![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
+ "\n",
"\n",
"[LATS (Language Agent Tree Search)](https://arxiv.org/pdf/2310.04406.pdf) by Zhou et al. combines LLM capabilities in planning, acting, and reasoning within a Monte Carlo tree search framework, allowing for deliberate and adaptive problem-solving guided by external feedback and self-reflection.\n",
"\n",
diff --git a/docs/docs/examples/agent/memory/composable_memory.ipynb b/docs/docs/examples/agent/memory/composable_memory.ipynb
index 144e9fdfa395c..9710b498da7c4 100644
--- a/docs/docs/examples/agent/memory/composable_memory.ipynb
+++ b/docs/docs/examples/agent/memory/composable_memory.ipynb
@@ -510,7 +510,7 @@
"source": [
"from llama_index.llms.openai import OpenAI\n",
"from llama_index.core.tools import FunctionTool\n",
- "from llama_index.core.agent import FunctionCallingAgentWorker\n",
+ "from llama_index.core.agent import FunctionCallingAgent\n",
"\n",
"import nest_asyncio\n",
"\n",
@@ -583,10 +583,12 @@
"outputs": [],
"source": [
"llm = OpenAI(model=\"gpt-3.5-turbo-0613\")\n",
- "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
- " [multiply_tool, mystery_tool], llm=llm, verbose=True\n",
- ")\n",
- "agent = agent_worker.as_agent(memory=composable_memory)"
+ "agent = FunctionCallingAgent.from_tools(\n",
+ " [multiply_tool, mystery_tool],\n",
+ " llm=llm,\n",
+ " memory=composable_memory,\n",
+ " verbose=True,\n",
+ ")"
]
},
{
@@ -685,10 +687,9 @@
"outputs": [],
"source": [
"llm = OpenAI(model=\"gpt-3.5-turbo-0613\")\n",
- "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ "agent_without_memory = FunctionCallingAgent.from_tools(\n",
" [multiply_tool, mystery_tool], llm=llm, verbose=True\n",
- ")\n",
- "agent_without_memory = agent_worker.as_agent()"
+ ")"
]
},
{
@@ -737,9 +738,7 @@
"outputs": [],
"source": [
"llm = OpenAI(model=\"gpt-3.5-turbo-0613\")\n",
- "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
- " [multiply_tool, mystery_tool], llm=llm, verbose=True\n",
- ")\n",
+ "\n",
"composable_memory = SimpleComposableMemory.from_defaults(\n",
" primary_memory=ChatMemoryBuffer.from_defaults(),\n",
" secondary_memory_sources=[\n",
@@ -749,7 +748,13 @@
" # later will use original vector_memory again\n",
" ],\n",
")\n",
- "agent_with_memory = agent_worker.as_agent(memory=composable_memory)"
+ "\n",
+ "agent_with_memory = FunctionCallingAgent.from_tools(\n",
+ " [multiply_tool, mystery_tool],\n",
+ " llm=llm,\n",
+ " memory=composable_memory,\n",
+ " verbose=True,\n",
+ ")"
]
},
{
@@ -937,9 +942,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "llama-index-core",
+ "display_name": "llama-index-caVs7DDe-py3.11",
"language": "python",
- "name": "llama-index-core"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
diff --git a/docs/docs/examples/agent/mistral_agent.ipynb b/docs/docs/examples/agent/mistral_agent.ipynb
index d3da7876f3345..d69a729336767 100644
--- a/docs/docs/examples/agent/mistral_agent.ipynb
+++ b/docs/docs/examples/agent/mistral_agent.ipynb
@@ -172,15 +172,14 @@
"metadata": {},
"outputs": [],
"source": [
- "from llama_index.core.agent import FunctionCallingAgentWorker\n",
+ "from llama_index.core.agent import FunctionCallingAgent\n",
"\n",
- "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ "agent = FunctionCallingAgent.from_tools(\n",
" [multiply_tool, add_tool],\n",
" llm=llm,\n",
" verbose=True,\n",
" allow_parallel_tool_calls=False,\n",
- ")\n",
- "agent = agent_worker.as_agent()"
+ ")"
]
},
{
@@ -267,13 +266,13 @@
],
"source": [
"# enable parallel function calling\n",
- "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ "agent = FunctionCallingAgent.from_tools(\n",
" [multiply_tool, add_tool],\n",
" llm=llm,\n",
" verbose=True,\n",
" allow_parallel_tool_calls=True,\n",
")\n",
- "agent = agent_worker.as_agent()\n",
+ "\n",
"response = await agent.achat(\"What is (121 * 3) + (5 * 8)?\")\n",
"print(str(response))"
]
@@ -360,12 +359,11 @@
"metadata": {},
"outputs": [],
"source": [
- "from llama_index.core.agent import FunctionCallingAgentWorker\n",
+ "from llama_index.core.agent import FunctionCallingAgent\n",
"\n",
- "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ "agent = FunctionCallingAgent.from_tools(\n",
" [query_engine_tool], llm=llm, verbose=True\n",
- ")\n",
- "agent = agent_worker.as_agent()"
+ ")"
]
},
{
diff --git a/docs/docs/examples/agent/nvidia_agent.ipynb b/docs/docs/examples/agent/nvidia_agent.ipynb
new file mode 100644
index 0000000000000..d7711bc4538c1
--- /dev/null
+++ b/docs/docs/examples/agent/nvidia_agent.ipynb
@@ -0,0 +1,734 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Function Calling NVIDIA Agent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This notebook shows you how to use our NVIDIA agent, powered by function calling capabilities."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initial Setup "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's start by importing some simple building blocks. \n",
+ "\n",
+ "The main thing we need is:\n",
+ "1. the NVIDIA NIM Endpoint (using our own `llama_index` LLM class)\n",
+ "2. a place to keep conversation history \n",
+ "3. a definition for tools that our agent can use."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33mDEPRECATION: pytest-httpx 0.21.0 has a non-standard dependency specifier pytest<8.*,>=6.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytest-httpx or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
+ "\u001b[0m\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+ "Note: you may need to restart the kernel to use updated packages.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install --upgrade --quiet llama-index-llms-nvidia"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Valid NVIDIA_API_KEY already in environment. Delete to reset\n"
+ ]
+ }
+ ],
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "# del os.environ['NVIDIA_API_KEY'] ## delete key and reset\n",
+ "if os.environ.get(\"NVIDIA_API_KEY\", \"\").startswith(\"nvapi-\"):\n",
+ " print(\"Valid NVIDIA_API_KEY already in environment. Delete to reset\")\n",
+ "else:\n",
+ " nvapi_key = getpass.getpass(\"NVAPI Key (starts with nvapi-): \")\n",
+ " assert nvapi_key.startswith(\n",
+ " \"nvapi-\"\n",
+ " ), f\"{nvapi_key[:5]}... is not a valid key\"\n",
+ " os.environ[\"NVIDIA_API_KEY\"] = nvapi_key"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.llms.nvidia import NVIDIA\n",
+ "from llama_index.core.tools import FunctionTool\n",
+ "from llama_index.embeddings.nvidia import NVIDIAEmbedding"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's define some very simple calculator tools for our agent."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def multiply(a: int, b: int) -> int:\n",
+ " \"\"\"Multiple two integers and returns the result integer\"\"\"\n",
+ " return a * b\n",
+ "\n",
+ "\n",
+ "multiply_tool = FunctionTool.from_defaults(fn=multiply)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def add(a: int, b: int) -> int:\n",
+ " \"\"\"Add two integers and returns the result integer\"\"\"\n",
+ " return a + b\n",
+ "\n",
+ "\n",
+ "add_tool = FunctionTool.from_defaults(fn=add)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we initialize a simple NVIDIA agent with calculator functions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm = NVIDIA(\"meta/llama-3.1-70b-instruct\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.agent import FunctionCallingAgent\n",
+ "\n",
+ "agent = FunctionCallingAgent.from_tools(\n",
+ " [multiply_tool, add_tool],\n",
+ " llm=llm,\n",
+ " verbose=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Chat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Added user message to memory: What is (121 * 3) + 42?\n",
+ "=== Calling Function ===\n",
+ "Calling function: multiply with args: {\"a\": 121, \"b\": 3}\n",
+ "=== Function Output ===\n",
+ "363\n",
+ "=== Calling Function ===\n",
+ "Calling function: add with args: {\"a\": 363, \"b\": 42}\n",
+ "=== Function Output ===\n",
+ "405\n",
+ "=== LLM Response ===\n",
+ "The answer is 405.\n",
+ "The answer is 405.\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = agent.chat(\"What is (121 * 3) + 42?\")\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ToolOutput(content='363', tool_name='multiply', raw_input={'args': (), 'kwargs': {'a': 121, 'b': 3}}, raw_output=363, is_error=False), ToolOutput(content='405', tool_name='add', raw_input={'args': (), 'kwargs': {'a': 363, 'b': 42}}, raw_output=405, is_error=False)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# inspect sources\n",
+ "print(response.sources)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Async Chat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Added user message to memory: What is 121 * 3?\n",
+ "=== Calling Function ===\n",
+ "Calling function: multiply with args: {\"a\": 121, \"b\": 3}\n",
+ "=== Function Output ===\n",
+ "363\n",
+ "=== LLM Response ===\n",
+ "The answer is 363.\n",
+ "The answer is 363.\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = await agent.achat(\"What is 121 * 3?\")\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Agent with Personality"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can specify a system prompt to give the agent additional instruction or personality."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.prompts.system import SHAKESPEARE_WRITING_ASSISTANT"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "agent = FunctionCallingAgent.from_tools(\n",
+ " [multiply_tool, add_tool],\n",
+ " llm=llm,\n",
+ " verbose=True,\n",
+ " system_prompt=SHAKESPEARE_WRITING_ASSISTANT,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Added user message to memory: Hi\n",
+ "Fair greeting unto thee, kind sir or madam! 'Tis a pleasure to make thy acquaintance. How doth thy day fare? Doth thou seek inspiration for a tale, a poem, or perhaps a song, penned in the grand style of the Bard himself?\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = agent.chat(\"Hi\")\n",
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = agent.chat(\"Tell me a story\")\n",
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# NVIDIA Agent with RAG/Query Engine Tools"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!mkdir -p 'data/10k/'\n",
+ "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n",
+ "from llama_index.core import SimpleDirectoryReader, VectorStoreIndex\n",
+ "\n",
+ "embed_model = NVIDIAEmbedding(model=\"NV-Embed-QA\", truncate=\"END\")\n",
+ "\n",
+ "# load data\n",
+ "uber_docs = SimpleDirectoryReader(\n",
+ " input_files=[\"./data/10k/uber_2021.pdf\"]\n",
+ ").load_data()\n",
+ "# build index\n",
+ "uber_index = VectorStoreIndex.from_documents(\n",
+ " uber_docs, embed_model=embed_model\n",
+ ")\n",
+ "uber_engine = uber_index.as_query_engine(similarity_top_k=3, llm=llm)\n",
+ "query_engine_tool = QueryEngineTool(\n",
+ " query_engine=uber_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=\"uber_10k\",\n",
+ " description=(\n",
+ " \"Provides information about Uber financials for year 2021. \"\n",
+ " \"Use a detailed plain text question as input to the tool.\"\n",
+ " ),\n",
+ " ),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "agent = FunctionCallingAgent.from_tools(\n",
+ " [query_engine_tool], llm=llm, verbose=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = agent.chat(\n",
+ " \"Tell me both the risk factors and tailwinds for Uber? Do two parallel tool calls.\",\n",
+ " allow_parallel_tool_calls=True,\n",
+ ")\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# ReAct Agent "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.agent import ReActAgent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "agent = ReActAgent.from_tools([multiply_tool, add_tool], llm=llm, verbose=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Running step a61e9980-2b9c-4a78-9950-cabe13827f73. Step input: What is 20+(2*4)? Calculate step by step \n",
+ "\u001b[1;3;38;5;200mThought: To calculate 20+(2*4), I need to follow the order of operations (PEMDAS). First, I need to calculate the multiplication part, which is 2*4. I will use the multiply tool to do this.\n",
+ "Action: multiply\n",
+ "Action Input: {'a': 2, 'b': 4}\n",
+ "\u001b[0m\u001b[1;3;34mObservation: 8\n",
+ "\u001b[0m> Running step 73418308-49cc-4689-bb39-b83d6a7cf7ac. Step input: None\n",
+ "\u001b[1;3;38;5;200mThought: Now that I have the result of the multiplication, which is 8, I can proceed to add 20 to it. I will use the add tool to do this.\n",
+ "Action: add\n",
+ "Action Input: {'a': 20, 'b': 8}\n",
+ "\u001b[0m\u001b[1;3;34mObservation: 28\n",
+ "\u001b[0m> Running step 73464ecd-b266-47ef-8f1b-c0aa0e43ad60. Step input: None\n",
+ "\u001b[1;3;38;5;200mThought: I have now calculated the entire expression 20+(2*4) and have the final result.\n",
+ "Answer: 28\n",
+ "\u001b[0m"
+ ]
+ }
+ ],
+ "source": [
+ "response = agent.chat(\"What is 20+(2*4)? Calculate step by step \")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Running step 079f0d12-e0f7-48cd-a1be-9f8a9ce98b9c. Step input: What is 20+2*4? Calculate step by step\n",
+ "\u001b[1;3;38;5;200mThought: To calculate 20+2*4, I need to follow the order of operations (PEMDAS). First, I need to calculate the multiplication.\n",
+ "Action: multiply\n",
+ "Action Input: {'a': 2, 'b': 4}\n",
+ "\u001b[0m\u001b[1;3;34mObservation: 8\n",
+ "\u001b[0m> Running step 9d3a64e8-0b14-4721-9d5c-dd51b03ff3fa. Step input: None\n",
+ "\u001b[1;3;38;5;200mThought: Now that I have the result of the multiplication, I can add 20 to it.\n",
+ "Action: add\n",
+ "Action Input: {'a': 20, 'b': 8}\n",
+ "\u001b[0m\u001b[1;3;34mObservation: 28\n",
+ "\u001b[0m> Running step 14d6f623-5c92-405b-88d5-468805429e0b. Step input: None\n",
+ " 20+2*4 = 28"
+ ]
+ }
+ ],
+ "source": [
+ "response_gen = agent.stream_chat(\"What is 20+2*4? Calculate step by step\")\n",
+ "response_gen.print_response_stream()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## View Prompts\n",
+ "\n",
+ "Let's take a look at the core system prompt powering the ReAct agent! \n",
+ "\n",
+ "Within the agent, the current conversation history is dumped below this line."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prompt_dict = agent.get_prompts()\n",
+ "for k, v in prompt_dict.items():\n",
+ " print(f\"Prompt: {k}\\n\\nValue: {v.template}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Customizing the Prompt\n",
+ "\n",
+ "For fun, let's try instructing the agent to output the answer along with reasoning in bullet points. See \"## Additional Rules\" section."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import PromptTemplate\n",
+ "\n",
+ "react_system_header_str = \"\"\"\\\n",
+ "\n",
+ "You are designed to help with a variety of tasks, from answering questions \\\n",
+ " to providing summaries to other types of analyses.\n",
+ "\n",
+ "## Tools\n",
+ "You have access to a wide variety of tools. You are responsible for using\n",
+ "the tools in any sequence you deem appropriate to complete the task at hand.\n",
+ "This may require breaking the task into subtasks and using different tools\n",
+ "to complete each subtask.\n",
+ "\n",
+ "You have access to the following tools:\n",
+ "{tool_desc}\n",
+ "\n",
+ "## Output Format\n",
+ "To answer the question, please use the following format.\n",
+ "\n",
+ "```\n",
+ "Thought: I need to use a tool to help me answer the question.\n",
+ "Action: tool name (one of {tool_names}) if using a tool.\n",
+ "Action Input: the input to the tool, in a JSON format representing the kwargs (e.g. {{\"input\": \"hello world\", \"num_beams\": 5}})\n",
+ "```\n",
+ "\n",
+ "Please ALWAYS start with a Thought.\n",
+ "\n",
+ "Please use a valid JSON format for the Action Input. Do NOT do this {{'input': 'hello world', 'num_beams': 5}}.\n",
+ "\n",
+ "If this format is used, the user will respond in the following format:\n",
+ "\n",
+ "```\n",
+ "Observation: tool response\n",
+ "```\n",
+ "\n",
+ "You should keep repeating the above format until you have enough information\n",
+ "to answer the question without using any more tools. At that point, you MUST respond\n",
+ "in the one of the following two formats:\n",
+ "\n",
+ "```\n",
+ "Thought: I can answer without using any more tools.\n",
+ "Answer: [your answer here]\n",
+ "```\n",
+ "\n",
+ "```\n",
+ "Thought: I cannot answer the question with the provided tools.\n",
+ "Answer: Sorry, I cannot answer your query.\n",
+ "```\n",
+ "\n",
+ "## Additional Rules\n",
+ "- The answer MUST contain a sequence of bullet points that explain how you arrived at the answer. This can include aspects of the previous conversation history.\n",
+ "- You MUST obey the function signature of each tool. Do NOT pass in no arguments if the function expects arguments.\n",
+ "\n",
+ "## Current Conversation\n",
+ "Below is the current conversation consisting of interleaving human and assistant messages.\n",
+ "\n",
+ "\"\"\"\n",
+ "react_system_prompt = PromptTemplate(react_system_header_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'agent_worker:system_prompt': PromptTemplate(metadata={'prompt_type': "
+ ""
]
},
{
diff --git a/docs/docs/examples/chat_engine/chat_engine_condense_plus_context.ipynb b/docs/docs/examples/chat_engine/chat_engine_condense_plus_context.ipynb
index b9402cfa2de4d..7678c4ef65059 100644
--- a/docs/docs/examples/chat_engine/chat_engine_condense_plus_context.ipynb
+++ b/docs/docs/examples/chat_engine/chat_engine_condense_plus_context.ipynb
@@ -5,11 +5,10 @@
"id": "616a781c",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "18e20fbc-056b-44ac-b1fc-2d34b8e99bcc",
"metadata": {},
@@ -19,7 +18,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "b99eea02-429c-40e4-99be-b82a89c8d070",
"metadata": {},
@@ -28,7 +26,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "34d34fcc-e247-4d55-ab16-c3d633e2385a",
"metadata": {},
@@ -40,7 +37,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "f1c3cbc6-98a8-4e0e-98eb-3c7fa09ba79f",
"metadata": {},
@@ -77,7 +73,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "79db0610",
"metadata": {},
@@ -97,7 +92,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "b314f279-bf7f-4e67-9f66-ebf783f08d38",
"metadata": {},
@@ -106,7 +100,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "40d3d9e4",
"metadata": {},
@@ -145,7 +138,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "e58d7ad9-d246-477e-acac-894ad5402f24",
"metadata": {},
@@ -182,7 +174,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "63a4259d-89b5-49f8-b158-9eba5353d6f5",
"metadata": {},
@@ -219,7 +210,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "67021e64-8665-4338-9fb4-c0f1d6361092",
"metadata": {},
@@ -264,7 +254,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "c2c68de8-af58-4f7e-8759-19fc072873fd",
"metadata": {},
@@ -311,7 +300,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"id": "a65ad1a2",
"metadata": {},
diff --git a/docs/docs/examples/chat_engine/chat_engine_condense_question.ipynb b/docs/docs/examples/chat_engine/chat_engine_condense_question.ipynb
index 08d1c6cdb554c..414631bc8aa31 100644
--- a/docs/docs/examples/chat_engine/chat_engine_condense_question.ipynb
+++ b/docs/docs/examples/chat_engine/chat_engine_condense_question.ipynb
@@ -5,7 +5,7 @@
"id": "dae1c2c1",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/chat_engine/chat_engine_context.ipynb b/docs/docs/examples/chat_engine/chat_engine_context.ipynb
index 6749ba1447e9a..11a25716b7986 100644
--- a/docs/docs/examples/chat_engine/chat_engine_context.ipynb
+++ b/docs/docs/examples/chat_engine/chat_engine_context.ipynb
@@ -5,7 +5,7 @@
"id": "616a781c",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/chat_engine/chat_engine_openai.ipynb b/docs/docs/examples/chat_engine/chat_engine_openai.ipynb
index 4303cde620fe2..4b3efffb4250f 100644
--- a/docs/docs/examples/chat_engine/chat_engine_openai.ipynb
+++ b/docs/docs/examples/chat_engine/chat_engine_openai.ipynb
@@ -5,7 +5,7 @@
"id": "23e5dc2d",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/chat_engine/chat_engine_personality.ipynb b/docs/docs/examples/chat_engine/chat_engine_personality.ipynb
index 1ed6f254deb77..5248d893d881e 100644
--- a/docs/docs/examples/chat_engine/chat_engine_personality.ipynb
+++ b/docs/docs/examples/chat_engine/chat_engine_personality.ipynb
@@ -5,7 +5,7 @@
"id": "5a19abd0",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/chat_engine/chat_engine_react.ipynb b/docs/docs/examples/chat_engine/chat_engine_react.ipynb
index ddd8f9e44e296..8d8d6e74c0f8b 100644
--- a/docs/docs/examples/chat_engine/chat_engine_react.ipynb
+++ b/docs/docs/examples/chat_engine/chat_engine_react.ipynb
@@ -5,7 +5,7 @@
"id": "beda3039",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/chat_engine/chat_engine_repl.ipynb b/docs/docs/examples/chat_engine/chat_engine_repl.ipynb
index 581265d9dbbac..aa85e0c84e8f6 100644
--- a/docs/docs/examples/chat_engine/chat_engine_repl.ipynb
+++ b/docs/docs/examples/chat_engine/chat_engine_repl.ipynb
@@ -5,7 +5,7 @@
"id": "bdb508b5",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/citation/pdf_page_reference.ipynb b/docs/docs/examples/citation/pdf_page_reference.ipynb
index 7aad4bae9fb52..2ecff553a4215 100644
--- a/docs/docs/examples/citation/pdf_page_reference.ipynb
+++ b/docs/docs/examples/citation/pdf_page_reference.ipynb
@@ -17,7 +17,7 @@
"id": "18ffbd8c",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/cookbooks/codestral.ipynb b/docs/docs/examples/cookbooks/codestral.ipynb
index 8a53a165a8adc..f3e7cf24dd802 100644
--- a/docs/docs/examples/cookbooks/codestral.ipynb
+++ b/docs/docs/examples/cookbooks/codestral.ipynb
@@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/cookbooks/cohere_retriever_eval.ipynb b/docs/docs/examples/cookbooks/cohere_retriever_eval.ipynb
index b7fef176f6018..27899185bfe62 100644
--- a/docs/docs/examples/cookbooks/cohere_retriever_eval.ipynb
+++ b/docs/docs/examples/cookbooks/cohere_retriever_eval.ipynb
@@ -6,7 +6,7 @@
"id": "8a5706df",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/cookbooks/mistralai.ipynb b/docs/docs/examples/cookbooks/mistralai.ipynb
index 8d55c1d727378..d615670af5a6b 100644
--- a/docs/docs/examples/cookbooks/mistralai.ipynb
+++ b/docs/docs/examples/cookbooks/mistralai.ipynb
@@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -218,7 +218,7 @@
"outputs": [],
"source": [
"from llama_index.core.tools import QueryEngineTool, ToolMetadata\n",
- "from llama_index.core.agent import FunctionCallingAgentWorker\n",
+ "from llama_index.core.agent import FunctionCallingAgent\n",
"\n",
"query_engine_tools = [\n",
" QueryEngineTool(\n",
@@ -237,13 +237,12 @@
" ),\n",
"]\n",
"\n",
- "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ "agent = FunctionCallingAgent.from_tools(\n",
" query_engine_tools,\n",
" llm=llm,\n",
" verbose=True,\n",
" allow_parallel_tool_calls=False,\n",
- ")\n",
- "agent = agent_worker.as_agent()"
+ ")"
]
},
{
@@ -378,7 +377,7 @@
"source": [
"from llama_index.core.tools import FunctionTool\n",
"from llama_index.core.agent import (\n",
- " FunctionCallingAgentWorker,\n",
+ " FunctionCallingAgent,\n",
" ReActAgent,\n",
")"
]
@@ -423,13 +422,12 @@
"metadata": {},
"outputs": [],
"source": [
- "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ "agent = FunctionCallingAgent.from_tools(\n",
" [multiply_tool, add_tool, subtract_tool],\n",
" llm=llm,\n",
" verbose=True,\n",
" allow_parallel_tool_calls=False,\n",
- ")\n",
- "agent = agent_worker.as_agent()"
+ ")"
]
},
{
diff --git a/docs/docs/examples/cookbooks/mixedbread_reranker.ipynb b/docs/docs/examples/cookbooks/mixedbread_reranker.ipynb
index 786d236ed4b63..ce365e1612b1a 100644
--- a/docs/docs/examples/cookbooks/mixedbread_reranker.ipynb
+++ b/docs/docs/examples/cookbooks/mixedbread_reranker.ipynb
@@ -5,7 +5,7 @@
"id": "964030f7-40e4-4398-a5ab-668aabcf3bad",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/cookbooks/oreilly_course_cookbooks/Module-2/Components_Of_LlamaIndex.ipynb b/docs/docs/examples/cookbooks/oreilly_course_cookbooks/Module-2/Components_Of_LlamaIndex.ipynb
new file mode 100644
index 0000000000000..e0648ed5bad0f
--- /dev/null
+++ b/docs/docs/examples/cookbooks/oreilly_course_cookbooks/Module-2/Components_Of_LlamaIndex.ipynb
@@ -0,0 +1,1179 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Components Of LlamaIndex\n",
+ "\n",
+ "In this notebook we will demonstrate building RAG application and customize it using different components of LlamaIndex.\n",
+ "\n",
+ "1. Question Answering\n",
+ "2. Summarization.\n",
+ "3. ChatEngine.\n",
+ "4. Customizing QA System.\n",
+ "5. Index as Retriever.\n",
+ "\n",
+ "[ChatEngine Documentation](https://docs.llamaindex.ai/en/stable/module_guides/deploying/chat_engines/usage_pattern/ )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Installation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install llama-index\n",
+ "# !pip install llama-index-llms-openai\n",
+ "# !pip install llama-index-embeddings-openai"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup API Key"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Download Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2024-04-28 04:34:09-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 75042 (73K) [text/plain]\n",
+ "Saving to: ‘paul_graham_essay.txt’\n",
+ "\n",
+ "\r",
+ "paul_graham_essay.t 0%[ ] 0 --.-KB/s \r",
+ "paul_graham_essay.t 100%[===================>] 73.28K --.-KB/s in 0.01s \n",
+ "\n",
+ "2024-04-28 04:34:09 (5.45 MB/s) - ‘paul_graham_essay.txt’ saved [75042/75042]\n",
+ "\n",
+ "--2024-04-28 04:34:09-- http://paul_graham_essay.txt/\n",
+ "Resolving paul_graham_essay.txt (paul_graham_essay.txt)... failed: Name or service not known.\n",
+ "wget: unable to resolve host address ‘paul_graham_essay.txt’\n",
+ "FINISHED --2024-04-28 04:34:09--\n",
+ "Total wall clock time: 0.2s\n",
+ "Downloaded: 1 files, 73K in 0.01s (5.45 MB/s)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!wget \"https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\" \"paul_graham_essay.txt\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
+ "\n",
+ "documents = SimpleDirectoryReader(\n",
+ " input_files=[\"paul_graham_essay.txt\"]\n",
+ ").load_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Set LLM and Embedding Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+ "from llama_index.llms.openai import OpenAI\n",
+ "from llama_index.core import Settings\n",
+ "\n",
+ "llm = OpenAI(model=\"gpt-3.5-turbo\", temperature=0.2)\n",
+ "embed_model = OpenAIEmbedding()\n",
+ "\n",
+ "Settings.llm = llm\n",
+ "Settings.embed_model = embed_model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create Nodes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.node_parser import TokenTextSplitter\n",
+ "\n",
+ "splitter = TokenTextSplitter(chunk_size=1024, chunk_overlap=20)\n",
+ "nodes = splitter.get_nodes_from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "TextNode(id_='957f1fe7-c30c-4ad4-a11d-5abd8df895e0', embedding=None, metadata={'file_path': 'paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2024-04-28', 'last_modified_date': '2024-04-28'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={{nodes[1].get_text()}
'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Faithfullness Evaluator\n", + "\n", + " Measures if the response from a query engine matches any source nodes. This is useful for measuring if the response was hallucinated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "faithfulness_evaluator = FaithfulnessEvaluator(llm=gpt4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate response\n", + "response_vector = query_engine.query(eval_query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "eval_result = faithfulness_evaluator.evaluate_response(\n", + " response=response_vector\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_result.passing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EvaluationResult(query=None, contexts=[\"Now that I could write essays again, I wrote a bunch about topics I'd had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who'd lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nNotes\\n\\n[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting.\\n\\n[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It's the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way.\\n\\n[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco. I saw Florence at street level in every possible condition, from empty dark winter evenings to sweltering summer days when the streets were packed with tourists.\\n\\n[4] You can of course paint people like still lives if you want to, and they're willing. That sort of portrait is arguably the apex of still life painting, though the long sitting does tend to produce pained expressions in the sitters.\\n\\n[5] Interleaf was one of many companies that had smart people and built impressive technology, and yet got crushed by Moore's Law. In the 1990s the exponential growth in the power of commodity (i.e. Intel) processors rolled up high-end, special-purpose hardware and software companies like a bulldozer.\\n\\n[6] The signature style seekers at RISD weren't specifically mercenary. In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive.\\n\\n[7] Technically the apartment wasn't rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price.\\n\\n[8] Most software you can launch as soon as it's done. But when the software is an online store builder and you're hosting the stores, if you don't have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores.\\n\\n[9] We'd had a code editor in Viaweb for users to define their own page styles. They didn't know it, but they were editing Lisp expressions underneath. But this wasn't an app editor, because the code ran when the merchants' sites were generated, not when shoppers visited them.\\n\\n[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren't they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I'm not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don't already know, and some people dislike being told such things.\\n\\n[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version.\\n\\n[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare.\", 'What I Worked On\\n\\nFebruary 2021\\n\\nBefore college the two main things I worked on, outside of school, were writing and programming. I didn\\'t write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\\n\\nThe first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district\\'s 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain\\'s lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\\n\\nThe language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.\\n\\nI was puzzled by the 1401. I couldn\\'t figure out what to do with it. And in retrospect there\\'s not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn\\'t have any data stored on punched cards. The only other option was to do things that didn\\'t rely on any input, like calculate approximations of pi, but I didn\\'t know enough math to do anything interesting of that type. So I\\'m not surprised I can\\'t remember any programs I wrote, because they can\\'t have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn\\'t. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager\\'s expression made clear.\\n\\nWith microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]\\n\\nThe first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.\\n\\nComputers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he\\'d write 2 pages at a time and then print them out, but it was a lot better than a typewriter.\\n\\nThough I liked programming, I didn\\'t plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn\\'t much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.\\n\\nI couldn\\'t have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.\\n\\nAI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven\\'t tried rereading The Moon is a Harsh Mistress, so I don\\'t know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we\\'d have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most.', 'I didn\\'t want to drop out of grad school, but how else was I going to get out? I remember when my friend Robert Morris got kicked out of Cornell for writing the internet worm of 1988, I was envious that he\\'d found such a spectacular way to get out of grad school.\\n\\nThen one day in April 1990 a crack appeared in the wall. I ran into professor Cheatham and he asked if I was far enough along to graduate that June. I didn\\'t have a word of my dissertation written, but in what must have been the quickest bit of thinking in my life, I decided to take a shot at writing one in the 5 weeks or so that remained before the deadline, reusing parts of On Lisp where I could, and I was able to respond, with no perceptible delay \"Yes, I think so. I\\'ll give you something to read in a few days.\"\\n\\nI picked applications of continuations as the topic. In retrospect I should have written about macros and embedded languages. There\\'s a whole world there that\\'s barely been explored. But all I wanted was to get out of grad school, and my rapidly written dissertation sufficed, just barely.\\n\\nMeanwhile I was applying to art schools. I applied to two: RISD in the US, and the Accademia di Belli Arti in Florence, which, because it was the oldest art school, I imagined would be good. RISD accepted me, and I never heard back from the Accademia, so off to Providence I went.\\n\\nI\\'d applied for the BFA program at RISD, which meant in effect that I had to go to college again. This was not as strange as it sounds, because I was only 25, and art schools are full of people of different ages. RISD counted me as a transfer sophomore and said I had to do the foundation that summer. The foundation means the classes that everyone has to take in fundamental subjects like drawing, color, and design.\\n\\nToward the end of the summer I got a big surprise: a letter from the Accademia, which had been delayed because they\\'d sent it to Cambridge England instead of Cambridge Massachusetts, inviting me to take the entrance exam in Florence that fall. This was now only weeks away. My nice landlady let me leave my stuff in her attic. I had some money saved from consulting work I\\'d done in grad school; there was probably enough to last a year if I lived cheaply. Now all I had to do was learn Italian.\\n\\nOnly stranieri (foreigners) had to take this entrance exam. In retrospect it may well have been a way of excluding them, because there were so many stranieri attracted by the idea of studying art in Florence that the Italian students would otherwise have been outnumbered. I was in decent shape at painting and drawing from the RISD foundation that summer, but I still don\\'t know how I managed to pass the written exam. I remember that I answered the essay question by writing about Cezanne, and that I cranked up the intellectual level as high as I could to make the most of my limited vocabulary. [2]\\n\\nI\\'m only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn\\'t require the faculty to teach anything, and in return the faculty wouldn\\'t require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they\\'d seen in American art magazines.\\n\\nOur model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She\\'d copy an obscure old painting out of a book, and then he\\'d take the copy and maltreat it to make it look old. [3]\\n\\nWhile I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time.'], response='The author described their early attempts at writing short stories as lacking in plot, focusing mainly on characters with strong feelings, which the author believed made the stories deep.', passing=True, feedback='YES', score=1.0, pairwise_source=None, invalid_result=False, invalid_reason=None)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Relevency Evaluation\n", + "\n", + "Measures if the response + source nodes match the query." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create RelevancyEvaluator using GPT-4 LLM\n", + "relevancy_evaluator = RelevancyEvaluator(llm=gpt4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "# Generate response\n", + "response_vector = query_engine.query(eval_query)\n", + "\n", + "# Evaluation\n", + "eval_result = relevancy_evaluator.evaluate_response(\n", + " query=eval_query, response=response_vector\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'How did the author describe their early attempts at writing short stories?'" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_result.query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'The author described their early attempts at writing short stories as being focused more on characters with strong feelings rather than having a strong plot.'" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_result.response" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_result.passing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Relevancy evaluation with multiple source nodes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "# Create Query Engine with similarity_top_k=3\n", + "query_engine = vector_index.as_query_engine(similarity_top_k=3)\n", + "\n", + "# Create response\n", + "response_vector = query_engine.query(eval_query)\n", + "\n", + "# Evaluate with each source node\n", + "eval_source_result_full = [\n", + " relevancy_evaluator.evaluate(\n", + " query=eval_query,\n", + " response=response_vector.response,\n", + " contexts=[source_node.get_content()],\n", + " )\n", + " for source_node in response_vector.source_nodes\n", + "]\n", + "\n", + "# Evaluation result\n", + "eval_source_result = [\n", + " \"Pass\" if result.passing else \"Fail\" for result in eval_source_result_full\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Fail', 'Pass', 'Fail']" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_source_result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Correctness Evaluator\n", + "\n", + "Evaluates the relevance and correctness of a generated answer against a reference answer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "correctness_evaluator = CorrectnessEvaluator(llm=gpt4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query = \"Can you explain the theory of relativity proposed by Albert Einstein in detail?\"\n", + "\n", + "reference = \"\"\"\n", + "Certainly! Albert Einstein's theory of relativity consists of two main components: special relativity and general relativity. Special relativity, published in 1905, introduced the concept that the laws of physics are the same for all non-accelerating observers and that the speed of light in a vacuum is a constant, regardless of the motion of the source or observer. It also gave rise to the famous equation E=mc², which relates energy (E) and mass (m).\n", + "\n", + "General relativity, published in 1915, extended these ideas to include the effects of gravity. According to general relativity, gravity is not a force between masses, as described by Newton's theory of gravity, but rather the result of the warping of space and time by mass and energy. Massive objects, such as planets and stars, cause a curvature in spacetime, and smaller objects follow curved paths in response to this curvature. This concept is often illustrated using the analogy of a heavy ball placed on a rubber sheet, causing it to create a depression that other objects (representing smaller masses) naturally move towards.\n", + "\n", + "In essence, general relativity provided a new understanding of gravity, explaining phenomena like the bending of light by gravity (gravitational lensing) and the precession of the orbit of Mercury. It has been confirmed through numerous experiments and observations and has become a fundamental theory in modern physics.\n", + "\"\"\"\n", + "\n", + "response = \"\"\"\n", + "Certainly! Albert Einstein's theory of relativity consists of two main components: special relativity and general relativity. Special relativity, published in 1905, introduced the concept that the laws of physics are the same for all non-accelerating observers and that the speed of light in a vacuum is a constant, regardless of the motion of the source or observer. It also gave rise to the famous equation E=mc², which relates energy (E) and mass (m).\n", + "\n", + "However, general relativity, published in 1915, extended these ideas to include the effects of magnetism. According to general relativity, gravity is not a force between masses but rather the result of the warping of space and time by magnetic fields generated by massive objects. Massive objects, such as planets and stars, create magnetic fields that cause a curvature in spacetime, and smaller objects follow curved paths in response to this magnetic curvature. This concept is often illustrated using the analogy of a heavy ball placed on a rubber sheet with magnets underneath, causing it to create a depression that other objects (representing smaller masses) naturally move towards due to magnetic attraction.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "correctness_result = correctness_evaluator.evaluate(\n", + " query=query,\n", + " response=response,\n", + " reference=reference,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EvaluationResult(query='Can you explain the theory of relativity proposed by Albert Einstein in detail?', contexts=None, response=\"\\nCertainly! Albert Einstein's theory of relativity consists of two main components: special relativity and general relativity. Special relativity, published in 1905, introduced the concept that the laws of physics are the same for all non-accelerating observers and that the speed of light in a vacuum is a constant, regardless of the motion of the source or observer. It also gave rise to the famous equation E=mc², which relates energy (E) and mass (m).\\n\\nHowever, general relativity, published in 1915, extended these ideas to include the effects of magnetism. According to general relativity, gravity is not a force between masses but rather the result of the warping of space and time by magnetic fields generated by massive objects. Massive objects, such as planets and stars, create magnetic fields that cause a curvature in spacetime, and smaller objects follow curved paths in response to this magnetic curvature. This concept is often illustrated using the analogy of a heavy ball placed on a rubber sheet with magnets underneath, causing it to create a depression that other objects (representing smaller masses) naturally move towards due to magnetic attraction.\\n\", passing=False, feedback='The generated answer is relevant to the user query as it attempts to explain the theory of relativity proposed by Albert Einstein. However, it contains significant mistakes. The general relativity part of the answer incorrectly attributes the warping of space and time to magnetic fields, which is not accurate. General relativity is about the effects of gravity, not magnetism. The analogy used also incorrectly introduces magnets, which is misleading.', score=2.5, pairwise_source=None, invalid_result=False, invalid_reason=None)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "correctness_result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.5" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "correctness_result.score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "correctness_result.passing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'The generated answer is relevant to the user query as it attempts to explain the theory of relativity proposed by Albert Einstein. However, it contains significant mistakes. The general relativity part of the answer incorrectly attributes the warping of space and time to magnetic fields, which is not accurate. General relativity is about the effects of gravity, not magnetism. The analogy used also incorrectly introduces magnets, which is misleading.'" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "correctness_result.feedback" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieval Evaluation\n", + "\n", + "Evaluates the quality of any Retriever module defined in LlamaIndex.\n", + "\n", + "To assess the quality of a Retriever module in LlamaIndex, we use metrics like hit-rate and MRR. These compare retrieved results to ground-truth context for any question. For simpler evaluation dataset creation, we utilize synthetic data generation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "reader = SimpleDirectoryReader(\"./data/paul_graham/\")\n", + "documents = reader.load_data()\n", + "\n", + "from llama_index.core.text_splitter import SentenceSplitter\n", + "\n", + "# create parser and parse document into nodes\n", + "parser = SentenceSplitter(chunk_size=1024, chunk_overlap=100)\n", + "nodes = parser(documents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "vector_index = VectorStoreIndex(nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the retriever\n", + "retriever = vector_index.as_retriever(similarity_top_k=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "retrieved_nodes = retriever.retrieve(eval_query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**Node ID:** 6447ac8e-b79c-48e2-a725-a29b79eabc32| \n", + " | retrievers | \n", + "hit_rate | \n", + "mrr | \n", + "
|---|---|---|---|
| 0 | \n", + "top-2 eval | \n", + "0.894737 | \n", + "0.776316 | \n", + "
2076
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### With Function Calling." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agent_worker = FunctionCallingAgentWorker.from_tools(\n", + " [multiply_tool, add_tool, subtract_tool],\n", + " llm=llm,\n", + " verbose=True,\n", + " allow_parallel_tool_calls=False,\n", + ")\n", + "agent = agent_worker.as_agent()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added user message to memory: What is (26 * 2) + 2024?\n", + "=== Calling Function ===\n", + "Calling function: multiply with args: {\"a\": 26, \"b\": 2}\n", + "=== Function Output ===\n", + "52\n", + "=== Calling Function ===\n", + "Calling function: add with args: {\"a\": 52, \"b\": 2024}\n", + "=== Function Output ===\n", + "2076\n", + "=== LLM Response ===\n", + "The result of (26 * 2) + 2024 is 2076.\n" + ] + } + ], + "source": [ + "response = agent.chat(\"What is (26 * 2) + 2024?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "assistant: The result of (26 * 2) + 2024 is 2076.
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Agent with RAG Query Engine Tools" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download Data\n", + "\n", + "We will use `Uber-2021` and `Lyft-2021` 10K SEC filings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-05-16 14:00:56-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1880483 (1.8M) [application/octet-stream]\n", + "Saving to: ‘./uber_2021.pdf’\n", + "\n", + "./uber_2021.pdf 100%[===================>] 1.79M 8.54MB/s in 0.2s \n", + "\n", + "2024-05-16 14:00:57 (8.54 MB/s) - ‘./uber_2021.pdf’ saved [1880483/1880483]\n", + "\n", + "--2024-05-16 14:00:57-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1440303 (1.4M) [application/octet-stream]\n", + "Saving to: ‘./lyft_2021.pdf’\n", + "\n", + "./lyft_2021.pdf 100%[===================>] 1.37M 6.90MB/s in 0.2s \n", + "\n", + "2024-05-16 14:00:58 (6.90 MB/s) - ‘./lyft_2021.pdf’ saved [1440303/1440303]\n", + "\n" + ] + } + ], + "source": [ + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O './uber_2021.pdf'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf' -O './lyft_2021.pdf'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "\n", + "uber_docs = SimpleDirectoryReader(input_files=[\"./uber_2021.pdf\"]).load_data()\n", + "lyft_docs = SimpleDirectoryReader(input_files=[\"./lyft_2021.pdf\"]).load_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build RAG on uber and lyft docs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex\n", + "\n", + "uber_index = VectorStoreIndex.from_documents(uber_docs)\n", + "uber_query_engine = uber_index.as_query_engine(similarity_top_k=3)\n", + "\n", + "lyft_index = VectorStoreIndex.from_documents(lyft_docs)\n", + "lyft_query_engine = lyft_index.as_query_engine(similarity_top_k=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = uber_query_engine.query(\"What are the investments of Uber in 2021?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "In 2021, Uber made significant investments to expand its international operations and compete with local and other global competitors. This included the acquisitions of Careem and Cornershop. Additionally, Uber continued to develop new technologies to enhance existing offerings and services, and to expand the range of its offerings through research and development. They also launched Uber One in the United States, a cross-platform membership program that brings together the best of Uber.
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = lyft_query_engine.query(\"What are lyft investments in 2021?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "In 2021, Lyft continued to invest in expanding its network of Light Vehicles and Lyft Autonomous, focusing on the deployment and scaling of third-party self-driving technology on the Lyft network. The company also made a commitment to reach 100% electric vehicles on the Lyft network by the end of 2030. Additionally, Lyft completed a transaction with Woven Planet, a subsidiary of Toyota Motor Corporation, for the divestiture of certain assets related to its self-driving vehicle division, Level 5.
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `FunctionCallingAgent` with RAG QueryEngineTools.\n", + "\n", + "Here we use `Fuction Calling` capabilities of the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n", + "from llama_index.core.agent import FunctionCallingAgentWorker\n", + "\n", + "query_engine_tools = [\n", + " QueryEngineTool(\n", + " query_engine=lyft_query_engine,\n", + " metadata=ToolMetadata(\n", + " name=\"lyft_10k\",\n", + " description=\"Provides information about Lyft financials for year 2021\",\n", + " ),\n", + " ),\n", + " QueryEngineTool(\n", + " query_engine=uber_query_engine,\n", + " metadata=ToolMetadata(\n", + " name=\"uber_10k\",\n", + " description=\"Provides information about Uber financials for year 2021\",\n", + " ),\n", + " ),\n", + "]\n", + "\n", + "agent_worker = FunctionCallingAgentWorker.from_tools(\n", + " query_engine_tools,\n", + " llm=llm,\n", + " verbose=True,\n", + " allow_parallel_tool_calls=False,\n", + ")\n", + "agent = agent_worker.as_agent()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added user message to memory: What are the investments of Uber in 2021?\n", + "=== Calling Function ===\n", + "Calling function: uber_10k with args: {\"input\": \"investments\"}\n", + "=== Function Output ===\n", + "Uber's investments primarily consist of money market funds, cash deposits, U.S. government and agency securities, and investment-grade corporate debt securities. The company's investment policy aims to preserve capital and meet liquidity requirements without significantly increasing risk. As of December 31, 2021, Uber had cash and cash equivalents including restricted cash and cash equivalents totaling $7.8 billion. They also hold investments in other companies, including minority-owned, privately-held affiliates and recently public companies. The carrying value of these investments was $12.6 billion as of December 31, 2021.\n", + "=== LLM Response ===\n", + "In 2021, Uber's investments primarily consisted of money market funds, cash deposits, U.S. government and agency securities, and investment-grade corporate debt securities. Their investment policy aims to preserve capital and meet liquidity requirements without significantly increasing risk. As of December 31, 2021, Uber had cash and cash equivalents including restricted cash and cash equivalents totaling $7.8 billion. They also hold investments in other companies, including minority-owned, privately-held affiliates and recently public companies. The carrying value of these investments was $12.6 billion as of December 31, 2021.\n" + ] + } + ], + "source": [ + "response = agent.chat(\"What are the investments of Uber in 2021?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "assistant: In 2021, Uber's investments primarily consisted of money market funds, cash deposits, U.S. government and agency securities, and investment-grade corporate debt securities. Their investment policy aims to preserve capital and meet liquidity requirements without significantly increasing risk. As of December 31, 2021, Uber had cash and cash equivalents including restricted cash and cash equivalents totaling $7.8 billion. They also hold investments in other companies, including minority-owned, privately-held affiliates and recently public companies. The carrying value of these investments was $12.6 billion as of December 31, 2021.
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added user message to memory: What are lyft investments in 2021?\n", + "=== Calling Function ===\n", + "Calling function: lyft_10k with args: {\"input\": \"investments\"}\n", + "=== Function Output ===\n", + "The company's investments include cash and cash equivalents, short-term investments, and restricted investments. Cash and cash equivalents include certificates of deposits, commercial paper, and corporate bonds that have an original maturity of 90 days or less and are readily convertible to known amounts of cash. Short-term investments are comprised of commercial paper, certificates of deposit, and corporate bonds, which mature in twelve months or less. Restricted investments are comprised of debt security investments in commercial paper, certificates of deposit, corporate bonds, and U.S. government securities which are held in trust accounts at third-party financial institutions pursuant to certain contracts with insurance providers. The company also has investments in non-marketable equity securities, which are measured at cost, with remeasurements to fair value only upon the occurrence of observable transactions for identical or similar investments of the same issuer or impairment.\n", + "=== LLM Response ===\n", + "In 2021, Lyft's investments included cash and cash equivalents, short-term investments, and restricted investments. Cash and cash equivalents included certificates of deposits, commercial paper, and corporate bonds that have an original maturity of 90 days or less and are readily convertible to known amounts of cash. Short-term investments were comprised of commercial paper, certificates of deposit, and corporate bonds, which mature in twelve months or less. \n", + "\n", + "Restricted investments were comprised of debt security investments in commercial paper, certificates of deposit, corporate bonds, and U.S. government securities which are held in trust accounts at third-party financial institutions pursuant to certain contracts with insurance providers. \n", + "\n", + "The company also had investments in non-marketable equity securities, which are measured at cost, with remeasurements to fair value only upon the occurrence of observable transactions for identical or similar investments of the same issuer or impairment.\n" + ] + } + ], + "source": [ + "response = agent.chat(\"What are lyft investments in 2021?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "assistant: In 2021, Lyft's investments included cash and cash equivalents, short-term investments, and restricted investments. Cash and cash equivalents included certificates of deposits, commercial paper, and corporate bonds that have an original maturity of 90 days or less and are readily convertible to known amounts of cash. Short-term investments were comprised of commercial paper, certificates of deposit, and corporate bonds, which mature in twelve months or less. \n", + "\n", + "Restricted investments were comprised of debt security investments in commercial paper, certificates of deposit, corporate bonds, and U.S. government securities which are held in trust accounts at third-party financial institutions pursuant to certain contracts with insurance providers. \n", + "\n", + "The company also had investments in non-marketable equity securities, which are measured at cost, with remeasurements to fair value only upon the occurrence of observable transactions for identical or similar investments of the same issuer or impairment.
" + ], + "text/plain": [ + "{response.response}
'))" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "mistralai22b", + "language": "python", + "name": "mistralai22b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + }, + "vscode": { + "interpreter": { + "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/examples/cookbooks/oreilly_course_cookbooks/Module-6/Router_And_SubQuestion_QueryEngine.ipynb b/docs/docs/examples/cookbooks/oreilly_course_cookbooks/Module-6/Router_And_SubQuestion_QueryEngine.ipynb new file mode 100644 index 0000000000000..78ad08cec2097 --- /dev/null +++ b/docs/docs/examples/cookbooks/oreilly_course_cookbooks/Module-6/Router_And_SubQuestion_QueryEngine.ipynb @@ -0,0 +1,955 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Router QueryEngine and SubQuestion QueryEngine\n", + "\n", + "In this notebook we will demonstrate:\n", + "\n", + "1. **RouterQueryEngine** - Handle user queries to choose from predefined indices.\n", + "2. **SubQuestionQueryEngine** - breaks down the complex query into sub-questions for each relevant data source, then gathers all the intermediate responses and synthesizes a final response.\n", + "\n", + "[RouterQueryEngine Documentation](https://docs.llamaindex.ai/en/stable/examples/query_engine/RouterQueryEngine/)\n", + "\n", + "[SubQuestionQueryEngine Documentation](https://docs.llamaindex.ai/en/stable/examples/query_engine/sub_question_query_engine/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Router QueryEngine\n", + "\n", + "Routers act as specialized modules that handle user queries and choose from a set of predefined options, each defined by specific metadata.\n", + "\n", + "There are two main types of core router modules:\n", + "\n", + "1. **LLM Selectors**: These modules present the available options as a text prompt and use the LLM text completion endpoint to make decisions.\n", + "\n", + "2. **Pydantic Selectors**: These modules format the options as Pydantic schemas and pass them to a function-calling endpoint, returning the results as Pydantic objects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: This is ONLY necessary in jupyter notebook.\n", + "# Details: Jupyter runs an event-loop behind the scenes.\n", + "# This results in nested event-loops when we start an event-loop to make async queries.\n", + "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n", + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NumExpr defaulting to 2 threads.\n" + ] + } + ], + "source": [ + "import logging\n", + "import sys\n", + "\n", + "# Set up the root logger\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.INFO) # Set logger level to INFO\n", + "\n", + "# Clear out any existing handlers\n", + "logger.handlers = []\n", + "\n", + "# Set up the StreamHandler to output to sys.stdout (Colab's output)\n", + "handler = logging.StreamHandler(sys.stdout)\n", + "handler.setLevel(logging.INFO) # Set handler level to INFO\n", + "\n", + "# Add the handler to the logger\n", + "logger.addHandler(handler)\n", + "\n", + "from llama_index.core import (\n", + " VectorStoreIndex,\n", + " SummaryIndex,\n", + " SimpleDirectoryReader,\n", + " ServiceContext,\n", + " StorageContext,\n", + ")\n", + "\n", + "import openai\n", + "import os\n", + "from IPython.display import display, HTML\n", + "\n", + "\n", + "# Setup openai api key\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-05-16 05:27:42-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.111.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 75042 (73K) [text/plain]\n", + "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n", + "\n", + "\r", + " data/paul 0%[ ] 0 --.-KB/s \r", + "data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.002s \n", + "\n", + "2024-05-16 05:27:42 (46.3 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir -p 'data/paul_graham/'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load documents\n", + "documents = SimpleDirectoryReader(\"data/paul_graham\").load_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Nodes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.text_splitter import SentenceSplitter\n", + "\n", + "# create parser and parse document into nodes\n", + "parser = SentenceSplitter(chunk_size=1024, chunk_overlap=100)\n", + "nodes = parser(documents)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create VectorStoreIndex and SummaryIndex." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "# Summary Index for summarization questions\n", + "summary_index = SummaryIndex(nodes)\n", + "\n", + "# Vector Index for answering specific context questions\n", + "vector_index = VectorStoreIndex(nodes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Query Engines.\n", + "\n", + "1. Summary Index Query Engine.\n", + "2. Vector Index Query Engine." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Summary Index Query Engine\n", + "summary_query_engine = summary_index.as_query_engine(\n", + " response_mode=\"tree_summarize\",\n", + " use_async=True,\n", + ")\n", + "\n", + "# Vector Index Query Engine\n", + "vector_query_engine = vector_index.as_query_engine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build summary index and vector index tools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.tools.query_engine import QueryEngineTool, ToolMetadata\n", + "\n", + "# Summary Index tool\n", + "summary_tool = QueryEngineTool.from_defaults(\n", + " query_engine=summary_query_engine,\n", + " description=\"Useful for summarization questions related to Paul Graham eassy on What I Worked On.\",\n", + ")\n", + "\n", + "# Vector Index tool\n", + "vector_tool = QueryEngineTool.from_defaults(\n", + " query_engine=vector_query_engine,\n", + " description=\"Useful for retrieving specific context from Paul Graham essay on What I Worked On.\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Router Query Engine\n", + "\n", + "Various selectors are at your disposal, each offering unique characteristics.\n", + "\n", + "Pydantic selectors, supported exclusively by gpt-4 and the default gpt-3.5-turbo, utilize the OpenAI Function Call API. Instead of interpreting raw JSON, they yield pydantic selection objects.\n", + "\n", + "On the other hand, LLM selectors employ the LLM to generate a JSON output, which is then parsed to query the relevant indexes.\n", + "\n", + "For both selector types, you can opt to route to either a single index or multiple indexes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PydanticSingleSelector\n", + "\n", + "Use the OpenAI Function API to generate/parse pydantic objects under the hood for the router selector." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.query_engine.router_query_engine import RouterQueryEngine\n", + "from llama_index.core.selectors.llm_selectors import (\n", + " LLMSingleSelector,\n", + " LLMMultiSelector,\n", + ")\n", + "from llama_index.core.selectors.pydantic_selectors import (\n", + " PydanticMultiSelector,\n", + " PydanticSingleSelector,\n", + ")\n", + "\n", + "# Create Router Query Engine\n", + "query_engine = RouterQueryEngine(\n", + " selector=PydanticSingleSelector.from_defaults(),\n", + " query_engine_tools=[\n", + " summary_tool,\n", + " vector_tool,\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Selecting query engine 0: The choice is specifically related to summarization questions about Paul Graham's essay on What I Worked On..\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "response = query_engine.query(\"What is the summary of the document?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "The document chronicles Paul Graham's journey through various endeavors, including his experiences with writing, programming, and founding software companies like Viaweb and Y Combinator. It discusses his exploration of painting, personal challenges such as his mother's illness, and his decision to step back from Y Combinator to focus on painting before returning to Lisp programming with the development of a new dialect called Bel. The narrative also covers Graham's reflections on his work choices, the transition of Y Combinator's leadership to Sam Altman, and his contemplation on future projects and the impact of customs in evolving fields.
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LLMSingleSelector\n", + "\n", + "Utilize OpenAI (or another LLM) to internally interpret the generated JSON and determine a sub-index for routing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Router Query Engine\n", + "query_engine = RouterQueryEngine(\n", + " selector=LLMSingleSelector.from_defaults(),\n", + " query_engine_tools=[\n", + " summary_tool,\n", + " vector_tool,\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Selecting query engine 0: This choice indicates that the summary is related to summarization questions specifically about Paul Graham's essay on What I Worked On..\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "response = query_engine.query(\"What is the summary of the document?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "The document chronicles Paul Graham's journey through various projects and endeavors, from his early experiences with writing and programming to his involvement in building software companies like Viaweb and Y Combinator. It details his exploration of different projects, challenges faced, decisions made, and eventual transition to focusing on painting and writing essays. The narrative also discusses his experimentation with the Lisp programming language and the development of a new Lisp dialect called Bel. The document concludes with Graham reflecting on his past projects and contemplating his future endeavors, emphasizing the importance of pursuing projects aligned with personal goals and interests.
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Selecting query engine 1: This choice is more relevant as it focuses on retrieving specific context from Paul Graham's essay on What I Worked On, which would likely provide information on what he did after RICS..\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "response = query_engine.query(\"What did Paul Graham do after RICS?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Paul Graham started painting after RICS.
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PydanticMultiSelector\n", + "\n", + "If you anticipate queries being directed to multiple indexes, it's advisable to use a multi-selector. This selector dispatches the query to various sub-indexes and subsequently aggregates the responses through a summary index to deliver a comprehensive answer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's create a simplekeywordtable index and corresponding tool." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleKeywordTableIndex\n", + "\n", + "keyword_index = SimpleKeywordTableIndex(nodes)\n", + "\n", + "keyword_query_engine = keyword_index.as_query_engine()\n", + "\n", + "keyword_tool = QueryEngineTool.from_defaults(\n", + " query_engine=keyword_query_engine,\n", + " description=\"Useful for retrieving specific context using keywords from Paul Graham essay on What I Worked On.\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build a router query engine." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query_engine = RouterQueryEngine(\n", + " selector=PydanticMultiSelector.from_defaults(),\n", + " query_engine_tools=[vector_tool, keyword_tool, summary_tool],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Selecting query engine 0: Retrieving specific context from Paul Graham essay on What I Worked On can provide detailed information about noteable events and people from the author's time at Interleaf and YC..\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Selecting query engine 1: Retrieving specific context using keywords from Paul Graham essay on What I Worked On can help identify key events and people related to the author's time at Interleaf and YC..\n", + "> Starting query: What were noteable events and people from the authors time at Interleaf and YC?\n", + "query keywords: ['noteable', 'authors', 'time', 'interleaf', 'yc', 'events', 'people']\n", + "> Extracted keywords: ['time', 'interleaf', 'yc', 'people']\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Combining responses from multiple query engines.\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "# This query could use either a keyword or vector query engine, so it will combine responses from both\n", + "response = query_engine.query(\n", + " \"What were noteable events and people from the authors time at Interleaf and YC?\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Notable events from the author's time at Interleaf include working on software development, observing inefficiencies in managing versions and ports, and learning about the dynamics of technology companies. Notable people mentioned from this time include Robert and Trevor, with whom the author worked on developing software components like the editor, shopping cart, and manager. \n", + "\n", + "During the author's time at Y Combinator (YC), notable events include working on various projects such as Hacker News, transitioning leadership to Sam Altman, and shifting focus towards painting after leaving YC. Notable people mentioned from this time include Julian, who provided seed funding for Viaweb, and Robert Morris, who advised the author about not letting YC be the last significant endeavor.
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SubQuestion Query Engine\n", + "\n", + "Here, we will demonstrate how to use a sub-question query engine to address the challenge of answering a complex query using multiple data sources.\n", + "\n", + "The SubQuestion Query Engine first breaks down the complex query into sub-questions for each relevant data source, then gathers all the intermediate responses and synthesizes a final response." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-05-16 05:36:06-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1880483 (1.8M) [application/octet-stream]\n", + "Saving to: ‘data/10k/uber_2021.pdf’\n", + "\n", + "\r", + "data/10k/uber_2021. 0%[ ] 0 --.-KB/s \r", + "data/10k/uber_2021. 100%[===================>] 1.79M --.-KB/s in 0.01s \n", + "\n", + "2024-05-16 05:36:06 (184 MB/s) - ‘data/10k/uber_2021.pdf’ saved [1880483/1880483]\n", + "\n", + "--2024-05-16 05:36:06-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1440303 (1.4M) [application/octet-stream]\n", + "Saving to: ‘data/10k/lyft_2021.pdf’\n", + "\n", + "data/10k/lyft_2021. 100%[===================>] 1.37M --.-KB/s in 0.01s \n", + "\n", + "2024-05-16 05:36:06 (120 MB/s) - ‘data/10k/lyft_2021.pdf’ saved [1440303/1440303]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir -p 'data/10k/'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lyft_docs = SimpleDirectoryReader(\n", + " input_files=[\"./data/10k/lyft_2021.pdf\"]\n", + ").load_data()\n", + "uber_docs = SimpleDirectoryReader(\n", + " input_files=[\"./data/10k/uber_2021.pdf\"]\n", + ").load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded lyft 10-K with 238 pages\n", + "Loaded Uber 10-K with 307 pages\n" + ] + } + ], + "source": [ + "print(f\"Loaded lyft 10-K with {len(lyft_docs)} pages\")\n", + "print(f\"Loaded Uber 10-K with {len(uber_docs)} pages\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Indices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "lyft_index = VectorStoreIndex.from_documents(lyft_docs)\n", + "uber_index = VectorStoreIndex.from_documents(uber_docs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Query Engines" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lyft_engine = lyft_index.as_query_engine(similarity_top_k=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uber_engine = uber_index.as_query_engine(similarity_top_k=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "response = await lyft_engine.aquery(\n", + " \"What is the revenue of Lyft in 2021? Answer in millions with page reference\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "The revenue of Lyft in 2021 was $3.208 billion. (Page reference: 79)
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "response = await uber_engine.aquery(\n", + " \"What is the revenue of Uber in 2021? Answer in millions, with page reference\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "The revenue of Uber in 2021 was $17,455 million. (Reference: page 77)
" + ], + "text/plain": [ + "{response.response}
'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define QueryEngine Tools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query_engine_tools = [\n", + " QueryEngineTool(\n", + " query_engine=lyft_engine,\n", + " metadata=ToolMetadata(\n", + " name=\"lyft_10k\",\n", + " description=\"Provides information about Lyft financials for year 2021\",\n", + " ),\n", + " ),\n", + " QueryEngineTool(\n", + " query_engine=uber_engine,\n", + " metadata=ToolMetadata(\n", + " name=\"uber_10k\",\n", + " description=\"Provides information about Uber financials for year 2021\",\n", + " ),\n", + " ),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SubQuestion QueryEngine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.query_engine.sub_question_query_engine import (\n", + " SubQuestionQueryEngine,\n", + ")\n", + "\n", + "sub_question_query_engine = SubQuestionQueryEngine.from_defaults(\n", + " query_engine_tools=query_engine_tools\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Querying" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Generated 4 sub questions.\n", + "\u001b[1;3;38;2;237;90;200m[uber_10k] Q: What was the revenue of Uber in 2020?\n", + "\u001b[0m\u001b[1;3;38;2;90;149;237m[uber_10k] Q: What was the revenue of Uber in 2021?\n", + "\u001b[0m\u001b[1;3;38;2;11;159;203m[lyft_10k] Q: What was the revenue of Lyft in 2020?\n", + "\u001b[0m\u001b[1;3;38;2;155;135;227m[lyft_10k] Q: What was the revenue of Lyft in 2021?\n", + "\u001b[0mHTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "\u001b[1;3;38;2;155;135;227m[lyft_10k] A: $3,208,323\n", + "\u001b[0mHTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "\u001b[1;3;38;2;11;159;203m[lyft_10k] A: $2,364,681\n", + "\u001b[0mHTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "\u001b[1;3;38;2;237;90;200m[uber_10k] A: $11,139 million\n", + "\u001b[0mHTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "\u001b[1;3;38;2;90;149;237m[uber_10k] A: $17,455\n", + "\u001b[0mHTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "response = await sub_question_query_engine.aquery(\n", + " \"Compare revenue growth of Uber and Lyft from 2020 to 2021\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Lyft's revenue grew by approximately 35.4% from 2020 to 2021, while Uber's revenue increased by around 56.8% during the same period.
" + ], + "text/plain": [ + "{response.response}
'))" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/examples/cookbooks/oreilly_course_cookbooks/Module-7/Multi_Modal_RAG_System.ipynb b/docs/docs/examples/cookbooks/oreilly_course_cookbooks/Module-7/Multi_Modal_RAG_System.ipynb new file mode 100644 index 0000000000000..d4950ea498822 --- /dev/null +++ b/docs/docs/examples/cookbooks/oreilly_course_cookbooks/Module-7/Multi_Modal_RAG_System.ipynb @@ -0,0 +1,722 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-Modal RAG System\n", + "\n", + "In this notebook we will demonstrate the following:\n", + "\n", + "1. Querying images with a Multi-Modal LLM.\n", + "2. Buidling a Multi-Modal RAG System." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index-multi-modal-llms-openai\n", + "!pip install llama-index-vector-stores-qdrant\n", + "!pip install llama_index ftfy regex tqdm\n", + "!pip install llama-index-embeddings-clip\n", + "!pip install git+https://github.com/openai/CLIP.git\n", + "!pip install matplotlib scikit-image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set API Key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Images with `urls`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.multi_modal_llms.openai import OpenAIMultiModal\n", + "\n", + "from llama_index.core.multi_modal_llms.generic_utils import load_image_urls\n", + "\n", + "\n", + "image_urls = [\n", + " \"https://res.cloudinary.com/hello-tickets/image/upload/c_limit,f_auto,q_auto,w_1920/v1640835927/o3pfl41q7m5bj8jardk0.jpg\",\n", + "]\n", + "\n", + "image_documents = load_image_urls(image_urls)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Querying with `GPT-4V` vision API." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "openai_mm_llm = OpenAIMultiModal(\n", + " model=\"gpt-4-vision-preview\", max_new_tokens=300\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The image shows the Colosseum in Rome, Italy, illuminated at night with the colors of the Italian flag: green, white, and red. The ancient amphitheater's iconic arches are vividly lit, and the structure stands out against the dark blue evening sky. Some construction or excavation work appears to be taking place in the foreground, and a few people can be seen walking near the site.\n" + ] + } + ], + "source": [ + "response = openai_mm_llm.complete(\n", + " prompt=\"Describe the images as an alternative text\",\n", + " image_documents=image_documents,\n", + ")\n", + "\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Images from directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "input_image_path = Path(\"input_images\")\n", + "if not input_image_path.exists():\n", + " Path.mkdir(input_image_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-04-27 20:51:52-- https://docs.google.com/uc?export=download&id=1nUhsBRiSWxcVQv8t8Cvvro8HJZ88LCzj\n", + "Resolving docs.google.com (docs.google.com)... 172.253.63.113, 172.253.63.139, 172.253.63.100, ...\n", + "Connecting to docs.google.com (docs.google.com)|172.253.63.113|:443... connected.\n", + "HTTP request sent, awaiting response... 303 See Other\n", + "Location: https://drive.usercontent.google.com/download?id=1nUhsBRiSWxcVQv8t8Cvvro8HJZ88LCzj&export=download [following]\n", + "--2024-04-27 20:51:52-- https://drive.usercontent.google.com/download?id=1nUhsBRiSWxcVQv8t8Cvvro8HJZ88LCzj&export=download\n", + "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 172.253.122.132, 2607:f8b0:4004:c09::84\n", + "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|172.253.122.132|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 160684 (157K) [image/png]\n", + "Saving to: ‘./input_images/long_range_spec.png’\n", + "\n", + "./input_images/long 100%[===================>] 156.92K --.-KB/s in 0.01s \n", + "\n", + "2024-04-27 20:51:53 (12.3 MB/s) - ‘./input_images/long_range_spec.png’ saved [160684/160684]\n", + "\n", + "--2024-04-27 20:51:53-- https://docs.google.com/uc?export=download&id=19pLwx0nVqsop7lo0ubUSYTzQfMtKJJtJ\n", + "Resolving docs.google.com (docs.google.com)... 172.253.63.113, 172.253.63.139, 172.253.63.100, ...\n", + "Connecting to docs.google.com (docs.google.com)|172.253.63.113|:443... connected.\n", + "HTTP request sent, awaiting response... 303 See Other\n", + "Location: https://drive.usercontent.google.com/download?id=19pLwx0nVqsop7lo0ubUSYTzQfMtKJJtJ&export=download [following]\n", + "--2024-04-27 20:51:53-- https://drive.usercontent.google.com/download?id=19pLwx0nVqsop7lo0ubUSYTzQfMtKJJtJ&export=download\n", + "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 172.253.122.132, 2607:f8b0:4004:c09::84\n", + "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|172.253.122.132|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1308468 (1.2M) [image/png]\n", + "Saving to: ‘./input_images/model_y.png’\n", + "\n", + "./input_images/mode 100%[===================>] 1.25M --.-KB/s in 0.03s \n", + "\n", + "2024-04-27 20:51:55 (38.8 MB/s) - ‘./input_images/model_y.png’ saved [1308468/1308468]\n", + "\n", + "--2024-04-27 20:51:55-- https://docs.google.com/uc?export=download&id=1utu3iD9XEgR5Sb7PrbtMf1qw8T1WdNmF\n", + "Resolving docs.google.com (docs.google.com)... 172.253.63.113, 172.253.63.139, 172.253.63.100, ...\n", + "Connecting to docs.google.com (docs.google.com)|172.253.63.113|:443... connected.\n", + "HTTP request sent, awaiting response... 303 See Other\n", + "Location: https://drive.usercontent.google.com/download?id=1utu3iD9XEgR5Sb7PrbtMf1qw8T1WdNmF&export=download [following]\n", + "--2024-04-27 20:51:55-- https://drive.usercontent.google.com/download?id=1utu3iD9XEgR5Sb7PrbtMf1qw8T1WdNmF&export=download\n", + "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 172.253.122.132, 2607:f8b0:4004:c09::84\n", + "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|172.253.122.132|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 62818 (61K) [image/png]\n", + "Saving to: ‘./input_images/performance_spec.png’\n", + "\n", + "./input_images/perf 100%[===================>] 61.35K --.-KB/s in 0.007s \n", + "\n", + "2024-04-27 20:51:55 (8.12 MB/s) - ‘./input_images/performance_spec.png’ saved [62818/62818]\n", + "\n", + "--2024-04-27 20:51:55-- https://docs.google.com/uc?export=download&id=1dpUakWMqaXR4Jjn1kHuZfB0pAXvjn2-i\n", + "Resolving docs.google.com (docs.google.com)... 172.253.63.113, 172.253.63.139, 172.253.63.100, ...\n", + "Connecting to docs.google.com (docs.google.com)|172.253.63.113|:443... connected.\n", + "HTTP request sent, awaiting response... 303 See Other\n", + "Location: https://drive.usercontent.google.com/download?id=1dpUakWMqaXR4Jjn1kHuZfB0pAXvjn2-i&export=download [following]\n", + "--2024-04-27 20:51:55-- https://drive.usercontent.google.com/download?id=1dpUakWMqaXR4Jjn1kHuZfB0pAXvjn2-i&export=download\n", + "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 172.253.122.132, 2607:f8b0:4004:c09::84\n", + "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|172.253.122.132|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 128556 (126K) [image/png]\n", + "Saving to: ‘./input_images/price.png’\n", + "\n", + "./input_images/pric 100%[===================>] 125.54K --.-KB/s in 0.01s \n", + "\n", + "2024-04-27 20:51:56 (10.9 MB/s) - ‘./input_images/price.png’ saved [128556/128556]\n", + "\n", + "--2024-04-27 20:51:56-- https://docs.google.com/uc?export=download&id=1qNeT201QAesnAP5va1ty0Ky5Q_jKkguV\n", + "Resolving docs.google.com (docs.google.com)... 172.253.63.113, 172.253.63.139, 172.253.63.100, ...\n", + "Connecting to docs.google.com (docs.google.com)|172.253.63.113|:443... connected.\n", + "HTTP request sent, awaiting response... 303 See Other\n", + "Location: https://drive.usercontent.google.com/download?id=1qNeT201QAesnAP5va1ty0Ky5Q_jKkguV&export=download [following]\n", + "--2024-04-27 20:51:56-- https://drive.usercontent.google.com/download?id=1qNeT201QAesnAP5va1ty0Ky5Q_jKkguV&export=download\n", + "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 172.253.122.132, 2607:f8b0:4004:c09::84\n", + "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|172.253.122.132|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 160998 (157K) [image/png]\n", + "Saving to: ‘./input_images/real_wheel_spec.png’\n", + "\n", + "./input_images/real 100%[===================>] 157.22K --.-KB/s in 0.01s \n", + "\n", + "2024-04-27 20:51:57 (12.5 MB/s) - ‘./input_images/real_wheel_spec.png’ saved [160998/160998]\n", + "\n" + ] + } + ], + "source": [ + "!wget \"https://docs.google.com/uc?export=download&id=1nUhsBRiSWxcVQv8t8Cvvro8HJZ88LCzj\" -O ./input_images/long_range_spec.png\n", + "!wget \"https://docs.google.com/uc?export=download&id=19pLwx0nVqsop7lo0ubUSYTzQfMtKJJtJ\" -O ./input_images/model_y.png\n", + "!wget \"https://docs.google.com/uc?export=download&id=1utu3iD9XEgR5Sb7PrbtMf1qw8T1WdNmF\" -O ./input_images/performance_spec.png\n", + "!wget \"https://docs.google.com/uc?export=download&id=1dpUakWMqaXR4Jjn1kHuZfB0pAXvjn2-i\" -O ./input_images/price.png\n", + "!wget \"https://docs.google.com/uc?export=download&id=1qNeT201QAesnAP5va1ty0Ky5Q_jKkguV\" -O ./input_images/real_wheel_spec.png" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "\n", + "\n", + "def plot_images(image_paths):\n", + " images_shown = 0\n", + " plt.figure(figsize=(16, 9))\n", + " for img_path in image_paths:\n", + " if os.path.isfile(img_path):\n", + " image = Image.open(img_path)\n", + "\n", + " plt.subplot(2, 3, images_shown + 1)\n", + " plt.imshow(image)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + "\n", + " images_shown += 1\n", + " if images_shown >= 9:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + ""
+ ""
]
},
{
diff --git a/docs/docs/examples/customization/llms/SimpleIndexDemo-ChatGPT.ipynb b/docs/docs/examples/customization/llms/SimpleIndexDemo-ChatGPT.ipynb
index f8746b1b4924c..0264ed417a4ed 100644
--- a/docs/docs/examples/customization/llms/SimpleIndexDemo-ChatGPT.ipynb
+++ b/docs/docs/examples/customization/llms/SimpleIndexDemo-ChatGPT.ipynb
@@ -5,7 +5,7 @@
"id": "7ae43f8b",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_camel.ipynb b/docs/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_camel.ipynb
index e643d37c36ecc..5fd3303e56758 100644
--- a/docs/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_camel.ipynb
+++ b/docs/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_camel.ipynb
@@ -5,7 +5,7 @@
"id": "5f753ded",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_stablelm.ipynb b/docs/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_stablelm.ipynb
index a9bc05e564c76..df234e5d336e8 100644
--- a/docs/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_stablelm.ipynb
+++ b/docs/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_stablelm.ipynb
@@ -5,7 +5,7 @@
"id": "6b39b02d",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/customization/prompts/chat_prompts.ipynb b/docs/docs/examples/customization/prompts/chat_prompts.ipynb
index 6a62f431ed4d0..1c356a792ada6 100644
--- a/docs/docs/examples/customization/prompts/chat_prompts.ipynb
+++ b/docs/docs/examples/customization/prompts/chat_prompts.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/customization/prompts/completion_prompts.ipynb b/docs/docs/examples/customization/prompts/completion_prompts.ipynb
index 43137468571be..824d342a384d2 100644
--- a/docs/docs/examples/customization/prompts/completion_prompts.ipynb
+++ b/docs/docs/examples/customization/prompts/completion_prompts.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/customization/streaming/SimpleIndexDemo-streaming.ipynb b/docs/docs/examples/customization/streaming/SimpleIndexDemo-streaming.ipynb
index 7fa593ad2cc74..86515efff87c1 100644
--- a/docs/docs/examples/customization/streaming/SimpleIndexDemo-streaming.ipynb
+++ b/docs/docs/examples/customization/streaming/SimpleIndexDemo-streaming.ipynb
@@ -5,7 +5,7 @@
"id": "87854970",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/data_connectors/DatabaseReaderDemo.ipynb b/docs/docs/examples/data_connectors/DatabaseReaderDemo.ipynb
index 063621a07352a..a275cd40577d9 100644
--- a/docs/docs/examples/data_connectors/DatabaseReaderDemo.ipynb
+++ b/docs/docs/examples/data_connectors/DatabaseReaderDemo.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/data_connectors/MakeDemo.ipynb b/docs/docs/examples/data_connectors/MakeDemo.ipynb
index 72b7b282a3fc6..fbbc0b5176b30 100644
--- a/docs/docs/examples/data_connectors/MakeDemo.ipynb
+++ b/docs/docs/examples/data_connectors/MakeDemo.ipynb
@@ -5,7 +5,7 @@
"id": "b890c854",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/data_connectors/MboxReaderDemo.ipynb b/docs/docs/examples/data_connectors/MboxReaderDemo.ipynb
index f6ebf3d9668b1..d4b45a9c6fb35 100644
--- a/docs/docs/examples/data_connectors/MboxReaderDemo.ipynb
+++ b/docs/docs/examples/data_connectors/MboxReaderDemo.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/data_connectors/MilvusReaderDemo.ipynb b/docs/docs/examples/data_connectors/MilvusReaderDemo.ipynb
index d0ad942161f0f..4310fad9db9b7 100644
--- a/docs/docs/examples/data_connectors/MilvusReaderDemo.ipynb
+++ b/docs/docs/examples/data_connectors/MilvusReaderDemo.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/data_connectors/MyScaleReaderDemo.ipynb b/docs/docs/examples/data_connectors/MyScaleReaderDemo.ipynb
index 96c3172f90f93..583e1276552a4 100644
--- a/docs/docs/examples/data_connectors/MyScaleReaderDemo.ipynb
+++ b/docs/docs/examples/data_connectors/MyScaleReaderDemo.ipynb
@@ -5,7 +5,7 @@
"id": "105c464c",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/data_connectors/ObsidianReaderDemo.ipynb b/docs/docs/examples/data_connectors/ObsidianReaderDemo.ipynb
index 2894f307ca204..33d9a020d45ae 100644
--- a/docs/docs/examples/data_connectors/ObsidianReaderDemo.ipynb
+++ b/docs/docs/examples/data_connectors/ObsidianReaderDemo.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/data_connectors/PsychicDemo.ipynb b/docs/docs/examples/data_connectors/PsychicDemo.ipynb
index d58c75c3db46b..1ff4ea95938a8 100644
--- a/docs/docs/examples/data_connectors/PsychicDemo.ipynb
+++ b/docs/docs/examples/data_connectors/PsychicDemo.ipynb
@@ -5,7 +5,7 @@
"id": "8c35f3c4",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/data_connectors/html_tag_reader.ipynb b/docs/docs/examples/data_connectors/html_tag_reader.ipynb
index 6d366b0adf7b6..0c3074c01a9a7 100644
--- a/docs/docs/examples/data_connectors/html_tag_reader.ipynb
+++ b/docs/docs/examples/data_connectors/html_tag_reader.ipynb
@@ -5,7 +5,7 @@
"id": "d70938ec",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/docstore/DocstoreDemo.ipynb b/docs/docs/examples/docstore/DocstoreDemo.ipynb
index df3c9e5162d01..fe3d5b5aee1d4 100644
--- a/docs/docs/examples/docstore/DocstoreDemo.ipynb
+++ b/docs/docs/examples/docstore/DocstoreDemo.ipynb
@@ -9,7 +9,7 @@
"\n",
"This guide shows you how to directly use our `DocumentStore` abstraction. By putting nodes in the docstore, this allows you to define multiple indices over the same underlying docstore, instead of duplicating data across indices.\n",
"\n",
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/docstore/DynamoDBDocstoreDemo.ipynb b/docs/docs/examples/docstore/DynamoDBDocstoreDemo.ipynb
index 5b2d147a46617..2dc5f5b973d89 100644
--- a/docs/docs/examples/docstore/DynamoDBDocstoreDemo.ipynb
+++ b/docs/docs/examples/docstore/DynamoDBDocstoreDemo.ipynb
@@ -9,7 +9,7 @@
"\n",
"This guide shows you how to directly use our `DocumentStore` abstraction backed by DynamoDB. By putting nodes in the docstore, this allows you to define multiple indices over the same underlying docstore, instead of duplicating data across indices.\n",
"\n",
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/docstore/FirestoreDemo.ipynb b/docs/docs/examples/docstore/FirestoreDemo.ipynb
index d9c5ff10b2b64..9f4296c8e7716 100644
--- a/docs/docs/examples/docstore/FirestoreDemo.ipynb
+++ b/docs/docs/examples/docstore/FirestoreDemo.ipynb
@@ -8,7 +8,7 @@
"\n",
"This guide shows you how to directly use our `DocumentStore` abstraction backed by Google Firestore. By putting nodes in the docstore, this allows you to define multiple indices over the same underlying docstore, instead of duplicating data across indices.\n",
"\n",
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/docstore/MongoDocstoreDemo.ipynb b/docs/docs/examples/docstore/MongoDocstoreDemo.ipynb
index 8af5f771c890a..88cb20fca1daa 100644
--- a/docs/docs/examples/docstore/MongoDocstoreDemo.ipynb
+++ b/docs/docs/examples/docstore/MongoDocstoreDemo.ipynb
@@ -9,7 +9,7 @@
"\n",
"This guide shows you how to directly use our `DocumentStore` abstraction backed by MongoDB. By putting nodes in the docstore, this allows you to define multiple indices over the same underlying docstore, instead of duplicating data across indices.\n",
"\n",
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb b/docs/docs/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb
index 23b7a86b7b544..a163131777b1a 100644
--- a/docs/docs/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb
+++ b/docs/docs/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb
@@ -5,7 +5,7 @@
"id": "c59e5e4e",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/embeddings/Anyscale.ipynb b/docs/docs/examples/embeddings/Anyscale.ipynb
index 7193b1f232e3b..f82f69c8ae1cd 100644
--- a/docs/docs/examples/embeddings/Anyscale.ipynb
+++ b/docs/docs/examples/embeddings/Anyscale.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/embeddings/Langchain.ipynb b/docs/docs/examples/embeddings/Langchain.ipynb
index 97b3437ce4341..b20ea3a692b2d 100644
--- a/docs/docs/examples/embeddings/Langchain.ipynb
+++ b/docs/docs/examples/embeddings/Langchain.ipynb
@@ -9,7 +9,7 @@
"\n",
"This guide shows you how to use embedding models from [LangChain](https://python.langchain.com/docs/integrations/text_embedding/).\n",
"\n",
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/embeddings/OpenAI.ipynb b/docs/docs/examples/embeddings/OpenAI.ipynb
index 9697d2d7270bc..b5474da2893ed 100644
--- a/docs/docs/examples/embeddings/OpenAI.ipynb
+++ b/docs/docs/examples/embeddings/OpenAI.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/embeddings/alephalpha.ipynb b/docs/docs/examples/embeddings/alephalpha.ipynb
index 82bceabb46129..a2c42b3bdd54a 100644
--- a/docs/docs/examples/embeddings/alephalpha.ipynb
+++ b/docs/docs/examples/embeddings/alephalpha.ipynb
@@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/embeddings/bedrock.ipynb b/docs/docs/examples/embeddings/bedrock.ipynb
index c7fe8fe931b72..0f91ce15701a5 100644
--- a/docs/docs/examples/embeddings/bedrock.ipynb
+++ b/docs/docs/examples/embeddings/bedrock.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/embeddings/custom_embeddings.ipynb b/docs/docs/examples/embeddings/custom_embeddings.ipynb
index 647a761932321..b6b1956ce6b62 100644
--- a/docs/docs/examples/embeddings/custom_embeddings.ipynb
+++ b/docs/docs/examples/embeddings/custom_embeddings.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -88,9 +88,9 @@
" instruction: str = \"Represent a document for semantic search:\",\n",
" **kwargs: Any,\n",
" ) -> None:\n",
+ " super().__init__(**kwargs)\n",
" self._model = INSTRUCTOR(instructor_model_name)\n",
" self._instruction = instruction\n",
- " super().__init__(**kwargs)\n",
"\n",
" @classmethod\n",
" def class_name(cls) -> str:\n",
diff --git a/docs/docs/examples/embeddings/deepinfra.ipynb b/docs/docs/examples/embeddings/deepinfra.ipynb
index 591f1de537168..3f83759d7da74 100644
--- a/docs/docs/examples/embeddings/deepinfra.ipynb
+++ b/docs/docs/examples/embeddings/deepinfra.ipynb
@@ -6,7 +6,7 @@
"metadata": {},
"outputs": [],
"source": [
- "\n"
+ "\n"
]
},
{
diff --git a/docs/docs/examples/embeddings/fireworks.ipynb b/docs/docs/examples/embeddings/fireworks.ipynb
index 7ebeeb8f19844..d3b4daec65ef0 100644
--- a/docs/docs/examples/embeddings/fireworks.ipynb
+++ b/docs/docs/examples/embeddings/fireworks.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/embeddings/nvidia.ipynb b/docs/docs/examples/embeddings/nvidia.ipynb
index db7beaf3636d9..91cb1b732649f 100644
--- a/docs/docs/examples/embeddings/nvidia.ipynb
+++ b/docs/docs/examples/embeddings/nvidia.ipynb
@@ -128,12 +128,33 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/raspawar/Desktop/llama_index/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/llama_index/embeddings/nvidia/base.py:161: UserWarning: Default model is set as: NV-Embed-QA. \n",
+ "Set model using model parameter. \n",
+ "To get available models use available_models property.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[Model(id='NV-Embed-QA', base_model=None)]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"from llama_index.embeddings.nvidia import NVIDIAEmbedding\n",
"\n",
"# connect to an embedding NIM running at localhost:8080\n",
- "embedder = NVIDIAEmbeddings(base_url=\"http://localhost:8080/v1\")\n",
+ "embedder = NVIDIAEmbedding(base_url=\"http://localhost:8080/v1\")\n",
"embedder.available_models"
]
},
diff --git a/docs/docs/examples/embeddings/oci_genai.ipynb b/docs/docs/examples/embeddings/oci_genai.ipynb
index f3e6e6476a33d..f59422484fd3b 100644
--- a/docs/docs/examples/embeddings/oci_genai.ipynb
+++ b/docs/docs/examples/embeddings/oci_genai.ipynb
@@ -6,7 +6,7 @@
"id": "6d1ca9ac",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/embeddings/ollama_embedding.ipynb b/docs/docs/examples/embeddings/ollama_embedding.ipynb
index 0fc06d648449b..536d8164c4168 100644
--- a/docs/docs/examples/embeddings/ollama_embedding.ipynb
+++ b/docs/docs/examples/embeddings/ollama_embedding.ipynb
@@ -5,7 +5,7 @@
"id": "efe8f603c3a1ea67",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/embeddings/text_embedding_inference.ipynb b/docs/docs/examples/embeddings/text_embedding_inference.ipynb
index d7b8d5c68b5d2..2a08337c09c6b 100644
--- a/docs/docs/examples/embeddings/text_embedding_inference.ipynb
+++ b/docs/docs/examples/embeddings/text_embedding_inference.ipynb
@@ -16,7 +16,7 @@
"\n",
"This notebook demonstrates how to configure `TextEmbeddingInference` embeddings.\n",
"\n",
- "The first step is to deploy the embeddings server. For detailed instructions, see the [official repository for Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference).\n",
+ "The first step is to deploy the embeddings server. For detailed instructions, see the [official repository for Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference). Or [tei-gaudi repository](https://github.com/huggingface/tei-gaudi) if you are deploying on Habana Gaudi/Gaudi 2. \n",
"\n",
"Once deployed, the code below will connect to and submit embeddings for inference."
]
diff --git a/docs/docs/examples/embeddings/together.ipynb b/docs/docs/examples/embeddings/together.ipynb
index b132125c4e27c..e81c06dbbb06d 100644
--- a/docs/docs/examples/embeddings/together.ipynb
+++ b/docs/docs/examples/embeddings/together.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/embeddings/upstage.ipynb b/docs/docs/examples/embeddings/upstage.ipynb
index e1252bee5acc2..0c00df9f5f69e 100644
--- a/docs/docs/examples/embeddings/upstage.ipynb
+++ b/docs/docs/examples/embeddings/upstage.ipynb
@@ -28,7 +28,7 @@
"metadata": {},
"outputs": [],
"source": [
- "%pip install llama-index-embeddings-upstage==0.1.0"
+ "%pip install llama-index-embeddings-upstage==0.2.1"
]
},
{
diff --git a/docs/docs/examples/evaluation/BeirEvaluation.ipynb b/docs/docs/examples/evaluation/BeirEvaluation.ipynb
index fc5a973f2774f..9c3f4b04a4458 100644
--- a/docs/docs/examples/evaluation/BeirEvaluation.ipynb
+++ b/docs/docs/examples/evaluation/BeirEvaluation.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/Deepeval.ipynb b/docs/docs/examples/evaluation/Deepeval.ipynb
index c91b3fdcb63b9..5fadbd3e7da29 100644
--- a/docs/docs/examples/evaluation/Deepeval.ipynb
+++ b/docs/docs/examples/evaluation/Deepeval.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/HotpotQADistractor.ipynb b/docs/docs/examples/evaluation/HotpotQADistractor.ipynb
index 667356c07b355..94c28551f3ec2 100644
--- a/docs/docs/examples/evaluation/HotpotQADistractor.ipynb
+++ b/docs/docs/examples/evaluation/HotpotQADistractor.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/QuestionGeneration.ipynb b/docs/docs/examples/evaluation/QuestionGeneration.ipynb
index b327f7d0ef338..fbf51e902b839 100644
--- a/docs/docs/examples/evaluation/QuestionGeneration.ipynb
+++ b/docs/docs/examples/evaluation/QuestionGeneration.ipynb
@@ -5,7 +5,7 @@
"id": "af775680",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/RAGChecker.ipynb b/docs/docs/examples/evaluation/RAGChecker.ipynb
index 558a7c4125792..0bc7d26a10d42 100644
--- a/docs/docs/examples/evaluation/RAGChecker.ipynb
+++ b/docs/docs/examples/evaluation/RAGChecker.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/RetryQuery.ipynb b/docs/docs/examples/evaluation/RetryQuery.ipynb
index e399db922a350..ed056e79ef46c 100644
--- a/docs/docs/examples/evaluation/RetryQuery.ipynb
+++ b/docs/docs/examples/evaluation/RetryQuery.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/TonicValidateEvaluators.ipynb b/docs/docs/examples/evaluation/TonicValidateEvaluators.ipynb
index f13eff89b4a38..9c6946c51204b 100644
--- a/docs/docs/examples/evaluation/TonicValidateEvaluators.ipynb
+++ b/docs/docs/examples/evaluation/TonicValidateEvaluators.ipynb
@@ -5,7 +5,7 @@
"id": "55c4401c-71f2-4cf2-a7c3-5cd490302e1b",
"metadata": {},
"source": [
- "\n",
+ "\n",
" \n",
""
]
diff --git a/docs/docs/examples/evaluation/answer_and_context_relevancy.ipynb b/docs/docs/examples/evaluation/answer_and_context_relevancy.ipynb
index a61c0be134cb1..8de4c40653909 100644
--- a/docs/docs/examples/evaluation/answer_and_context_relevancy.ipynb
+++ b/docs/docs/examples/evaluation/answer_and_context_relevancy.ipynb
@@ -5,7 +5,7 @@
"id": "9ad59b9d",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/correctness_eval.ipynb b/docs/docs/examples/evaluation/correctness_eval.ipynb
index 8e8cb14ab1249..d816f1066f1f7 100644
--- a/docs/docs/examples/evaluation/correctness_eval.ipynb
+++ b/docs/docs/examples/evaluation/correctness_eval.ipynb
@@ -5,7 +5,7 @@
"id": "79c6ea87",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/guideline_eval.ipynb b/docs/docs/examples/evaluation/guideline_eval.ipynb
index aab198b3e0e0e..3d8739e5f91cb 100644
--- a/docs/docs/examples/evaluation/guideline_eval.ipynb
+++ b/docs/docs/examples/evaluation/guideline_eval.ipynb
@@ -5,7 +5,7 @@
"id": "9e22fa55",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/mt_bench_human_judgement.ipynb b/docs/docs/examples/evaluation/mt_bench_human_judgement.ipynb
index 077b73be239ce..b2ccb1bef4d8a 100644
--- a/docs/docs/examples/evaluation/mt_bench_human_judgement.ipynb
+++ b/docs/docs/examples/evaluation/mt_bench_human_judgement.ipynb
@@ -5,7 +5,7 @@
"id": "4bc26779",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/mt_bench_single_grading.ipynb b/docs/docs/examples/evaluation/mt_bench_single_grading.ipynb
index 9d5a9ccc59012..132bb4d58da88 100644
--- a/docs/docs/examples/evaluation/mt_bench_single_grading.ipynb
+++ b/docs/docs/examples/evaluation/mt_bench_single_grading.ipynb
@@ -5,7 +5,7 @@
"id": "39bbe88b",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/evaluation/multi_modal/multi_modal_rag_evaluation.ipynb b/docs/docs/examples/evaluation/multi_modal/multi_modal_rag_evaluation.ipynb
index 1b387513472f1..2a42b875b6205 100644
--- a/docs/docs/examples/evaluation/multi_modal/multi_modal_rag_evaluation.ipynb
+++ b/docs/docs/examples/evaluation/multi_modal/multi_modal_rag_evaluation.ipynb
@@ -5,7 +5,7 @@
"id": "f064137a-a77f-4745-bae5-6b7eb3cbbf3f",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/finetuning/embeddings/finetune_corpus_embedding.ipynb b/docs/docs/examples/finetuning/embeddings/finetune_corpus_embedding.ipynb
new file mode 100644
index 0000000000000..277d5bc9a8aa1
--- /dev/null
+++ b/docs/docs/examples/finetuning/embeddings/finetune_corpus_embedding.ipynb
@@ -0,0 +1,485 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Finetuning corpus embeddings using NUDGE\n",
+ "[NUDGE](https://www.arxiv.org/abs/2409.02343) is a novel simple and lightweight fine-tuning method that boosts accuracy when retrieving text using semantic similarity with pre-trained embedding models. NUDGE directly modifies the embeddings of data records to maximize the similarity between training queries and their ground-truth answers. NUDGE does so non-parametrically. Non-parametric means that NUDGE does not modify model parameters to generate better embeddings, as fine-tuning the embedding model, or training adaptors would. Instead, NUDGE directly changes the embeddings themselves. Compared with fine-tuning the pre-trained model and training adaptors, NUDGE provides 3.3x and 4.3x higher increase in accuracy and runs 200x and 3x faster, respectively. [Here](https://data-people-group.github.io/blogs/2024/09/05/nudge/) is a blog post on NUDGE, and [here](https://www.arxiv.org/abs/2409.02343) is the paper with more details.\n",
+ "\n",
+ "We demonstrate NUDGE's effectiveness on a commonly used Information Retrieval benchmark called Scifact."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install llama-index-experimental llama-index-embeddings-huggingface torch datasets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load the scifact benchmark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.finetuning import EmbeddingQAFinetuneDataset\n",
+ "from datasets import load_dataset\n",
+ "\n",
+ "\n",
+ "def load_hf_dataset(dataset_name):\n",
+ " hf_dataset_name = f\"sepz/{dataset_name}_ft\"\n",
+ " corpus = load_dataset(hf_dataset_name, \"data_records\", split=\"train\")\n",
+ "\n",
+ " queries_train = load_dataset(hf_dataset_name, \"qs\", split=\"train\")\n",
+ " queries_validation = load_dataset(hf_dataset_name, \"qs\", split=\"dev\")\n",
+ " queries_test = load_dataset(hf_dataset_name, \"qs\", split=\"test\")\n",
+ "\n",
+ " qrels_train = load_dataset(hf_dataset_name, \"qs_rel\", split=\"train\")\n",
+ " qrels_validation = load_dataset(hf_dataset_name, \"qs_rel\", split=\"dev\")\n",
+ " qrels_test = load_dataset(hf_dataset_name, \"qs_rel\", split=\"test\")\n",
+ "\n",
+ " corpus = {\n",
+ " str(corpus[i][\"record_id\"]): corpus[i][\"text\"]\n",
+ " for i in range(len(corpus))\n",
+ " }\n",
+ "\n",
+ " queries_train = {\n",
+ " str(queries_train[i][\"q_id\"]): queries_train[i][\"input\"]\n",
+ " for i in range(len(queries_train))\n",
+ " }\n",
+ " queries_validation = {\n",
+ " str(r[\"q_id\"]): r[\"input\"] for r in queries_validation\n",
+ " }\n",
+ " queries_test = {str(r[\"q_id\"]): r[\"input\"] for r in queries_test}\n",
+ "\n",
+ " qrels_train = (\n",
+ " qrels_train.to_pandas()\n",
+ " .groupby(\"q_id\")[\"record_id\"]\n",
+ " .apply(list)\n",
+ " .to_dict()\n",
+ " )\n",
+ " qrels_validation = (\n",
+ " qrels_validation.to_pandas()\n",
+ " .groupby(\"q_id\")[\"record_id\"]\n",
+ " .apply(list)\n",
+ " .to_dict()\n",
+ " )\n",
+ " qrels_test = (\n",
+ " qrels_test.to_pandas()\n",
+ " .groupby(\"q_id\")[\"record_id\"]\n",
+ " .apply(list)\n",
+ " .to_dict()\n",
+ " )\n",
+ " # convert to strings\n",
+ " qrels_train = {str(k): [str(i) for i in v] for k, v in qrels_train.items()}\n",
+ " qrels_validation = {\n",
+ " str(k): [str(i) for i in v] for k, v in qrels_validation.items()\n",
+ " }\n",
+ " qrels_test = {str(k): [str(i) for i in v] for k, v in qrels_test.items()}\n",
+ "\n",
+ " # Load the dataset\n",
+ " train_dataset = EmbeddingQAFinetuneDataset(\n",
+ " corpus=corpus, queries=queries_train, relevant_docs=qrels_train\n",
+ " )\n",
+ " validation_dataset = EmbeddingQAFinetuneDataset(\n",
+ " corpus=corpus,\n",
+ " queries=queries_validation,\n",
+ " relevant_docs=qrels_validation,\n",
+ " )\n",
+ " test_dataset = EmbeddingQAFinetuneDataset(\n",
+ " corpus=corpus, queries=queries_test, relevant_docs=qrels_test\n",
+ " )\n",
+ "\n",
+ " return train_dataset, validation_dataset, test_dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load the dataset and base embedding model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.embeddings import resolve_embed_model\n",
+ "\n",
+ "train_dataset, val_dataset, test_dataset = load_hf_dataset(\"scifact\")\n",
+ "base_embed_model = resolve_embed_model(\"local:BAAI/bge-small-en-v1.5\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we take a peek at the dataset, we can see that its structured as\n",
+ "- courpus: mapping of document ID to text\n",
+ "- queries: mapping of query ID to query text\n",
+ "- relevant_docs: a mapping of query ID to list of document IDs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Depletion of nitric oxide is responsible for vasospasm.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(val_dataset.queries[\"2\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['552']\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(val_dataset.relevant_docs[\"2\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CONTEXT Delayed cerebral vasospasm causes permanent neurological deficits or death in at least 15% of patients following otherwise successful treatment for ruptured intracranial aneurysm. Decreased bioavailability of nitric oxide has been associated with the development of cerebral vasospasm. OBJECTIVE To determine whether infusions of nitrite will prevent delayed cerebral vasospasm. DESIGN, SETTING, AND SUBJECTS A total of 14 anesthetized cynomolgus monkeys had an autologous blood clot placed around the right middle cerebral artery. Cerebral arteriography was performed before clot placement and on days 7 and 14 to assess vasospasm. The study was conducted from August 2003 to February 2004. INTERVENTIONS A 90-mg sodium nitrite intravenous solution infused over 24 hours plus a 45-mg sodium nitrite bolus daily (n = 3); a 180-mg sodium nitrite intravenous solution infused over 24 hours (n = 3); or a control saline solution infusion (n = 8). Each was infused continuously for 14 days. MAIN OUTCOME MEASURES Nitrite, S-nitrosothiol, and methemoglobin levels in blood and cerebrospinal fluid and degree of arteriographic vasospasm. RESULTS In control monkeys, mean (SD) cerebrospinal fluid nitrite levels decreased from 3.1 (1.5) micromol/L to 0.4 (0.1) micromol/L at day 7 and to 0.4 (0.4) micromol/L at day 14 (P = .03). All 8 control monkeys developed significant vasospasm of the right middle cerebral artery, which was complicated by stroke and death in 1 animal. Sodium nitrite infusions increased the nitrite and methemoglobin levels (<2.1% of total hemoglobin) in the blood and cerebrospinal fluid without evoking systemic hypotension. Nitrite infusion prevented development of vasospasm (no animals developed significant vasospasm; mean [SD] reduction in right middle cerebral artery area on day 7 after subarachnoid hemorrhage of 8% [9%] in nitrite-treated monkeys vs 47% [5%] in saline-treated controls; P<.001). There was a negative correlation between the concentration of nitrite in cerebrospinal fluid and the degree of cerebral vasospasm (P<.001). Pharmacological effects of nitrite infusion were also associated with the formation of S-nitrosothiol in cerebrospinal fluid. There was no clinical or pathological evidence of nitrite toxicity. CONCLUSION Subacute sodium nitrite infusions prevented delayed cerebral vasospasm in a primate model of subarachnoid hemorrhage.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(val_dataset.corpus[\"552\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Using your own Datasets\n",
+ "\n",
+ "As you can see, you can run this notebook on any dataset, as long as you have queries and a mapping to relevant documents! \n",
+ "\n",
+ "If you wanted, you could also write your own dataset, or even use llama-index to create your own.\n",
+ "\n",
+ "Uncomment the code below and add your own files if you want to try it out."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# This code would generate your own dataset against your own custom data\n",
+ "from llama_index.finetuning import generate_qa_embedding_pairs\n",
+ "from llama_index.core import SimpleDirectoryReader\n",
+ "from llama_index.core.node_parser import SentenceSplitter\n",
+ "from llama_index.core.evaluation import EmbeddingQAFinetuneDataset\n",
+ "\n",
+ "\n",
+ "def load_corpus(files, verbose=False):\n",
+ " if verbose:\n",
+ " print(f\"Loading files {files}\")\n",
+ "\n",
+ " reader = SimpleDirectoryReader(input_files=files)\n",
+ " docs = reader.load_data()\n",
+ " if verbose:\n",
+ " print(f\"Loaded {len(docs)} docs\")\n",
+ "\n",
+ " parser = SentenceSplitter()\n",
+ " nodes = parser.get_nodes_from_documents(docs, show_progress=verbose)\n",
+ "\n",
+ " if verbose:\n",
+ " print(f\"Parsed {len(nodes)} nodes\")\n",
+ "\n",
+ " return nodes\n",
+ "\n",
+ "\n",
+ "# Load data\n",
+ "# train_nodes = load_corpus([\"file1.pdf\", ...], verbose=True)\n",
+ "# val_nodes = load_corpus([\"file2.pdf\", ...], verbose=True)\n",
+ "\n",
+ "# Generate pairs\n",
+ "# train_dataset = generate_qa_embedding_pairs(train_nodes)\n",
+ "# val_dataset = generate_qa_embedding_pairs(val_nodes)\n",
+ "\n",
+ "# [Optional] Save to disk\n",
+ "# train_dataset.save_json(\"train_dataset.json\")\n",
+ "# val_dataset.save_json(\"val_dataset.json\")\n",
+ "\n",
+ "# [Optional] Load\n",
+ "# train_dataset = EmbeddingQAFinetuneDataset.from_json(\"train_dataset.json\")\n",
+ "# val_dataset = EmbeddingQAFinetuneDataset.from_json(\"val_dataset.json\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Evaluation\n",
+ "A common Information Retrieval metric to report during evaluation is NDCG@k."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Optional\n",
+ "\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from tqdm import tqdm\n",
+ "from llama_index.core.schema import TextNode\n",
+ "from llama_index.core.base.embeddings.base import BaseEmbedding\n",
+ "from llama_index.core.base.base_retriever import BaseRetriever\n",
+ "from llama_index.core import VectorStoreIndex\n",
+ "\n",
+ "\n",
+ "def build_retriever(\n",
+ " dataset: EmbeddingQAFinetuneDataset,\n",
+ " embed_model: BaseEmbedding | str,\n",
+ " corpus_embeddings: Optional[torch.Tensor] = None,\n",
+ " k: int = 10,\n",
+ ") -> BaseRetriever:\n",
+ " corpus = dataset.corpus\n",
+ "\n",
+ " nodes = []\n",
+ " for i, (id_, text) in enumerate(corpus.items()):\n",
+ " if corpus_embeddings is not None:\n",
+ " nodes.append(\n",
+ " TextNode(\n",
+ " id_=id_, text=text, embedding=corpus_embeddings[i].tolist()\n",
+ " )\n",
+ " )\n",
+ " else:\n",
+ " nodes.append(TextNode(id_=id_, text=text))\n",
+ "\n",
+ " index = VectorStoreIndex(\n",
+ " nodes=nodes,\n",
+ " embeddings=corpus_embeddings,\n",
+ " embed_model=embed_model,\n",
+ " show_progress=True,\n",
+ " )\n",
+ " return index.as_retriever(similarity_top_k=k)\n",
+ "\n",
+ "\n",
+ "def ndcg_at_k(\n",
+ " dataset: EmbeddingQAFinetuneDataset, retriever: BaseRetriever, k: int = 10\n",
+ "):\n",
+ " queries = dataset.queries\n",
+ " relevant_docs = dataset.relevant_docs\n",
+ "\n",
+ " ndcg_scores = []\n",
+ " for query_id, query in tqdm(queries.items()):\n",
+ " retrieved_nodes = retriever.retrieve(query)\n",
+ " retrieved_ids = [node.node.node_id for node in retrieved_nodes]\n",
+ " expected_ids = relevant_docs[query_id]\n",
+ "\n",
+ " # Calculate NDCG\n",
+ " ideal_dcg = np.sum(\n",
+ " [1 / np.log2(i + 2) for i in range(min(k, len(expected_ids)))]\n",
+ " )\n",
+ " rel_scores = np.zeros(k)\n",
+ " for j in range(min(k, len(retrieved_ids))):\n",
+ " if retrieved_ids[j] in expected_ids:\n",
+ " rel_scores[j] = 1\n",
+ " dcg = np.sum(\n",
+ " [rel_scores[i] / np.log2(i + 2) for i in range(len(rel_scores))]\n",
+ " )\n",
+ " ndcg = dcg / ideal_dcg if ideal_dcg > 0 else 0\n",
+ "\n",
+ " ndcg_scores.append(ndcg)\n",
+ "\n",
+ " mean_ndcg = np.mean(ndcg_scores)\n",
+ " return mean_ndcg"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get the corpus embedding finetuning results\n",
+ "Next we use, [NUDGE](https://www.arxiv.org/abs/2409.02343), the state of the art method for finetuning corpus embeddings to maximize the accuracy of k-NN retrieval. We then take our new corpus embeddings along with the original embedding model to build a retriever. NUDGE only finetunes the corpus embeddings and does not change any of the parameters in the base embedding model. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.experimental.nudge.base:Use pytorch device: cuda\n",
+ "Use pytorch device: cuda\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%capture\n",
+ "from llama_index.experimental import Nudge\n",
+ "\n",
+ "k = 10\n",
+ "\n",
+ "nudge = Nudge(\n",
+ " train_dataset=train_dataset,\n",
+ " val_dataset=val_dataset,\n",
+ " embed_model=base_embed_model,\n",
+ " epochs=10000,\n",
+ " train_batch_size=len(train_dataset.queries),\n",
+ " val_batch_size=len(val_dataset.queries),\n",
+ ")\n",
+ "nudge.finetune()\n",
+ "nudge_corpus_embeddings = nudge.get_finetuned_corpus_embeddings()\n",
+ "nudge_retriever = build_retriever(\n",
+ " train_dataset, base_embed_model, nudge_corpus_embeddings, k=k\n",
+ ")\n",
+ "nudge_ndcg_test = ndcg_at_k(test_dataset, nudge_retriever, k)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get the adapter finetuning results\n",
+ "We use a smaller batchsize than NUDGE above due to the adapter finetune baseline having a significantly slower training process. We also note that even with a batchsize the size of the dataset and 10k epochs the adapter finetuned model performs similarly to the hyperparams currently used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.finetuning.embeddings.adapter:Use pytorch device: cuda\n",
+ "Use pytorch device: cuda\n",
+ "INFO:llama_index.embeddings.adapter.base:Use pytorch device: cuda\n",
+ "Use pytorch device: cuda\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%capture\n",
+ "from llama_index.finetuning import EmbeddingAdapterFinetuneEngine\n",
+ "\n",
+ "embedding_adapater_finetune_engine = EmbeddingAdapterFinetuneEngine(\n",
+ " train_dataset,\n",
+ " base_embed_model,\n",
+ " epochs=4,\n",
+ " batch_size=10,\n",
+ ")\n",
+ "embedding_adapater_finetune_engine.finetune()\n",
+ "embedding_adapter_model = (\n",
+ " embedding_adapater_finetune_engine.get_finetuned_model()\n",
+ ")\n",
+ "ft_retriever = build_retriever(train_dataset, embedding_adapter_model, k=k)\n",
+ "ft_ndcg_test = ndcg_at_k(test_dataset, ft_retriever, k)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get the baseline results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "\n",
+ "base_retriever = build_retriever(train_dataset, base_embed_model, k=k)\n",
+ "bge_ndcg_test = ndcg_at_k(test_dataset, base_retriever, k)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Display the results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "bge test - ndcg@10: 0.71\n",
+ "adaptor finetune test - ndcg@10: 0.72\n",
+ "NUDGE test - ndcg@10: 0.83\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"bge test - ndcg@10: {bge_ndcg_test:.2f}\")\n",
+ "print(f\"adaptor finetune test - ndcg@10: {ft_ndcg_test:.2f}\")\n",
+ "print(f\"NUDGE test - ndcg@10: {nudge_ndcg_test:.2f}\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/docs/examples/finetuning/openai_fine_tuning_functions.ipynb b/docs/docs/examples/finetuning/openai_fine_tuning_functions.ipynb
index 223e57722ba9f..3724f2cc6aea7 100644
--- a/docs/docs/examples/finetuning/openai_fine_tuning_functions.ipynb
+++ b/docs/docs/examples/finetuning/openai_fine_tuning_functions.ipynb
@@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/finetuning/rerankers/cohere_custom_reranker.ipynb b/docs/docs/examples/finetuning/rerankers/cohere_custom_reranker.ipynb
index 012f56355ce15..aaf5c8426eaec 100644
--- a/docs/docs/examples/finetuning/rerankers/cohere_custom_reranker.ipynb
+++ b/docs/docs/examples/finetuning/rerankers/cohere_custom_reranker.ipynb
@@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/index_structs/doc_summary/DocSummary.ipynb b/docs/docs/examples/index_structs/doc_summary/DocSummary.ipynb
index e9f861fc08c53..aae60cc2cbeb1 100644
--- a/docs/docs/examples/index_structs/doc_summary/DocSummary.ipynb
+++ b/docs/docs/examples/index_structs/doc_summary/DocSummary.ipynb
@@ -5,7 +5,7 @@
"id": "20de0e22",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/index_structs/struct_indices/SQLIndexDemo.ipynb b/docs/docs/examples/index_structs/struct_indices/SQLIndexDemo.ipynb
index 4222d820953d8..833110e649f5a 100644
--- a/docs/docs/examples/index_structs/struct_indices/SQLIndexDemo.ipynb
+++ b/docs/docs/examples/index_structs/struct_indices/SQLIndexDemo.ipynb
@@ -5,7 +5,7 @@
"id": "1a558ee1",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb b/docs/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb
index a2af0fe02d08d..02cb61d3b398b 100644
--- a/docs/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb
+++ b/docs/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb
@@ -6,7 +6,7 @@
"id": "e4d8172b",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/ingestion/advanced_ingestion_pipeline.ipynb b/docs/docs/examples/ingestion/advanced_ingestion_pipeline.ipynb
index 48ff5533a2d39..38f10b712b6bf 100644
--- a/docs/docs/examples/ingestion/advanced_ingestion_pipeline.ipynb
+++ b/docs/docs/examples/ingestion/advanced_ingestion_pipeline.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/ingestion/parallel_execution_ingestion_pipeline.ipynb b/docs/docs/examples/ingestion/parallel_execution_ingestion_pipeline.ipynb
index e35be36a26b0b..ea4cd1d22617c 100644
--- a/docs/docs/examples/ingestion/parallel_execution_ingestion_pipeline.ipynb
+++ b/docs/docs/examples/ingestion/parallel_execution_ingestion_pipeline.ipynb
@@ -5,7 +5,7 @@
"id": "d1de0f1a",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llama_dataset/downloading_llama_datasets.ipynb b/docs/docs/examples/llama_dataset/downloading_llama_datasets.ipynb
index 9ee44266f6d90..1662be6bba6b4 100644
--- a/docs/docs/examples/llama_dataset/downloading_llama_datasets.ipynb
+++ b/docs/docs/examples/llama_dataset/downloading_llama_datasets.ipynb
@@ -5,7 +5,7 @@
"id": "28d433f8",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llama_dataset/labelled-rag-datasets.ipynb b/docs/docs/examples/llama_dataset/labelled-rag-datasets.ipynb
index 2524f9197b221..2be07a76bc8c9 100644
--- a/docs/docs/examples/llama_dataset/labelled-rag-datasets.ipynb
+++ b/docs/docs/examples/llama_dataset/labelled-rag-datasets.ipynb
@@ -5,7 +5,7 @@
"id": "e8761049",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llama_dataset/ragdataset_submission_template.ipynb b/docs/docs/examples/llama_dataset/ragdataset_submission_template.ipynb
index 8c11fe91b4b10..240f3154e6c4f 100644
--- a/docs/docs/examples/llama_dataset/ragdataset_submission_template.ipynb
+++ b/docs/docs/examples/llama_dataset/ragdataset_submission_template.ipynb
@@ -5,7 +5,7 @@
"id": "3cfd446f",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llama_dataset/uploading_llama_dataset.ipynb b/docs/docs/examples/llama_dataset/uploading_llama_dataset.ipynb
index 1970033c698ab..b113732fc48b5 100644
--- a/docs/docs/examples/llama_dataset/uploading_llama_dataset.ipynb
+++ b/docs/docs/examples/llama_dataset/uploading_llama_dataset.ipynb
@@ -5,7 +5,7 @@
"id": "9c8368b2",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llama_hub/llama_pack_resume.ipynb b/docs/docs/examples/llama_hub/llama_pack_resume.ipynb
index 3aaf8f92a978b..20858337c532f 100644
--- a/docs/docs/examples/llama_hub/llama_pack_resume.ipynb
+++ b/docs/docs/examples/llama_hub/llama_pack_resume.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Llama Pack - Resume Screener 📄\n",
"\n",
- "\n",
+ "\n",
"\n",
"This example shows you how to use the Resume Screener Llama Pack.\n",
"You can find all packs on https://llamahub.ai\n",
diff --git a/docs/docs/examples/llama_hub/llama_packs_example.ipynb b/docs/docs/examples/llama_hub/llama_packs_example.ipynb
index 4a50c13d6e700..d7ce1e14836ba 100644
--- a/docs/docs/examples/llama_hub/llama_packs_example.ipynb
+++ b/docs/docs/examples/llama_hub/llama_packs_example.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Llama Packs Example\n",
"\n",
- "\n",
+ "\n",
"\n",
"This example shows you how to use a simple Llama Pack with VoyageAI. We show the following:\n",
"- How to download a Llama Pack\n",
diff --git a/docs/docs/examples/llm/ai21.ipynb b/docs/docs/examples/llm/ai21.ipynb
index 579f604361a3d..c1b3640e8dec0 100644
--- a/docs/docs/examples/llm/ai21.ipynb
+++ b/docs/docs/examples/llm/ai21.ipynb
@@ -14,7 +14,8 @@
"source": [
"# AI21\n",
"\n",
- "This notebook shows how to use AI21's foundation models in LlamaIndex. The default model is `jamba-instruct`. If you want to use the older Jurassic models, specify the model name \"j2-mid\" or \"j2-ultra\"."
+ "This notebook shows how to use AI21's foundation models in LlamaIndex. The default model is `jamba-1.5-mini`.\n",
+ "Other supported models are `jamba-1.5-large` and `jamba-instruct`. If you want to use the older Jurassic models, specify the model name `j2-mid` or `j2-ultra`."
]
},
{
@@ -222,7 +223,7 @@
"from llama_index.llms.ai21 import AI21\n",
"\n",
"llm = AI21(\n",
- " model=\"jamba-instruct\", api_key=api_key, max_tokens=100, temperature=0.5\n",
+ " model=\"jamba-1.5-mini\", api_key=api_key, max_tokens=100, temperature=0.5\n",
")"
]
},
@@ -275,7 +276,7 @@
"from llama_index.llms.ai21 import AI21\n",
"from llama_index.core.llms import ChatMessage\n",
"\n",
- "llm = AI21(api_key=api_key, model=\"jamba-instruct\")\n",
+ "llm = AI21(api_key=api_key, model=\"jamba-1.5-mini\")\n",
"messages = [\n",
" ChatMessage(\n",
" role=\"system\", content=\"You are a pirate with a colorful personality\"\n",
@@ -339,7 +340,7 @@
"source": [
"from llama_index.llms.ai21 import AI21\n",
"\n",
- "llm = AI21(api_key=api_key, model=\"jamba-instruct\")\n",
+ "llm = AI21(api_key=api_key, model=\"jamba-1.5-mini\")\n",
"\n",
"tokenizer = llm.tokenizer\n",
"\n",
@@ -349,6 +350,64 @@
"\n",
"print(decoded)"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Tool Calling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.agent import FunctionCallingAgentWorker\n",
+ "from llama_index.llms.ai21 import AI21\n",
+ "from llama_index.core.tools import FunctionTool\n",
+ "\n",
+ "\n",
+ "def multiply(a: int, b: int) -> int:\n",
+ " \"\"\"Multiply two integers and returns the result integer\"\"\"\n",
+ " return a * b\n",
+ "\n",
+ "\n",
+ "def subtract(a: int, b: int) -> int:\n",
+ " \"\"\"Subtract two integers and returns the result integer\"\"\"\n",
+ " return a - b\n",
+ "\n",
+ "\n",
+ "def divide(a: int, b: int) -> float:\n",
+ " \"\"\"Divide two integers and returns the result float\"\"\"\n",
+ " return a - b\n",
+ "\n",
+ "\n",
+ "def add(a: int, b: int) -> int:\n",
+ " \"\"\"Add two integers and returns the result integer\"\"\"\n",
+ " return a + b\n",
+ "\n",
+ "\n",
+ "multiply_tool = FunctionTool.from_defaults(fn=multiply)\n",
+ "add_tool = FunctionTool.from_defaults(fn=add)\n",
+ "subtract_tool = FunctionTool.from_defaults(fn=subtract)\n",
+ "divide_tool = FunctionTool.from_defaults(fn=divide)\n",
+ "\n",
+ "llm = AI21(model=\"jamba-1.5-mini\", api_key=api_key)\n",
+ "\n",
+ "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ " [multiply_tool, add_tool, subtract_tool, divide_tool],\n",
+ " llm=llm,\n",
+ " verbose=True,\n",
+ " allow_parallel_tool_calls=True,\n",
+ ")\n",
+ "agent = agent_worker.as_agent()\n",
+ "\n",
+ "response = agent.chat(\n",
+ " \"My friend Moses had 10 apples. He ate 5 apples in the morning. Then he found a box with 25 apples. He divided all his apples between his 5 friends. How many apples did each friend get?\"\n",
+ ")"
+ ]
}
],
"metadata": {
diff --git a/docs/docs/examples/llm/alephalpha.ipynb b/docs/docs/examples/llm/alephalpha.ipynb
index 3ff23939bf9bf..94dcccaac73e2 100644
--- a/docs/docs/examples/llm/alephalpha.ipynb
+++ b/docs/docs/examples/llm/alephalpha.ipynb
@@ -5,7 +5,7 @@
"id": "6453d3d5",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/anthropic.ipynb b/docs/docs/examples/llm/anthropic.ipynb
index d712efa64f9f9..8479462e70246 100644
--- a/docs/docs/examples/llm/anthropic.ipynb
+++ b/docs/docs/examples/llm/anthropic.ipynb
@@ -5,7 +5,7 @@
"id": "6453d3d5",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/anyscale.ipynb b/docs/docs/examples/llm/anyscale.ipynb
index 927ce04b038f2..a0b4977aaa974 100644
--- a/docs/docs/examples/llm/anyscale.ipynb
+++ b/docs/docs/examples/llm/anyscale.ipynb
@@ -5,7 +5,7 @@
"id": "7f7c3284",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/azure_inference.ipynb b/docs/docs/examples/llm/azure_inference.ipynb
index b8884d5a726f7..b4f7280935288 100644
--- a/docs/docs/examples/llm/azure_inference.ipynb
+++ b/docs/docs/examples/llm/azure_inference.ipynb
@@ -5,7 +5,7 @@
"id": "6340e329",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -15,7 +15,7 @@
"source": [
"# Azure AI model inference\n",
"\n",
- "This notebook explains how to use `llama-index-llm-azure-inference` package with models deployed with the Azure AI model inference API in Azure AI studio or Azure Machine Learning."
+ "This notebook explains how to use `llama-index-llm-azure-inference` package with models deployed with the Azure AI model inference API in Azure AI studio or Azure Machine Learning. The package also support GitHub Models (Preview) endpoints."
]
},
{
@@ -68,7 +68,8 @@
"3. Deploy one model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference). In this example we use a `Mistral-Large` deployment. \n",
"\n",
" * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless).\n",
- "\n"
+ "\n",
+ "Alternatively, you can use GitHub Models endpoints with this integration, including the free tier experience. Read more about [GitHub models](https://github.com/marketplace/models)."
]
},
{
@@ -119,7 +120,7 @@
"id": "a593031b-c872-4360-8775-dff4844ccead",
"metadata": {},
"source": [
- "## Use the model"
+ "## Connect to your deployment and endpoint"
]
},
{
@@ -186,7 +187,7 @@
},
{
"cell_type": "markdown",
- "id": "97fb9877",
+ "id": "ed641e58",
"metadata": {},
"source": [
"If you are planning to use asynchronous calling, it's a best practice to use the asynchronous version for the credentials:"
@@ -195,7 +196,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "d7bc2c98",
+ "id": "8aa5e256",
"metadata": {},
"outputs": [],
"source": [
@@ -209,6 +210,36 @@
")"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "e3a6ad14",
+ "metadata": {},
+ "source": [
+ "If your endpoint is serving more than one model, like [GitHub Models](https://github.com/marketplace/models) or Azure AI Services, then you have to indicate the parameter `model_name`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f95b7416",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm = AzureAICompletionsModel(\n",
+ " endpoint=os.environ[\"AZURE_INFERENCE_ENDPOINT\"],\n",
+ " credential=os.environ[\"AZURE_INFERENCE_CREDENTIAL\"],\n",
+ " model_name=\"mistral-large\", # change it to the model you want to use\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f414500c",
+ "metadata": {},
+ "source": [
+ "## Use the model"
+ ]
+ },
{
"cell_type": "markdown",
"id": "579ce31c-7b51-471e-bcb5-47da90b3d555",
diff --git a/docs/docs/examples/llm/azure_openai.ipynb b/docs/docs/examples/llm/azure_openai.ipynb
index 5a6fe79c3428c..7a3d3121724fd 100644
--- a/docs/docs/examples/llm/azure_openai.ipynb
+++ b/docs/docs/examples/llm/azure_openai.ipynb
@@ -5,7 +5,7 @@
"id": "6340e329",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/cleanlab.ipynb b/docs/docs/examples/llm/cleanlab.ipynb
index aa3b7230a2b84..21a7df46746d4 100644
--- a/docs/docs/examples/llm/cleanlab.ipynb
+++ b/docs/docs/examples/llm/cleanlab.ipynb
@@ -5,7 +5,7 @@
"id": "4d1b897a",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/deepinfra.ipynb b/docs/docs/examples/llm/deepinfra.ipynb
index 8ae2c1a58d139..08d543f82747a 100644
--- a/docs/docs/examples/llm/deepinfra.ipynb
+++ b/docs/docs/examples/llm/deepinfra.ipynb
@@ -7,7 +7,7 @@
"metadata": {},
"outputs": [],
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/fireworks.ipynb b/docs/docs/examples/llm/fireworks.ipynb
index 2fd9883b88e93..e67751e68dcb0 100644
--- a/docs/docs/examples/llm/fireworks.ipynb
+++ b/docs/docs/examples/llm/fireworks.ipynb
@@ -5,7 +5,7 @@
"id": "9fd54a32",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/fireworks_cookbook.ipynb b/docs/docs/examples/llm/fireworks_cookbook.ipynb
index efe892665028c..d048f7e2d6cca 100644
--- a/docs/docs/examples/llm/fireworks_cookbook.ipynb
+++ b/docs/docs/examples/llm/fireworks_cookbook.ipynb
@@ -5,7 +5,7 @@
"id": "9fd54a32",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/groq.ipynb b/docs/docs/examples/llm/groq.ipynb
index 20e1bdbbd1643..ef24ae820933a 100644
--- a/docs/docs/examples/llm/groq.ipynb
+++ b/docs/docs/examples/llm/groq.ipynb
@@ -5,7 +5,7 @@
"id": "4d1b897a",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/langchain.ipynb b/docs/docs/examples/llm/langchain.ipynb
index 74a56ab3ee12f..3c66465765e94 100644
--- a/docs/docs/examples/llm/langchain.ipynb
+++ b/docs/docs/examples/llm/langchain.ipynb
@@ -5,7 +5,7 @@
"id": "f503d3ec",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/litellm.ipynb b/docs/docs/examples/llm/litellm.ipynb
index 2829f7494a4fb..94f076b07b61f 100755
--- a/docs/docs/examples/llm/litellm.ipynb
+++ b/docs/docs/examples/llm/litellm.ipynb
@@ -5,7 +5,7 @@
"id": "30311f71",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/llama_2.ipynb b/docs/docs/examples/llm/llama_2.ipynb
index 58faf193dd640..b73c3c90a97b9 100644
--- a/docs/docs/examples/llm/llama_2.ipynb
+++ b/docs/docs/examples/llm/llama_2.ipynb
@@ -5,7 +5,7 @@
"id": "bc2079ca",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/llama_2_llama_cpp.ipynb b/docs/docs/examples/llm/llama_2_llama_cpp.ipynb
index d707d2bccb7d9..427f24e815db2 100644
--- a/docs/docs/examples/llm/llama_2_llama_cpp.ipynb
+++ b/docs/docs/examples/llm/llama_2_llama_cpp.ipynb
@@ -5,7 +5,7 @@
"id": "3ac9adb4",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/llama_2_rap_battle.ipynb b/docs/docs/examples/llm/llama_2_rap_battle.ipynb
index fbb72030e2c23..e83994f7cffc3 100644
--- a/docs/docs/examples/llm/llama_2_rap_battle.ipynb
+++ b/docs/docs/examples/llm/llama_2_rap_battle.ipynb
@@ -5,7 +5,7 @@
"id": "c7f0f941",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/llama_api.ipynb b/docs/docs/examples/llm/llama_api.ipynb
index f5358a8a77f19..b90e037dc7513 100644
--- a/docs/docs/examples/llm/llama_api.ipynb
+++ b/docs/docs/examples/llm/llama_api.ipynb
@@ -5,7 +5,7 @@
"id": "7184eab5",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/llm_predictor.ipynb b/docs/docs/examples/llm/llm_predictor.ipynb
index 5bee613f1454d..7bc3fa8167a58 100644
--- a/docs/docs/examples/llm/llm_predictor.ipynb
+++ b/docs/docs/examples/llm/llm_predictor.ipynb
@@ -5,7 +5,7 @@
"id": "f8612a38",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/mistralai.ipynb b/docs/docs/examples/llm/mistralai.ipynb
index b5677de283c70..a4e91ece70881 100644
--- a/docs/docs/examples/llm/mistralai.ipynb
+++ b/docs/docs/examples/llm/mistralai.ipynb
@@ -5,7 +5,7 @@
"id": "6453d3d5",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -65,7 +65,7 @@
"# otherwise it will lookup MISTRAL_API_KEY from your env variable\n",
"# llm = MistralAI(api_key=\""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/neutrino.ipynb b/docs/docs/examples/llm/neutrino.ipynb
index c1a6e8fe9147f..412b9a18f13bb 100644
--- a/docs/docs/examples/llm/neutrino.ipynb
+++ b/docs/docs/examples/llm/neutrino.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/nvidia.ipynb b/docs/docs/examples/llm/nvidia.ipynb
index fedb3dc9d6315..ec60f15273ed4 100644
--- a/docs/docs/examples/llm/nvidia.ipynb
+++ b/docs/docs/examples/llm/nvidia.ipynb
@@ -585,6 +585,78 @@
"source": [
"streaming_response.print_response_stream()"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Tool calling\n",
+ "Starting in v0.2.1, NVIDIA supports tool calling.\n",
+ "\n",
+ "NVIDIA provides integration with the variety of models on build.nvidia.com as well as local NIMs. Not all these models are trained for tool calling. Be sure to select a model that does have tool calling for your experimention and applications.\n",
+ "\n",
+ "You can get a list of models that are known to support tool calling with,\n",
+ "\n",
+ "`NOTE:` For more examples refer : [nvidia_agent.ipynb](../agent/nvidia_agent.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tool_models = [\n",
+ " model\n",
+ " for model in NVIDIA().available_models\n",
+ " if model.is_function_calling_model\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With a tool capable model,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.tools import FunctionTool\n",
+ "\n",
+ "\n",
+ "def multiply(a: int, b: int) -> int:\n",
+ " \"\"\"Multiple two integers and returns the result integer\"\"\"\n",
+ " return a * b\n",
+ "\n",
+ "\n",
+ "multiply_tool = FunctionTool.from_defaults(fn=multiply)\n",
+ "\n",
+ "\n",
+ "def add(a: int, b: int) -> int:\n",
+ " \"\"\"Add two integers and returns the result integer\"\"\"\n",
+ " return a + b\n",
+ "\n",
+ "\n",
+ "add_tool = FunctionTool.from_defaults(fn=add)\n",
+ "\n",
+ "llm = NVIDIA(\"meta/llama-3.1-70b-instruct\")\n",
+ "from llama_index.core.agent import FunctionCallingAgentWorker\n",
+ "\n",
+ "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ " [multiply_tool, add_tool],\n",
+ " llm=llm,\n",
+ " verbose=True,\n",
+ ")\n",
+ "agent = agent_worker.as_agent()\n",
+ "\n",
+ "response = agent.chat(\"What is (121 * 3) + 42?\")\n",
+ "print(str(response))"
+ ]
}
],
"metadata": {
diff --git a/docs/docs/examples/llm/nvidia_tensorrt.ipynb b/docs/docs/examples/llm/nvidia_tensorrt.ipynb
index 32e030f76f147..abc5caa78574f 100644
--- a/docs/docs/examples/llm/nvidia_tensorrt.ipynb
+++ b/docs/docs/examples/llm/nvidia_tensorrt.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/nvidia_text_completion.ipynb b/docs/docs/examples/llm/nvidia_text_completion.ipynb
new file mode 100644
index 0000000000000..34244c8ce58d3
--- /dev/null
+++ b/docs/docs/examples/llm/nvidia_text_completion.ipynb
@@ -0,0 +1,258 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# NVIDIA's LLM Text Completion API\n",
+ "\n",
+ "Extending the NVIDIA class to support /completion API's for below models:\n",
+ "\n",
+ "- bigcode/starcoder2-7b\n",
+ "- bigcode/starcoder2-15b"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Installation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install --force-reinstall llama_index-llms-nvidia"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "\n",
+ "**To get started:**\n",
+ "\n",
+ "1. Create a free account with [NVIDIA](https://build.nvidia.com/), which hosts NVIDIA AI Foundation models.\n",
+ "\n",
+ "2. Click on your model of choice.\n",
+ "\n",
+ "3. Under Input select the Python tab, and click `Get API Key`. Then click `Generate Key`.\n",
+ "\n",
+ "4. Copy and save the generated key as NVIDIA_API_KEY. From there, you should have access to the endpoints."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!which python"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "# del os.environ['NVIDIA_API_KEY'] ## delete key and reset\n",
+ "if os.environ.get(\"NVIDIA_API_KEY\", \"\").startswith(\"nvapi-\"):\n",
+ " print(\"Valid NVIDIA_API_KEY already in environment. Delete to reset\")\n",
+ "else:\n",
+ " nvapi_key = getpass.getpass(\"NVAPI Key (starts with nvapi-): \")\n",
+ " assert nvapi_key.startswith(\n",
+ " \"nvapi-\"\n",
+ " ), f\"{nvapi_key[:5]}... is not a valid key\"\n",
+ " os.environ[\"NVIDIA_API_KEY\"] = nvapi_key"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "os.environ[\"NVIDIA_API_KEY\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio\n",
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Working with NVIDIA API Catalog"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.llms.nvidia import NVIDIA\n",
+ "\n",
+ "llm = NVIDIA(model=\"bigcode/starcoder2-15b\", use_chat_completions=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm.available_models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Working with NVIDIA NIMs\n",
+ "\n",
+ "In addition to connecting to hosted [NVIDIA NIMs](https://ai.nvidia.com), this connector can be used to connect to local microservice instances. This helps you take your applications local when necessary.\n",
+ "\n",
+ "For instructions on how to setup local microservice instances, see https://developer.nvidia.com/blog/nvidia-nim-offers-optimized-inference-microservices-for-deploying-ai-models-at-scale/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.llms.nvidia import NVIDIA\n",
+ "\n",
+ "# connect to an chat NIM running at localhost:8080, spcecifying a specific model\n",
+ "llm = NVIDIA(base_url=\"http://localhost:8080/v1\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Complete: `.complete()`\n",
+ "\n",
+ "We can use `.complete()`/`.acomplete()` (which takes a string) to prompt a response from the selected model.\n",
+ "\n",
+ "Let's use our default model for this task."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(llm.complete(\"# Function that does quicksort:\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As is expected by LlamaIndex - we get a `CompletionResponse` in response."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Async Complete: `.acomplete()`\n",
+ "\n",
+ "There is also an async implementation which can be leveraged in the same way!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "await llm.acomplete(\"# Function that does quicksort:\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Streaming"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = llm.stream_complete(prompt=\"# Reverse string in python:\", max_tokens=512)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Async Streaming"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = await llm.astream_complete(\n",
+ " prompt=\"# Reverse program in python:\", max_tokens=512\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "async for t in x:\n",
+ " print(t.delta, end=\"\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/docs/examples/llm/nvidia_triton.ipynb b/docs/docs/examples/llm/nvidia_triton.ipynb
index 66be41c519343..c516158badb49 100644
--- a/docs/docs/examples/llm/nvidia_triton.ipynb
+++ b/docs/docs/examples/llm/nvidia_triton.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/oci_genai.ipynb b/docs/docs/examples/llm/oci_genai.ipynb
index f9a0584f15672..3caeb6ca6d1d0 100644
--- a/docs/docs/examples/llm/oci_genai.ipynb
+++ b/docs/docs/examples/llm/oci_genai.ipynb
@@ -6,7 +6,7 @@
"id": "6d1ca9ac",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/ollama.ipynb b/docs/docs/examples/llm/ollama.ipynb
index fef6be0fba5d2..cc53efe836354 100644
--- a/docs/docs/examples/llm/ollama.ipynb
+++ b/docs/docs/examples/llm/ollama.ipynb
@@ -5,7 +5,7 @@
"id": "4d1b897a",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/ollama_gemma.ipynb b/docs/docs/examples/llm/ollama_gemma.ipynb
index 3960aadfa4918..a04a6f980cffe 100644
--- a/docs/docs/examples/llm/ollama_gemma.ipynb
+++ b/docs/docs/examples/llm/ollama_gemma.ipynb
@@ -5,7 +5,7 @@
"id": "4d1b897a",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/openai.ipynb b/docs/docs/examples/llm/openai.ipynb
index 85f37fdcea90d..5d681cd9e8af7 100644
--- a/docs/docs/examples/llm/openai.ipynb
+++ b/docs/docs/examples/llm/openai.ipynb
@@ -5,7 +5,7 @@
"id": "9fd54a32",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -455,7 +455,7 @@
"source": [
"from llama_index.llms.openai import OpenAI\n",
"from llama_index.core.prompts import PromptTemplate\n",
- "from pydantic.v1 import BaseModel\n",
+ "from pydantic import BaseModel\n",
"from typing import List\n",
"\n",
"\n",
diff --git a/docs/docs/examples/llm/openrouter.ipynb b/docs/docs/examples/llm/openrouter.ipynb
index 7bbdb926a884d..cc96e8cf06a30 100644
--- a/docs/docs/examples/llm/openrouter.ipynb
+++ b/docs/docs/examples/llm/openrouter.ipynb
@@ -5,7 +5,7 @@
"id": "7f7c3284",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/paieas.ipynb b/docs/docs/examples/llm/paieas.ipynb
index 4060000d4f8cc..e568f4672fbb2 100644
--- a/docs/docs/examples/llm/paieas.ipynb
+++ b/docs/docs/examples/llm/paieas.ipynb
@@ -5,7 +5,7 @@
"id": "9fd54a32",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/palm.ipynb b/docs/docs/examples/llm/palm.ipynb
index 14917868d79cf..f1942a781d7e1 100644
--- a/docs/docs/examples/llm/palm.ipynb
+++ b/docs/docs/examples/llm/palm.ipynb
@@ -5,7 +5,7 @@
"id": "4d4991c2",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/portkey.ipynb b/docs/docs/examples/llm/portkey.ipynb
index 5f67556d7785f..70c8b1de670a6 100644
--- a/docs/docs/examples/llm/portkey.ipynb
+++ b/docs/docs/examples/llm/portkey.ipynb
@@ -44,7 +44,7 @@
"\n",
"To harness these features, let's start with the setup:\n",
"\n",
- "\n",
+ "\n",
" \n",
""
]
@@ -359,7 +359,7 @@
"- [LLMOptions Constructor](https://github.com/Portkey-AI/rubeus-python-sdk/blob/4cf3e17b847225123e92f8e8467b41d082186d60/rubeus/api_resources/utils.py#L179)\n",
"- [List of Portkey + Llamaindex Features](#portkeys-integration-with-llamaindex-adds-the-following-production-capabilities-to-your-apps-out-of-the-box)\n",
"\n",
- "\n",
+ "\n",
" \n",
"\n",
"\n"
diff --git a/docs/docs/examples/llm/predibase.ipynb b/docs/docs/examples/llm/predibase.ipynb
index f196e1b627ebe..c20e9db68220f 100644
--- a/docs/docs/examples/llm/predibase.ipynb
+++ b/docs/docs/examples/llm/predibase.ipynb
@@ -5,7 +5,7 @@
"id": "4ec7cd6e",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/rungpt.ipynb b/docs/docs/examples/llm/rungpt.ipynb
index cd91b6f2252e8..01af7738d81dc 100644
--- a/docs/docs/examples/llm/rungpt.ipynb
+++ b/docs/docs/examples/llm/rungpt.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/together.ipynb b/docs/docs/examples/llm/together.ipynb
index ba7b1116a2203..1962a57dbe3db 100644
--- a/docs/docs/examples/llm/together.ipynb
+++ b/docs/docs/examples/llm/together.ipynb
@@ -5,7 +5,7 @@
"id": "4d1b897a",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/upstage.ipynb b/docs/docs/examples/llm/upstage.ipynb
index 3e4021c976950..a9d4f99f4107b 100644
--- a/docs/docs/examples/llm/upstage.ipynb
+++ b/docs/docs/examples/llm/upstage.ipynb
@@ -5,7 +5,7 @@
"id": "9fd54a32",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/vicuna.ipynb b/docs/docs/examples/llm/vicuna.ipynb
index 9e96df22bcfab..7cf6419cba523 100644
--- a/docs/docs/examples/llm/vicuna.ipynb
+++ b/docs/docs/examples/llm/vicuna.ipynb
@@ -5,7 +5,7 @@
"id": "c798b3ad",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/llm/xinference_local_deployment.ipynb b/docs/docs/examples/llm/xinference_local_deployment.ipynb
index e0790f6d207a5..745974be963b9 100644
--- a/docs/docs/examples/llm/xinference_local_deployment.ipynb
+++ b/docs/docs/examples/llm/xinference_local_deployment.ipynb
@@ -6,7 +6,7 @@
"id": "c6218e91",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/low_level/ingestion.ipynb b/docs/docs/examples/low_level/ingestion.ipynb
index 8f916b8e93161..bbcad29f39ccc 100644
--- a/docs/docs/examples/low_level/ingestion.ipynb
+++ b/docs/docs/examples/low_level/ingestion.ipynb
@@ -5,7 +5,7 @@
"id": "57c676db",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/low_level/response_synthesis.ipynb b/docs/docs/examples/low_level/response_synthesis.ipynb
index 566bf441ffd73..7ca91161f7b31 100644
--- a/docs/docs/examples/low_level/response_synthesis.ipynb
+++ b/docs/docs/examples/low_level/response_synthesis.ipynb
@@ -5,7 +5,7 @@
"id": "e5df0f51",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/low_level/retrieval.ipynb b/docs/docs/examples/low_level/retrieval.ipynb
index f26e84fc4dbd1..faed60438f706 100644
--- a/docs/docs/examples/low_level/retrieval.ipynb
+++ b/docs/docs/examples/low_level/retrieval.ipynb
@@ -5,7 +5,7 @@
"id": "62ac6c38",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/managed/PostgresMLDemo.ipynb b/docs/docs/examples/managed/PostgresMLDemo.ipynb
index 0b20023189feb..ebdaf78d9d792 100644
--- a/docs/docs/examples/managed/PostgresMLDemo.ipynb
+++ b/docs/docs/examples/managed/PostgresMLDemo.ipynb
@@ -5,7 +5,7 @@
"id": "adf7d63d",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/managed/vectaraDemo.ipynb b/docs/docs/examples/managed/vectaraDemo.ipynb
index 611c153a77e31..e760573e8ee38 100644
--- a/docs/docs/examples/managed/vectaraDemo.ipynb
+++ b/docs/docs/examples/managed/vectaraDemo.ipynb
@@ -5,7 +5,7 @@
"id": "adf7d63d",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -19,8 +19,8 @@
"Vectara provides an end-to-end managed service for Retrieval Augmented Generation or RAG, which includes:\n",
"1. A way to extract text from document files and chunk them into sentences.\n",
"2. The state-of-the-art [Boomerang](https://vectara.com/how-boomerang-takes-retrieval-augmented-generation-to-the-next-level-via-grounded-generation/) embeddings model. Each text chunk is encoded into a vector embedding using Boomerang, and stored in the Vectara internal vector store. Thus, when using Vectara with LlamaIndex you do not need to call a separate embedding model - this happens automatically within the Vectara backend.\n",
- "3. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) and [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/))\n",
- "4. An option to a create [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents, including citations.\n",
+ "3. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) and [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/), [Multilingual](https://vectara.com/blog/unlocking-the-state-of-the-art-reranker-introducing-the-vectara-multilingual-reranker_v1/), or [User Defined Function](https://vectara.com/blog/introducing-user-defined-functions-for-vectara/) reranking)\n",
+ "4. An option to a create [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview) with a selection of different LLMs (including [Mockingbird](https://vectara.com/blog/mockingbird-is-a-rag-specific-llm-that-beats-gpt-4-gemini-1-5-pro-in-rag-output-quality/)), based on the retrieved documents, including citations.\n",
"\n",
"See the [Vectara API documentation](https://docs.vectara.com/docs/) for more information on how to use the API."
]
@@ -86,7 +86,18 @@
"execution_count": null,
"id": "c154dd4b",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'ai-bill-of-rights.pdf'"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"from llama_index.indices.managed.vectara import VectaraIndex\n",
"import requests\n",
@@ -137,7 +148,7 @@
{
"data": {
"text/plain": [
- "\"The risks associated with AI include potential biases leading to discriminatory outcomes, lack of transparency in decision-making processes, and challenges in establishing public trust and understanding of algorithmic systems [1]. Safety and efficacy concerns arise in the context of complex technologies like AI, necessitating strong regulations and proactive risk mitigation strategies [2]. The process of identifying and addressing risks before and during the deployment of automated systems is crucial to prevent harm to individuals' rights, opportunities, and access [5]. Furthermore, the impact of AI risks can be most visible at the community level, emphasizing the importance of considering and mitigating harms to various communities [6]. Efforts are being made to translate principles into practice through laws, policies, and technical approaches to ensure AI systems are lawful, respectful, accurate, safe, understandable, responsible, and accountable [7].\""
+ "'The risks of AI include biased data and discriminatory outcomes, opaque decision-making processes, and lack of public trust and understanding of algorithmic systems [1]. These risks can lead to harm to individuals and communities, including image-based abuse, incorrect evaluations, and violations of safety [7]. To mitigate these risks, it is essential to have ongoing transparency, value-sensitive and participatory design, explanations designed for relevant stakeholders, and public consultation [1]. Strong safety regulations and measures to address harms when they occur can enhance innovation in the context of complex technologies [2]. Additionally, industry is providing innovative solutions to mitigate risks to the safety and efficacy of AI systems, including risk assessments, auditing mechanisms, and documentation procedures [2].'"
]
},
"execution_count": null,
@@ -146,7 +157,9 @@
}
],
"source": [
- "qe = index.as_query_engine(summary_enabled=True)\n",
+ "qe = index.as_query_engine(\n",
+ " summary_enabled=True, summary_prompt_name=\"mockingbird-1.0-2024-07-16\"\n",
+ ")\n",
"qe.query(questions[0]).response"
]
},
@@ -168,12 +181,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "The risks of AI include biased data leading to discriminatory outcomes, opaque decision-making processes, and lack of public trust and understanding in algorithmic systems [1]. Organizations are implementing innovative solutions like risk assessments, auditing mechanisms, and ongoing monitoring to mitigate safety and efficacy risks of AI systems [2]. Stakeholder engagement and a risk management framework by institutions like NIST aim to address risks to individuals, organizations, and society posed by AI technology [3]. Risk identification, mitigation, and focusing on safety and effectiveness of AI systems are crucial before and during deployment to protect people’s rights, opportunities, and access [5]. The concept of communities is integral in understanding the impact of AI and automated systems, as the potential harm may be most visible at the community level [6]. Practical implementation of principles such as lawful, purposeful, accurate, safe, and accountable AI is essential to address risks, with federal agencies adhering to guidelines promoting trustworthy AI [7]."
+ "The risks of AI include biased data and discriminatory outcomes, opaque decision-making processes, and lack of public trust and understanding of algorithmic systems [1]. These risks can lead to harm to individuals and communities, including image-based abuse, incorrect evaluations, and violations of safety [7]. To mitigate these risks, it is essential to have ongoing transparency, value-sensitive and participatory design, explanations designed for relevant stakeholders, and public consultation [1]. Strong safety regulations and measures to address harms when they occur can enhance innovation in the context of complex technologies [2]. Additionally, industry is providing innovative solutions to mitigate risks to the safety and efficacy of AI systems, including risk assessments, auditing mechanisms, and documentation procedures [2]."
]
}
],
"source": [
- "qe = index.as_query_engine(summary_enabled=True, streaming=True)\n",
+ "qe = index.as_query_engine(\n",
+ " summary_enabled=True,\n",
+ " summary_prompt_name=\"mockingbird-1.0-2024-07-16\",\n",
+ " streaming=True,\n",
+ ")\n",
"response = qe.query(questions[0])\n",
"\n",
"for chunk in response.response_gen:\n",
@@ -214,15 +231,15 @@
"text": [
"Question: What are the risks of AI?\n",
"\n",
- "Response: The risks of AI involve potential biases, opaque decision-making processes, and lack of public trust due to discriminatory outcomes and biased data [1]. To mitigate these risks, industry is implementing innovative solutions like risk assessments and monitoring mechanisms [2]. Stakeholder engagement and the development of a risk management framework by organizations like the National Institute of Standards and Technology aim to manage risks posed by AI to individuals, organizations, and society [3]. Identification and mitigation of potential risks, impact assessments, and balancing high impact risks with appropriate mitigation are crucial before and during the deployment of AI systems [5]. The Blueprint for an AI Bill of Rights emphasizes the protection of individuals from unsafe or ineffective AI systems [7].\n",
+ "Response: The risks of AI include potential biased data leading to discriminatory outcomes, opaque decision-making processes, lack of public trust, and understanding of algorithmic systems. Risks also involve safety concerns, such as AI systems violating the safety of individuals, and the proliferation of harmful technologies like AI-enabled \"nudification\" tools. Furthermore, the incorrect penalization by AI systems, as seen in the case of AI-powered cameras in delivery vans, can lead to adverse consequences for individuals. To mitigate these risks, ongoing transparency, participatory design, explanations for stakeholders, and public consultation are essential. Organizations are implementing innovative solutions like risk assessments, auditing mechanisms, and monitoring tools to address safety and efficacy concerns related to AI systems. The involvement of communities and stakeholders in the design and evaluation of AI systems is crucial to ensure trustworthiness and mitigate potential harms.\n",
"\n",
"Question: What should we do to prevent bad actors from using AI?\n",
"\n",
- "Response: To prevent the misuse of AI by malicious entities, several key measures can be implemented. Firstly, it is crucial to ensure that automated systems are designed with safety and effectiveness in mind, following principles such as being lawful, purposeful, accurate, secure, and transparent [2]. Entities should proactively identify and manage risks associated with sensitive data, conducting regular audits and limiting access to prevent misuse [3], [4], [5]. Additionally, ongoing monitoring of automated systems is essential to detect and address algorithmic discrimination and unforeseen interactions that could lead to misuse [6], [7]. By incorporating these practices into the design, development, and deployment of AI technologies, the potential for misuse by malicious entities can be significantly reduced.\n",
+ "Response: To stop malicious individuals from exploiting artificial intelligence, measures can be taken such as implementing ethical principles for the use of AI, ensuring transparency, accountability, and regular monitoring of AI systems, conducting equity assessments, using representative data, protecting against biases, and providing clear organizational oversight. Additionally, it is crucial to address algorithmic discrimination through proactive measures, ongoing disparity testing, and independent evaluations to protect individuals and communities from unjust treatment based on various characteristics. Furthermore, incorporating safeguards like plain language reporting, algorithmic impact assessments, and public disclosure of mitigation efforts can help prevent the misuse of AI by malicious actors [1][2][4].\n",
"\n",
"Question: What are the benefits?\n",
"\n",
- "Response: Artificial Intelligence (AI) offers various advantages, such as promoting the use of trustworthy AI systems with principles focusing on legality, performance, safety, transparency, and accountability [1]. Organizations are incorporating protections and ethical principles in AI development, aligning with global recommendations for responsible AI stewardship [2]. Furthermore, research is ongoing to enhance explainable AI systems for better human understanding and trust in AI outcomes [5]. The U.S. government is establishing councils and frameworks to advance AI technologies, ensuring responsible AI implementation across sectors [4], . AI can streamline processes, improve decision-making, and enhance efficiency, although challenges like bias, flaws, and accessibility issues need to be addressed to maximize its benefits [5].\n",
+ "Response: The advantages of using artificial intelligence include providing systems that are lawful, purposeful, accurate, safe, secure, and transparent. AI can be accountable, reliable, and effective, leading to trustworthy automated systems. Additionally, AI can enhance efficiency, decision-making processes, and innovation while supporting democratic values and ethical principles [1][2][7].\n",
"\n"
]
}
@@ -262,12 +279,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "The search results indicate a focus on the relationship between humans and robots, emphasizing the need for co-intelligence and the best use of automated systems [2]. The discussions revolve around ensuring that automated systems are designed, tested, and protected to prevent potential harmful outcomes [1]. While there are concerns about the use of surveillance technology by companies like Amazon and Walmart, the emphasis is on balancing equities and maintaining oversight in law enforcement activities [5]. The search results do not directly answer whether robots will kill us all, but they highlight the importance of proactive protections, context-specific guidance, and existing policies to govern the use of automated systems in various settings [6]."
+ "Artificial intelligence will not rule the government. The government is implementing principles and guidelines to ensure the ethical and responsible use of AI in various sectors, including the federal government. These measures focus on transparency, accountability, safety, and adherence to national values, ensuring that AI is used in a manner that upholds civil rights, democratic values, and national security [1] [2] [3]."
]
}
],
"source": [
- "response = ce.stream_chat(\"Will robots kill us all?\")\n",
+ "response = ce.stream_chat(\"Will artificial intelligence rule the government?\")\n",
"for chunk in response.chat_stream:\n",
" print(chunk.delta or \"\", end=\"\", flush=True)"
]
@@ -279,7 +296,9 @@
"source": [
"### Agentic RAG\n",
"\n",
- "Let's create a ReAct Agent using LlamaIndex that utilizes Vectara as its RAG tool.\n",
+ "Vectara also has its own package, [vectara-agentic](https://github.com/vectara/py-vectara-agentic), built on top of many features from LlamaIndex to easily implement agentic RAG applications. It allows you to create your own AI assistant with RAG query tools and other custom tools, such as making API calls to retrieve information from financial websites. You can find the full documentation for vectara-agentic [here](https://vectara.github.io/vectara-agentic-docs/).\n",
+ "\n",
+ "Let's create a ReAct Agent with a single RAG tool using vectara-agentic.\n",
"For this you would need to use another LLM as the driver of the agent resoning, and we are using OpenAI's GPT4o here as an example.\n",
"(for this to work, please make sure you have `OPENAI_API_KEY` defined in your environment)."
]
@@ -287,129 +306,109 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "c6766a84-b228-4b80-a32b-0c167b843819",
+ "id": "69f0d504-bc72-4dfc-8cdf-83b8aa69206c",
"metadata": {},
"outputs": [],
"source": [
- "from llama_index.core.agent import ReActAgent\n",
- "from llama_index.llms.openai import OpenAI\n",
- "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n",
- "\n",
- "llm = OpenAI(model=\"gpt-4o\", temperature=0)\n",
- "vectara_tool = QueryEngineTool(\n",
- " query_engine=index.as_query_engine(\n",
- " summary_enabled=True,\n",
- " summary_num_results=5,\n",
- " summary_response_lang=\"en\",\n",
- " summary_prompt_name=\"vectara-summary-ext-24-05-large\",\n",
- " reranker=\"mmr\",\n",
- " rerank_k=50,\n",
- " mmr_diversity_bias=0.2,\n",
- " ),\n",
- " metadata=ToolMetadata(\n",
- " name=\"Vectara\",\n",
- " description=\"Vectara Query Engine that is able to answer Questions about AI regulation.\",\n",
- " ),\n",
- ")\n",
- "agent = ReActAgent.from_tools(\n",
- " tools=[vectara_tool],\n",
- " llm=llm,\n",
- " context=\"\"\"\n",
- " You are a helpful chatbot that answers any user questions around AI regulations using the Vectara tool.\n",
- " You break down complex questions into simpler ones.\n",
- " You use the Vectara query engine to help provide answers to simpler questions.\n",
- " \"\"\",\n",
- " verbose=True,\n",
- ")"
+ "!pip install vectara-agentic"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b8425b7-6235-408b-a02e-ff332916b4d6",
+ "metadata": {},
+ "source": [
+ "UNCOMMENT NAME ARGUMENT WHEN NEW VERSION OF VECTARA AGENTIC IS RELEASED. MAKE SURE THE AGENT DOES NOT USE \"vectara_11\" as the first Action"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "8a68dbca-613a-4f44-aa9e-67a758f76683",
+ "id": "ee674bf5-fccb-42cb-8499-77aad14fc538",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "> Running step e6ab056d-843c-489d-91ad-fc0245168f7e. Step input: What are the risks of AI? What are the benefits? Compare and contrast and provide a summary with arguments for and against from experts.\n",
"\u001b[1;3;38;5;200mThought: The current language of the user is: English. I need to use a tool to help me answer the question.\n",
- "Action: Vectara\n",
- "Action Input: {'input': 'What are the risks of AI?'}\n",
- "\u001b[0m\u001b[1;3;34mObservation: The risks of AI include biased data leading to discriminatory outcomes, opaque decision-making processes, and a lack of public trust and understanding in algorithmic systems. Mitigation strategies discussed involve ongoing transparency, participatory design, and engaging with impacted communities to understand potential harms and integrate protections into the design of AI systems [1]. Additionally, there's a focus on identifying and mitigating risks before deployment, particularly those impacting people's rights, opportunities, or safety, with a strong emphasis on avoiding systems that inherently violate safety norms [5].\n",
- "\u001b[0m\u001b[1;3;38;5;200mThought: I have obtained information about the risks of AI. Now, I need to gather information about the benefits of AI to provide a comprehensive comparison.\n",
- "Action: Vectara\n",
- "Action Input: {'input': 'What are the benefits of AI?'}\n",
- "\u001b[0m\u001b[1;3;34mObservation: The benefits of AI include its transformative potential to improve people's lives by building better and more innovative infrastructure. It also offers the possibility to enhance community health, safety, and welfare by ensuring better representation of all voices, particularly those traditionally marginalized by technological advances [1]. AI can also prevent harms and improve opportunities, rights, and access for Americans, playing a central role in shaping important policies like the Blueprint for an AI Bill of Rights [2].\n",
- "\u001b[0m\u001b[1;3;38;5;200mThought: I have gathered information about both the risks and benefits of AI. Now, I need to compare and contrast these points and provide a summary with arguments for and against from experts.\n",
- "Answer: ### Comparison of Risks and Benefits of AI\n",
- "\n",
- "#### Risks of AI:\n",
- "1. **Biased Data and Discriminatory Outcomes**: AI systems can perpetuate and even exacerbate biases present in the data they are trained on, leading to unfair and discriminatory outcomes.\n",
- "2. **Opaque Decision-Making**: The decision-making processes of AI systems can be complex and not easily understandable, leading to a lack of transparency.\n",
- "3. **Lack of Public Trust**: The opacity and potential biases in AI systems can result in a lack of trust and understanding from the public.\n",
- "4. **Safety and Rights Violations**: There is a risk of AI systems violating safety norms and impacting people's rights, opportunities, or safety.\n",
- "\n",
- "#### Benefits of AI:\n",
- "1. **Improved Infrastructure**: AI has the potential to transform and improve infrastructure, making it more innovative and efficient.\n",
- "2. **Enhanced Community Health and Safety**: AI can play a significant role in improving community health, safety, and welfare by ensuring better representation and inclusivity.\n",
- "3. **Prevention of Harms**: AI can help prevent harms and improve opportunities, rights, and access, particularly for marginalized communities.\n",
- "4. **Policy Shaping**: AI is central to shaping important policies, such as the Blueprint for an AI Bill of Rights, which aims to protect and enhance the rights of individuals.\n",
+ "Action: query_ai\n",
+ "Action Input: {'query': 'What are the risks and benefits of AI? Provide a summary with arguments for and against from experts.'}\n",
+ "\u001b[0m\u001b[1;3;34mObservation: {'response': \"Here is a summary of the risks and benefits of AI based on the provided sources:\\n\\n**Benefits of AI:**\\n\\n* AI has transformative potential to improve Americans' lives [1]\\n* AI-enabled systems can build better and more innovative infrastructure [3]\\n* AI can be integrated into communities in a thoughtful and responsible way, benefiting from lessons learned from urban planning [3]\\n\\n**Risks of AI:**\\n\\n* AI can lead to biased data and discriminatory outcomes [2]\\n* Opaque decision-making processes can lack public trust and understanding [2]\\n* AI can pose risks to individuals, organizations, and society, highlighting the need for risk management frameworks [5]\\n\\n**Addressing Risks and Benefits:**\\n\\n* The White House Office of Science and Technology Policy has led a year-long process to seek input from experts and stakeholders on the issue of algorithmic and data-driven harms [1]\\n* The Blueprint for an AI Bill of Rights emphasizes the importance of safe and effective systems, ongoing transparency, and value-sensitive and participatory design [1]\\n* Industry is providing innovative solutions to mitigate risks to the safety and efficacy of AI systems, including risk assessments, auditing mechanisms, and stakeholder engagement [5]\\n* The National Institute of Standards and Technology (NIST) is developing a risk management framework to better manage risks posed to individuals, organizations, and society by AI [5]\\n\\nOverall, the sources suggest that AI has the potential to bring significant benefits, but also poses risks that need to be addressed through thoughtful and responsible development and integration into communities.\", 'citation_metadata': {'metadata for citation 1': {'page': '1', 'lang': 'eng', 'section': '1', 'offset': '10959', 'len': '208', 'CreationDate': '1663695035', 'Producer': 'iLovePDF', 'Title': 'Blueprint for an AI Bill of Rights', 'Creator': 'Adobe Illustrator 26.3 (Macintosh)', 'ModDate': '1664808078', 'name': 'AI bill of rights', 'year': '2022', 'framework': 'llama_index', 'title': 'Blueprint for an AI Bill of Rights'}, 'metadata for citation 3': {'page': '56', 'title': 'Blueprint for an AI Bill of Rights', 'lang': 'eng', 'section': '4', 'offset': '1', 'len': '140', 'CreationDate': '1663695035', 'Producer': 'iLovePDF', 'Title': 'Blueprint for an AI Bill of Rights', 'Creator': 'Adobe Illustrator 26.3 (Macintosh)', 'ModDate': '1664808078', 'name': 'AI bill of rights', 'year': '2022', 'framework': 'llama_index'}, 'metadata for citation 2': {'page': '56', 'title': 'Blueprint for an AI Bill of Rights', 'lang': 'eng', 'section': '4', 'offset': '9099', 'len': '689', 'CreationDate': '1663695035', 'Producer': 'iLovePDF', 'Title': 'Blueprint for an AI Bill of Rights', 'Creator': 'Adobe Illustrator 26.3 (Macintosh)', 'ModDate': '1664808078', 'name': 'AI bill of rights', 'year': '2022', 'framework': 'llama_index'}, 'metadata for citation 5': {'page': '1', 'lang': 'eng', 'section': '1', 'offset': '52952', 'len': '1025', 'CreationDate': '1663695035', 'Producer': 'iLovePDF', 'Title': 'Blueprint for an AI Bill of Rights', 'Creator': 'Adobe Illustrator 26.3 (Macintosh)', 'ModDate': '1664808078', 'name': 'AI bill of rights', 'year': '2022', 'framework': 'llama_index', 'title': 'Blueprint for an AI Bill of Rights'}}, 'factual_consistency': 0.41484985}\n",
+ "\u001b[0m> Running step fe08e033-f8d6-4596-af28-68c156737665. Step input: None\n",
+ "\u001b[1;3;38;5;200mThought: I can answer without using any more tools. I'll use the user's language to answer\n",
+ "Answer: The benefits and risks of AI are widely discussed among experts, and here is a summary based on the provided sources:\n",
"\n",
- "### Summary with Arguments For and Against AI\n",
+ "**Benefits of AI:**\n",
+ "- AI has the potential to transform lives by improving various aspects of daily living.\n",
+ "- It can lead to the development of better and more innovative infrastructure.\n",
+ "- When integrated thoughtfully and responsibly, AI can enhance community development, drawing lessons from urban planning.\n",
"\n",
- "#### Arguments For AI:\n",
- "- **Innovation and Efficiency**: AI can drive significant advancements in technology and infrastructure, leading to more efficient and innovative solutions.\n",
- "- **Inclusivity and Representation**: AI can ensure better representation of marginalized voices, leading to more equitable outcomes.\n",
- "- **Health and Safety**: AI can enhance community health and safety by providing better tools and systems for monitoring and intervention.\n",
- "- **Policy and Rights**: AI can play a crucial role in shaping policies that protect and enhance individual rights and opportunities.\n",
+ "**Risks of AI:**\n",
+ "- AI systems can result in biased data and discriminatory outcomes.\n",
+ "- The decision-making processes of AI can be opaque, leading to a lack of public trust and understanding.\n",
+ "- AI poses risks to individuals, organizations, and society, necessitating the development of risk management frameworks.\n",
"\n",
- "#### Arguments Against AI:\n",
- "- **Bias and Discrimination**: The risk of biased data leading to discriminatory outcomes is a significant concern.\n",
- "- **Transparency and Trust**: The opaque nature of AI decision-making processes can erode public trust and understanding.\n",
- "- **Safety Risks**: There is a potential for AI systems to violate safety norms and impact people's rights and safety negatively.\n",
- "- **Complexity of Mitigation**: Mitigating the risks associated with AI requires ongoing transparency, participatory design, and engagement with impacted communities, which can be complex and resource-intensive.\n",
+ "**Addressing Risks and Benefits:**\n",
+ "- The White House Office of Science and Technology Policy has engaged in a process to gather input from experts and stakeholders on algorithmic and data-driven harms.\n",
+ "- The Blueprint for an AI Bill of Rights emphasizes the need for safe and effective systems, transparency, and participatory design.\n",
+ "- The industry is working on innovative solutions to mitigate AI risks, including risk assessments, auditing mechanisms, and stakeholder engagement.\n",
+ "- The National Institute of Standards and Technology (NIST) is developing a risk management framework to manage AI-related risks effectively.\n",
"\n",
- "In conclusion, while AI offers numerous benefits, including innovation, improved infrastructure, and enhanced community welfare, it also poses significant risks related to bias, transparency, and safety. Experts argue that a balanced approach, involving robust mitigation strategies and inclusive design, is essential to harness the benefits of AI while minimizing its risks.\n",
- "\u001b[0m### Comparison of Risks and Benefits of AI\n",
+ "Overall, while AI offers significant benefits, it also presents risks that require careful management and responsible integration into society.\n",
+ "\u001b[0mThe benefits and risks of AI are widely discussed among experts, and here is a summary based on the provided sources:\n",
"\n",
- "#### Risks of AI:\n",
- "1. **Biased Data and Discriminatory Outcomes**: AI systems can perpetuate and even exacerbate biases present in the data they are trained on, leading to unfair and discriminatory outcomes.\n",
- "2. **Opaque Decision-Making**: The decision-making processes of AI systems can be complex and not easily understandable, leading to a lack of transparency.\n",
- "3. **Lack of Public Trust**: The opacity and potential biases in AI systems can result in a lack of trust and understanding from the public.\n",
- "4. **Safety and Rights Violations**: There is a risk of AI systems violating safety norms and impacting people's rights, opportunities, or safety.\n",
+ "**Benefits of AI:**\n",
+ "- AI has the potential to transform lives by improving various aspects of daily living.\n",
+ "- It can lead to the development of better and more innovative infrastructure.\n",
+ "- When integrated thoughtfully and responsibly, AI can enhance community development, drawing lessons from urban planning.\n",
"\n",
- "#### Benefits of AI:\n",
- "1. **Improved Infrastructure**: AI has the potential to transform and improve infrastructure, making it more innovative and efficient.\n",
- "2. **Enhanced Community Health and Safety**: AI can play a significant role in improving community health, safety, and welfare by ensuring better representation and inclusivity.\n",
- "3. **Prevention of Harms**: AI can help prevent harms and improve opportunities, rights, and access, particularly for marginalized communities.\n",
- "4. **Policy Shaping**: AI is central to shaping important policies, such as the Blueprint for an AI Bill of Rights, which aims to protect and enhance the rights of individuals.\n",
+ "**Risks of AI:**\n",
+ "- AI systems can result in biased data and discriminatory outcomes.\n",
+ "- The decision-making processes of AI can be opaque, leading to a lack of public trust and understanding.\n",
+ "- AI poses risks to individuals, organizations, and society, necessitating the development of risk management frameworks.\n",
"\n",
- "### Summary with Arguments For and Against AI\n",
+ "**Addressing Risks and Benefits:**\n",
+ "- The White House Office of Science and Technology Policy has engaged in a process to gather input from experts and stakeholders on algorithmic and data-driven harms.\n",
+ "- The Blueprint for an AI Bill of Rights emphasizes the need for safe and effective systems, transparency, and participatory design.\n",
+ "- The industry is working on innovative solutions to mitigate AI risks, including risk assessments, auditing mechanisms, and stakeholder engagement.\n",
+ "- The National Institute of Standards and Technology (NIST) is developing a risk management framework to manage AI-related risks effectively.\n",
"\n",
- "#### Arguments For AI:\n",
- "- **Innovation and Efficiency**: AI can drive significant advancements in technology and infrastructure, leading to more efficient and innovative solutions.\n",
- "- **Inclusivity and Representation**: AI can ensure better representation of marginalized voices, leading to more equitable outcomes.\n",
- "- **Health and Safety**: AI can enhance community health and safety by providing better tools and systems for monitoring and intervention.\n",
- "- **Policy and Rights**: AI can play a crucial role in shaping policies that protect and enhance individual rights and opportunities.\n",
- "\n",
- "#### Arguments Against AI:\n",
- "- **Bias and Discrimination**: The risk of biased data leading to discriminatory outcomes is a significant concern.\n",
- "- **Transparency and Trust**: The opaque nature of AI decision-making processes can erode public trust and understanding.\n",
- "- **Safety Risks**: There is a potential for AI systems to violate safety norms and impact people's rights and safety negatively.\n",
- "- **Complexity of Mitigation**: Mitigating the risks associated with AI requires ongoing transparency, participatory design, and engagement with impacted communities, which can be complex and resource-intensive.\n",
- "\n",
- "In conclusion, while AI offers numerous benefits, including innovation, improved infrastructure, and enhanced community welfare, it also poses significant risks related to bias, transparency, and safety. Experts argue that a balanced approach, involving robust mitigation strategies and inclusive design, is essential to harness the benefits of AI while minimizing its risks.\n"
+ "Overall, while AI offers significant benefits, it also presents risks that require careful management and responsible integration into society.\n"
]
}
],
"source": [
- "question = \"\"\"\n",
- " What are the risks of AI? What are the benefits?\n",
- " Compare and contrast and provide a summary with arguments for and against from experts.\n",
- "\"\"\"\n",
+ "from vectara_agentic.agent import Agent\n",
+ "import os\n",
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "load_dotenv(override=True)\n",
+ "\n",
+ "api_key = str(os.environ[\"VECTARA_API_KEY\"])\n",
+ "corpus_id = str(os.environ[\"VECTARA_CORPUS_ID\"])\n",
+ "customer_id = str(os.environ[\"VECTARA_CUSTOMER_ID\"])\n",
"\n",
- "print(agent.chat(question).response)"
+ "agent = Agent.from_corpus(\n",
+ " tool_name=\"query_ai\",\n",
+ " vectara_customer_id=customer_id,\n",
+ " vectara_corpus_id=corpus_id,\n",
+ " vectara_api_key=api_key,\n",
+ " data_description=\"AI regulations\",\n",
+ " assistant_specialty=\"artificial intelligence\",\n",
+ " verbose=True,\n",
+ " vectara_summary_num_results=5,\n",
+ " vectara_summarizer=\"mockingbird-1.0-2024-07-16\",\n",
+ " vectara_reranker=\"mmr\",\n",
+ " vectara_rerank_k=50,\n",
+ ")\n",
+ "\n",
+ "print(\n",
+ " agent.chat(\n",
+ " \"What are the risks of AI? What are the benefits? Compare and contrast and provide a summary with arguments for and against from experts.\"\n",
+ " )\n",
+ ")"
]
}
],
diff --git a/docs/docs/examples/managed/zcpDemo.ipynb b/docs/docs/examples/managed/zcpDemo.ipynb
index 10e194c7b49e6..7e13d46d764f7 100644
--- a/docs/docs/examples/managed/zcpDemo.ipynb
+++ b/docs/docs/examples/managed/zcpDemo.ipynb
@@ -5,7 +5,7 @@
"id": "adf7d63d",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/metadata_extraction/EntityExtractionClimate.ipynb b/docs/docs/examples/metadata_extraction/EntityExtractionClimate.ipynb
index 5c638323ba90d..6b3cced776420 100644
--- a/docs/docs/examples/metadata_extraction/EntityExtractionClimate.ipynb
+++ b/docs/docs/examples/metadata_extraction/EntityExtractionClimate.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/metadata_extraction/MetadataExtractionSEC.ipynb b/docs/docs/examples/metadata_extraction/MetadataExtractionSEC.ipynb
index 6c5ca04b1ad1a..337ace51daeec 100644
--- a/docs/docs/examples/metadata_extraction/MetadataExtractionSEC.ipynb
+++ b/docs/docs/examples/metadata_extraction/MetadataExtractionSEC.ipynb
@@ -5,7 +5,7 @@
"id": "76534cce",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/metadata_extraction/MetadataExtraction_LLMSurvey.ipynb b/docs/docs/examples/metadata_extraction/MetadataExtraction_LLMSurvey.ipynb
index cfeec5999290e..b851c14b06c08 100644
--- a/docs/docs/examples/metadata_extraction/MetadataExtraction_LLMSurvey.ipynb
+++ b/docs/docs/examples/metadata_extraction/MetadataExtraction_LLMSurvey.ipynb
@@ -5,7 +5,7 @@
"id": "41aecfc5",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/multi_modal/mm_agent.ipynb b/docs/docs/examples/multi_modal/mm_agent.ipynb
index 1dd5a00d36baa..97f6b222644b6 100644
--- a/docs/docs/examples/multi_modal/mm_agent.ipynb
+++ b/docs/docs/examples/multi_modal/mm_agent.ipynb
@@ -7,7 +7,7 @@
"source": [
"# [Beta] Multi-modal ReAct Agent\n",
"\n",
- "\n",
+ "\n",
"\n",
"In this tutorial we show you how to construct a multi-modal ReAct agent.\n",
"\n",
diff --git a/docs/docs/examples/node_postprocessor/MixedbreadAIRerank.ipynb b/docs/docs/examples/node_postprocessor/MixedbreadAIRerank.ipynb
index 60cadf8f8ae20..fac05e6406c75 100644
--- a/docs/docs/examples/node_postprocessor/MixedbreadAIRerank.ipynb
+++ b/docs/docs/examples/node_postprocessor/MixedbreadAIRerank.ipynb
@@ -5,7 +5,7 @@
"id": "4b5daafbac08a79e",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/node_postprocessor/NVIDIARerank.ipynb b/docs/docs/examples/node_postprocessor/NVIDIARerank.ipynb
index a6733818b797e..eee4783a5a459 100644
--- a/docs/docs/examples/node_postprocessor/NVIDIARerank.ipynb
+++ b/docs/docs/examples/node_postprocessor/NVIDIARerank.ipynb
@@ -63,7 +63,7 @@
"metadata": {},
"outputs": [],
"source": [
- "%pip install --upgrade --quiet llama-index-postprocessor-nvidia-rerank"
+ "%pip install --upgrade --quiet llama-index-postprocessor-nvidia-rerank llama-index-llms-nvidia llama-index-readers-file"
]
},
{
@@ -114,13 +114,120 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
+ ]
+ }
+ ],
"source": [
"from llama_index.postprocessor.nvidia_rerank import NVIDIARerank\n",
+ "from llama_index.core import SimpleDirectoryReader, Settings, VectorStoreIndex\n",
+ "from llama_index.embeddings.nvidia import NVIDIAEmbedding\n",
+ "from llama_index.llms.nvidia import NVIDIA\n",
+ "from llama_index.core.node_parser import SentenceSplitter\n",
+ "from llama_index.core import Settings\n",
+ "import os\n",
"\n",
"reranker = NVIDIARerank(top_n=4)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mkdir: cannot create directory ‘data’: File exists\n",
+ "--2024-07-03 10:33:17-- https://www.dropbox.com/scl/fi/p33j9112y0ysgwg77fdjz/2021_Housing_Inventory.pdf?rlkey=yyok6bb18s5o31snjd2dxkxz3&dl=0\n",
+ "Resolving www.dropbox.com (www.dropbox.com)... 162.125.81.18, 2620:100:6031:18::a27d:5112\n",
+ "Connecting to www.dropbox.com (www.dropbox.com)|162.125.81.18|:443... connected.\n",
+ "HTTP request sent, awaiting response... 302 Found\n",
+ "Location: https://uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com/cd/0/inline/CV9Hy3nIrjnOf-Fqsgd-YhHcMaj0AHvOQaE1b4sdiKnOBqZL_u9ml6dAGctGxr5I79yD_kI8BNwDtFl_ll_sdfdt0iXcIYosfxaPr2NdbkRAMR6vg9UXuCU8kNEFi0D3Grs/file# [following]\n",
+ "--2024-07-03 10:33:18-- https://uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com/cd/0/inline/CV9Hy3nIrjnOf-Fqsgd-YhHcMaj0AHvOQaE1b4sdiKnOBqZL_u9ml6dAGctGxr5I79yD_kI8BNwDtFl_ll_sdfdt0iXcIYosfxaPr2NdbkRAMR6vg9UXuCU8kNEFi0D3Grs/file\n",
+ "Resolving uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com (uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com)... 162.125.81.15, 2620:100:6031:15::a27d:510f\n",
+ "Connecting to uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com (uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com)|162.125.81.15|:443... connected.\n",
+ "HTTP request sent, awaiting response... 302 Found\n",
+ "Location: /cd/0/inline2/CV9Ugj_mK7TSMb3sw_BdQFrj2rzx-SI2cfGU7-VF4bcW3PdhxO4qw--AXQKUidWtDL_54rViwvbaBGHMvtMEAK_lCIwXXj5XwkKpJKTmP0mDrz8eU2qu0FGyi4uOGfO7TeNLFMFY_bBGUMHMatvKJVPF59Ps94-8LC40ba-Cgv2YKZtcU-UjFpLh-Fnf6emkG-c8eUWB2uKPX_Lx0E4hCENQEPOGOfMhDHU0DC8k6khZiilmLtjXsDJ0H4y3efQ-Fz-VsWCC2FcoGpDcxXGu1Ysp5-mP2eHpH3qOx20d2IrndwN4RGLAqzR6cfsOHPMvoYPyLjOW1322t1O46mXqcjv94OPEEIIHI-2K8xL4pBjLUQ/file [following]\n",
+ "--2024-07-03 10:33:18-- https://uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com/cd/0/inline2/CV9Ugj_mK7TSMb3sw_BdQFrj2rzx-SI2cfGU7-VF4bcW3PdhxO4qw--AXQKUidWtDL_54rViwvbaBGHMvtMEAK_lCIwXXj5XwkKpJKTmP0mDrz8eU2qu0FGyi4uOGfO7TeNLFMFY_bBGUMHMatvKJVPF59Ps94-8LC40ba-Cgv2YKZtcU-UjFpLh-Fnf6emkG-c8eUWB2uKPX_Lx0E4hCENQEPOGOfMhDHU0DC8k6khZiilmLtjXsDJ0H4y3efQ-Fz-VsWCC2FcoGpDcxXGu1Ysp5-mP2eHpH3qOx20d2IrndwN4RGLAqzR6cfsOHPMvoYPyLjOW1322t1O46mXqcjv94OPEEIIHI-2K8xL4pBjLUQ/file\n",
+ "Reusing existing connection to uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com:443.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 4808625 (4.6M) [application/pdf]\n",
+ "Saving to: ‘data/housing_data.pdf’\n",
+ "\n",
+ "data/housing_data.p 100%[===================>] 4.58M 2.68MB/s in 1.7s \n",
+ "\n",
+ "2024-07-03 10:33:21 (2.68 MB/s) - ‘data/housing_data.pdf’ saved [4808625/4808625]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!mkdir data\n",
+ "!wget \"https://www.dropbox.com/scl/fi/p33j9112y0ysgwg77fdjz/2021_Housing_Inventory.pdf?rlkey=yyok6bb18s5o31snjd2dxkxz3&dl=0\" -O \"data/housing_data.pdf\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Settings.text_splitter = SentenceSplitter(chunk_size=500)\n",
+ "\n",
+ "documents = SimpleDirectoryReader(\"./data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Settings.embed_model = NVIDIAEmbedding(model=\"NV-Embed-QA\", truncate=\"END\")\n",
+ "\n",
+ "index = VectorStoreIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Settings.llm = NVIDIA()\n",
+ "\n",
+ "query_engine = index.as_query_engine(\n",
+ " similarity_top_k=20, node_postprocessors=[reranker]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The net gain in housing units in the Mission in 2021 was not specified in the provided context information.\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"What was the net gain in housing units in the Mission in 2021?\"\n",
+ ")\n",
+ "print(response)"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/docs/docs/examples/node_postprocessor/openvino_rerank.ipynb b/docs/docs/examples/node_postprocessor/openvino_rerank.ipynb
index 62afb44bdb7ab..12eac60e32b7a 100644
--- a/docs/docs/examples/node_postprocessor/openvino_rerank.ipynb
+++ b/docs/docs/examples/node_postprocessor/openvino_rerank.ipynb
@@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/node_postprocessor/rankLLM.ipynb b/docs/docs/examples/node_postprocessor/rankLLM.ipynb
index 6da31bd383195..eaac6373ab9da 100644
--- a/docs/docs/examples/node_postprocessor/rankLLM.ipynb
+++ b/docs/docs/examples/node_postprocessor/rankLLM.ipynb
@@ -6,23 +6,23 @@
"source": [
"# RankLLM Reranker Demonstration (Van Gogh Wiki)\n",
"\n",
- "This demo showcases how to use RankLLM (https://github.com/castorini/rank_llm) to rerank passages. RankLLM offers a suite of listwise rerankers, albeit with focus on open source LLMs finetuned for the task - RankVicuna and RankZephyr being two of them.\n",
+ "This demo showcases how to use [RankLLM](https://github.com/castorini/rank_llm) to rerank passages. \n",
"\n",
- "It compares query search results from Van Gogh’s wikipedia with just retrieval (using VectorIndexRetriever from llama-index) and retrieval+reranking with RankLLM. It demonstrates two models from RankLLM:\n",
+ "RankLLM offers a suite of listwise rerankers, albeit with focus on open source LLMs finetuned for the task - RankVicuna and RankZephyr being two of them.\n",
"\n",
- "- ```RankVicuna 7B V1```\n",
- "- ```RankZephyr 7B V1 - Full - BF16```\n",
+ "It compares query search results from Van Gogh’s wikipedia with just retrieval (using VectorIndexRetriever from llama-index) and retrieval+reranking with RankLLM. We show an example of reranking 50 candidates using the RankZephyr reranker, which uses a listwise sliding window algorithm.\n",
"\n",
+ "\n",
+ "_______________________________\n",
"Dependencies:\n",
"\n",
- "- Currently, RankLLM's rerankers require requires `CUDA`\n",
+ "- **CUDA**\n",
"- The built-in retriever, which uses [Pyserini](https://github.com/castorini/pyserini), requires `JDK11`, `PyTorch`, and `Faiss`\n",
"\n",
"\n",
"### castorini/rank_llm\n",
- "Repository for prompt-decoding using LLMs (```GPT3.5```, ```GPT4```, ```Vicuna```, and ```Zephyr```)\\\n",
- "Website: [http://rankllm.ai](http://rankllm.ai)\\\n",
- "Stars: 193"
+ "Suite of LLM-based reranking models (e.g, `RankZephyr`, `LiT5`, `RankVicuna`)\n",
+ "Website: [http://rankllm.ai](http://rankllm.ai)\\"
]
},
{
@@ -165,12 +165,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Retrieval + RankLLM Reranking\n",
+ "### Retrieval + RankLLM Reranking (sliding window)\n",
"\n",
"1. Set up retriever and reranker\n",
"2. Retrieve results given search query without reranking\n",
- "3. Retrieve results given search query with RankZephyr reranking\n",
- "4. Retrieve results given search query with RankVicuna reranking"
+ "3. Retrieve results given search query with RankZephyr reranking"
]
},
{
@@ -181,9 +180,10 @@
"source": [
"from llama_index.core.retrievers import VectorIndexRetriever\n",
"from llama_index.core import QueryBundle\n",
- "from llama_index.postprocessor.rankLLM_rerank import RankLLMRerank\n",
+ "from llama_index.postprocessor.rankllm_rerank import RankLLMRerank\n",
"\n",
"import pandas as pd\n",
+ "import torch\n",
"from IPython.display import display, HTML\n",
"\n",
"\n",
@@ -192,9 +192,8 @@
" vector_top_k=10,\n",
" reranker_top_n=3,\n",
" with_reranker=False,\n",
- " with_retrieval=False,\n",
- " model=\"zephyr\",\n",
- " gpt_model=\"gpt-3.5-turbo\",\n",
+ " model=\"rank_zephyr\",\n",
+ " window_size=None,\n",
"):\n",
" query_bundle = QueryBundle(query_str)\n",
" # configure retriever\n",
@@ -203,19 +202,21 @@
" similarity_top_k=vector_top_k,\n",
" )\n",
" retrieved_nodes = retriever.retrieve(query_bundle)\n",
+ " retrieved_nodes.reverse()\n",
"\n",
" if with_reranker:\n",
" # configure reranker\n",
" reranker = RankLLMRerank(\n",
- " top_n=reranker_top_n,\n",
- " model=model,\n",
- " with_retrieval=with_retrieval,\n",
- " gpt_model=gpt_model,\n",
+ " model=model, top_n=reranker_top_n, window_size=window_size\n",
" )\n",
" retrieved_nodes = reranker.postprocess_nodes(\n",
" retrieved_nodes, query_bundle\n",
" )\n",
"\n",
+ " # clear cache, rank_zephyr uses 16GB of GPU VRAM\n",
+ " del reranker\n",
+ " torch.cuda.empty_cache()\n",
+ "\n",
" return retrieved_nodes\n",
"\n",
"\n",
@@ -236,9 +237,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Retrieval top 3 results without reranking\n",
+ "### Without `RankZephyr` reranking, the correct result is ranked `47`th/50.\n",
"\n",
- "## Expected result:\n",
+ "#### Expected result:\n",
"```After much pleading from Van Gogh, Gauguin arrived in Arles on 23 October and, in November, the two painted together. Gauguin depicted Van Gogh in his The Painter of Sunflowers;```"
]
},
@@ -269,18 +270,18 @@
" \n",
" | \n", - " | Score | \n", - "Text | \n", - "
|---|---|---|
| 0 | \n", - "0.752651 | \n", - "== Nazi-looted art == During the Nazi period (1933–1945) a great number of artworks by Van Gogh changed hands, many of them looted from Jewish collectors who were forced into exile or murdered. Some of these works have disappeared into private collections. Others have since resurfaced in museums, or at auction, or have been reclaimed, often in high-profile lawsuits, by their former owners. The German Lost Art Foundation still lists dozens of missing van Goghs and the American Alliance of Museums lists 73 van Goghs on the Nazi Era Provenance Internet Portal. == References == === Explanatory footnotes === === Citations === === General and cited sources === == External links == The Vincent van Gogh Gallery, the complete works and letters of Van Gogh Vincent van Gogh The letters, the complete letters of Van Gogh (translated into English and annotated) Vincent van Gogh, teaching resource on Van Gogh Works by Vincent van Gogh at Project Gutenberg Works by or about Vincent van Gogh at Internet Archive Works by Vincent van Gogh at LibriVox (public domain audiobooks) Vincent van Gogh at IMDb | \n",
- "
| 1 | \n", - "0.765492 | \n", - "Yellow meant the most to him, because it symbolised emotional truth. He used yellow as a symbol for sunlight, life, and God.Van Gogh strove to be a painter of rural life and nature; during his first summer in Arles he used his new palette to paint landscapes and traditional rural life. His belief that a power existed behind the natural led him to try to capture a sense of that power, or the essence of nature in his art, sometimes through the use of symbols. His renditions of the sower, at first copied from Jean-François Millet, reflect the influence of Thomas Carlyle and Friedrich Nietzsche's thoughts on the heroism of physical labour, as well as Van Gogh's religious beliefs: the sower as Christ sowing life beneath the hot sun. These were themes and motifs he returned to often to rework and develop. His paintings of flowers are filled with symbolism, but rather than use traditional Christian iconography he made up his own, where life is lived under the sun and work is an allegory of life. In Arles, having gained confidence after painting spring blossoms and learning to capture bright sunlight, he was ready to paint The Sower. Van Gogh stayed within what he called the \"guise of reality\" and was critical of overly stylised works. He wrote afterwards that the abstraction of Starry Night had gone too far and that reality had \"receded too far in the background\". Hughes describes it as a moment of extreme visionary ecstasy: the stars are in a great whirl, reminiscent of Hokusai's Great Wave, the movement in the heaven above is reflected by the movement of the cypress on the earth below, and the painter's vision is \"translated into a thick, emphatic plasma of paint\".Between 1885 and his death in 1890, Van Gogh appears to have been building an oeuvre, a collection that reflected his personal vision and could be commercially successful. He was influenced by Blanc's definition of style, that a true painting required optimal use of colour, perspective and brushstrokes. Van Gogh applied the word \"purposeful\" to paintings he thought he had mastered, as opposed to those he thought of as studies. | \n",
- "
| 2 | \n", - "0.768011 | \n", - "His legacy is honored and celebrated by the Van Gogh Museum in Amsterdam, which holds the world's largest collection of his paintings and drawings. == Letters == The most comprehensive primary source on Van Gogh is his correspondence with his younger brother, Theo. Their lifelong friendship, and most of what is known of Vincent's thoughts and theories of art, are recorded in the hundreds of letters they exchanged from 1872 until 1890. Theo van Gogh was an art dealer and provided his brother with financial and emotional support as well as access to influential people on the contemporary art scene.Theo kept all of Vincent's letters to him; but Vincent kept only a few of the letters he received. After both had died, Theo's widow Jo Bonger-van Gogh arranged for the publication of some of their letters. A few appeared in 1906 and 1913; the majority were published in 1914. Vincent's letters are eloquent and expressive, have been described as having a \"diary-like intimacy\", and read in parts like autobiography. Translator Arnold Pomerans wrote that their publication adds a \"fresh dimension to the understanding of Van Gogh's artistic achievement, an understanding granted to us by virtually no other painter\". There are more than 600 letters from Vincent to Theo and around 40 from Theo to Vincent. There are 22 to his sister Wil, 58 to the painter Anthon van Rappard, 22 to Émile Bernard as well as individual letters to Paul Signac, Paul Gauguin, and the critic Albert Aurier. Some are illustrated with sketches. Many are undated, but art historians have been able to place most in chronological order. Problems in transcription and dating remain, mainly with those posted from Arles. While there, Vincent wrote around 200 letters in Dutch, French, and English. There is a gap in the record when he lived in Paris as the brothers lived together and had no need to correspond.The highly paid contemporary artist Jules Breton was frequently mentioned in Vincent's letters. In 1875 letters to Theo, Vincent mentions he saw Breton, discusses the Breton paintings he saw at a Salon, and discusses sending one of Breton's books but only on the condition that it be returned. | \n",
- "
| 3 | \n", - "0.769008 | \n", - "==== Self-portraits ==== Van Gogh created more than 43 self-portraits between 1885 and 1889. They were usually completed in series, such as those painted in Paris in mid-1887, and continued until shortly before his death. Generally the portraits were studies, created during periods when he was reluctant to mix with others or when he lacked models and painted himself.Van Gogh's self-portraits reflect a high degree of self-scrutiny. Often they were intended to mark important periods in his life; for example, the mid-1887 Paris series were painted at the point where he became aware of Claude Monet, Paul Cézanne and Signac. In Self-Portrait with Grey Felt Hat, heavy strains of paint spread outwards across the canvas. It is one of his most renowned self-portraits of that period, \"with its highly organized rhythmic brushstrokes, and the novel halo derived from the Neo-impressionist repertoire was what Van Gogh himself called a 'purposeful' canvas\".They contain a wide array of physiognomical representations. Van Gogh's mental and physical condition is usually apparent; he may appear unkempt, unshaven or with a neglected beard, with deeply sunken eyes, a weak jaw, or having lost teeth. Some show him with full lips, a long face or prominent skull, or sharpened, alert features. His hair is sometimes depicted in a vibrant reddish hue and at other times ash colored.Van Gogh's self-portraits vary stylistically. In those painted after December 1888, the strong contrast of vivid colors highlight the haggard pallor of his skin. Some depict the artist with a beard, others without. He can be seen with bandages in portraits executed just after he mutilated his ear. In only a few does he depict himself as a painter. Those painted in Saint-Rémy show the head from the right, the side opposite his damaged ear, as he painted himself reflected in his mirror. | \n",
- "
| 4 | \n", - "0.769796 | \n", - "Sien gave her daughter to her mother and baby Willem to her brother. Willem remembered visiting Rotterdam when he was about 12, when an uncle tried to persuade Sien to marry to legitimise the child. He believed Van Gogh was his father, but the timing of his birth makes this unlikely. Sien drowned herself in the River Scheldt in 1904.In September 1883, Van Gogh moved to Drenthe in the northern Netherlands. In December driven by loneliness, he went to live with his parents, then in Nuenen, North Brabant. === Emerging artist === ==== Nuenen and Antwerp (1883–1886) ==== In Nuenen, Van Gogh focused on painting and drawing. Working outside and very quickly, he completed sketches and paintings of weavers and their cottages. Van Gogh also completed The Parsonage Garden at Nuenen, which was stolen from the Singer Laren in March 2020. From August 1884, Margot Begemann, a neighbour's daughter ten years his senior, joined him on his forays; she fell in love and he reciprocated, though less enthusiastically. They wanted to marry, but neither side of their families were in favor. Margot was distraught and took an overdose of strychnine, but survived after Van Gogh rushed her to a nearby hospital. On 26 March 1885, his father died of a heart attack.Van Gogh painted several groups of still lifes in 1885. During his two-year stay in Nuenen, he completed numerous drawings and watercolours and nearly 200 oil paintings. His palette consisted mainly of sombre earth tones, particularly dark brown, and showed no sign of the vivid colours that distinguished his later work.There was interest from a dealer in Paris early in 1885. Theo asked Vincent if he had paintings ready to exhibit. In May, Van Gogh responded with his first major work, The Potato Eaters, and a series of \"peasant character studies\" which were the culmination of several years of work. | \n",
- "
| 5 | \n", - "0.770298 | \n", - "==== Cypresses and olives ==== Fifteen canvases depict cypresses, a tree he became fascinated with in Arles. He brought life to the trees, which were traditionally seen as emblematic of death. The series of cypresses he began in Arles featured the trees in the distance, as windbreaks in fields; when he was at Saint-Rémy he brought them to the foreground. Vincent wrote to Theo in May 1889: \"Cypresses still preoccupy me, I should like to do something with them like my canvases of sunflowers\"; he went on to say, \"They are beautiful in line and proportion like an Egyptian obelisk.\"In mid-1889, and at his sister Wil's request, Van Gogh painted several smaller versions of Wheat Field with Cypresses. The works are characterised by swirls and densely painted impasto, and include The Starry Night, in which cypresses dominate the foreground. In addition to this, other notable works on cypresses include Cypresses (1889), Cypresses with Two Figures (1889–90), and Road with Cypress and Star (1890).During the last six or seven months of the year 1889, he had also created at least fifteen paintings of olive trees, a subject which he considered as demanding and compelling. Among these works are Olive Trees with the Alpilles in the Background (1889), about which in a letter to his brother Van Gogh wrote, \"At last I have a landscape with olives\". While in Saint-Rémy, Van Gogh spent time outside the asylum, where he painted trees in the olive groves. In these works, natural life is rendered as gnarled and arthritic as if a personification of the natural world, which are, according to Hughes, filled with \"a continuous field of energy of which nature is a manifestation\". | \n",
- "
| 6 | \n", - "0.771924 | \n", - "He wrote afterwards that the abstraction of Starry Night had gone too far and that reality had \"receded too far in the background\". Hughes describes it as a moment of extreme visionary ecstasy: the stars are in a great whirl, reminiscent of Hokusai's Great Wave, the movement in the heaven above is reflected by the movement of the cypress on the earth below, and the painter's vision is \"translated into a thick, emphatic plasma of paint\".Between 1885 and his death in 1890, Van Gogh appears to have been building an oeuvre, a collection that reflected his personal vision and could be commercially successful. He was influenced by Blanc's definition of style, that a true painting required optimal use of colour, perspective and brushstrokes. Van Gogh applied the word \"purposeful\" to paintings he thought he had mastered, as opposed to those he thought of as studies. He painted many series of studies; most of which were still lifes, many executed as colour experiments or as gifts to friends. The work in Arles contributed considerably to his oeuvre: those he thought the most important from that time were The Sower, Night Cafe, Memory of the Garden in Etten and Starry Night. With their broad brushstrokes, inventive perspectives, colours, contours and designs, these paintings represent the style he sought. === Major series === Van Gogh's stylistic developments are usually linked to the periods he spent living in different places across Europe. He was inclined to immerse himself in local cultures and lighting conditions, although he maintained a highly individual visual outlook throughout. His evolution as an artist was slow and he was aware of his painterly limitations. Van Gogh moved home often, perhaps to expose himself to new visual stimuli, and through exposure develop his technical skill. Art historian Melissa McQuillan believes the moves also reflect later stylistic changes and that Van Gogh used the moves to avoid conflict, and as a coping mechanism for when the idealistic artist was faced with the realities of his then current situation. | \n",
- "
| 7 | \n", - "0.772942 | \n", - "Among these was Two Peasant Women Digging in a Snow-Covered Field at Sunset. Hulsker believes that this small group of paintings formed the nucleus of many drawings and study sheets depicting landscapes and figures that Van Gogh worked on during this time. He comments that this short period was the only time that Van Gogh's illness had a significant effect on his work. Van Gogh asked his mother and his brother to send him drawings and rough work he had done in the early 1880s so he could work on new paintings from his old sketches. Belonging to this period is Sorrowing Old Man (\"At Eternity's Gate\"), a colour study Hulsker describes as \"another unmistakable remembrance of times long past\". His late paintings show an artist at the height of his abilities, according to the art critic Robert Hughes, \"longing for concision and grace\".After the birth of his nephew, Van Gogh wrote, \"I started right away to make a picture for him, to hang in their bedroom, branches of white almond blossom against a blue sky.\" | \n", - "
| 8 | \n", - "0.774411 | \n", - "He moved to Nuenen after a short period of time in Drenthe and began work on several large paintings but destroyed most of them. The Potato Eaters and its companion pieces are the only ones to have survived. Following a visit to the Rijksmuseum Van Gogh wrote of his admiration for the quick, economical brushwork of the Dutch Masters, especially Rembrandt and Frans Hals. He was aware many of his faults were due to lack of experience and technical expertise, so in November 1885 he travelled to Antwerp and later Paris to learn and develop his skills. Theo criticised The Potato Eaters for its dark palette, which he thought unsuitable for a modern style. During Van Gogh's stay in Paris between 1886 and 1887, he tried to master a new, lighter palette. His Portrait of Père Tanguy (1887) shows his success with the brighter palette and is evidence of an evolving personal style. Charles Blanc's treatise on colour interested him greatly and led him to work with complementary colours. Van Gogh came to believe that the effect of colour went beyond the descriptive; he said that \"colour expresses something in itself\". According to Hughes, Van Gogh perceived colour as having a \"psychological and moral weight\", as exemplified in the garish reds and greens of The Night Café, a work he wanted to \"express the terrible passions of humanity\". Yellow meant the most to him, because it symbolised emotional truth. He used yellow as a symbol for sunlight, life, and God.Van Gogh strove to be a painter of rural life and nature; during his first summer in Arles he used his new palette to paint landscapes and traditional rural life. His belief that a power existed behind the natural led him to try to capture a sense of that power, or the essence of nature in his art, sometimes through the use of symbols. His renditions of the sower, at first copied from Jean-François Millet, reflect the influence of Thomas Carlyle and Friedrich Nietzsche's thoughts on the heroism of physical labour, as well as Van Gogh's religious beliefs: the sower as Christ sowing life beneath the hot sun. These were themes and motifs he returned to often to rework and develop. | \n",
- "
| 9 | \n", - "0.774471 | \n", - "With their broad brushstrokes, inventive perspectives, colours, contours and designs, these paintings represent the style he sought. === Major series === Van Gogh's stylistic developments are usually linked to the periods he spent living in different places across Europe. He was inclined to immerse himself in local cultures and lighting conditions, although he maintained a highly individual visual outlook throughout. His evolution as an artist was slow and he was aware of his painterly limitations. Van Gogh moved home often, perhaps to expose himself to new visual stimuli, and through exposure develop his technical skill. Art historian Melissa McQuillan believes the moves also reflect later stylistic changes and that Van Gogh used the moves to avoid conflict, and as a coping mechanism for when the idealistic artist was faced with the realities of his then current situation. ==== Portraits ==== Van Gogh said portraiture was his greatest interest. \"What I'm most passionate about, much much more than all the rest in my profession\", he wrote in 1890, \"is the portrait, the modern portrait.\" It is \"the only thing in painting that moves me deeply and that gives me a sense of the infinite.\" He wrote to his sister that he wished to paint portraits that would endure, and that he would use colour to capture their emotions and character rather than aiming for photographic realism. Those closest to Van Gogh are mostly absent from his portraits; he rarely painted Theo, Van Rappard or Bernard. The portraits of his mother were from photographs.Van Gogh painted Arles' postmaster Joseph Roulin and his family repeatedly. In five versions of La Berceuse (The Lullaby), Van Gogh painted Augustine Roulin quietly holding a rope that rocks the unseen cradle of her infant daughter. Van Gogh had planned for it to be the central image of a triptych, flanked by paintings of sunflowers. | \n",
- "
| 10 | \n", - "0.776510 | \n", - "These began a compelling mythology of Van Gogh as an intense and dedicated painter who suffered for his art and died young. In 1934, the novelist Irving Stone wrote a biographical novel of Van Gogh's life titled Lust for Life, based on Van Gogh's letters to Theo. This novel and the 1956 film further enhanced his fame, especially in the United States where Stone surmised only a few hundred people had heard of Van Gogh prior to his surprise best-selling book.In 1957, Francis Bacon based a series of paintings on reproductions of Van Gogh's The Painter on the Road to Tarascon, the original of which was destroyed during the Second World War. Bacon was inspired by an image he described as \"haunting\", and regarded Van Gogh as an alienated outsider, a position which resonated with him. Bacon identified with Van Gogh's theories of art and quoted lines written to Theo: \"[R]eal painters do not paint things as they are ... [T]hey paint them as they themselves feel them to be.\"Van Gogh's works are among the world's most expensive paintings. Those sold for over US$100 million (today's equivalent) include Portrait of Dr Gachet, Portrait of Joseph Roulin and Irises. The Metropolitan Museum of Art acquired a copy of Wheat Field with Cypresses in 1993 for US$57 million by using funds donated by publisher, diplomat and philanthropist Walter Annenberg. In 2015, L'Allée des Alyscamps sold for US$66.3 million at Sotheby's, New York, exceeding its reserve of US$40 million.Minor planet 4457 van Gogh is named in his honour.In October 2022, two activists protesting the effects of the fossil fuel industry on climate change threw a can of tomato soup on Van Gogh's Sunflowers in the National Gallery, London, and then glued their hands to the gallery wall. As the painting was covered by glass it was not damaged. | \n", - "
| 11 | \n", - "0.776510 | \n", - "==== Flowers ==== Van Gogh painted several landscapes with flowers, including roses, lilacs, irises, and sunflowers. Some reflect his interests in the language of colour, and also in Japanese ukiyo-e. There are two series of dying sunflowers. The first was painted in Paris in 1887 and shows flowers lying on the ground. The second set was completed a year later in Arles and is of bouquets in a vase positioned in early morning light. Both are built from thickly layered paintwork, which, according to the London National Gallery, evoke the \"texture of the seed-heads\".In these series, Van Gogh was not preoccupied by his usual interest in filling his paintings with subjectivity and emotion; rather, the two series are intended to display his technical skill and working methods to Gauguin, who was about to visit. The 1888 paintings were created during a rare period of optimism for the artist. Vincent wrote to Theo in August 1888: I'm painting with the gusto of a Marseillais eating bouillabaisse, which won't surprise you when it's a question of painting large sunflowers ... If I carry out this plan there'll be a dozen or so panels. The whole thing will therefore be a symphony in blue and yellow. I work on it all these mornings, from sunrise. Because the flowers wilt quickly and it's a matter of doing the whole thing in one go. The sunflowers were painted to decorate the walls in anticipation of Gauguin's visit, and Van Gogh placed individual works around the Yellow House's guest room in Arles. Gauguin was deeply impressed and later acquired two of the Paris versions. After Gauguin's departure, Van Gogh imagined the two major versions of the sunflowers as wings of the Berceuse Triptych, and included them in his Les XX in Brussels exhibit. Today the major pieces of the series are among his best known, celebrated for the sickly connotations of the colour yellow and its tie-in with the Yellow House, the expressionism of the brush strokes, and their contrast against often dark backgrounds. | \n",
- "
| 12 | \n", - "0.778443 | \n", - "His father was the youngest son of a minister. The two met when Anna's younger sister, Cornelia, married Theodorus's older brother Vincent (Cent). Van Gogh's parents married in May 1851 and moved to Zundert. His brother Theo was born on 1 May 1857. There was another brother, Cor, and three sisters: Elisabeth, Anna, and Willemina (known as \"Wil\"). In later life, Van Gogh remained in touch only with Willemina and Theo. Theodorus's salary as a minister was modest, but the Church also supplied the family with a house, a maid, two cooks, a gardener, a carriage and horse; his mother Anna instilled in the children a duty to uphold the family's high social position.Van Gogh was a serious and thoughtful child. He was taught at home by his mother and a governess, and in 1860, was sent to the village school. In 1864, he was placed in a boarding school at Zevenbergen, where he felt abandoned, and he campaigned to come home. Instead, in 1866, his parents sent him to the middle school in Tilburg, where he was also deeply unhappy. His interest in art began at a young age. He was encouraged to draw as a child by his mother, and his early drawings are expressive, but do not approach the intensity of his later work. Constant Cornelis Huijsmans, who had been a successful artist in Paris, taught the students at Tilburg. His philosophy was to reject technique in favour of capturing the impressions of things, particularly nature or common objects. Van Gogh's profound unhappiness seems to have overshadowed the lessons, which had little effect. In March 1868, he abruptly returned home. He later wrote that his youth was \"austere and cold, and sterile\".In July 1869, Van Gogh's uncle Cent obtained a position for him at the art dealers Goupil & Cie in The Hague. After completing his training in 1873, he was transferred to Goupil's London branch on Southampton Street, and took lodgings at 87 Hackford Road, Stockwell. | \n", - "
| 13 | \n", - "0.778511 | \n", - "=== Death === On 27 July 1890, aged 37, Van Gogh shot himself in the chest with a revolver. The shooting may have taken place in the wheat field in which he had been painting, or in a local barn. The bullet was deflected by a rib and passed through his chest without doing apparent damage to internal organs – possibly stopped by his spine. He was able to walk back to the Auberge Ravoux, where he was attended to by two doctors. One of them, Dr Gachet, served as a war surgeon in 1870 and had extensive knowledge of gunshots. Vincent was possibly attended to during the night by Dr Gachet's son Paul Louis Gachet and the innkeeper, Arthur Ravoux. The following morning, Theo rushed to his brother's side, finding him in good spirits. But within hours Vincent's health began to fail, suffering from an infection resulting from the wound. He died in the early hours of 29 July. According to Theo, Vincent's last words were: \"The sadness will last forever\". Van Gogh was buried on 30 July, in the municipal cemetery of Auvers-sur-Oise. The funeral was attended by Theo van Gogh, Andries Bonger, Charles Laval, Lucien Pissarro, Émile Bernard, Julien Tanguy and Paul Gachet, among twenty family members, friends and locals. Theo suffered from syphilis, and his health began to decline further after his brother's death. Weak and unable to come to terms with Vincent's absence, he died on 25 January 1891 at Den Dolder and was buried in Utrecht. In 1914, Johanna van Gogh-Bonger had Theo's body exhumed and moved from Utrecht to be re-buried alongside Vincent's at Auvers-sur-Oise.There have been numerous debates as to the nature of Van Gogh's illness and its effect on his work, and many retrospective diagnoses have been proposed. The consensus is that Van Gogh had an episodic condition with periods of normal functioning. Perry was the first to suggest bipolar disorder in 1947, and this has been supported by the psychiatrists Hemphill and Blumer. | \n",
- "
| 14 | \n", - "0.780200 | \n", - "He was encouraged to draw as a child by his mother, and his early drawings are expressive, but do not approach the intensity of his later work. Constant Cornelis Huijsmans, who had been a successful artist in Paris, taught the students at Tilburg. His philosophy was to reject technique in favour of capturing the impressions of things, particularly nature or common objects. Van Gogh's profound unhappiness seems to have overshadowed the lessons, which had little effect. In March 1868, he abruptly returned home. He later wrote that his youth was \"austere and cold, and sterile\".In July 1869, Van Gogh's uncle Cent obtained a position for him at the art dealers Goupil & Cie in The Hague. After completing his training in 1873, he was transferred to Goupil's London branch on Southampton Street, and took lodgings at 87 Hackford Road, Stockwell. This was a happy time for Van Gogh; he was successful at work and, at 20, was earning more than his father. Theo's wife, Jo Van Gogh-Bonger, later remarked that this was the best year of Vincent's life. He became infatuated with his landlady's daughter, Eugénie Loyer, but she rejected him after he confessed his feelings; she was secretly engaged to a former lodger. He grew more isolated and religiously fervent. His father and uncle arranged a transfer to Paris in 1875, where he became resentful of issues such as the degree to which the art dealers commodified art, and he was dismissed a year later. In April 1876, he returned to England to take unpaid work as a supply teacher in a small boarding school in Ramsgate. When the proprietor moved to Isleworth in Middlesex, Van Gogh went with him. The arrangement was not successful; he left to become a Methodist minister's assistant. His parents had meanwhile moved to Etten; in 1876 he returned home at Christmas for six months and took work at a bookshop in Dordrecht. He was unhappy in the position, and spent his time doodling or translating passages from the Bible into English, French, and German. | \n",
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| 15 | \n", - "0.780329 | \n", - "Influenced by Van Gogh, Matisse abandoned his earth-coloured palette for bright colours.In Paris in 1901, a large Van Gogh retrospective was held at the Bernheim-Jeune Gallery, which excited André Derain and Maurice de Vlaminck, and contributed to the emergence of Fauvism. Important group exhibitions took place with the Sonderbund artists in Cologne in 1912, the Armory Show, New York in 1913, and Berlin in 1914. Henk Bremmer was instrumental in teaching and talking about Van Gogh, and introduced Helene Kröller-Müller to Van Gogh's art; she became an avid collector of his work. The early figures in German Expressionism such as Emil Nolde acknowledged a debt to Van Gogh's work. Bremmer assisted Jacob Baart de la Faille, whose catalogue raisonné L'Oeuvre de Vincent van Gogh appeared in 1928. Van Gogh's fame reached its first peak in Austria and Germany before World War I, helped by the publication of his letters in three volumes in 1914. His letters are expressive and literate, and have been described as among the foremost 19th-century writings of their kind. These began a compelling mythology of Van Gogh as an intense and dedicated painter who suffered for his art and died young. In 1934, the novelist Irving Stone wrote a biographical novel of Van Gogh's life titled Lust for Life, based on Van Gogh's letters to Theo. This novel and the 1956 film further enhanced his fame, especially in the United States where Stone surmised only a few hundred people had heard of Van Gogh prior to his surprise best-selling book.In 1957, Francis Bacon based a series of paintings on reproductions of Van Gogh's The Painter on the Road to Tarascon, the original of which was destroyed during the Second World War. Bacon was inspired by an image he described as \"haunting\", and regarded Van Gogh as an alienated outsider, a position which resonated with him. | \n",
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| 16 | \n", - "0.780921 | \n", - "Theo suffered from syphilis, and his health began to decline further after his brother's death. Weak and unable to come to terms with Vincent's absence, he died on 25 January 1891 at Den Dolder and was buried in Utrecht. In 1914, Johanna van Gogh-Bonger had Theo's body exhumed and moved from Utrecht to be re-buried alongside Vincent's at Auvers-sur-Oise.There have been numerous debates as to the nature of Van Gogh's illness and its effect on his work, and many retrospective diagnoses have been proposed. The consensus is that Van Gogh had an episodic condition with periods of normal functioning. Perry was the first to suggest bipolar disorder in 1947, and this has been supported by the psychiatrists Hemphill and Blumer. Biochemist Wilfred Arnold has countered that the symptoms are more consistent with acute intermittent porphyria, noting that the popular link between bipolar disorder and creativity might be spurious. Temporal lobe epilepsy with bouts of depression has also been suggested. Whatever the diagnosis, his condition was likely worsened by malnutrition, overwork, insomnia and alcohol. == Style and works == === Artistic development === Van Gogh drew and painted with watercolours while at school, but only a few examples survive and the authorship of some has been challenged. When he took up art as an adult, he began at an elementary level. In early 1882, his uncle, Cornelis Marinus, owner of a well-known gallery of contemporary art in Amsterdam, asked for drawings of The Hague. Van Gogh's work did not live up to expectations. Marinus offered a second commission, specifying the subject matter in detail, but was again disappointed with the result. Van Gogh persevered; he experimented with lighting in his studio using variable shutters and different drawing materials. For more than a year he worked on single figures – highly elaborate studies in black and white, which at the time gained him only criticism. Later, they were recognised as early masterpieces.In August 1882, Theo gave Vincent money to buy materials for working en plein air. Vincent wrote that he could now \"go on painting with new vigour\". | \n",
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| 17 | \n", - "0.784223 | \n", - "Those sold for over US$100 million (today's equivalent) include Portrait of Dr Gachet, Portrait of Joseph Roulin and Irises. The Metropolitan Museum of Art acquired a copy of Wheat Field with Cypresses in 1993 for US$57 million by using funds donated by publisher, diplomat and philanthropist Walter Annenberg. In 2015, L'Allée des Alyscamps sold for US$66.3 million at Sotheby's, New York, exceeding its reserve of US$40 million.Minor planet 4457 van Gogh is named in his honour.In October 2022, two activists protesting the effects of the fossil fuel industry on climate change threw a can of tomato soup on Van Gogh's Sunflowers in the National Gallery, London, and then glued their hands to the gallery wall. As the painting was covered by glass it was not damaged. === Van Gogh Museum === Van Gogh's nephew and namesake, Vincent Willem van Gogh (1890–1978), inherited the estate after his mother's death in 1925. During the early 1950s he arranged for the publication of a complete edition of the letters presented in four volumes and several languages. He then began negotiations with the Dutch government to subsidise a foundation to purchase and house the entire collection. Theo's son participated in planning the project in the hope that the works would be exhibited under the best possible conditions. The project began in 1963; architect Gerrit Rietveld was commissioned to design it, and after his death in 1964 Kisho Kurokawa took charge. Work progressed throughout the 1960s, with 1972 as the target for its grand opening.The Van Gogh Museum opened in the Museumplein in Amsterdam in 1973. It became the second most popular museum in the Netherlands, after the Rijksmuseum, regularly receiving more than 1.5 million visitors a year. In 2015 it had a record 1.9 million. Eighty-five percent of the visitors come from other countries. | \n",
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| 18 | \n", - "0.786045 | \n", - "They wanted to marry, but neither side of their families were in favor. Margot was distraught and took an overdose of strychnine, but survived after Van Gogh rushed her to a nearby hospital. On 26 March 1885, his father died of a heart attack.Van Gogh painted several groups of still lifes in 1885. During his two-year stay in Nuenen, he completed numerous drawings and watercolours and nearly 200 oil paintings. His palette consisted mainly of sombre earth tones, particularly dark brown, and showed no sign of the vivid colours that distinguished his later work.There was interest from a dealer in Paris early in 1885. Theo asked Vincent if he had paintings ready to exhibit. In May, Van Gogh responded with his first major work, The Potato Eaters, and a series of \"peasant character studies\" which were the culmination of several years of work. When he complained that Theo was not making enough effort to sell his paintings in Paris, his brother responded that they were too dark and not in keeping with the bright style of Impressionism. In August his work was publicly exhibited for the first time, in the shop windows of the dealer Leurs in The Hague. One of his young peasant sitters became pregnant in September 1885; Van Gogh was accused of forcing himself upon her, and the village priest forbade parishioners to model for him. \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t He moved to Antwerp that November and rented a room above a paint dealer's shop in the rue des Images (Lange Beeldekensstraat). He lived in poverty and ate poorly, preferring to spend the money Theo sent on painting materials and models. Bread, coffee and tobacco became his staple diet. In February 1886, he wrote to Theo that he could only remember eating six hot meals since the previous May. His teeth became loose and painful. In Antwerp he applied himself to the study of colour theory and spent time in museums—particularly studying the work of Peter Paul Rubens—and broadened his palette to include carmine, cobalt blue and emerald green. | \n",
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| 19 | \n", - "0.786288 | \n", - "Vincent Willem van Gogh (Dutch: [ˈvɪnsɛnt ˈʋɪləɱ‿vɑŋ‿ˈɣɔx] ; 30 March 1853 – 29 July 1890) was a Dutch Post-Impressionist painter who is among the most famous and influential figures in the history of Western art. In just over a decade, he created approximately 2100 artworks, including around 860 oil paintings, most of them in the last two years of his life. His oeuvre includes landscapes, still lifes, portraits, and self-portraits, most of which are characterized by bold colors and dramatic brushwork that contributed to the rise of expressionism in modern art. Van Gogh's work was beginning to gain critical attention before he died at age 37, by what was suspected at the time to be a suicide. During his lifetime, only one of Van Gogh's paintings, The Red Vineyard, was sold. Born into an upper-middle-class family, Van Gogh drew as a child and was serious, quiet and thoughtful, but showed signs of mental instability. As a young man, he worked as an art dealer, often travelling, but became depressed after he was transferred to London. He turned to religion and spent time as a missionary in southern Belgium. Later he drifted into ill-health and solitude. He was keenly aware of modernist trends in art and, while back with his parents, took up painting in 1881. His younger brother, Theo, supported him financially, and the two of them maintained a long correspondence. Van Gogh's early works consist of mostly still lifes and depictions of peasant laborers. In 1886, he moved to Paris, where he met members of the artistic avant-garde, including Émile Bernard and Paul Gauguin, who were seeking new paths beyond Impressionism. Frustrated in Paris and inspired by a growing spirit of artistic change and collaboration, in February 1888, Van Gogh moved to Arles in southern France to establish an artistic retreat and commune. Once there, Van Gogh's art changed. | \n",
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| 20 | \n", - "0.788811 | \n", - "==== Wheat fields ==== Van Gogh made several painting excursions during visits to the landscape around Arles. He made paintings of harvests, wheat fields and other rural landmarks of the area, including The Old Mill (1888); a good example of a picturesque structure bordering the wheat fields beyond. At various points, Van Gogh painted the view from his window – at The Hague, Antwerp, and Paris. These works culminated in The Wheat Field series, which depicted the view from his cells in the asylum at Saint-Rémy.Many of the late paintings are sombre but essentially optimistic and, right up to the time of Van Gogh's death, reflect his desire to return to lucid mental health. Yet some of his final works reflect his deepening concerns. Writing in July 1890, from Auvers, Van Gogh said that he had become absorbed \"in the immense plain against the hills, boundless as the sea, delicate yellow\".Van Gogh was captivated by the fields in May when the wheat was young and green. His Wheatfields at Auvers with White House shows a more subdued palette of yellows and blues, which creates a sense of idyllic harmony.About 10 July 1890, Van Gogh wrote to Theo of \"vast fields of wheat under troubled skies\". Wheatfield with Crows shows the artist's state of mind in his final days; Hulsker describes the work as a \"doom-filled painting with threatening skies and ill-omened crows\". Its dark palette and heavy brushstrokes convey a sense of menace. \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t == Reputation and legacy == After Van Gogh's first exhibitions in the late 1880s, his reputation grew steadily among artists, art critics, dealers and collectors. In 1887, André Antoine hung Van Gogh's alongside works of Georges Seurat and Paul Signac, at the Théâtre Libre in Paris; some were acquired by Julien Tanguy. In 1889, his work was described in the journal Le Moderniste Illustré by Albert Aurier as characterised by \"fire, intensity, sunshine\". Ten paintings were shown at the Société des Artistes Indépendants, in Brussels in January 1890. | \n",
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| 21 | \n", - "0.789036 | \n", - "== Reputation and legacy == After Van Gogh's first exhibitions in the late 1880s, his reputation grew steadily among artists, art critics, dealers and collectors. In 1887, André Antoine hung Van Gogh's alongside works of Georges Seurat and Paul Signac, at the Théâtre Libre in Paris; some were acquired by Julien Tanguy. In 1889, his work was described in the journal Le Moderniste Illustré by Albert Aurier as characterised by \"fire, intensity, sunshine\". Ten paintings were shown at the Société des Artistes Indépendants, in Brussels in January 1890. French president Marie François Sadi Carnot was said to have been impressed by Van Gogh's work.After Van Gogh's death, memorial exhibitions were held in Brussels, Paris, The Hague and Antwerp. His work was shown in several high-profile exhibitions, including six works at Les XX; in 1891 there was a retrospective exhibition in Brussels. In 1892, Octave Mirbeau wrote that Van Gogh's suicide was an \"infinitely sadder loss for art ... even though the populace has not crowded to a magnificent funeral, and poor Vincent van Gogh, whose demise means the extinction of a beautiful flame of genius, has gone to his death as obscure and neglected as he lived.\"Theo died in January 1891, removing Vincent's most vocal and well-connected champion. Theo's widow Johanna van Gogh-Bonger was a Dutchwoman in her twenties who had not known either her husband or her brother-in-law very long and who suddenly had to take care of several hundreds of paintings, letters and drawings, as well as her infant son, Vincent Willem van Gogh. Gauguin was not inclined to offer assistance in promoting Van Gogh's reputation, and Johanna's brother Andries Bonger also seemed lukewarm about his work. Aurier, one of Van Gogh's earliest supporters among the critics, died of typhoid fever in 1892 at the age of 27. | \n",
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| 22 | \n", - "0.790108 | \n", - "When he took up art as an adult, he began at an elementary level. In early 1882, his uncle, Cornelis Marinus, owner of a well-known gallery of contemporary art in Amsterdam, asked for drawings of The Hague. Van Gogh's work did not live up to expectations. Marinus offered a second commission, specifying the subject matter in detail, but was again disappointed with the result. Van Gogh persevered; he experimented with lighting in his studio using variable shutters and different drawing materials. For more than a year he worked on single figures – highly elaborate studies in black and white, which at the time gained him only criticism. Later, they were recognised as early masterpieces.In August 1882, Theo gave Vincent money to buy materials for working en plein air. Vincent wrote that he could now \"go on painting with new vigour\". From early 1883, he worked on multi-figure compositions. He had some of them photographed, but when his brother remarked that they lacked liveliness and freshness, he destroyed them and turned to oil painting. Van Gogh turned to well-known Hague School artists like Weissenbruch and Blommers, and he received technical advice from them as well as from painters like De Bock and Van der Weele, both of the Hague School's second generation. He moved to Nuenen after a short period of time in Drenthe and began work on several large paintings but destroyed most of them. The Potato Eaters and its companion pieces are the only ones to have survived. Following a visit to the Rijksmuseum Van Gogh wrote of his admiration for the quick, economical brushwork of the Dutch Masters, especially Rembrandt and Frans Hals. He was aware many of his faults were due to lack of experience and technical expertise, so in November 1885 he travelled to Antwerp and later Paris to learn and develop his skills. Theo criticised The Potato Eaters for its dark palette, which he thought unsuitable for a modern style. During Van Gogh's stay in Paris between 1886 and 1887, he tried to master a new, lighter palette. | \n",
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| 23 | \n", - "0.790171 | \n", - "Soon after, he first painted in oils, bought with money borrowed from Theo. He liked the medium, and he spread the paint liberally, scraping from the canvas and working back with the brush. He wrote that he was surprised at how good the results were. By March 1882, Mauve appeared to have gone cold towards Van Gogh, and he stopped replying to his letters. He had learned of Van Gogh's new domestic arrangement with an alcoholic prostitute, Clasina Maria \"Sien\" Hoornik (1850–1904), and her young daughter. Van Gogh had met Sien towards the end of January 1882, when she had a five-year-old daughter and was pregnant. She had previously borne two children who died, but Van Gogh was unaware of this. On 2 July, she gave birth to a baby boy, Willem. When Van Gogh's father discovered the details of their relationship, he put pressure on his son to abandon Sien and her two children. Vincent at first defied him, and considered moving the family out of the city, but in late 1883, he left Sien and the children.Poverty may have pushed Sien back into prostitution; the home became less happy and Van Gogh may have felt family life was irreconcilable with his artistic development. Sien gave her daughter to her mother and baby Willem to her brother. Willem remembered visiting Rotterdam when he was about 12, when an uncle tried to persuade Sien to marry to legitimise the child. He believed Van Gogh was his father, but the timing of his birth makes this unlikely. Sien drowned herself in the River Scheldt in 1904.In September 1883, Van Gogh moved to Drenthe in the northern Netherlands. In December driven by loneliness, he went to live with his parents, then in Nuenen, North Brabant. === Emerging artist === ==== Nuenen and Antwerp (1883–1886) ==== In Nuenen, Van Gogh focused on painting and drawing. Working outside and very quickly, he completed sketches and paintings of weavers and their cottages. | \n",
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| 24 | \n", - "0.791175 | \n", - "His friendship with Gauguin ended after a confrontation with a razor when, in a rage, he severed his left ear. Van Gogh spent time in psychiatric hospitals, including a period at Saint-Rémy. After he discharged himself and moved to the Auberge Ravoux in Auvers-sur-Oise near Paris, he came under the care of the homeopathic doctor Paul Gachet. His depression persisted, and on 27 July 1890, Van Gogh is believed to have shot himself in the chest with a revolver, dying from his injuries two days later. Van Gogh's work began to attract critical artistic attention in the last year of his life. After his death, Van Gogh's art and life story captured public imagination as an emblem of misunderstood genius, due in large part to the efforts of his widowed sister-in-law Johanna van Gogh-Bonger. His bold use of color, expressive line and thick application of paint inspired avant-garde artistic groups like the Fauves and German Expressionists in the early 20th century. Van Gogh's work gained widespread critical and commercial success in the following decades, and he has become a lasting icon of the romantic ideal of the tortured artist. Today, Van Gogh's works are among the world's most expensive paintings ever sold. His legacy is honored and celebrated by the Van Gogh Museum in Amsterdam, which holds the world's largest collection of his paintings and drawings. == Letters == The most comprehensive primary source on Van Gogh is his correspondence with his younger brother, Theo. Their lifelong friendship, and most of what is known of Vincent's thoughts and theories of art, are recorded in the hundreds of letters they exchanged from 1872 until 1890. Theo van Gogh was an art dealer and provided his brother with financial and emotional support as well as access to influential people on the contemporary art scene.Theo kept all of Vincent's letters to him; but Vincent kept only a few of the letters he received. After both had died, Theo's widow Jo Bonger-van Gogh arranged for the publication of some of their letters. A few appeared in 1906 and 1913; the majority were published in 1914. | \n",
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| 25 | \n", - "0.792039 | \n", - "He turned around and returned without making his presence known. It appears Breton was unaware of Van Gogh or his attempted visit. There are no known letters between the two artists and Van Gogh is not one of the contemporary artists discussed by Breton in his 1891 autobiography Life of an Artist. == Life == === Early years === Vincent Willem van Gogh was born on 30 March 1853 in Groot-Zundert, in the predominantly Catholic province of North Brabant in the Netherlands. He was the oldest surviving child of Theodorus van Gogh (1822–1885), a minister of the Dutch Reformed Church, and his wife, Anna Cornelia Carbentus (1819–1907). Van Gogh was given the name of his grandfather and of a brother stillborn exactly a year before his birth. Vincent was a common name in the Van Gogh family. The name had been borne by his grandfather, the prominent art dealer Vincent (1789–1874), and a theology graduate at the University of Leiden in 1811. This Vincent had six sons, three of whom became art dealers, and may have been named after his great-uncle, a sculptor (1729–1802).Van Gogh's mother came from a prosperous family in The Hague. His father was the youngest son of a minister. The two met when Anna's younger sister, Cornelia, married Theodorus's older brother Vincent (Cent). Van Gogh's parents married in May 1851 and moved to Zundert. His brother Theo was born on 1 May 1857. There was another brother, Cor, and three sisters: Elisabeth, Anna, and Willemina (known as \"Wil\"). In later life, Van Gogh remained in touch only with Willemina and Theo. Theodorus's salary as a minister was modest, but the Church also supplied the family with a house, a maid, two cooks, a gardener, a carriage and horse; his mother Anna instilled in the children a duty to uphold the family's high social position.Van Gogh was a serious and thoughtful child. | \n",
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| 26 | \n", - "0.792563 | \n", - "The physician was not fond of the painting and used it to repair a chicken coop, then gave it away. In 2016, the portrait was housed at the Pushkin Museum of Fine Arts and estimated to be worth over $50 million. \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t ==== Saint-Rémy (May 1889 – May 1890) ==== Van Gogh entered the Saint-Paul-de-Mausole asylum on 8 May 1889, accompanied by his caregiver, Frédéric Salles, a Protestant clergyman. Saint-Paul was a former monastery in Saint-Rémy, located less than 30 kilometres (19 mi) from Arles, and it was run by a former naval doctor, Théophile Peyron. Van Gogh had two cells with barred windows, one of which he used as a studio. The clinic and its garden became the main subjects of his paintings. He made several studies of the hospital's interiors, such as Vestibule of the Asylum and Saint-Rémy (September 1889), and its gardens, such as Lilacs (May 1889). Some of his works from this time are characterised by swirls, such as The Starry Night. He was allowed short supervised walks, during which time he painted cypresses and olive trees, including Valley with Ploughman Seen from Above, Olive Trees with the Alpilles in the Background 1889, Cypresses 1889, Cornfield with Cypresses (1889), Country road in Provence by Night (1890). In September 1889, he produced two further versions of Bedroom in Arles and The Gardener.Limited access to life outside the clinic resulted in a shortage of subject matter. Van Gogh instead worked on interpretations of other artist's paintings, such as Millet's The Sower and Noonday Rest, and variations on his own earlier work. Van Gogh was an admirer of the Realism of Jules Breton, Gustave Courbet and Millet, and he compared his copies to a musician's interpreting Beethoven.His Prisoners' Round (after Gustave Doré) (1890) was painted after an engraving by Gustave Doré (1832–1883). | \n",
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| 27 | \n", - "0.792953 | \n", - "One of his young peasant sitters became pregnant in September 1885; Van Gogh was accused of forcing himself upon her, and the village priest forbade parishioners to model for him. \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t He moved to Antwerp that November and rented a room above a paint dealer's shop in the rue des Images (Lange Beeldekensstraat). He lived in poverty and ate poorly, preferring to spend the money Theo sent on painting materials and models. Bread, coffee and tobacco became his staple diet. In February 1886, he wrote to Theo that he could only remember eating six hot meals since the previous May. His teeth became loose and painful. In Antwerp he applied himself to the study of colour theory and spent time in museums—particularly studying the work of Peter Paul Rubens—and broadened his palette to include carmine, cobalt blue and emerald green. Van Gogh bought Japanese ukiyo-e woodcuts in the docklands, later incorporating elements of their style into the background of some of his paintings. He was drinking heavily again, and was hospitalised between February and March 1886, when he was possibly also treated for syphilis. After his recovery, despite his antipathy towards academic teaching, he took the higher-level admission exams at the Academy of Fine Arts in Antwerp and, in January 1886, matriculated in painting and drawing. He became ill and run down by overwork, poor diet and excessive smoking. He started to attend drawing classes after plaster models at the Antwerp Academy on 18 January 1886. He quickly got into trouble with Charles Verlat, the director of the academy and teacher of a painting class, because of his unconventional painting style. Van Gogh had also clashed with the instructor of the drawing class Franz Vinck. Van Gogh finally started to attend the drawing classes after antique plaster models given by Eugène Siberdt. Soon Siberdt and Van Gogh came into conflict when the latter did not comply with Siberdt's requirement that drawings express the contour and concentrate on the line. When Van Gogh was required to draw the Venus de Milo during a drawing class, he produced the limbless, naked torso of a Flemish peasant woman. | \n",
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| 28 | \n", - "0.793931 | \n", - "In September 1889, he produced two further versions of Bedroom in Arles and The Gardener.Limited access to life outside the clinic resulted in a shortage of subject matter. Van Gogh instead worked on interpretations of other artist's paintings, such as Millet's The Sower and Noonday Rest, and variations on his own earlier work. Van Gogh was an admirer of the Realism of Jules Breton, Gustave Courbet and Millet, and he compared his copies to a musician's interpreting Beethoven.His Prisoners' Round (after Gustave Doré) (1890) was painted after an engraving by Gustave Doré (1832–1883). Tralbaut suggests that the face of the prisoner in the centre of the painting looking towards the viewer is Van Gogh himself; Jan Hulsker discounts this.Between February and April 1890, Van Gogh suffered a severe relapse. Depressed and unable to bring himself to write, he was still able to paint and draw a little during this time, and he later wrote to Theo that he had made a few small canvases \"from memory ... reminisces of the North\". Among these was Two Peasant Women Digging in a Snow-Covered Field at Sunset. Hulsker believes that this small group of paintings formed the nucleus of many drawings and study sheets depicting landscapes and figures that Van Gogh worked on during this time. He comments that this short period was the only time that Van Gogh's illness had a significant effect on his work. Van Gogh asked his mother and his brother to send him drawings and rough work he had done in the early 1880s so he could work on new paintings from his old sketches. Belonging to this period is Sorrowing Old Man (\"At Eternity's Gate\"), a colour study Hulsker describes as \"another unmistakable remembrance of times long past\". | \n", - "
| 29 | \n", - "0.798987 | \n", - "He grew more isolated and religiously fervent. His father and uncle arranged a transfer to Paris in 1875, where he became resentful of issues such as the degree to which the art dealers commodified art, and he was dismissed a year later. In April 1876, he returned to England to take unpaid work as a supply teacher in a small boarding school in Ramsgate. When the proprietor moved to Isleworth in Middlesex, Van Gogh went with him. The arrangement was not successful; he left to become a Methodist minister's assistant. His parents had meanwhile moved to Etten; in 1876 he returned home at Christmas for six months and took work at a bookshop in Dordrecht. He was unhappy in the position, and spent his time doodling or translating passages from the Bible into English, French, and German. He immersed himself in Christianity and became increasingly pious and monastic. According to his flatmate of the time, Paulus van Görlitz, Van Gogh ate frugally, avoiding meat.To support his religious conviction and his desire to become a pastor, in 1877, the family sent him to live with his uncle Johannes Stricker, a respected theologian, in Amsterdam. Van Gogh prepared for the University of Amsterdam theology entrance examination; he failed the exam and left his uncle's house in July 1878. He undertook, but also failed, a three-month course at a Protestant missionary school in Laken, near Brussels. In January 1879, he took up a post as a missionary at Petit-Wasmes in the working class, coal-mining district of Borinage in Belgium. To show support for his impoverished congregation, he gave up his comfortable lodgings at a bakery to a homeless person and moved to a small hut, where he slept on straw. His humble living conditions did not endear him to church authorities, who dismissed him for \"undermining the dignity of the priesthood\". He then walked the 75 kilometres (47 mi) to Brussels, returned briefly to Cuesmes in the Borinage, but he gave in to pressure from his parents to return home to Etten. He stayed there until around March 1880, which caused concern and frustration for his parents. | \n",
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| 30 | \n", - "0.800982 | \n", - "\"Theo died in January 1891, removing Vincent's most vocal and well-connected champion. Theo's widow Johanna van Gogh-Bonger was a Dutchwoman in her twenties who had not known either her husband or her brother-in-law very long and who suddenly had to take care of several hundreds of paintings, letters and drawings, as well as her infant son, Vincent Willem van Gogh. Gauguin was not inclined to offer assistance in promoting Van Gogh's reputation, and Johanna's brother Andries Bonger also seemed lukewarm about his work. Aurier, one of Van Gogh's earliest supporters among the critics, died of typhoid fever in 1892 at the age of 27. In 1892, Émile Bernard organised a small solo show of Van Gogh's paintings in Paris, and Julien Tanguy exhibited his Van Gogh paintings with several consigned from Johanna van Gogh-Bonger. In April 1894, the Durand-Ruel Gallery in Paris agreed to take 10 paintings on consignment from Van Gogh's estate. In 1896, the Fauvist painter Henri Matisse, then an unknown art student, visited John Russell on Belle Île off Brittany. Russell had been a close friend of Van Gogh; he introduced Matisse to the Dutchman's work, and gave him a Van Gogh drawing. Influenced by Van Gogh, Matisse abandoned his earth-coloured palette for bright colours.In Paris in 1901, a large Van Gogh retrospective was held at the Bernheim-Jeune Gallery, which excited André Derain and Maurice de Vlaminck, and contributed to the emergence of Fauvism. Important group exhibitions took place with the Sonderbund artists in Cologne in 1912, the Armory Show, New York in 1913, and Berlin in 1914. Henk Bremmer was instrumental in teaching and talking about Van Gogh, and introduced Helene Kröller-Müller to Van Gogh's art; she became an avid collector of his work. The early figures in German Expressionism such as Emil Nolde acknowledged a debt to Van Gogh's work. | \n",
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| 31 | \n", - "0.801200 | \n", - "==== Orchards ==== The Flowering Orchards (also the Orchards in Blossom) are among the first groups of work completed after Van Gogh's arrival in Arles in February 1888. The 14 paintings are optimistic, joyous and visually expressive of the burgeoning spring. They are delicately sensitive and unpopulated. He painted swiftly, and although he brought to this series a version of Impressionism, a strong sense of personal style began to emerge during this period. The transience of the blossoming trees, and the passing of the season, seemed to align with his sense of impermanence and belief in a new beginning in Arles. During the blossoming of the trees that spring, he found \"a world of motifs that could not have been more Japanese\". Vincent wrote to Theo on 21 April 1888 that he had 10 orchards and \"one big [painting] of a cherry tree, which I've spoiled\".During this period Van Gogh mastered the use of light by subjugating shadows and painting the trees as if they are the source of light – almost in a sacred manner. Early the following year he painted another smaller group of orchards, including View of Arles, Flowering Orchards. Van Gogh was enthralled by the landscape and vegetation of the south of France, and often visited the farm gardens near Arles. In the vivid light of the Mediterranean climate his palette significantly brightened. | \n",
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| 32 | \n", - "0.801701 | \n", - "Within days he left for Amsterdam. Kee would not meet him, and her parents wrote that his \"persistence is disgusting\". In despair, he held his left hand in the flame of a lamp, with the words: \"Let me see her for as long as I can keep my hand in the flame.\" He did not recall the event well, but later assumed that his uncle had blown out the flame. Kee's father made it clear that her refusal should be heeded and that the two would not marry, largely because of Van Gogh's inability to support himself.Mauve took Van Gogh on as a student and introduced him to watercolour, which he worked on for the next month before returning home for Christmas. He quarrelled with his father, refusing to attend church, and left for The Hague. In January 1882, Mauve introduced him to painting in oil and lent him money to set up a studio. Within a month Van Gogh and Mauve fell out, possibly over the viability of drawing from plaster casts. Van Gogh could afford to hire only people from the street as models, a practice of which Mauve seems to have disapproved. In June, Van Gogh suffered a bout of gonorrhoea and spent three weeks in hospital. Soon after, he first painted in oils, bought with money borrowed from Theo. He liked the medium, and he spread the paint liberally, scraping from the canvas and working back with the brush. He wrote that he was surprised at how good the results were. By March 1882, Mauve appeared to have gone cold towards Van Gogh, and he stopped replying to his letters. He had learned of Van Gogh's new domestic arrangement with an alcoholic prostitute, Clasina Maria \"Sien\" Hoornik (1850–1904), and her young daughter. Van Gogh had met Sien towards the end of January 1882, when she had a five-year-old daughter and was pregnant. She had previously borne two children who died, but Van Gogh was unaware of this. On 2 July, she gave birth to a baby boy, Willem. | \n",
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| 33 | \n", - "0.803687 | \n", - "He comments that this short period was the only time that Van Gogh's illness had a significant effect on his work. Van Gogh asked his mother and his brother to send him drawings and rough work he had done in the early 1880s so he could work on new paintings from his old sketches. Belonging to this period is Sorrowing Old Man (\"At Eternity's Gate\"), a colour study Hulsker describes as \"another unmistakable remembrance of times long past\". His late paintings show an artist at the height of his abilities, according to the art critic Robert Hughes, \"longing for concision and grace\".After the birth of his nephew, Van Gogh wrote, \"I started right away to make a picture for him, to hang in their bedroom, branches of white almond blossom against a blue sky.\" \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t ==== 1890 Exhibitions and recognition ==== See also Vincent van Gogh's display at Les XX, 1890 Albert Aurier praised his work in the Mercure de France in January 1890 and described him as \"a genius\". In February, Van Gogh painted five versions of L'Arlésienne (Madame Ginoux), based on a charcoal sketch Gauguin had produced when she sat for both artists in November 1888. Also in February, Van Gogh was invited by Les XX, a society of avant-garde painters in Brussels, to participate in their annual exhibition. At the opening dinner a Les XX member, Henry de Groux, insulted Van Gogh's work. Toulouse-Lautrec demanded satisfaction, and Signac declared he would continue to fight for Van Gogh's honour if Lautrec surrendered. De Groux apologised for the slight and left the group.From 20 March to 27 April 1890, Van Gogh was included in the sixth exhibition of the Société des Artistes Indépendants in the Pavillon de la Ville de Paris on the Champs-Elysées. Van Gogh exhibited ten paintings. While the exhibition was on display with the Artistes Indépendants in Paris, Claude Monet said that Van Gogh's work was the best in the show. | \n",
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| 34 | \n", - "0.804031 | \n", - "He registered at the Académie in November 1880, where he studied anatomy and the standard rules of modelling and perspective. === Etten, Drenthe and The Hague === Van Gogh returned to Etten in April 1881 for an extended stay with his parents. He continued to draw, often using his neighbours as subjects. In August 1881, his recently widowed cousin, Cornelia \"Kee\" Vos-Stricker, daughter of his mother's older sister Willemina and Johannes Stricker, arrived for a visit. He was thrilled and took long walks with her. Kee was seven years older than he was and had an eight-year-old son. Van Gogh surprised everyone by declaring his love to her and proposing marriage. She refused with the words \"No, nay, never\" (\"nooit, neen, nimmer\"). After Kee returned to Amsterdam, Van Gogh went to The Hague to try to sell paintings and to meet with his second cousin, Anton Mauve. Mauve was the successful artist Van Gogh longed to be. Mauve invited him to return in a few months and suggested he spend the intervening time working in charcoal and pastels; Van Gogh returned to Etten and followed this advice.Late in November 1881, Van Gogh wrote a letter to Johannes Stricker, one which he described to Theo as an attack. Within days he left for Amsterdam. Kee would not meet him, and her parents wrote that his \"persistence is disgusting\". In despair, he held his left hand in the flame of a lamp, with the words: \"Let me see her for as long as I can keep my hand in the flame.\" He did not recall the event well, but later assumed that his uncle had blown out the flame. Kee's father made it clear that her refusal should be heeded and that the two would not marry, largely because of Van Gogh's inability to support himself.Mauve took Van Gogh on as a student and introduced him to watercolour, which he worked on for the next month before returning home for Christmas. He quarrelled with his father, refusing to attend church, and left for The Hague. | \n",
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| 35 | \n", - "0.804749 | \n", - "He turned to religion and spent time as a missionary in southern Belgium. Later he drifted into ill-health and solitude. He was keenly aware of modernist trends in art and, while back with his parents, took up painting in 1881. His younger brother, Theo, supported him financially, and the two of them maintained a long correspondence. Van Gogh's early works consist of mostly still lifes and depictions of peasant laborers. In 1886, he moved to Paris, where he met members of the artistic avant-garde, including Émile Bernard and Paul Gauguin, who were seeking new paths beyond Impressionism. Frustrated in Paris and inspired by a growing spirit of artistic change and collaboration, in February 1888, Van Gogh moved to Arles in southern France to establish an artistic retreat and commune. Once there, Van Gogh's art changed. His paintings grew brighter and he turned his attention to the natural world, depicting local olive groves, wheat fields and sunflowers. Van Gogh invited Gauguin to join him in Arles and eagerly anticipated Gauguin's arrival in the fall of 1888. Van Gogh suffered from psychotic episodes and delusions. Though he worried about his mental stability, he often neglected his physical health, did not eat properly and drank heavily. His friendship with Gauguin ended after a confrontation with a razor when, in a rage, he severed his left ear. Van Gogh spent time in psychiatric hospitals, including a period at Saint-Rémy. After he discharged himself and moved to the Auberge Ravoux in Auvers-sur-Oise near Paris, he came under the care of the homeopathic doctor Paul Gachet. His depression persisted, and on 27 July 1890, Van Gogh is believed to have shot himself in the chest with a revolver, dying from his injuries two days later. Van Gogh's work began to attract critical artistic attention in the last year of his life. After his death, Van Gogh's art and life story captured public imagination as an emblem of misunderstood genius, due in large part to the efforts of his widowed sister-in-law Johanna van Gogh-Bonger. | \n",
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| 36 | \n", - "0.805063 | \n", - "There are 22 to his sister Wil, 58 to the painter Anthon van Rappard, 22 to Émile Bernard as well as individual letters to Paul Signac, Paul Gauguin, and the critic Albert Aurier. Some are illustrated with sketches. Many are undated, but art historians have been able to place most in chronological order. Problems in transcription and dating remain, mainly with those posted from Arles. While there, Vincent wrote around 200 letters in Dutch, French, and English. There is a gap in the record when he lived in Paris as the brothers lived together and had no need to correspond.The highly paid contemporary artist Jules Breton was frequently mentioned in Vincent's letters. In 1875 letters to Theo, Vincent mentions he saw Breton, discusses the Breton paintings he saw at a Salon, and discusses sending one of Breton's books but only on the condition that it be returned. In a March 1884 letter to Rappard he discusses one of Breton's poems that had inspired one of his paintings. In 1885 he describes Breton's famous work The Song of the Lark as being \"fine\". In March 1880, roughly midway between these letters, Van Gogh set out on an 80-kilometre trip on foot to meet with Breton in the village of Courrières; however, he was intimidated by Breton's success and/or the high wall around his estate. He turned around and returned without making his presence known. It appears Breton was unaware of Van Gogh or his attempted visit. There are no known letters between the two artists and Van Gogh is not one of the contemporary artists discussed by Breton in his 1891 autobiography Life of an Artist. == Life == === Early years === Vincent Willem van Gogh was born on 30 March 1853 in Groot-Zundert, in the predominantly Catholic province of North Brabant in the Netherlands. He was the oldest surviving child of Theodorus van Gogh (1822–1885), a minister of the Dutch Reformed Church, and his wife, Anna Cornelia Carbentus (1819–1907). | \n",
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| 37 | \n", - "0.806510 | \n", - "He became ill and run down by overwork, poor diet and excessive smoking. He started to attend drawing classes after plaster models at the Antwerp Academy on 18 January 1886. He quickly got into trouble with Charles Verlat, the director of the academy and teacher of a painting class, because of his unconventional painting style. Van Gogh had also clashed with the instructor of the drawing class Franz Vinck. Van Gogh finally started to attend the drawing classes after antique plaster models given by Eugène Siberdt. Soon Siberdt and Van Gogh came into conflict when the latter did not comply with Siberdt's requirement that drawings express the contour and concentrate on the line. When Van Gogh was required to draw the Venus de Milo during a drawing class, he produced the limbless, naked torso of a Flemish peasant woman. Siberdt regarded this as defiance against his artistic guidance and made corrections to Van Gogh's drawing with his crayon so vigorously that he tore the paper. Van Gogh then flew into a violent rage and shouted at Siberdt: 'You clearly do not know what a young woman is like, God damn it! A woman must have hips, buttocks, a pelvis in which she can carry a baby!' According to some accounts, this was the last time Van Gogh attended classes at the academy and he left later for Paris. On 31 March 1886, which was about a month after the confrontation with Siberdt, the teachers of the academy decided that 17 students, including Van Gogh, had to repeat a year. The story that Van Gogh was expelled from the academy by Siberdt is therefore unfounded. ==== Paris (1886–1888) ==== Van Gogh moved to Paris in March 1886 where he shared Theo's rue Laval apartment in Montmartre and studied at Fernand Cormon's studio. In June the brothers took a larger flat at 54 rue Lepic. In Paris, Vincent painted portraits of friends and acquaintances, still life paintings, views of Le Moulin de la Galette, scenes in Montmartre, Asnières and along the Seine. | \n",
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| 38 | \n", - "0.807259 | \n", - "He undertook, but also failed, a three-month course at a Protestant missionary school in Laken, near Brussels. In January 1879, he took up a post as a missionary at Petit-Wasmes in the working class, coal-mining district of Borinage in Belgium. To show support for his impoverished congregation, he gave up his comfortable lodgings at a bakery to a homeless person and moved to a small hut, where he slept on straw. His humble living conditions did not endear him to church authorities, who dismissed him for \"undermining the dignity of the priesthood\". He then walked the 75 kilometres (47 mi) to Brussels, returned briefly to Cuesmes in the Borinage, but he gave in to pressure from his parents to return home to Etten. He stayed there until around March 1880, which caused concern and frustration for his parents. His father was especially frustrated and advised that his son be committed to the lunatic asylum in Geel.Van Gogh returned to Cuesmes in August 1880, where he lodged with a miner until October. He became interested in the people and scenes around him, and he recorded them in drawings after Theo's suggestion that he take up art in earnest. He traveled to Brussels later in the year, to follow Theo's recommendation that he study with the Dutch artist Willem Roelofs, who persuaded him – in spite of his dislike of formal schools of art – to attend the Académie Royale des Beaux-Arts. He registered at the Académie in November 1880, where he studied anatomy and the standard rules of modelling and perspective. === Etten, Drenthe and The Hague === Van Gogh returned to Etten in April 1881 for an extended stay with his parents. He continued to draw, often using his neighbours as subjects. In August 1881, his recently widowed cousin, Cornelia \"Kee\" Vos-Stricker, daughter of his mother's older sister Willemina and Johannes Stricker, arrived for a visit. He was thrilled and took long walks with her. Kee was seven years older than he was and had an eight-year-old son. Van Gogh surprised everyone by declaring his love to her and proposing marriage. | \n",
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| 39 | \n", - "0.816479 | \n", - "==== Hospital in Arles (December 1888) ==== The exact sequence that led to the mutilation of van Gogh's ear is not known. Gauguin said, fifteen years later, that the night followed several instances of physically threatening behaviour. Their relationship was complex and Theo may have owed money to Gauguin, who suspected the brothers were exploiting him financially. It seems likely that Vincent realised that Gauguin was planning to leave. The following days saw heavy rain, leading to the two men being shut in the Yellow House. Gauguin recalled that Van Gogh followed him after he left for a walk and \"rushed towards me, an open razor in his hand.\" This account is uncorroborated; Gauguin was almost certainly absent from the Yellow House that night, most likely staying in a hotel.After an altercation on the evening of 23 December 1888, Van Gogh returned to his room where he seemingly heard voices and either wholly or in part severed his left ear with a razor causing severe bleeding. He bandaged the wound, wrapped the ear in paper and delivered the package to a woman at a brothel Van Gogh and Gauguin both frequented. Van Gogh was found unconscious the next morning by a policeman and taken to hospital, where he was treated by Félix Rey, a young doctor still in training. The ear was brought to the hospital, but Rey did not attempt to reattach it as too much time had passed. Van Gogh researcher and art historian Bernadette Murphy discovered the true identity of the woman named Gabrielle, who died in Arles at the age of 80 in 1952, and whose descendants still lived (as of 2020) just outside Arles. Gabrielle, known in her youth as \"Gaby,\" was a 17-year-old cleaning girl at the brothel and other local establishments at the time Van Gogh presented her with his ear.Van Gogh had no recollection of the event, suggesting that he may have suffered an acute mental breakdown. The hospital diagnosis was \"acute mania with generalised delirium\", and within a few days, the local police ordered that he be placed in hospital care. | \n",
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| 40 | \n", - "0.819207 | \n", - "The ear was brought to the hospital, but Rey did not attempt to reattach it as too much time had passed. Van Gogh researcher and art historian Bernadette Murphy discovered the true identity of the woman named Gabrielle, who died in Arles at the age of 80 in 1952, and whose descendants still lived (as of 2020) just outside Arles. Gabrielle, known in her youth as \"Gaby,\" was a 17-year-old cleaning girl at the brothel and other local establishments at the time Van Gogh presented her with his ear.Van Gogh had no recollection of the event, suggesting that he may have suffered an acute mental breakdown. The hospital diagnosis was \"acute mania with generalised delirium\", and within a few days, the local police ordered that he be placed in hospital care. Gauguin immediately notified Theo, who, on 24 December, had proposed marriage to his old friend Andries Bonger's sister Johanna. That evening, Theo rushed to the station to board a night train to Arles. He arrived on Christmas Day and comforted Vincent, who seemed to be semi-lucid. That evening, he left Arles for the return trip to Paris.During the first days of his treatment, Van Gogh repeatedly and unsuccessfully asked for Gauguin, who asked a policeman attending the case to \"be kind enough, Monsieur, to awaken this man with great care, and if he asks for me tell him I have left for Paris; the sight of me might prove fatal for him.\" Gauguin fled Arles, never to see Van Gogh again. They continued to correspond, and in 1890, Gauguin proposed they form a studio in Antwerp. Meanwhile, other visitors to the hospital included Marie Ginoux and Roulin.Despite a pessimistic diagnosis, Van Gogh recovered and returned to the Yellow House on 7 January 1889. He spent the following month between hospital and home, suffering from hallucinations and delusions of poisoning. In March, the police closed his house after a petition by 30 townspeople (including the Ginoux family) who described him as le fou roux \"the redheaded madman\"; Van Gogh returned to hospital. | \n", - "
| 41 | \n", - "0.820484 | \n", - "Two years later, Vincent and Theo paid for the publication of a book on Monticelli paintings, and Vincent bought some of Monticelli's works to add to his collection.Van Gogh learned about Fernand Cormon's atelier from Theo. He worked at the studio in April and May 1886, where he frequented the circle of the Australian artist John Russell, who painted his portrait in 1886. Van Gogh also met fellow students Émile Bernard, Louis Anquetin and Henri de Toulouse-Lautrec – who painted a portrait of him in pastel. They met at Julien \"Père\" Tanguy's paint shop, (which was, at that time, the only place where Paul Cézanne's paintings were displayed). In 1886, two large exhibitions were staged there, showing Pointillism and Neo-impressionism for the first time and bringing attention to Georges Seurat and Paul Signac. Theo kept a stock of Impressionist paintings in his gallery on boulevard Montmartre, but Van Gogh was slow to acknowledge the new developments in art.Conflicts arose between the brothers. At the end of 1886 Theo found living with Vincent to be \"almost unbearable\". By early 1887, they were again at peace, and Vincent had moved to Asnières, a northwestern suburb of Paris, where he got to know Signac. He adopted elements of Pointillism, a technique in which a multitude of small coloured dots are applied to the canvas so that when seen from a distance they create an optical blend of hues. The style stresses the ability of complementary colours – including blue and orange – to form vibrant contrasts. \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t While in Asnières Van Gogh painted parks, restaurants and the Seine, including Bridges across the Seine at Asnières. In November 1887, Theo and Vincent befriended Paul Gauguin who had just arrived in Paris. Towards the end of the year, Vincent arranged an exhibition alongside Bernard, Anquetin, and probably Toulouse-Lautrec, at the Grand-Bouillon Restaurant du Chalet, 43 avenue de Clichy, Montmartre. In a contemporary account, Bernard wrote that the exhibition was ahead of anything else in Paris. | \n",
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| 42 | \n", - "0.820913 | \n", - "According to some accounts, this was the last time Van Gogh attended classes at the academy and he left later for Paris. On 31 March 1886, which was about a month after the confrontation with Siberdt, the teachers of the academy decided that 17 students, including Van Gogh, had to repeat a year. The story that Van Gogh was expelled from the academy by Siberdt is therefore unfounded. ==== Paris (1886–1888) ==== Van Gogh moved to Paris in March 1886 where he shared Theo's rue Laval apartment in Montmartre and studied at Fernand Cormon's studio. In June the brothers took a larger flat at 54 rue Lepic. In Paris, Vincent painted portraits of friends and acquaintances, still life paintings, views of Le Moulin de la Galette, scenes in Montmartre, Asnières and along the Seine. In 1885 in Antwerp he had become interested in Japanese ukiyo-e woodblock prints and had used them to decorate the walls of his studio; while in Paris he collected hundreds of them. He tried his hand at Japonaiserie, tracing a figure from a reproduction on the cover of the magazine Paris Illustre, The Courtesan or Oiran (1887), after Keisai Eisen, which he then graphically enlarged in a painting.After seeing the portrait of Adolphe Monticelli at the Galerie Delareybarette, Van Gogh adopted a brighter palette and a bolder attack, particularly in paintings such as his Seascape at Saintes-Maries (1888). Two years later, Vincent and Theo paid for the publication of a book on Monticelli paintings, and Vincent bought some of Monticelli's works to add to his collection.Van Gogh learned about Fernand Cormon's atelier from Theo. He worked at the studio in April and May 1886, where he frequented the circle of the Australian artist John Russell, who painted his portrait in 1886. Van Gogh also met fellow students Émile Bernard, Louis Anquetin and Henri de Toulouse-Lautrec – who painted a portrait of him in pastel. | \n",
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| 43 | \n", - "0.823414 | \n", - "==== Auvers-sur-Oise (May–July 1890) ==== In May 1890, Van Gogh left the clinic in Saint-Rémy to move nearer to both Dr Paul Gachet in the Paris suburb of Auvers-sur-Oise and to Theo. Gachet was an amateur painter and had treated several other artists – Camille Pissarro had recommended him. Van Gogh's first impression was that Gachet was \"iller than I am, it seemed to me, or let's say just as much.\"The painter Charles Daubigny moved to Auvers in 1861 and in turn drew other artists there, including Camille Corot and Honoré Daumier. In July 1890, Van Gogh completed two paintings of Daubigny's Garden, one of which is likely his final work. During his last weeks at Saint-Rémy, his thoughts returned to \"memories of the North\", and several of the approximately 70 oils, painted during as many days in Auvers-sur-Oise, are reminiscent of northern scenes. In June 1890, he painted several portraits of his doctor, including Portrait of Dr Gachet, and his only etching. In each the emphasis is on Gachet's melancholic disposition. There are other paintings which are probably unfinished, including Thatched Cottages by a Hill.In July, Van Gogh wrote that he had become absorbed \"in the immense plain against the hills, boundless as the sea, delicate yellow\". He had first become captivated by the fields in May, when the wheat was young and green. In July, he described to Theo \"vast fields of wheat under turbulent skies\".He wrote that they represented his \"sadness and extreme loneliness\" and that the \"canvases will tell you what I cannot say in words, that is, how healthy and invigorating I find the countryside\". Wheatfield with Crows, although not his last oil work, is from July 1890 and Hulsker discusses it as being associated with \"melancholy and extreme loneliness\". Hulsker identifies seven oil paintings from Auvers that follow the completion of Wheatfield with Crows. Hulsker also expressed concern about the number of paintings attributed to Van Gogh from the period. | \n",
- "
| 44 | \n", - "0.823965 | \n", - "He adopted elements of Pointillism, a technique in which a multitude of small coloured dots are applied to the canvas so that when seen from a distance they create an optical blend of hues. The style stresses the ability of complementary colours – including blue and orange – to form vibrant contrasts. \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t While in Asnières Van Gogh painted parks, restaurants and the Seine, including Bridges across the Seine at Asnières. In November 1887, Theo and Vincent befriended Paul Gauguin who had just arrived in Paris. Towards the end of the year, Vincent arranged an exhibition alongside Bernard, Anquetin, and probably Toulouse-Lautrec, at the Grand-Bouillon Restaurant du Chalet, 43 avenue de Clichy, Montmartre. In a contemporary account, Bernard wrote that the exhibition was ahead of anything else in Paris. There, Bernard and Anquetin sold their first paintings, and Van Gogh exchanged work with Gauguin. Discussions on art, artists, and their social situations started during this exhibition, continued and expanded to include visitors to the show, like Camille Pissarro and his son Lucien, Signac and Seurat. In February 1888, feeling worn out from life in Paris, Van Gogh left, having painted more than 200 paintings during his two years there. Hours before his departure, accompanied by Theo, he paid his first and only visit to Seurat in his studio. === Artistic breakthrough === ==== Arles (1888–89) ==== Ill from drink and suffering from smoker's cough, in February 1888, Van Gogh sought refuge in Arles. He seems to have moved with thoughts of founding an art colony. The Danish artist Christian Mourier-Petersen was his companion for two months and at first, Arles appeared exotic to Van Gogh. In a letter, he described it as a foreign country: \"The Zouaves, the brothels, the adorable little Arlésienne going to her First Communion, the priest in his surplice, who looks like a dangerous rhinoceros, the people drinking absinthe, all seem to me creatures from another world. | \n",
- "
| 45 | \n", - "0.828397 | \n", - "In April, he was visited by the American artist Dodge MacKnight, who was living nearby at Fontvieille.On 1 May 1888, Van Gogh signed a lease for four rooms in the Yellow House. The house at 2 place Lamartine cost 15 francs per month. The rooms were unfurnished and had been uninhabited for months. Because the Yellow House had to be furnished before he could fully move in, Van Gogh moved from the Hôtel Carrel to the Café de la Gare on 7 May 1888. He had befriended the Yellow House's proprietors, Joseph and Marie Ginoux, and was able to use it as a studio. Van Gogh wanted a gallery to display his work and started a series of paintings that eventually included Van Gogh's Chair (1888), Bedroom in Arles (1888), The Night Café (1888), Café Terrace at Night (September 1888), Starry Night Over the Rhone (1888), and Still Life: Vase with Twelve Sunflowers (1888), all intended for the decoration for the Yellow House.Van Gogh wrote that with The Night Café he tried \"to express the idea that the café is a place where one can ruin oneself, go mad, or commit a crime\". When he visited Saintes-Maries-de-la-Mer in June, he gave lessons to a Zouave second lieutenant – Paul-Eugène Milliet – and painted boats on the sea and the village. MacKnight introduced Van Gogh to Eugène Boch, a Belgian painter who sometimes stayed in Fontvieille, and the two exchanged visits in July. \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t ==== Gauguin's visit (1888) ==== When Gauguin agreed to visit Arles in 1888, Van Gogh hoped for friendship and to realize his idea of an artists' collective. Van Gogh prepared for Gauguin's arrival by painting four versions of Sunflowers in one week. \"In the hope of living in a studio of our own with Gauguin,\" he wrote in a letter to Theo, \"I'd like to do a decoration for the studio. Nothing but large Sunflowers. | \n",
- "
| 46 | \n", - "0.838895 | \n", - "Hours before his departure, accompanied by Theo, he paid his first and only visit to Seurat in his studio. === Artistic breakthrough === ==== Arles (1888–89) ==== Ill from drink and suffering from smoker's cough, in February 1888, Van Gogh sought refuge in Arles. He seems to have moved with thoughts of founding an art colony. The Danish artist Christian Mourier-Petersen was his companion for two months and at first, Arles appeared exotic to Van Gogh. In a letter, he described it as a foreign country: \"The Zouaves, the brothels, the adorable little Arlésienne going to her First Communion, the priest in his surplice, who looks like a dangerous rhinoceros, the people drinking absinthe, all seem to me creatures from another world.\"The time in Arles was one of Van Gogh's more prolific periods: he completed 200 paintings and more than 100 drawings and watercolors. He was energized by the local countryside and light; his works from this period are rich in yellow, ultramarine and mauve. They include harvests, wheat fields and general rural landmarks from the area, including The Old Mill (1888), one of seven canvases sent to Pont-Aven on 4 October 1888 in an exchange of works with Paul Gauguin, Émile Bernard, Charles Laval and others.In March 1888, Van Gogh created landscapes using a gridded \"perspective frame\"and three of those works were shown at the annual exhibition of the Société des Artistes Indépendants. In April, he was visited by the American artist Dodge MacKnight, who was living nearby at Fontvieille.On 1 May 1888, Van Gogh signed a lease for four rooms in the Yellow House. The house at 2 place Lamartine cost 15 francs per month. The rooms were unfurnished and had been uninhabited for months. Because the Yellow House had to be furnished before he could fully move in, Van Gogh moved from the Hôtel Carrel to the Café de la Gare on 7 May 1888. | \n",
- "
| 47 | \n", - "0.848972 | \n", - "When he visited Saintes-Maries-de-la-Mer in June, he gave lessons to a Zouave second lieutenant – Paul-Eugène Milliet – and painted boats on the sea and the village. MacKnight introduced Van Gogh to Eugène Boch, a Belgian painter who sometimes stayed in Fontvieille, and the two exchanged visits in July. \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t ==== Gauguin's visit (1888) ==== When Gauguin agreed to visit Arles in 1888, Van Gogh hoped for friendship and to realize his idea of an artists' collective. Van Gogh prepared for Gauguin's arrival by painting four versions of Sunflowers in one week. \"In the hope of living in a studio of our own with Gauguin,\" he wrote in a letter to Theo, \"I'd like to do a decoration for the studio. Nothing but large Sunflowers.\"When Boch visited again, Van Gogh painted a portrait of him, as well as the study The Poet Against a Starry Sky.In preparation for Gauguin's visit, Van Gogh bought two beds on advice from the station's postal supervisor Joseph Roulin, whose portrait he painted. On 17 September, he spent his first night in the still sparsely furnished Yellow House. When Gauguin consented to work and live in Arles with him, Van Gogh started to work on the Décoration for the Yellow House, probably the most ambitious effort he ever undertook. He completed two chair paintings: Van Gogh's Chair and Gauguin's Chair.After much pleading from Van Gogh, Gauguin arrived in Arles on 23 October and, in November, the two painted together. Gauguin depicted Van Gogh in his The Painter of Sunflowers; Van Gogh painted pictures from memory, following Gauguin's suggestion. Among these \"imaginative\" paintings is Memory of the Garden at Etten. Their first joint outdoor venture was at the Alyscamps, when they produced the pendants Les Alyscamps. | \n",
- "
| 48 | \n", - "0.849399 | \n", - "On 17 September, he spent his first night in the still sparsely furnished Yellow House. When Gauguin consented to work and live in Arles with him, Van Gogh started to work on the Décoration for the Yellow House, probably the most ambitious effort he ever undertook. He completed two chair paintings: Van Gogh's Chair and Gauguin's Chair.After much pleading from Van Gogh, Gauguin arrived in Arles on 23 October and, in November, the two painted together. Gauguin depicted Van Gogh in his The Painter of Sunflowers; Van Gogh painted pictures from memory, following Gauguin's suggestion. Among these \"imaginative\" paintings is Memory of the Garden at Etten. Their first joint outdoor venture was at the Alyscamps, when they produced the pendants Les Alyscamps. The single painting Gauguin completed during his visit was his portrait of Van Gogh.Van Gogh and Gauguin visited Montpellier in December 1888, where they saw works by Courbet and Delacroix in the Musée Fabre. Their relationship began to deteriorate; Van Gogh admired Gauguin and wanted to be treated as his equal, but Gauguin was arrogant and domineering, which frustrated Van Gogh. They often quarrelled; Van Gogh increasingly feared that Gauguin was going to desert him, and the situation, which Van Gogh described as one of \"excessive tension\", rapidly headed towards crisis point. \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t ==== Hospital in Arles (December 1888) ==== The exact sequence that led to the mutilation of van Gogh's ear is not known. Gauguin said, fifteen years later, that the night followed several instances of physically threatening behaviour. Their relationship was complex and Theo may have owed money to Gauguin, who suspected the brothers were exploiting him financially. It seems likely that Vincent realised that Gauguin was planning to leave. The following days saw heavy rain, leading to the two men being shut in the Yellow House. Gauguin recalled that Van Gogh followed him after he left for a walk and \"rushed towards me, an open razor in his hand.\" | \n",
- "
| 49 | \n", - "0.851540 | \n", - "Gauguin fled Arles, never to see Van Gogh again. They continued to correspond, and in 1890, Gauguin proposed they form a studio in Antwerp. Meanwhile, other visitors to the hospital included Marie Ginoux and Roulin.Despite a pessimistic diagnosis, Van Gogh recovered and returned to the Yellow House on 7 January 1889. He spent the following month between hospital and home, suffering from hallucinations and delusions of poisoning. In March, the police closed his house after a petition by 30 townspeople (including the Ginoux family) who described him as le fou roux \"the redheaded madman\"; Van Gogh returned to hospital. Paul Signac visited him twice in March; in April, Van Gogh moved into rooms owned by Dr Rey after floods damaged paintings in his own home. Two months later, he left Arles and voluntarily entered an asylum in Saint-Rémy-de-Provence. Around this time, he wrote, \"Sometimes moods of indescribable anguish, sometimes moments when the veil of time and fatality of circumstances seemed to be torn apart for an instant.\"Van Gogh gave his 1889 Portrait of Doctor Félix Rey to Dr Rey. The physician was not fond of the painting and used it to repair a chicken coop, then gave it away. In 2016, the portrait was housed at the Pushkin Museum of Fine Arts and estimated to be worth over $50 million. \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t \\t\\t \\t\\t\\t \\t\\t\\t \\t\\t ==== Saint-Rémy (May 1889 – May 1890) ==== Van Gogh entered the Saint-Paul-de-Mausole asylum on 8 May 1889, accompanied by his caregiver, Frédéric Salles, a Protestant clergyman. Saint-Paul was a former monastery in Saint-Rémy, located less than 30 kilometres (19 mi) from Arles, and it was run by a former naval doctor, Théophile Peyron. Van Gogh had two cells with barred windows, one of which he used as a studio. The clinic and its garden became the main subjects of his paintings. | \n",
- "
"
+ ""
]
},
{
diff --git a/docs/docs/examples/observability/AimCallback.ipynb b/docs/docs/examples/observability/AimCallback.ipynb
index 22f55128f826f..3b6708a4a36a1 100644
--- a/docs/docs/examples/observability/AimCallback.ipynb
+++ b/docs/docs/examples/observability/AimCallback.ipynb
@@ -6,7 +6,7 @@
"id": "b4698442",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/observability/HoneyHiveLlamaIndexTracer.ipynb b/docs/docs/examples/observability/HoneyHiveLlamaIndexTracer.ipynb
index fe0277c2fee11..c71797b0883f2 100644
--- a/docs/docs/examples/observability/HoneyHiveLlamaIndexTracer.ipynb
+++ b/docs/docs/examples/observability/HoneyHiveLlamaIndexTracer.ipynb
@@ -6,7 +6,7 @@
"id": "13d2b729",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/observability/LangfuseCallbackHandler.ipynb b/docs/docs/examples/observability/LangfuseCallbackHandler.ipynb
index 539c16e6e00b5..fcf331708f49a 100644
--- a/docs/docs/examples/observability/LangfuseCallbackHandler.ipynb
+++ b/docs/docs/examples/observability/LangfuseCallbackHandler.ipynb
@@ -6,7 +6,7 @@
"id": "d6509c3a",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/observability/LlamaDebugHandler.ipynb b/docs/docs/examples/observability/LlamaDebugHandler.ipynb
index 322244d026fe8..00a11606c37d4 100644
--- a/docs/docs/examples/observability/LlamaDebugHandler.ipynb
+++ b/docs/docs/examples/observability/LlamaDebugHandler.ipynb
@@ -5,7 +5,7 @@
"id": "dcab9129",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/observability/OpenInferenceCallback.ipynb b/docs/docs/examples/observability/OpenInferenceCallback.ipynb
index 94457e4f48bde..82cdc19490c3e 100644
--- a/docs/docs/examples/observability/OpenInferenceCallback.ipynb
+++ b/docs/docs/examples/observability/OpenInferenceCallback.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/observability/OpenLLMetry.ipynb b/docs/docs/examples/observability/OpenLLMetry.ipynb
index 6cc030ed64892..90946081daa60 100644
--- a/docs/docs/examples/observability/OpenLLMetry.ipynb
+++ b/docs/docs/examples/observability/OpenLLMetry.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/observability/OpikCallback.ipynb b/docs/docs/examples/observability/OpikCallback.ipynb
new file mode 100644
index 0000000000000..67a2fb456aa0c
--- /dev/null
+++ b/docs/docs/examples/observability/OpikCallback.ipynb
@@ -0,0 +1,215 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Logging traces with Opik\n",
+ "\n",
+ "For this guide we will be downloading the essays from Paul Graham and use them as our data source. We will then start querying these essays with LlamaIndex and logging the traces to Opik.\n",
+ "\n",
+ "## Creating an account on Comet.com\n",
+ "\n",
+ "[Comet](https://www.comet.com/site) provides a hosted version of the Opik platform, [simply create an account](https://www.comet.com/signup?from=llm) and grab you API Key.\n",
+ "\n",
+ "> You can also run the Opik platform locally, see the [installation guide](https://www.comet.com/docs/opik/self-host/self_hosting_opik/) for more information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import getpass\n",
+ "\n",
+ "if \"OPIK_API_KEY\" not in os.environ:\n",
+ " os.environ[\"OPIK_API_KEY\"] = getpass.getpass(\"Opik API Key: \")\n",
+ "if \"OPIK_WORKSPACE\" not in os.environ:\n",
+ " os.environ[\"OPIK_WORKSPACE\"] = input(\n",
+ " \"Comet workspace (often the same as your username): \"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you are running the Opik platform locally, simply set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import os\n",
+ "# os.environ[\"OPIK_URL_OVERRIDE\"] = \"http://localhost:5173/api\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Preparing our environment\n",
+ "\n",
+ "First, we will install the necessary libraries, download the Chinook database and set up our different API keys."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install opik llama-index llama-index-agent-openai llama-index-llms-openai --upgrade --quiet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And configure the required environment variables:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import getpass\n",
+ "\n",
+ "if \"OPENAI_API_KEY\" not in os.environ:\n",
+ " os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\n",
+ " \"Enter your OpenAI API key: \"\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In addition, we will download the Paul Graham essays:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import requests\n",
+ "\n",
+ "# Create directory if it doesn't exist\n",
+ "os.makedirs(\"./data/paul_graham/\", exist_ok=True)\n",
+ "\n",
+ "# Download the file using requests\n",
+ "url = \"https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\"\n",
+ "response = requests.get(url)\n",
+ "with open(\"./data/paul_graham/paul_graham_essay.txt\", \"wb\") as f:\n",
+ " f.write(response.content)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Using LlamaIndex\n",
+ "\n",
+ "### Configuring the Opik integration\n",
+ "\n",
+ "You can use the Opik callback directly by calling:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import set_global_handler\n",
+ "\n",
+ "# You should provide your OPIK API key and Workspace using the following environment variables:\n",
+ "# OPIK_API_KEY, OPIK_WORKSPACE\n",
+ "set_global_handler(\n",
+ " \"opik\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that the callback handler is configured, all traces will automatically be logged to Opik.\n",
+ "\n",
+ "### Using LLamaIndex\n",
+ "\n",
+ "The first step is to load the data into LlamaIndex. We will use the `SimpleDirectoryReader` to load the data from the `data/paul_graham` directory. We will also create the vector store to index all the loaded documents."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
+ "\n",
+ "documents = SimpleDirectoryReader(\"./data/paul_graham\").load_data()\n",
+ "index = VectorStoreIndex.from_documents(documents)\n",
+ "query_engine = index.as_query_engine()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now query the index using the `query_engine` object:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(\"What did the author do growing up?\")\n",
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can now go to the Opik app to see the trace:\n",
+ "\n",
+ ""
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "py312_llm_eval",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/docs/examples/observability/PromptLayerHandler.ipynb b/docs/docs/examples/observability/PromptLayerHandler.ipynb
index 92646aafb1e2a..bffd6b6beba05 100644
--- a/docs/docs/examples/observability/PromptLayerHandler.ipynb
+++ b/docs/docs/examples/observability/PromptLayerHandler.ipynb
@@ -5,7 +5,7 @@
"id": "dcab9129",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/observability/TokenCountingHandler.ipynb b/docs/docs/examples/observability/TokenCountingHandler.ipynb
index c65fc78d2ad8f..a424aca3ae634 100644
--- a/docs/docs/examples/observability/TokenCountingHandler.ipynb
+++ b/docs/docs/examples/observability/TokenCountingHandler.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/observability/UpTrainCallback.ipynb b/docs/docs/examples/observability/UpTrainCallback.ipynb
index eb1bddf1f7202..aa04b60037ab2 100644
--- a/docs/docs/examples/observability/UpTrainCallback.ipynb
+++ b/docs/docs/examples/observability/UpTrainCallback.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/observability/WandbCallbackHandler.ipynb b/docs/docs/examples/observability/WandbCallbackHandler.ipynb
index 15e3801aafb59..cd1a5d565dac1 100644
--- a/docs/docs/examples/observability/WandbCallbackHandler.ipynb
+++ b/docs/docs/examples/observability/WandbCallbackHandler.ipynb
@@ -6,7 +6,7 @@
"id": "d6509c3a",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/output_parsing/nvidia_output_parsing.ipynb b/docs/docs/examples/output_parsing/nvidia_output_parsing.ipynb
new file mode 100644
index 0000000000000..501e5b0dbfd4c
--- /dev/null
+++ b/docs/docs/examples/output_parsing/nvidia_output_parsing.ipynb
@@ -0,0 +1,660 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "30815a85",
+ "metadata": {},
+ "source": [
+ "# LLM Pydantic Program - NVIDIA"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "311e16cb",
+ "metadata": {},
+ "source": [
+ "This guide shows you how to generate structured data with our `LLMTextCompletionProgram`. Given an LLM as well as an output Pydantic class, generate a structured Pydantic object.\n",
+ "\n",
+ "In terms of the target object, you can choose to directly specify `output_cls`, or specify a `PydanticOutputParser` or any other BaseOutputParser that generates a Pydantic object.\n",
+ "\n",
+ "in the examples below, we show you different ways of extracting into the `Album` object (which can contain a list of Song objects)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e0611198",
+ "metadata": {},
+ "source": [
+ "## Extract into `Album` class\n",
+ "\n",
+ "This is a simple example of parsing an output into an `Album` schema, which can contain multiple songs.\n",
+ "\n",
+ "Just pass `Album` into the `output_cls` property on initialization of the `LLMTextCompletionProgram`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "511a8171",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install llama-index-readers-file llama-index-embeddings-nvidia llama-index-llms-nvidia"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b029b7e6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "# del os.environ['NVIDIA_API_KEY'] ## delete key and reset\n",
+ "if os.environ.get(\"NVIDIA_API_KEY\", \"\").startswith(\"nvapi-\"):\n",
+ " print(\"Valid NVIDIA_API_KEY already in environment. Delete to reset\")\n",
+ "else:\n",
+ " nvapi_key = getpass.getpass(\"NVAPI Key (starts with nvapi-): \")\n",
+ " assert nvapi_key.startswith(\n",
+ " \"nvapi-\"\n",
+ " ), f\"{nvapi_key[:5]}... is not a valid key\"\n",
+ " os.environ[\"NVIDIA_API_KEY\"] = nvapi_key"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7a83b49-5c34-45d5-8cf4-62f348fb1299",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pydantic import BaseModel\n",
+ "from typing import List\n",
+ "from llama_index.core import Settings\n",
+ "from llama_index.llms.nvidia import NVIDIA\n",
+ "from llama_index.embeddings.nvidia import NVIDIAEmbedding\n",
+ "from llama_index.core.program import LLMTextCompletionProgram\n",
+ "from llama_index.core.program import FunctionCallingProgram"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4e4fc4b9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm = NVIDIA()\n",
+ "\n",
+ "embedder = NVIDIAEmbedding(model=\"NV-Embed-QA\", truncate=\"END\")\n",
+ "Settings.embed_model = embedder\n",
+ "Settings.llm = llm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d92e739",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Song(BaseModel):\n",
+ " \"\"\"Data model for a song.\"\"\"\n",
+ "\n",
+ " title: str\n",
+ " length_seconds: int\n",
+ "\n",
+ "\n",
+ "class Album(BaseModel):\n",
+ " \"\"\"Data model for an album.\"\"\"\n",
+ "\n",
+ " name: str\n",
+ " artist: str\n",
+ " songs: List[Song]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "46c2d509",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prompt_template_str = \"\"\"\\\n",
+ "Generate an example album, with an artist and a list of songs. \\\n",
+ "Using the movie {movie_name} as inspiration.\\\n",
+ "\"\"\"\n",
+ "program = LLMTextCompletionProgram.from_defaults(\n",
+ " output_cls=Album,\n",
+ " prompt_template_str=prompt_template_str,\n",
+ " verbose=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "498370f4",
+ "metadata": {},
+ "source": [
+ "Run program to get structured output. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ca490bf8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "output = program(movie_name=\"The Shining\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40cce83f",
+ "metadata": {},
+ "source": [
+ "The output is a valid Pydantic object that we can then use to call functions/APIs. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53934d3d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6401ab8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.output_parsers import PydanticOutputParser\n",
+ "\n",
+ "program = LLMTextCompletionProgram.from_defaults(\n",
+ " output_parser=PydanticOutputParser(output_cls=Album),\n",
+ " prompt_template_str=prompt_template_str,\n",
+ " verbose=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1adc5b2b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "output = program(movie_name=\"Lord of the Rings\")\n",
+ "output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a41391d9",
+ "metadata": {},
+ "source": [
+ "## Define a Custom Output Parser\n",
+ "\n",
+ "Sometimes you may want to parse an output your own way into a JSON object. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "60b7b669",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.output_parsers import ChainableOutputParser\n",
+ "\n",
+ "\n",
+ "class CustomAlbumOutputParser(ChainableOutputParser):\n",
+ " \"\"\"Custom Album output parser.\n",
+ "\n",
+ " Assume first line is name and artist.\n",
+ "\n",
+ " Assume each subsequent line is the song.\n",
+ "\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, verbose: bool = False):\n",
+ " self.verbose = verbose\n",
+ "\n",
+ " def parse(self, output: str) -> Album:\n",
+ " \"\"\"Parse output.\"\"\"\n",
+ " if self.verbose:\n",
+ " print(f\"> Raw output: {output}\")\n",
+ " lines = output.split(\"\\n\")\n",
+ " lines = list(filter(None, (line.strip() for line in lines)))\n",
+ " name, artist = lines[1].split(\",\")\n",
+ " songs = []\n",
+ " for i in range(2, len(lines)):\n",
+ " title, length_seconds = lines[i].split(\",\")\n",
+ " songs.append(Song(title=title, length_seconds=length_seconds))\n",
+ "\n",
+ " return Album(name=name, artist=artist, songs=songs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e165a0f9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prompt_template_str = \"\"\"\\\n",
+ "Generate an example album, with an artist and a list of songs. \\\n",
+ "Using the movie {movie_name} as inspiration.\\\n",
+ "\n",
+ "Return answer in following format.\n",
+ "The first line is:\n",
+ "\n",
+ "\n",
"\n",
"In this guide we show you how to do hyperparameter optimization for RAG.\n",
"\n",
diff --git a/docs/docs/examples/property_graph/agentic_graph_rag_vertex.ipynb b/docs/docs/examples/property_graph/agentic_graph_rag_vertex.ipynb
index 4fe56f0aeb212..578e2920378ad 100644
--- a/docs/docs/examples/property_graph/agentic_graph_rag_vertex.ipynb
+++ b/docs/docs/examples/property_graph/agentic_graph_rag_vertex.ipynb
@@ -212,8 +212,7 @@
"from llama_index.core.vector_stores import MetadataFilters\n",
"from pathlib import Path\n",
"\n",
- "from llama_index.core.agent import FunctionCallingAgentWorker\n",
- "from llama_index.core.agent import AgentRunner"
+ "from llama_index.core.agent import FunctionCallingAgent"
]
},
{
@@ -1085,10 +1084,9 @@
"outputs": [],
"source": [
"# Create Agent Runner\n",
- "agent_worker = FunctionCallingAgentWorker.from_tools(\n",
+ "agent = FunctionCallingAgent.from_tools(\n",
" [summary_tool, vector_query_tool], llm=vertex_gemini, verbose=True\n",
- ")\n",
- "agent = AgentRunner(agent_worker)"
+ ")"
]
},
{
diff --git a/docs/docs/examples/property_graph/property_graph_kuzu.ipynb b/docs/docs/examples/property_graph/property_graph_kuzu.ipynb
new file mode 100644
index 0000000000000..47ecb197a8d6e
--- /dev/null
+++ b/docs/docs/examples/property_graph/property_graph_kuzu.ipynb
@@ -0,0 +1,450 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %pip install llama-index llama-index-embeddings-openai llama-index-graph-stores-kuzu"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[Kùzu](https://kuzudb.com/) is an open source, embedded graph database that's designed for query speed and scalability. It implements the Cypher query language, and utilizes a structured property graph model (a variant of the labelled property graph model) with support for ACID transactions. Because Kùzu is embedded, there's no requirement for a server to set up and use the database.\n",
+ "\n",
+ "If you already have an existing graph, please skip to the end of this notebook. Otherwise, let's begin by creating a graph from unstructured text to demonstrate how to use Kùzu as a graph store."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Environment Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"enter your key here\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will be using OpenAI models for this example, so we'll specify the OpenAI API key."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2024-08-27 16:12:46-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8001::154, 2606:50c0:8002::154, 2606:50c0:8000::154, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8001::154|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 75042 (73K) [text/plain]\n",
+ "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n",
+ "\n",
+ "data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.04s \n",
+ "\n",
+ "2024-08-27 16:12:47 (1.61 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!mkdir -p 'data/paul_graham/'\n",
+ "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import SimpleDirectoryReader\n",
+ "\n",
+ "documents = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Graph Construction\n",
+ "\n",
+ "We first need to create an empty Kùzu database directory by calling the `kuzu.Database` constructor. This step instantiates the database and creates the necessary directories and files within a local directory that stores the graph. This `Database` object is then passed to the `KuzuPropertyGraph` constructor."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import shutil\n",
+ "import kuzu\n",
+ "\n",
+ "shutil.rmtree(\"test_db\", ignore_errors=True)\n",
+ "db = kuzu.Database(\"test_db\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.graph_stores.kuzu import KuzuPropertyGraphStore\n",
+ "\n",
+ "graph_store = KuzuPropertyGraphStore(db)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Because Kùzu implements the structured graph property model, it imposes some level of structure on the schema of the graph. In the above case, because we did not specify a relationship schema that we want in our graph, it uses a generic schema, where the relationship types are not constrained, allowing the extracted triples from the LLM to be stored as relationships in the graph."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define models\n",
+ "\n",
+ "Below, we'll define the models used for embedding the text and the LLMs that are used to extract triples from the text and generate the response.\n",
+ "In this case, we specify different temperature settings for the same model - the extraction model has a temperature of 0."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+ "from llama_index.llms.openai import OpenAI\n",
+ "from llama_index.core import Settings\n",
+ "\n",
+ "embed_model = OpenAIEmbedding(model_name=\"text-embedding-3-small\")\n",
+ "extract_llm = OpenAI(model=\"gpt-4o-mini\", temperature=0.0)\n",
+ "generate_llm = OpenAI(model=\"gpt-4o-mini\", temperature=0.3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Create property graph index without imposing structure\n",
+ "\n",
+ "Because we didn't specify the relationship schema above, we can simply invoke the `SchemaLLMPathExtractor` to extract the triples from the text and store them in the graph. We can define the property graph index using Kùzu as the graph store, as shown below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 17.81it/s]\n",
+ "Extracting paths from text with schema: 100%|██████████| 22/22 [00:31<00:00, 1.43s/it]\n",
+ "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 1.34it/s]\n",
+ "Generating embeddings: 100%|██████████| 2/2 [00:00<00:00, 3.06it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.core import PropertyGraphIndex\n",
+ "from llama_index.core.indices.property_graph import SchemaLLMPathExtractor\n",
+ "\n",
+ "index = PropertyGraphIndex.from_documents(\n",
+ " documents,\n",
+ " embed_model=embed_model,\n",
+ " kg_extractors=[SchemaLLMPathExtractor(extract_llm)],\n",
+ " property_graph_store=graph_store,\n",
+ " show_progress=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that the graph is created, we can explore it in [Kùzu Explorer](https://docs.kuzudb.com/visualization/), a web-base UI, by running a Docker container that pulls the latest image of Kùzu Explorer as follows:\n",
+ "```bash\n",
+ "docker run -p 8000:8000 \\\n",
+ " -v ./test_db:/database \\\n",
+ " --rm kuzudb/explorer:latest\n",
+ "```\n",
+ "\n",
+ "Then, launch the UI and then visting [http://localhost:8000/](http://localhost:8000/). \n",
+ "\n",
+ "The easiest way to see the entire graph is to use a Cypher query like `\"match (a)-[b]->(c) return * limit 200\"`.\n",
+ "\n",
+ "To delete the entire graph, you can either delete the `./test_db` directory that contains the database files, or run the Cypher query `\"match (n) detach delete n\"` in the Kùzu Explorer shell."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Querying and Retrieval"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Interleaf and Viaweb are both products associated with the development of software solutions. Interleaf is linked to Lisp, indicating a relationship where Interleaf may utilize or be built upon Lisp programming language capabilities. Viaweb, on the other hand, is identified as an ecommerce software product and also has a connection to Lisp, suggesting that it may incorporate Lisp in its architecture or functionality. Both products are documented in a text file, which includes details about their creation and modification dates, file size, and type.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Switch to the generate LLM during retrieval\n",
+ "Settings.llm = generate_llm\n",
+ "\n",
+ "query_engine = index.as_query_engine(include_text=False)\n",
+ "\n",
+ "response = query_engine.query(\"Tell me more about Interleaf and Viaweb\")\n",
+ "\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Create property graph index with structure\n",
+ "\n",
+ "The recommended way to use Kùzu is to apply a structured schema to the graph. The schema is defined by specifying the relationship types (including direction) that we want in the graph. The imposition of structure helps with generating triples that are more meaningful for the types of questions we may want to answer from the graph.\n",
+ "\n",
+ "By specifying the below validation schema, we can enforce that the graph only contains relationships of the specified types."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import Literal\n",
+ "\n",
+ "entities = Literal[\"PERSON\", \"PLACE\", \"ORGANIZATION\"]\n",
+ "relations = Literal[\"HAS\", \"PART_OF\", \"WORKED_ON\", \"WORKED_WITH\", \"WORKED_AT\"]\n",
+ "# Define the relationship schema that we will pass to our graph store\n",
+ "# This must be a list of valid triples in the form (head_entity, relation, tail_entity)\n",
+ "validation_schema = [\n",
+ " (\"ORGANIZATION\", \"HAS\", \"PERSON\"),\n",
+ " (\"PERSON\", \"WORKED_AT\", \"ORGANIZATION\"),\n",
+ " (\"PERSON\", \"WORKED_WITH\", \"PERSON\"),\n",
+ " (\"PERSON\", \"WORKED_ON\", \"ORGANIZATION\"),\n",
+ " (\"PERSON\", \"PART_OF\", \"ORGANIZATION\"),\n",
+ " (\"ORGANIZATION\", \"PART_OF\", \"ORGANIZATION\"),\n",
+ " (\"PERSON\", \"WORKED_AT\", \"PLACE\"),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create a new empty database\n",
+ "shutil.rmtree(\"test_db\", ignore_errors=True)\n",
+ "db = kuzu.Database(\"test_db\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Along with the `Database` constructor, we also specify two additional arguments to the property graph store: `has_structured_schema=True` and `relationship_schema=validation_schema`, which provides Kùzu additional information as it instantiates a new graph."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "graph_store = KuzuPropertyGraphStore(\n",
+ " db,\n",
+ " has_structured_schema=True,\n",
+ " relationship_schema=validation_schema,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To construct a property graph with the desired schema, observe that we specify a few additional arguments to the `SchemaLLMPathExtractor`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 16.23it/s]\n",
+ "Extracting paths from text with schema: 100%|██████████| 22/22 [00:29<00:00, 1.34s/it]\n",
+ "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s]\n",
+ "Generating embeddings: 100%|██████████| 4/4 [00:01<00:00, 3.69it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = PropertyGraphIndex.from_documents(\n",
+ " documents,\n",
+ " embed_model=OpenAIEmbedding(model_name=\"text-embedding-3-small\"),\n",
+ " kg_extractors=[\n",
+ " SchemaLLMPathExtractor(\n",
+ " llm=OpenAI(model=\"gpt-4o-mini\", temperature=0.0),\n",
+ " possible_entities=entities,\n",
+ " possible_relations=relations,\n",
+ " kg_validation_schema=validation_schema,\n",
+ " strict=True, # if false, will allow triples outside of the schema\n",
+ " )\n",
+ " ],\n",
+ " property_graph_store=graph_store,\n",
+ " show_progress=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now apply the query engine on the index as before."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Interleaf and Viaweb are both organizations mentioned in the provided information. Interleaf is associated with Emacs, indicating a connection to text editing or software development environments. Viaweb, on the other hand, has several associations, including individuals like Julian and Idelle, as well as the programming language Lisp. This suggests that Viaweb may have a broader scope, potentially involving web development or e-commerce, given its historical context as an early web application platform. Both organizations appear to have been referenced in a document related to Paul Graham, indicating their relevance in discussions around technology or entrepreneurship.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Switch to the generate LLM during retrieval\n",
+ "Settings.llm = generate_llm\n",
+ "\n",
+ "query_engine = index.as_query_engine(include_text=False)\n",
+ "\n",
+ "response2 = query_engine.query(\"Tell me more about Interleaf and Viaweb\")\n",
+ "print(str(response2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use existing graph\n",
+ "\n",
+ "You can reuse an existing `Database` object to connect to its underlying `PropertyGraphIndex`. This is useful when you want to query the graph without having to re-extract the triples from the text."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Viaweb was founded by Paul Graham. The specific founding date is not provided in the information available.\n"
+ ]
+ }
+ ],
+ "source": [
+ "graph_store = KuzuPropertyGraphStore(db)\n",
+ "\n",
+ "# Set up the property graph index\n",
+ "index = PropertyGraphIndex.from_existing(\n",
+ " embed_model=embed_model,\n",
+ " llm=generate_llm,\n",
+ " property_graph_store=graph_store,\n",
+ ")\n",
+ "\n",
+ "query_engine = index.as_query_engine(include_text=False)\n",
+ "\n",
+ "response3 = query_engine.query(\"When was Viaweb founded, and by whom?\")\n",
+ "print(str(response3))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For full details on construction, retrieval, querying of a property graph, see the [full docs page](../../module_guides/indexing/lpg_index_guide.md)."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama",
+ "language": "python",
+ "name": "llama"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/docs/examples/property_graph/property_graph_neptune.ipynb b/docs/docs/examples/property_graph/property_graph_neptune.ipynb
index 547c39490adbc..0a7122e1a8bad 100644
--- a/docs/docs/examples/property_graph/property_graph_neptune.ipynb
+++ b/docs/docs/examples/property_graph/property_graph_neptune.ipynb
@@ -273,7 +273,7 @@
"metadata": {},
"outputs": [],
"source": [
- "from pydantic.v1 import BaseModel, \n",
+ "from pydantic import BaseModel, Field\n",
"from llama_index.core.indices.property_graph import CypherTemplateRetriever\n",
"\n",
"cypher_query = \"\"\"\n",
@@ -282,12 +282,14 @@
" RETURN c.text, o.name, o.label;\n",
" \"\"\"\n",
"\n",
+ "\n",
"class TemplateParams(BaseModel):\n",
" \"\"\"Template params for a cypher query.\"\"\"\n",
"\n",
" names: list[str] = Field(\n",
- " description=\"A list of entity names or keywords to use for lookup in a knowledge graph.\"\n",
- " )\n",
+ " description=\"A list of entity names or keywords to use for lookup in a knowledge graph.\"\n",
+ " )\n",
+ "\n",
"\n",
"cypher_retriever = CypherTemplateRetriever(\n",
" index.property_graph_store, TemplateParams, cypher_query\n",
diff --git a/docs/docs/examples/query_engine/CustomRetrievers.ipynb b/docs/docs/examples/query_engine/CustomRetrievers.ipynb
index 507df4a1651cd..a4d7c1da67086 100644
--- a/docs/docs/examples/query_engine/CustomRetrievers.ipynb
+++ b/docs/docs/examples/query_engine/CustomRetrievers.ipynb
@@ -5,7 +5,7 @@
"id": "7f6607dd",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/JSONalyze_query_engine.ipynb b/docs/docs/examples/query_engine/JSONalyze_query_engine.ipynb
index 217066c181b39..62809ccda16b5 100644
--- a/docs/docs/examples/query_engine/JSONalyze_query_engine.ipynb
+++ b/docs/docs/examples/query_engine/JSONalyze_query_engine.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n",
+ "\n",
"\n",
"# JSONalyze Query Engine"
]
diff --git a/docs/docs/examples/query_engine/JointQASummary.ipynb b/docs/docs/examples/query_engine/JointQASummary.ipynb
index 9323280878c5e..abf133da7499d 100644
--- a/docs/docs/examples/query_engine/JointQASummary.ipynb
+++ b/docs/docs/examples/query_engine/JointQASummary.ipynb
@@ -5,7 +5,7 @@
"id": "5f1c83e5",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/SQLAutoVectorQueryEngine.ipynb b/docs/docs/examples/query_engine/SQLAutoVectorQueryEngine.ipynb
index 5a5e023828979..a98e6db414711 100644
--- a/docs/docs/examples/query_engine/SQLAutoVectorQueryEngine.ipynb
+++ b/docs/docs/examples/query_engine/SQLAutoVectorQueryEngine.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/SQLJoinQueryEngine.ipynb b/docs/docs/examples/query_engine/SQLJoinQueryEngine.ipynb
index 1b2bc22e29de4..60079406078b0 100644
--- a/docs/docs/examples/query_engine/SQLJoinQueryEngine.ipynb
+++ b/docs/docs/examples/query_engine/SQLJoinQueryEngine.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/SQLRouterQueryEngine.ipynb b/docs/docs/examples/query_engine/SQLRouterQueryEngine.ipynb
index f533822dd3295..b1af9610cf423 100644
--- a/docs/docs/examples/query_engine/SQLRouterQueryEngine.ipynb
+++ b/docs/docs/examples/query_engine/SQLRouterQueryEngine.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/citation_query_engine.ipynb b/docs/docs/examples/query_engine/citation_query_engine.ipynb
index 2e03423a87ec9..5f65806d58ce4 100644
--- a/docs/docs/examples/query_engine/citation_query_engine.ipynb
+++ b/docs/docs/examples/query_engine/citation_query_engine.ipynb
@@ -5,7 +5,7 @@
"id": "f560d34d",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/ensemble_query_engine.ipynb b/docs/docs/examples/query_engine/ensemble_query_engine.ipynb
index ebbdccd6db885..890b606d28039 100644
--- a/docs/docs/examples/query_engine/ensemble_query_engine.ipynb
+++ b/docs/docs/examples/query_engine/ensemble_query_engine.ipynb
@@ -5,7 +5,7 @@
"id": "9bbed780",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/flare_query_engine.ipynb b/docs/docs/examples/query_engine/flare_query_engine.ipynb
index 73290b04d0266..54f1d4c5272f7 100644
--- a/docs/docs/examples/query_engine/flare_query_engine.ipynb
+++ b/docs/docs/examples/query_engine/flare_query_engine.ipynb
@@ -5,7 +5,7 @@
"id": "5e329054",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/json_query_engine.ipynb b/docs/docs/examples/query_engine/json_query_engine.ipynb
index 5471e8688f8fc..5c9f4fdee9727 100644
--- a/docs/docs/examples/query_engine/json_query_engine.ipynb
+++ b/docs/docs/examples/query_engine/json_query_engine.ipynb
@@ -5,7 +5,7 @@
"id": "fa753135",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/knowledge_graph_query_engine.ipynb b/docs/docs/examples/query_engine/knowledge_graph_query_engine.ipynb
index ce04d70d9b504..915643f7babdd 100644
--- a/docs/docs/examples/query_engine/knowledge_graph_query_engine.ipynb
+++ b/docs/docs/examples/query_engine/knowledge_graph_query_engine.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/knowledge_graph_rag_query_engine.ipynb b/docs/docs/examples/query_engine/knowledge_graph_rag_query_engine.ipynb
index 4d82a472fc03b..61c10608c096b 100644
--- a/docs/docs/examples/query_engine/knowledge_graph_rag_query_engine.ipynb
+++ b/docs/docs/examples/query_engine/knowledge_graph_rag_query_engine.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/pandas_query_engine.ipynb b/docs/docs/examples/query_engine/pandas_query_engine.ipynb
index 3343353f2b7f7..63e1d832e170d 100644
--- a/docs/docs/examples/query_engine/pandas_query_engine.ipynb
+++ b/docs/docs/examples/query_engine/pandas_query_engine.ipynb
@@ -5,7 +5,7 @@
"id": "8329aae0",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_engine/sub_question_query_engine.ipynb b/docs/docs/examples/query_engine/sub_question_query_engine.ipynb
index c496bd350ba04..40eac6b8cc637 100644
--- a/docs/docs/examples/query_engine/sub_question_query_engine.ipynb
+++ b/docs/docs/examples/query_engine/sub_question_query_engine.ipynb
@@ -5,7 +5,7 @@
"id": "0e3e4245",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_transformations/HyDEQueryTransformDemo.ipynb b/docs/docs/examples/query_transformations/HyDEQueryTransformDemo.ipynb
index ac6b2633128e4..0cf642d327f0b 100644
--- a/docs/docs/examples/query_transformations/HyDEQueryTransformDemo.ipynb
+++ b/docs/docs/examples/query_transformations/HyDEQueryTransformDemo.ipynb
@@ -5,7 +5,7 @@
"id": "033d7aed",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_transformations/SimpleIndexDemo-multistep.ipynb b/docs/docs/examples/query_transformations/SimpleIndexDemo-multistep.ipynb
index 6ac46ee828be1..6ef079b814277 100644
--- a/docs/docs/examples/query_transformations/SimpleIndexDemo-multistep.ipynb
+++ b/docs/docs/examples/query_transformations/SimpleIndexDemo-multistep.ipynb
@@ -5,7 +5,7 @@
"id": "5e0768d5",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/query_transformations/query_transform_cookbook.ipynb b/docs/docs/examples/query_transformations/query_transform_cookbook.ipynb
index 79a9cd4c01c18..1270db6f92dae 100644
--- a/docs/docs/examples/query_transformations/query_transform_cookbook.ipynb
+++ b/docs/docs/examples/query_transformations/query_transform_cookbook.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Query Transform Cookbook \n",
"\n",
- "\n",
+ "\n",
"\n",
"A user query can be transformed and decomposed in many ways before being executed as part of a RAG query engine, agent, or any other pipeline.\n",
"\n",
diff --git a/docs/docs/examples/response_synthesizers/long_context_test.ipynb b/docs/docs/examples/response_synthesizers/long_context_test.ipynb
index be351f1befdba..ca836e0c82a56 100644
--- a/docs/docs/examples/response_synthesizers/long_context_test.ipynb
+++ b/docs/docs/examples/response_synthesizers/long_context_test.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Stress-Testing Long Context LLMs with a Recall Task\n",
"\n",
- "\n",
+ "\n",
"\n",
"In this section we stress-test long context recall capabilities of GPT-4 and Claude v2. This is inspired by [Greg Kamradt's tweet](https://x.com/GregKamradt/status/1722386725635580292?s=20). \n",
"\n",
diff --git a/docs/docs/examples/response_synthesizers/refine.ipynb b/docs/docs/examples/response_synthesizers/refine.ipynb
index 0941662a9fd87..acd847322f6af 100644
--- a/docs/docs/examples/response_synthesizers/refine.ipynb
+++ b/docs/docs/examples/response_synthesizers/refine.ipynb
@@ -5,7 +5,7 @@
"id": "91dea8fd",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/response_synthesizers/structured_refine.ipynb b/docs/docs/examples/response_synthesizers/structured_refine.ipynb
index 825b0df0a0f6d..f06f43ba7e116 100644
--- a/docs/docs/examples/response_synthesizers/structured_refine.ipynb
+++ b/docs/docs/examples/response_synthesizers/structured_refine.ipynb
@@ -5,7 +5,7 @@
"id": "def266be",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/response_synthesizers/tree_summarize.ipynb b/docs/docs/examples/response_synthesizers/tree_summarize.ipynb
index 493f83a30c2c6..554ecedf381e0 100644
--- a/docs/docs/examples/response_synthesizers/tree_summarize.ipynb
+++ b/docs/docs/examples/response_synthesizers/tree_summarize.ipynb
@@ -5,7 +5,7 @@
"id": "dad51427",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/retrievers/auto_merging_retriever.ipynb b/docs/docs/examples/retrievers/auto_merging_retriever.ipynb
index f1f8d5f387f0c..17dff92bd3c17 100644
--- a/docs/docs/examples/retrievers/auto_merging_retriever.ipynb
+++ b/docs/docs/examples/retrievers/auto_merging_retriever.ipynb
@@ -5,7 +5,7 @@
"id": "b39acfe0",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/retrievers/bm25_retriever.ipynb b/docs/docs/examples/retrievers/bm25_retriever.ipynb
index a4a4ab2e1a5d9..27443b4ec00fc 100644
--- a/docs/docs/examples/retrievers/bm25_retriever.ipynb
+++ b/docs/docs/examples/retrievers/bm25_retriever.ipynb
@@ -5,7 +5,7 @@
"id": "723fcf40",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/retrievers/multi_doc_together_hybrid.ipynb b/docs/docs/examples/retrievers/multi_doc_together_hybrid.ipynb
index 7a7e976423b3b..9e4fe03904b82 100644
--- a/docs/docs/examples/retrievers/multi_doc_together_hybrid.ipynb
+++ b/docs/docs/examples/retrievers/multi_doc_together_hybrid.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/retrievers/recursive_retriever_nodes.ipynb b/docs/docs/examples/retrievers/recursive_retriever_nodes.ipynb
index 8e0b8da30911d..8febccad0e726 100644
--- a/docs/docs/examples/retrievers/recursive_retriever_nodes.ipynb
+++ b/docs/docs/examples/retrievers/recursive_retriever_nodes.ipynb
@@ -5,7 +5,7 @@
"id": "94f8a023",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/retrievers/vectara_auto_retriever.ipynb b/docs/docs/examples/retrievers/vectara_auto_retriever.ipynb
index 3cc4b043ab243..4e3484b999230 100644
--- a/docs/docs/examples/retrievers/vectara_auto_retriever.ipynb
+++ b/docs/docs/examples/retrievers/vectara_auto_retriever.ipynb
@@ -5,7 +5,7 @@
"id": "0e81b124",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/selectors/not_diamond_selector.ipynb b/docs/docs/examples/selectors/not_diamond_selector.ipynb
new file mode 100644
index 0000000000000..d3b3ddddec3e3
--- /dev/null
+++ b/docs/docs/examples/selectors/not_diamond_selector.ipynb
@@ -0,0 +1,343 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using Not Diamond to Select LLMs For Indexes\n",
+ "In this tutorial, we demonstrate how to use a router query engine with a selector powered by [Not Diamond](https://www.notdiamond.ai). You can automatically route a query to one of several available LLMs, which will then select the best index for your needs."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
+ "Note: you may need to restart the kernel to use updated packages.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install -q llama-index-llms-anthropic llama-index-llms-openai"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -q llama-index notdiamond"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: This is ONLY necessary in jupyter notebook.\n",
+ "# Details: Jupyter runs an event-loop behind the scenes.\n",
+ "# This results in nested event-loops when we start an event-loop to make async queries.\n",
+ "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!mkdir -p 'data/paul_graham/'\n",
+ "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Routing Queries With Not Diamond"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from typing import List\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
+ "os.environ[\"ANTHROPIC_API_KEY\"] = \"sk-ant-...\"\n",
+ "os.environ[\"NOTDIAMOND_API_KEY\"] = \"sk-...\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create Indexes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import (\n",
+ " SimpleDirectoryReader,\n",
+ " VectorStoreIndex,\n",
+ " SummaryIndex,\n",
+ " Settings,\n",
+ ")\n",
+ "from llama_index.core.query_engine import RouterQueryEngine\n",
+ "from llama_index.core.tools import QueryEngineTool\n",
+ "from llama_index.selectors.notdiamond.base import NotDiamondSelector\n",
+ "\n",
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"data/paul_graham\").load_data()\n",
+ "nodes = Settings.node_parser.get_nodes_from_documents(documents)\n",
+ "\n",
+ "# index documents\n",
+ "vector_index = VectorStoreIndex.from_documents(documents)\n",
+ "summary_index = SummaryIndex.from_documents(documents)\n",
+ "query_text = \"What was Paul Graham's role at Yahoo?\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Set up Tools for the QueryEngine"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "list_query_engine = summary_index.as_query_engine(\n",
+ " response_mode=\"tree_summarize\",\n",
+ " use_async=True,\n",
+ ")\n",
+ "vector_query_engine = vector_index.as_query_engine()\n",
+ "\n",
+ "list_tool = QueryEngineTool.from_defaults(\n",
+ " query_engine=list_query_engine,\n",
+ " description=(\n",
+ " \"Useful for summarization questions related to Paul Graham eassy on\"\n",
+ " \" What I Worked On.\"\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "vector_tool = QueryEngineTool.from_defaults(\n",
+ " query_engine=vector_query_engine,\n",
+ " description=(\n",
+ " \"Useful for retrieving specific context from Paul Graham essay on What\"\n",
+ " \" I Worked On.\"\n",
+ " ),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create a NotDiamondSelector and RouterQueryEngine"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from notdiamond import NotDiamond\n",
+ "\n",
+ "client = NotDiamond(\n",
+ " api_key=os.environ[\"NOTDIAMOND_API_KEY\"],\n",
+ " llm_configs=[\"openai/gpt-4o\", \"anthropic/claude-3-5-sonnet-20240620\"],\n",
+ ")\n",
+ "preference_id = client.create_preference_id()\n",
+ "client.preference_id = preference_id\n",
+ "\n",
+ "nd_selector = NotDiamondSelector(client=client)\n",
+ "\n",
+ "query_engine = RouterQueryEngine(\n",
+ " selector=nd_selector,\n",
+ " query_engine_tools=[\n",
+ " list_tool,\n",
+ " vector_tool,\n",
+ " ],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Use Not Diamond to Query Indexes\n",
+ "\n",
+ "Once we've set up our indexes and query engine, we can submit queries as usual."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Paul Graham has contributed to the field of technology and entrepreneurship through his essays and datasets. He has provided a labelled RAG dataset based on one of his essays, which includes queries, reference answers, and reference contexts. Additionally, he has shared insights and code related to using the LlamaIndex RAG pipeline for evaluation purposes.\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"Please summarize Paul Graham's working experience.\"\n",
+ ")\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Paul Graham founded Viaweb after RICS.\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\"What did Paul Graham do after RICS?\")\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Using NotDiamondSelector as a standalone selector\n",
+ "\n",
+ "As with LlamaIndex's built-in selectors, you can also use the `NotDiamondSelector` to select an index."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "selections=[SingleSelection(index=1, reason=\"The question asks for a summary of a recipe for deviled eggs, which directly aligns with option 2: 'Great for summarizing recipes.' This choice is most relevant as it specifically addresses the task of summarizing recipes, which is exactly what the question is asking for.\")] session_id='078a837f-d8da-4de4-aec9-b44df5ea32ba' llm=LLMConfig(anthropic/claude-3-5-sonnet-20240620)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.core.tools import ToolMetadata\n",
+ "from llama_index.selectors.notdiamond.base import NotDiamondSelector\n",
+ "\n",
+ "from notdiamond import NotDiamond, Metric\n",
+ "\n",
+ "choices = [\n",
+ " ToolMetadata(\n",
+ " name=\"vector_index\",\n",
+ " description=\"Great for asking questions about recipes.\",\n",
+ " ),\n",
+ " ToolMetadata(\n",
+ " name=\"list_index\", description=\"Great for summarizing recipes.\"\n",
+ " ),\n",
+ "]\n",
+ "\n",
+ "llm_configs = [\"openai/gpt-4o\", \"anthropic/claude-3-5-sonnet-20240620\"]\n",
+ "nd_client = NotDiamond(\n",
+ " api_key=os.environ[\"NOTDIAMOND_API_KEY\"],\n",
+ " llm_configs=llm_configs,\n",
+ " preference_id=preference_id,\n",
+ ")\n",
+ "preference_id = nd_client.create_preference_id()\n",
+ "nd_client.preference_id = preference_id\n",
+ "nd_selector = NotDiamondSelector(client=nd_client)\n",
+ "\n",
+ "nd_result = nd_selector.select(\n",
+ " choices, query=\"What is the summary of this recipe for deviled eggs?\"\n",
+ ")\n",
+ "print(nd_result)\n",
+ "\n",
+ "# Use the result's session_id to customize your routing logic.\n",
+ "metric = Metric(\"accuracy\")\n",
+ "score = metric.feedback(\n",
+ " session_id=nd_result.session_id,\n",
+ " llm_config=nd_result.llm,\n",
+ " value=1,\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/docs/examples/structured_outputs/structured_outputs.ipynb b/docs/docs/examples/structured_outputs/structured_outputs.ipynb
index 13ac310276d74..b248c1a5c6e70 100644
--- a/docs/docs/examples/structured_outputs/structured_outputs.ipynb
+++ b/docs/docs/examples/structured_outputs/structured_outputs.ipynb
@@ -67,7 +67,7 @@
"outputs": [],
"source": [
"from typing import List\n",
- "from pydantic.v1 import BaseModel, Field\n",
+ "from pydantic import BaseModel, Field\n",
"\n",
"\n",
"class Song(BaseModel):\n",
@@ -565,7 +565,7 @@
"metadata": {},
"outputs": [],
"source": [
- "from pydantic.v1 import BaseModel, Field\n",
+ "from pydantic import BaseModel, Field\n",
"from typing import List\n",
"\n",
"\n",
diff --git a/docs/docs/examples/tools/OnDemandLoaderTool.ipynb b/docs/docs/examples/tools/OnDemandLoaderTool.ipynb
index a26cd92705457..12b6df18cdc90 100644
--- a/docs/docs/examples/tools/OnDemandLoaderTool.ipynb
+++ b/docs/docs/examples/tools/OnDemandLoaderTool.ipynb
@@ -5,7 +5,7 @@
"id": "4f4b3e2a",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/vector_stores/AnalyticDBDemo.ipynb b/docs/docs/examples/vector_stores/AnalyticDBDemo.ipynb
index c866c237f393d..1e77d9f8ebeb6 100644
--- a/docs/docs/examples/vector_stores/AnalyticDBDemo.ipynb
+++ b/docs/docs/examples/vector_stores/AnalyticDBDemo.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb b/docs/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb
index d7b2cb5a5fdc1..97ea0fa21c774 100644
--- a/docs/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb
+++ b/docs/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb
@@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/vector_stores/AzureCosmosDBNoSqlDemo.ipynb b/docs/docs/examples/vector_stores/AzureCosmosDBNoSqlDemo.ipynb
new file mode 100644
index 0000000000000..33c5e2091ea02
--- /dev/null
+++ b/docs/docs/examples/vector_stores/AzureCosmosDBNoSqlDemo.ipynb
@@ -0,0 +1,246 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6463dfe0-31f0-494e-995e-9d3b96db0eeb",
+ "metadata": {},
+ "source": [
+ "# Azure Cosmos DB No SQL Vector Store\n",
+ "\n",
+ "In this notebook we are going to show a quick demo of how to use AzureCosmosDBNoSqlVectorSearch to perform vector searches in LlamaIndex.\n",
+ "\n",
+ "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d865e38e-7cfb-44fc-a811-ccbbb6bd5c8e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install llama-index-embeddings-openai\n",
+ "%pip install llama-index-llms-azure-openai"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5ed73758-4a14-4c9e-a4de-7c9c584fbdc0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install llama-index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0609a213-479b-4924-8a31-07f9076bcb4a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import json\n",
+ "import openai\n",
+ "from llama_index.llms.azure_openai import AzureOpenAI\n",
+ "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+ "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d1cf060d-7ab1-4a56-8098-4fb306d3401e",
+ "metadata": {},
+ "source": [
+ "# Setup Azure OpenAI\n",
+ "\n",
+ "The first step is to configure the llm and the embeddings model. These models will be used to create embeddings for the documents loaded into the database and for llm completions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "230c386e-b118-4cef-aabe-37f78e478f97",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm = AzureOpenAI(\n",
+ " model=\"AZURE_OPENAI_MODEL\",\n",
+ " deployment_name=\"AZURE_OPENAI_DEPLOYMENT_NAME\",\n",
+ " azure_endpoint=\"AZURE_OPENAI_BASE\",\n",
+ " api_key=\"AZURE_OPENAI_KEY\",\n",
+ " api_version=\"AZURE_OPENAI_VERSION\",\n",
+ ")\n",
+ "\n",
+ "embed_model = AzureOpenAIEmbedding(\n",
+ " model=\"AZURE_OPENAI_EMBEDDING_MODEL\",\n",
+ " deployment_name=\"AZURE_OPENAI_EMBEDDING_MODEL_DEPLOYMENT_NAME\",\n",
+ " azure_endpoint=\"AZURE_OPENAI_BASE\",\n",
+ " api_key=\"AZURE_OPENAI_KEY\",\n",
+ " api_version=\"AZURE_OPENAI_VERSION\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8da35d45-9689-4f3a-9011-1cda0fb361ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import Settings\n",
+ "\n",
+ "Settings.llm = llm\n",
+ "Settings.embed_model = embed_model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "084aa964-7222-47b2-bdab-825c85a6ffed",
+ "metadata": {},
+ "source": [
+ "# Loading Documents\n",
+ "\n",
+ "In this example we will be using the paul_graham essay which will be processed by the SimpleDirectoryReader."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8f689978-93c6-4c34-9a6e-9fca606a1058",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core import SimpleDirectoryReader\n",
+ "\n",
+ "documents = SimpleDirectoryReader(\n",
+ " input_files=[r\"\\docs\\examples\\data\\paul_graham\\paul_graham_essay.txt\"]\n",
+ ").load_data()\n",
+ "\n",
+ "print(\"Document ID:\", documents[0].doc_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e6c5f4bf-411e-482d-8ada-580dad6575ee",
+ "metadata": {},
+ "source": [
+ "# Create the index\n",
+ "\n",
+ "Here we establish the connection to cosmos db nosql and create a vector store index."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4eb1251c-8bbb-416d-9c32-c7260d039900",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from azure.cosmos import CosmosClient, PartitionKey\n",
+ "from llama_index.vector_stores.azurecosmosnosql import (\n",
+ " AzureCosmosDBNoSqlVectorSearch,\n",
+ ")\n",
+ "from llama_index.core import StorageContext\n",
+ "\n",
+ "# create cosmos client\n",
+ "URI = \"AZURE_COSMOSDB_URI\"\n",
+ "KEY = \"AZURE_COSMOSDB_KEY\"\n",
+ "client = CosmosClient(URI, credential=KEY)\n",
+ "\n",
+ "# specify vector store properties\n",
+ "indexing_policy = {\n",
+ " \"indexingMode\": \"consistent\",\n",
+ " \"includedPaths\": [{\"path\": \"/*\"}],\n",
+ " \"excludedPaths\": [{\"path\": '/\"_etag\"/?'}],\n",
+ " \"vectorIndexes\": [{\"path\": \"/embedding\", \"type\": \"quantizedFlat\"}],\n",
+ "}\n",
+ "\n",
+ "vector_embedding_policy = {\n",
+ " \"vectorEmbeddings\": [\n",
+ " {\n",
+ " \"path\": \"/embedding\",\n",
+ " \"dataType\": \"float32\",\n",
+ " \"distanceFunction\": \"cosine\",\n",
+ " \"dimensions\": 3072,\n",
+ " }\n",
+ " ]\n",
+ "}\n",
+ "\n",
+ "partition_key = PartitionKey(path=\"/id\")\n",
+ "cosmos_container_properties_test = {\"partition_key\": partition_key}\n",
+ "cosmos_database_properties_test = {}\n",
+ "\n",
+ "# create vector store\n",
+ "store = AzureCosmosDBNoSqlVectorSearch(\n",
+ " cosmos_client=client,\n",
+ " vector_embedding_policy=vector_embedding_policy,\n",
+ " indexing_policy=indexing_policy,\n",
+ " cosmos_container_properties=cosmos_container_properties_test,\n",
+ " cosmos_database_properties=cosmos_database_properties_test,\n",
+ " create_container=True,\n",
+ ")\n",
+ "\n",
+ "storage_context = StorageContext.from_defaults(vector_store=store)\n",
+ "\n",
+ "index = VectorStoreIndex.from_documents(\n",
+ " documents, storage_context=storage_context\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "70671760-c408-4f94-b4c8-f9b7aad47644",
+ "metadata": {},
+ "source": [
+ "# Query the index\n",
+ "We can now ask questions using our index."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "930a6143-62c9-4377-8955-0c05bfb7e1a2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author love working on?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c572a6cd-34db-47e1-897c-a03048173882",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import textwrap\n",
+ "\n",
+ "print(textwrap.fill(str(response), 100))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/examples/vector_stores/FirestoreVectorStore.ipynb b/docs/docs/examples/vector_stores/FirestoreVectorStore.ipynb
index e1a15daf4114f..0aa5e3ae5fd60 100644
--- a/docs/docs/examples/vector_stores/FirestoreVectorStore.ipynb
+++ b/docs/docs/examples/vector_stores/FirestoreVectorStore.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/vector_stores/HologresDemo.ipynb b/docs/docs/examples/vector_stores/HologresDemo.ipynb
index dd6667d8cdabd..97cb079ef9984 100644
--- a/docs/docs/examples/vector_stores/HologresDemo.ipynb
+++ b/docs/docs/examples/vector_stores/HologresDemo.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/vector_stores/LanternIndexDemo.ipynb b/docs/docs/examples/vector_stores/LanternIndexDemo.ipynb
index e602b5f529c1c..37d9f024e038b 100644
--- a/docs/docs/examples/vector_stores/LanternIndexDemo.ipynb
+++ b/docs/docs/examples/vector_stores/LanternIndexDemo.ipynb
@@ -6,7 +6,7 @@
"id": "bccd47fc",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/vector_stores/LindormDemo.ipynb b/docs/docs/examples/vector_stores/LindormDemo.ipynb
index 6e7cf94382ecc..7f45f409e38d6 100644
--- a/docs/docs/examples/vector_stores/LindormDemo.ipynb
+++ b/docs/docs/examples/vector_stores/LindormDemo.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/docs/docs/examples/vector_stores/TablestoreDemo.ipynb b/docs/docs/examples/vector_stores/TablestoreDemo.ipynb
new file mode 100644
index 0000000000000..b92c4b6d92c20
--- /dev/null
+++ b/docs/docs/examples/vector_stores/TablestoreDemo.ipynb
@@ -0,0 +1,301 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# TablestoreVectorStore\n",
+ "\n",
+ "> [Tablestore](https://www.aliyun.com/product/ots) is a fully managed NoSQL cloud database service that enables storage of a massive amount of structured\n",
+ "and semi-structured data.\n",
+ "\n",
+ "This notebook shows how to use functionality related to the `Tablestore` vector database.\n",
+ "\n",
+ "To use Tablestore, you must create an instance.\n",
+ "Here are the [creating instance instructions](https://help.aliyun.com/zh/tablestore/getting-started/manage-the-wide-column-model-in-the-tablestore-console)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Install"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install llama-index-vector-stores-tablestore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"end_point\"] = getpass.getpass(\"Tablestore end_point:\")\n",
+ "os.environ[\"instance_name\"] = getpass.getpass(\"Tablestore instance_name:\")\n",
+ "os.environ[\"access_key_id\"] = getpass.getpass(\"Tablestore access_key_id:\")\n",
+ "os.environ[\"access_key_secret\"] = getpass.getpass(\n",
+ " \"Tablestore access_key_secret:\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create vector store. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "from llama_index.core import MockEmbedding\n",
+ "from llama_index.core.schema import TextNode\n",
+ "from llama_index.core.vector_stores import (\n",
+ " VectorStoreQuery,\n",
+ " MetadataFilters,\n",
+ " MetadataFilter,\n",
+ " FilterCondition,\n",
+ " FilterOperator,\n",
+ ")\n",
+ "from tablestore import FieldSchema, FieldType, VectorMetricType\n",
+ "\n",
+ "from llama_index.vector_stores.tablestore import TablestoreVectorStore\n",
+ "\n",
+ "vector_dimension = 4\n",
+ "\n",
+ "store = TablestoreVectorStore(\n",
+ " endpoint=os.getenv(\"end_point\"),\n",
+ " instance_name=os.getenv(\"instance_name\"),\n",
+ " access_key_id=os.getenv(\"access_key_id\"),\n",
+ " access_key_secret=os.getenv(\"access_key_secret\"),\n",
+ " vector_dimension=vector_dimension,\n",
+ " vector_metric_type=VectorMetricType.VM_COSINE,\n",
+ " # metadata mapping is used to filter non-vector fields.\n",
+ " metadata_mappings=[\n",
+ " FieldSchema(\n",
+ " \"type\", FieldType.KEYWORD, index=True, enable_sort_and_agg=True\n",
+ " ),\n",
+ " FieldSchema(\n",
+ " \"time\", FieldType.LONG, index=True, enable_sort_and_agg=True\n",
+ " ),\n",
+ " ],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create table and index."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "store.create_table_if_not_exist()\n",
+ "store.create_search_index_if_not_exist()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "New a mock embedding for test."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "embedder = MockEmbedding(vector_dimension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Prepare some docs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "texts = [\n",
+ " TextNode(\n",
+ " id_=\"1\",\n",
+ " text=\"The lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.\",\n",
+ " metadata={\"type\": \"a\", \"time\": 1995},\n",
+ " ),\n",
+ " TextNode(\n",
+ " id_=\"2\",\n",
+ " text=\"When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.\",\n",
+ " metadata={\"type\": \"a\", \"time\": 1990},\n",
+ " ),\n",
+ " TextNode(\n",
+ " id_=\"3\",\n",
+ " text=\"An insomniac office worker and a devil-may-care soapmaker form an underground fight club that evolves into something much, much more.\",\n",
+ " metadata={\"type\": \"a\", \"time\": 2009},\n",
+ " ),\n",
+ " TextNode(\n",
+ " id_=\"4\",\n",
+ " text=\"A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.\",\n",
+ " metadata={\"type\": \"a\", \"time\": 2023},\n",
+ " ),\n",
+ " TextNode(\n",
+ " id_=\"5\",\n",
+ " text=\"A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers.\",\n",
+ " metadata={\"type\": \"b\", \"time\": 2018},\n",
+ " ),\n",
+ " TextNode(\n",
+ " id_=\"6\",\n",
+ " text=\"Two detectives, a rookie and a veteran, hunt a serial killer who uses the seven deadly sins as his motives.\",\n",
+ " metadata={\"type\": \"c\", \"time\": 2010},\n",
+ " ),\n",
+ " TextNode(\n",
+ " id_=\"7\",\n",
+ " text=\"An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.\",\n",
+ " metadata={\"type\": \"a\", \"time\": 2023},\n",
+ " ),\n",
+ "]\n",
+ "for t in texts:\n",
+ " t.embedding = embedder.get_text_embedding(t.text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Write some docs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['1', '2', '3', '4', '5', '6', '7']"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "store.add(texts)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Delete docs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "store.delete(\"1\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Query with filters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "VectorStoreQueryResult(nodes=[TextNode(id_='2', embedding=[0.5, 0.5, 0.5, 0.5], metadata={'time': 1990, 'type': 'a'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), TextNode(id_='3', embedding=[0.5, 0.5, 0.5, 0.5], metadata={'time': 2009, 'type': 'a'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='An insomniac office worker and a devil-may-care soapmaker form an underground fight club that evolves into something much, much more.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')], similarities=None, ids=['2', '3'])"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "store.query(\n",
+ " query=VectorStoreQuery(\n",
+ " query_embedding=embedder.get_text_embedding(\"nature fight physical\"),\n",
+ " similarity_top_k=5,\n",
+ " filters=MetadataFilters(\n",
+ " filters=[\n",
+ " MetadataFilter(\n",
+ " key=\"type\", value=\"a\", operator=FilterOperator.EQ\n",
+ " ),\n",
+ " MetadataFilter(\n",
+ " key=\"time\", value=2020, operator=FilterOperator.LTE\n",
+ " ),\n",
+ " ],\n",
+ " condition=FilterCondition.AND,\n",
+ " ),\n",
+ " ),\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb
index 9d3c3e59fd194..c180152a2b21b 100644
--- a/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb
+++ b/docs/docs/examples/vector_stores/VespaIndexDemo.ipynb
@@ -18,7 +18,7 @@
"id": "23cf319b",
"metadata": {},
"source": [
- "\n"
+ "\n"
]
},
{
diff --git a/docs/docs/vector_stores/wordlift_vector_store_demo.ipynb b/docs/docs/examples/vector_stores/WordLiftDemo.ipynb
similarity index 100%
rename from docs/docs/vector_stores/wordlift_vector_store_demo.ipynb
rename to docs/docs/examples/vector_stores/WordLiftDemo.ipynb
diff --git a/docs/docs/examples/workflow/JSONalyze_query_engine.ipynb b/docs/docs/examples/workflow/JSONalyze_query_engine.ipynb
index cfef1ffc8f75e..17ee27b7d458d 100644
--- a/docs/docs/examples/workflow/JSONalyze_query_engine.ipynb
+++ b/docs/docs/examples/workflow/JSONalyze_query_engine.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -222,8 +222,8 @@
"\n",
" raise ImportError(IMPORT_ERROR_MSG) from exc\n",
"\n",
- " ctx.data[\"query\"] = ev.get(\"query\")\n",
- " ctx.data[\"llm\"] = ev.get(\"llm\")\n",
+ " await ctx.set(\"query\", ev.get(\"query\"))\n",
+ " await ctx.set(\"llm\", ev.get(\"llm\"))\n",
"\n",
" query = ev.get(\"query\")\n",
" table_name = ev.get(\"table_name\")\n",
@@ -273,8 +273,8 @@
" self, ctx: Context, ev: JsonAnalyzerEvent\n",
" ) -> StopEvent:\n",
" \"\"\"Synthesize the response.\"\"\"\n",
- " llm = ctx.data.get(\"llm\")\n",
- " query = ctx.data.get(\"query\")\n",
+ " llm = await ctx.get(\"llm\", default=None)\n",
+ " query = await ctx.get(\"query\", default=None)\n",
"\n",
" response_str = llm.predict(\n",
" DEFAULT_RESPONSE_SYNTHESIS_PROMPT,\n",
diff --git a/docs/docs/examples/workflow/advanced_text_to_sql.ipynb b/docs/docs/examples/workflow/advanced_text_to_sql.ipynb
new file mode 100644
index 0000000000000..23ee38209f9a8
--- /dev/null
+++ b/docs/docs/examples/workflow/advanced_text_to_sql.ipynb
@@ -0,0 +1,1289 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5fb96106-e662-4a0f-9673-843c685c111e",
+ "metadata": {},
+ "source": [
+ "# Workflows for Advanced Text-to-SQL\n",
+ "\n",
+ "\n",
+ "\n",
+ "In this guide we show you how to setup a text-to-SQL workflow over your data with our [workflows](https://docs.llamaindex.ai/en/stable/module_guides/workflow/#workflows) syntax.\n",
+ "\n",
+ "This gives you flexibility to enhance text-to-SQL with additional techniques. We show these in the below sections: \n",
+ "1. **Query-Time Table Retrieval**: Dynamically retrieve relevant tables in the text-to-SQL prompt.\n",
+ "2. **Query-Time Sample Row retrieval**: Embed/Index each row, and dynamically retrieve example rows for each table in the text-to-SQL prompt.\n",
+ "\n",
+ "Our out-of-the box workflows include our `NLSQLTableQueryEngine` and `SQLTableRetrieverQueryEngine`. (if you want to check out our text-to-SQL guide using these modules, take a look [here](https://docs.llamaindex.ai/en/stable/examples/index_structs/struct_indices/SQLIndexDemo.html)). This guide implements an advanced version of those modules, giving you the utmost flexibility to apply this to your own setting.\n",
+ "\n",
+ "**NOTE:** Any Text-to-SQL application should be aware that executing \n",
+ "arbitrary SQL queries can be a security risk. It is recommended to\n",
+ "take precautions as needed, such as using restricted roles, read-only\n",
+ "databases, sandboxing, etc."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d5c0cbbb-0d20-4233-b2f6-9e8ff3f6d04c",
+ "metadata": {},
+ "source": [
+ "## Load and Ingest Data\n",
+ "\n",
+ "\n",
+ "### Load Data\n",
+ "We use the [WikiTableQuestions dataset](https://ppasupat.github.io/WikiTableQuestions/) (Pasupat and Liang 2015) as our test dataset.\n",
+ "\n",
+ "We go through all the csv's in one folder, store each in a sqlite database (we will then build an object index over each table schema)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dae8fb42-d65a-456c-b5bf-613efa43e9f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install llama-index-llms-openai"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "468a4900-a1ba-471d-a839-8cf56dfb5cf1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!wget \"https://github.com/ppasupat/WikiTableQuestions/releases/download/v1.0.2/WikiTableQuestions-1.0.2-compact.zip\" -O data.zip\n",
+ "!unzip data.zip"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cba67be2-343b-4acd-b128-b26a79bf4504",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pathlib import Path\n",
+ "\n",
+ "data_dir = Path(\"./WikiTableQuestions/csv/200-csv\")\n",
+ "csv_files = sorted([f for f in data_dir.glob(\"*.csv\")])\n",
+ "dfs = []\n",
+ "for csv_file in csv_files:\n",
+ " print(f\"processing file: {csv_file}\")\n",
+ " try:\n",
+ " df = pd.read_csv(csv_file)\n",
+ " dfs.append(df)\n",
+ " except Exception as e:\n",
+ " print(f\"Error parsing {csv_file}: {str(e)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2bf32f7b-dad2-4aca-88b1-1ec0bc221ebb",
+ "metadata": {},
+ "source": [
+ "### Extract Table Name and Summary from each Table\n",
+ "\n",
+ "Here we use gpt-4o-mini to extract a table name (with underscores) and summary from each table with our Pydantic program."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c3040ec-9523-492b-870b-754feffca0c7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mkdir: WikiTableQuestions_TableInfo: File exists\n"
+ ]
+ }
+ ],
+ "source": [
+ "tableinfo_dir = \"WikiTableQuestions_TableInfo\"\n",
+ "!mkdir {tableinfo_dir}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e921b5d0-662b-4991-b904-090f6cf3eb8c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.prompts import ChatPromptTemplate\n",
+ "from llama_index.core.bridge.pydantic import BaseModel, Field\n",
+ "from llama_index.llms.openai import OpenAI\n",
+ "from llama_index.core.llms import ChatMessage\n",
+ "\n",
+ "\n",
+ "class TableInfo(BaseModel):\n",
+ " \"\"\"Information regarding a structured table.\"\"\"\n",
+ "\n",
+ " table_name: str = Field(\n",
+ " ..., description=\"table name (must be underscores and NO spaces)\"\n",
+ " )\n",
+ " table_summary: str = Field(\n",
+ " ..., description=\"short, concise summary/caption of the table\"\n",
+ " )\n",
+ "\n",
+ "\n",
+ "prompt_str = \"\"\"\\\n",
+ "Give me a summary of the table with the following JSON format.\n",
+ "\n",
+ "- The table name must be unique to the table and describe it while being concise. \n",
+ "- Do NOT output a generic table name (e.g. table, my_table).\n",
+ "\n",
+ "Do NOT make the table name one of the following: {exclude_table_name_list}\n",
+ "\n",
+ "Table:\n",
+ "{table_str}\n",
+ "\n",
+ "Summary: \"\"\"\n",
+ "prompt_tmpl = ChatPromptTemplate(\n",
+ " message_templates=[ChatMessage.from_str(prompt_str, role=\"user\")]\n",
+ ")\n",
+ "\n",
+ "llm = OpenAI(model=\"gpt-4o-mini\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15277c5d-9a12-4ae9-b7ef-de58139ea66b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "\n",
+ "\n",
+ "def _get_tableinfo_with_index(idx: int) -> str:\n",
+ " results_gen = Path(tableinfo_dir).glob(f\"{idx}_*\")\n",
+ " results_list = list(results_gen)\n",
+ " if len(results_list) == 0:\n",
+ " return None\n",
+ " elif len(results_list) == 1:\n",
+ " path = results_list[0]\n",
+ " return TableInfo.parse_file(path)\n",
+ " else:\n",
+ " raise ValueError(\n",
+ " f\"More than one file matching index: {list(results_gen)}\"\n",
+ " )\n",
+ "\n",
+ "\n",
+ "table_names = set()\n",
+ "table_infos = []\n",
+ "for idx, df in enumerate(dfs):\n",
+ " table_info = _get_tableinfo_with_index(idx)\n",
+ " if table_info:\n",
+ " table_infos.append(table_info)\n",
+ " else:\n",
+ " while True:\n",
+ " df_str = df.head(10).to_csv()\n",
+ " table_info = llm.structured_predict(\n",
+ " TableInfo,\n",
+ " prompt_tmpl,\n",
+ " table_str=df_str,\n",
+ " exclude_table_name_list=str(list(table_names)),\n",
+ " )\n",
+ " table_name = table_info.table_name\n",
+ " print(f\"Processed table: {table_name}\")\n",
+ " if table_name not in table_names:\n",
+ " table_names.add(table_name)\n",
+ " break\n",
+ " else:\n",
+ " # try again\n",
+ " print(f\"Table name {table_name} already exists, trying again.\")\n",
+ " pass\n",
+ "\n",
+ " out_file = f\"{tableinfo_dir}/{idx}_{table_name}.json\"\n",
+ " json.dump(table_info.dict(), open(out_file, \"w\"))\n",
+ " table_infos.append(table_info)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f67987b-6906-42e0-b80c-3ba096b71b42",
+ "metadata": {},
+ "source": [
+ "### Put Data in SQL Database\n",
+ "\n",
+ "We use `sqlalchemy`, a popular SQL database toolkit, to load all the tables."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d315556-e83a-4181-935a-1f067b12052a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# put data into sqlite db\n",
+ "from sqlalchemy import (\n",
+ " create_engine,\n",
+ " MetaData,\n",
+ " Table,\n",
+ " Column,\n",
+ " String,\n",
+ " Integer,\n",
+ ")\n",
+ "import re\n",
+ "\n",
+ "\n",
+ "# Function to create a sanitized column name\n",
+ "def sanitize_column_name(col_name):\n",
+ " # Remove special characters and replace spaces with underscores\n",
+ " return re.sub(r\"\\W+\", \"_\", col_name)\n",
+ "\n",
+ "\n",
+ "# Function to create a table from a DataFrame using SQLAlchemy\n",
+ "def create_table_from_dataframe(\n",
+ " df: pd.DataFrame, table_name: str, engine, metadata_obj\n",
+ "):\n",
+ " # Sanitize column names\n",
+ " sanitized_columns = {col: sanitize_column_name(col) for col in df.columns}\n",
+ " df = df.rename(columns=sanitized_columns)\n",
+ "\n",
+ " # Dynamically create columns based on DataFrame columns and data types\n",
+ " columns = [\n",
+ " Column(col, String if dtype == \"object\" else Integer)\n",
+ " for col, dtype in zip(df.columns, df.dtypes)\n",
+ " ]\n",
+ "\n",
+ " # Create a table with the defined columns\n",
+ " table = Table(table_name, metadata_obj, *columns)\n",
+ "\n",
+ " # Create the table in the database\n",
+ " metadata_obj.create_all(engine)\n",
+ "\n",
+ " # Insert data from DataFrame into the table\n",
+ " with engine.connect() as conn:\n",
+ " for _, row in df.iterrows():\n",
+ " insert_stmt = table.insert().values(**row.to_dict())\n",
+ " conn.execute(insert_stmt)\n",
+ " conn.commit()\n",
+ "\n",
+ "\n",
+ "# engine = create_engine(\"sqlite:///:memory:\")\n",
+ "engine = create_engine(\"sqlite:///wiki_table_questions.db\")\n",
+ "metadata_obj = MetaData()\n",
+ "for idx, df in enumerate(dfs):\n",
+ " tableinfo = _get_tableinfo_with_index(idx)\n",
+ " print(f\"Creating table: {tableinfo.table_name}\")\n",
+ " create_table_from_dataframe(df, tableinfo.table_name, engine, metadata_obj)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "982aa74e-323f-4c85-b0e2-1acf91d6b841",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # setup Arize Phoenix for logging/observability\n",
+ "# import phoenix as px\n",
+ "# import llama_index.core\n",
+ "\n",
+ "# px.launch_app()\n",
+ "# llama_index.core.set_global_handler(\"arize_phoenix\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f5492e07-234e-4b08-bf37-e4fac422927b",
+ "metadata": {},
+ "source": [
+ "## Advanced Capability 1: Text-to-SQL with Query-Time Table Retrieval.\n",
+ "\n",
+ "We now show you how to setup an e2e text-to-SQL with table retrieval.\n",
+ "\n",
+ "### Define Modules\n",
+ "\n",
+ "Here we define the core modules.\n",
+ "1. Object index + retriever to store table schemas\n",
+ "2. SQLDatabase object to connect to the above tables + SQLRetriever.\n",
+ "3. Text-to-SQL Prompt\n",
+ "4. Response synthesis Prompt\n",
+ "5. LLM"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "926c03f4-0934-4e47-a11f-a7722e66feec",
+ "metadata": {},
+ "source": [
+ "Object index, retriever, SQLDatabase"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "709f0692-ddd1-4ff6-975a-1df5abfea1ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.objects import (\n",
+ " SQLTableNodeMapping,\n",
+ " ObjectIndex,\n",
+ " SQLTableSchema,\n",
+ ")\n",
+ "from llama_index.core import SQLDatabase, VectorStoreIndex\n",
+ "\n",
+ "sql_database = SQLDatabase(engine)\n",
+ "\n",
+ "table_node_mapping = SQLTableNodeMapping(sql_database)\n",
+ "table_schema_objs = [\n",
+ " SQLTableSchema(table_name=t.table_name, context_str=t.table_summary)\n",
+ " for t in table_infos\n",
+ "] # add a SQLTableSchema for each table\n",
+ "\n",
+ "obj_index = ObjectIndex.from_objects(\n",
+ " table_schema_objs,\n",
+ " table_node_mapping,\n",
+ " VectorStoreIndex,\n",
+ ")\n",
+ "obj_retriever = obj_index.as_retriever(similarity_top_k=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf8ce73a-5f21-4977-87ee-7752da4fed63",
+ "metadata": {},
+ "source": [
+ "SQLRetriever + Table Parser"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3047347e-362e-46c2-9ac8-ee3b8730fe3c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.retrievers import SQLRetriever\n",
+ "from typing import List\n",
+ "\n",
+ "sql_retriever = SQLRetriever(sql_database)\n",
+ "\n",
+ "\n",
+ "def get_table_context_str(table_schema_objs: List[SQLTableSchema]):\n",
+ " \"\"\"Get table context string.\"\"\"\n",
+ " context_strs = []\n",
+ " for table_schema_obj in table_schema_objs:\n",
+ " table_info = sql_database.get_single_table_info(\n",
+ " table_schema_obj.table_name\n",
+ " )\n",
+ " if table_schema_obj.context_str:\n",
+ " table_opt_context = \" The table description is: \"\n",
+ " table_opt_context += table_schema_obj.context_str\n",
+ " table_info += table_opt_context\n",
+ "\n",
+ " context_strs.append(table_info)\n",
+ " return \"\\n\\n\".join(context_strs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d5e693fd-18b4-4d3e-9877-7ca527d83f57",
+ "metadata": {},
+ "source": [
+ "Text-to-SQL Prompt + Output Parser"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c708fba-0551-4e14-938d-5c32eae52595",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. You can order the results by a relevant column to return the most interesting examples in the database.\n",
+ "\n",
+ "Never query for all the columns from a specific table, only ask for a few relevant columns given the question.\n",
+ "\n",
+ "Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Pay attention to which column is in which table. Also, qualify column names with the table name when needed. You are required to use the following format, each taking one line:\n",
+ "\n",
+ "Question: Question here\n",
+ "SQLQuery: SQL Query to run\n",
+ "SQLResult: Result of the SQLQuery\n",
+ "Answer: Final answer here\n",
+ "\n",
+ "Only use tables listed below.\n",
+ "{schema}\n",
+ "\n",
+ "Question: {query_str}\n",
+ "SQLQuery: \n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.core.prompts.default_prompts import DEFAULT_TEXT_TO_SQL_PROMPT\n",
+ "from llama_index.core import PromptTemplate\n",
+ "from llama_index.core.llms import ChatResponse\n",
+ "\n",
+ "\n",
+ "def parse_response_to_sql(chat_response: ChatResponse) -> str:\n",
+ " \"\"\"Parse response to SQL.\"\"\"\n",
+ " response = chat_response.message.content\n",
+ " sql_query_start = response.find(\"SQLQuery:\")\n",
+ " if sql_query_start != -1:\n",
+ " response = response[sql_query_start:]\n",
+ " # TODO: move to removeprefix after Python 3.9+\n",
+ " if response.startswith(\"SQLQuery:\"):\n",
+ " response = response[len(\"SQLQuery:\") :]\n",
+ " sql_result_start = response.find(\"SQLResult:\")\n",
+ " if sql_result_start != -1:\n",
+ " response = response[:sql_result_start]\n",
+ " return response.strip().strip(\"```\").strip()\n",
+ "\n",
+ "\n",
+ "text2sql_prompt = DEFAULT_TEXT_TO_SQL_PROMPT.partial_format(\n",
+ " dialect=engine.dialect.name\n",
+ ")\n",
+ "print(text2sql_prompt.template)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf67d6a9-f94c-4c27-b09b-2dbe1952727d",
+ "metadata": {},
+ "source": [
+ "Response Synthesis Prompt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1757f33-f1ca-4a31-8cce-36be9bb9a989",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response_synthesis_prompt_str = (\n",
+ " \"Given an input question, synthesize a response from the query results.\\n\"\n",
+ " \"Query: {query_str}\\n\"\n",
+ " \"SQL: {sql_query}\\n\"\n",
+ " \"SQL Response: {context_str}\\n\"\n",
+ " \"Response: \"\n",
+ ")\n",
+ "response_synthesis_prompt = PromptTemplate(\n",
+ " response_synthesis_prompt_str,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "95b197ba-9c26-4e6f-9300-0e90a474911c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# llm = OpenAI(model=\"gpt-3.5-turbo\")\n",
+ "llm = OpenAI(model=\"gpt-4o-mini\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "655a23f3-06e3-45cf-9988-bf1cf5c4a5ad",
+ "metadata": {},
+ "source": [
+ "### Define Workflow\n",
+ "\n",
+ "Now that the components are in place, let's define the full workflow! "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3f3b808c-df2a-4bc1-89a3-ba75f6377f68",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.core.workflow import (\n",
+ " Workflow,\n",
+ " StartEvent,\n",
+ " StopEvent,\n",
+ " step,\n",
+ " Context,\n",
+ " Event,\n",
+ ")\n",
+ "\n",
+ "\n",
+ "class TableRetrieveEvent(Event):\n",
+ " \"\"\"Result of running table retrieval.\"\"\"\n",
+ "\n",
+ " table_context_str: str\n",
+ " query: str\n",
+ "\n",
+ "\n",
+ "class TextToSQLEvent(Event):\n",
+ " \"\"\"Text-to-SQL event.\"\"\"\n",
+ "\n",
+ " sql: str\n",
+ " query: str\n",
+ "\n",
+ "\n",
+ "class TextToSQLWorkflow1(Workflow):\n",
+ " \"\"\"Text-to-SQL Workflow that does query-time table retrieval.\"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self,\n",
+ " obj_retriever,\n",
+ " text2sql_prompt,\n",
+ " sql_retriever,\n",
+ " response_synthesis_prompt,\n",
+ " llm,\n",
+ " *args,\n",
+ " **kwargs\n",
+ " ) -> None:\n",
+ " \"\"\"Init params.\"\"\"\n",
+ " super().__init__(*args, **kwargs)\n",
+ " self.obj_retriever = obj_retriever\n",
+ " self.text2sql_prompt = text2sql_prompt\n",
+ " self.sql_retriever = sql_retriever\n",
+ " self.response_synthesis_prompt = response_synthesis_prompt\n",
+ " self.llm = llm\n",
+ "\n",
+ " @step\n",
+ " def retrieve_tables(\n",
+ " self, ctx: Context, ev: StartEvent\n",
+ " ) -> TableRetrieveEvent:\n",
+ " \"\"\"Retrieve tables.\"\"\"\n",
+ " table_schema_objs = self.obj_retriever.retrieve(ev.query)\n",
+ " table_context_str = get_table_context_str(table_schema_objs)\n",
+ " return TableRetrieveEvent(\n",
+ " table_context_str=table_context_str, query=ev.query\n",
+ " )\n",
+ "\n",
+ " @step\n",
+ " def generate_sql(\n",
+ " self, ctx: Context, ev: TableRetrieveEvent\n",
+ " ) -> TextToSQLEvent:\n",
+ " \"\"\"Generate SQL statement.\"\"\"\n",
+ " fmt_messages = self.text2sql_prompt.format_messages(\n",
+ " query_str=ev.query, schema=ev.table_context_str\n",
+ " )\n",
+ " chat_response = self.llm.chat(fmt_messages)\n",
+ " sql = parse_response_to_sql(chat_response)\n",
+ " return TextToSQLEvent(sql=sql, query=ev.query)\n",
+ "\n",
+ " @step\n",
+ " def generate_response(self, ctx: Context, ev: TextToSQLEvent) -> StopEvent:\n",
+ " \"\"\"Run SQL retrieval and generate response.\"\"\"\n",
+ " retrieved_rows = self.sql_retriever.retrieve(ev.sql)\n",
+ " fmt_messages = self.response_synthesis_prompt.format_messages(\n",
+ " sql_query=ev.sql,\n",
+ " context_str=str(retrieved_rows),\n",
+ " query_str=ev.query,\n",
+ " )\n",
+ " chat_response = llm.chat(fmt_messages)\n",
+ " return StopEvent(result=chat_response)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2922dc47-1f63-4f6a-a795-c3b3e60fd354",
+ "metadata": {},
+ "source": [
+ "### Visualize Workflow\n",
+ "\n",
+ "A really nice property of workflows is that you can both visualize the execution graph as well as a trace of the most recent execution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5ac1a1bc-7cc6-49e7-a7cd-31ca9e347e5b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "text_to_sql_table_retrieval.html\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.utils.workflow import draw_all_possible_flows\n",
+ "\n",
+ "draw_all_possible_flows(\n",
+ " TextToSQLWorkflow1, filename=\"text_to_sql_table_retrieval.html\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "74f3aaca-59b0-478e-9652-7ccddd1cb4d4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ ""
+ ""
]
},
{
@@ -263,7 +263,7 @@
" print(f\"Query the database with: {query}\")\n",
"\n",
" # store the query in the global context\n",
- " ctx.data[\"query\"] = query\n",
+ " await ctx.set(\"query\", query)\n",
"\n",
" if ev.index is None:\n",
" print(\"Index is empty, load some documents before querying!\")\n",
@@ -276,7 +276,7 @@
"\n",
" @step\n",
" async def create_citation_nodes(\n",
- " self, ctx: Context, ev: RetrieverEvent\n",
+ " self, ev: RetrieverEvent\n",
" ) -> CreateCitationsEvent:\n",
" \"\"\"\n",
" Modify retrieved nodes to create granular sources for citations.\n",
@@ -323,7 +323,7 @@
" ) -> StopEvent:\n",
" \"\"\"Return a streaming response using the retrieved nodes.\"\"\"\n",
" llm = OpenAI(model=\"gpt-4o-mini\")\n",
- " query = ctx.data.get(\"query\")\n",
+ " query = await ctx.get(\"query\", default=None)\n",
"\n",
" synthesizer = get_response_synthesizer(\n",
" llm=llm,\n",
diff --git a/docs/docs/examples/workflow/corrective_rag_pack.ipynb b/docs/docs/examples/workflow/corrective_rag_pack.ipynb
index 2653662cbdc3c..73809573d51c5 100644
--- a/docs/docs/examples/workflow/corrective_rag_pack.ipynb
+++ b/docs/docs/examples/workflow/corrective_rag_pack.ipynb
@@ -44,7 +44,7 @@
"source": [
"import os\n",
"\n",
- "os.environ[\"OPENAI_API_KEY\"] = \""
+ ""
]
},
{
@@ -206,7 +206,7 @@
" source_nodes = []\n",
"\n",
" query = ev.get(\"query\")\n",
- " ctx.data[\"query\"] = ev.get(\"query\")\n",
+ " await ctx.set(\"query\", ev.get(\"query\"))\n",
"\n",
" llm = Settings.llm\n",
" stop_fn = self.default_stop_fn\n",
@@ -274,8 +274,9 @@
" ) -> StopEvent:\n",
" \"\"\"Synthesize the response.\"\"\"\n",
" response_synthesizer = get_response_synthesizer()\n",
+ " query = await ctx.get(\"query\", default=None)\n",
" final_response = await response_synthesizer.asynthesize(\n",
- " query=ctx.data.get(\"query\"),\n",
+ " query=query,\n",
" nodes=ev.nodes,\n",
" additional_source_nodes=ev.source_nodes,\n",
" )\n",
diff --git a/docs/docs/examples/workflow/multi_strategy_workflow.ipynb b/docs/docs/examples/workflow/multi_strategy_workflow.ipynb
index 1c0f4fc5a9a46..8b99576a27f94 100644
--- a/docs/docs/examples/workflow/multi_strategy_workflow.ipynb
+++ b/docs/docs/examples/workflow/multi_strategy_workflow.ipynb
@@ -218,14 +218,17 @@
" self, ctx: Context, ev: StartEvent | JudgeEvent\n",
" ) -> BadQueryEvent | NaiveRAGEvent | HighTopKEvent | RerankEvent:\n",
" # initialize\n",
- " if not hasattr(ctx.data, \"llm\"):\n",
- " ctx.data[\"llm\"] = OpenAI(model=\"gpt-4o\", temperature=0.1)\n",
- " ctx.data[\"index\"] = self.load_or_create_index(\"data\", \"storage\")\n",
+ " llm = await ctx.get(\"llm\", default=None)\n",
+ " if llm is None:\n",
+ " await ctx.set(\"llm\", OpenAI(model=\"gpt-4o\", temperature=0.1))\n",
+ " await ctx.set(\n",
+ " \"index\", self.load_or_create_index(\"data\", \"storage\")\n",
+ " )\n",
"\n",
" # we use a chat engine so it remembers previous interactions\n",
- " ctx.data[\"judge\"] = SimpleChatEngine.from_defaults()\n",
+ " await ctx.set(\"judge\", SimpleChatEngine.from_defaults())\n",
"\n",
- " response = ctx.data[\"judge\"].chat(\n",
+ " response = await ctx.get(\"judge\").chat(\n",
" f\"\"\"\n",
" Given a user query, determine if this is likely to yield good results from a RAG system as-is. If it's good, return 'good', if it's bad, return 'bad'.\n",
" Good queries use a lot of relevant keywords and are detailed. Bad queries are vague or ambiguous.\n",
@@ -246,7 +249,7 @@
" async def improve_query(\n",
" self, ctx: Context, ev: BadQueryEvent\n",
" ) -> JudgeEvent:\n",
- " response = ctx.data[\"llm\"].complete(\n",
+ " response = await ctx.get(\"llm\").complete(\n",
" f\"\"\"\n",
" This is a query to a RAG system: {ev.query}\n",
"\n",
@@ -259,7 +262,7 @@
" async def naive_rag(\n",
" self, ctx: Context, ev: NaiveRAGEvent\n",
" ) -> ResponseEvent:\n",
- " index = ctx.data[\"index\"]\n",
+ " index = await ctx.get(\"index\")\n",
" engine = index.as_query_engine(similarity_top_k=5)\n",
" response = engine.query(ev.query)\n",
" print(\"Naive response:\", response)\n",
@@ -271,7 +274,7 @@
" async def high_top_k(\n",
" self, ctx: Context, ev: HighTopKEvent\n",
" ) -> ResponseEvent:\n",
- " index = ctx.data[\"index\"]\n",
+ " index = await ctx.get(\"index\")\n",
" engine = index.as_query_engine(similarity_top_k=20)\n",
" response = engine.query(ev.query)\n",
" print(\"High top k response:\", response)\n",
@@ -281,8 +284,8 @@
"\n",
" @step\n",
" async def rerank(self, ctx: Context, ev: RerankEvent) -> ResponseEvent:\n",
- " index = ctx.data[\"index\"]\n",
- " reranker = RankGPTRerank(top_n=5, llm=ctx.data[\"llm\"])\n",
+ " index = await ctx.get(\"index\")\n",
+ " reranker = RankGPTRerank(top_n=5, llm=await ctx.get(\"llm\"))\n",
" retriever = index.as_retriever(similarity_top_k=20)\n",
" engine = RetrieverQueryEngine.from_args(\n",
" retriever=retriever,\n",
@@ -300,7 +303,7 @@
" if ready is None:\n",
" return None\n",
"\n",
- " response = ctx.data[\"judge\"].chat(\n",
+ " response = await ctx.get(\"judge\").chat(\n",
" f\"\"\"\n",
" A user has provided a query and 3 different strategies have been used\n",
" to try to answer the query. Your job is to decide which strategy best\n",
diff --git a/docs/docs/examples/workflow/parallel_execution.ipynb b/docs/docs/examples/workflow/parallel_execution.ipynb
index 03fde6084fc70..68536e65c64ff 100644
--- a/docs/docs/examples/workflow/parallel_execution.ipynb
+++ b/docs/docs/examples/workflow/parallel_execution.ipynb
@@ -108,7 +108,7 @@
" @step\n",
" async def start(self, ctx: Context, ev: StartEvent) -> ProcessEvent:\n",
" data_list = [\"A\", \"B\", \"C\"]\n",
- " ctx.data[\"num_to_collect\"] = len(data_list)\n",
+ " await ctx.set(\"num_to_collect\", len(data_list))\n",
" for item in data_list:\n",
" self.send_event(ProcessEvent(data=item))\n",
" return None\n",
@@ -125,7 +125,7 @@
" async def combine_results(\n",
" self, ctx: Context, ev: ResultEvent\n",
" ) -> StopEvent | None:\n",
- " num_to_collect = ctx.data[\"num_to_collect\"]\n",
+ " num_to_collect = await ctx.get(\"num_to_collect\")\n",
" results = ctx.collect_events(ev, [ResultEvent] * num_to_collect)\n",
" if results is None:\n",
" return None\n",
@@ -138,7 +138,7 @@
" @step\n",
" async def start(self, ctx: Context, ev: StartEvent) -> ProcessEvent:\n",
" data_list = [\"A\", \"B\", \"C\"]\n",
- " ctx.data[\"num_to_collect\"] = len(data_list)\n",
+ " await ctx.set(\"num_to_collect\", len(data_list))\n",
" for item in data_list:\n",
" self.send_event(ProcessEvent(data=item))\n",
" return None\n",
@@ -155,7 +155,7 @@
" async def combine_results(\n",
" self, ctx: Context, ev: ResultEvent\n",
" ) -> StopEvent | None:\n",
- " num_to_collect = ctx.data[\"num_to_collect\"]\n",
+ " num_to_collect = await ctx.get(\"num_to_collect\")\n",
" results = ctx.collect_events(ev, [ResultEvent] * num_to_collect)\n",
" if results is None:\n",
" return None\n",
diff --git a/docs/docs/examples/workflow/rag.ipynb b/docs/docs/examples/workflow/rag.ipynb
index afcece0e5cb86..a1d94d4453027 100644
--- a/docs/docs/examples/workflow/rag.ipynb
+++ b/docs/docs/examples/workflow/rag.ipynb
@@ -44,7 +44,7 @@
"metadata": {},
"outputs": [],
"source": [
- "!pip install \"llama-index-core>=0.10.43\" \"openinference-instrumentation-llama-index>=2.2.2\" \"opentelemetry-proto>=1.12.0\" opentelemetry-exporter-otlp opentelemetry-sdk"
+ "%pip install \"openinference-instrumentation-llama-index>=3.0.0\" \"opentelemetry-proto>=1.12.0\" opentelemetry-exporter-otlp opentelemetry-sdk"
]
},
{
@@ -190,11 +190,11 @@
" return None\n",
"\n",
" documents = SimpleDirectoryReader(dirname).load_data()\n",
- " ctx.data[\"index\"] = VectorStoreIndex.from_documents(\n",
+ " index = VectorStoreIndex.from_documents(\n",
" documents=documents,\n",
" embed_model=OpenAIEmbedding(model_name=\"text-embedding-3-small\"),\n",
" )\n",
- " return StopEvent(result=f\"Indexed {len(documents)} documents.\")\n",
+ " return StopEvent(result=index)\n",
"\n",
" @step\n",
" async def retrieve(\n",
@@ -202,22 +202,23 @@
" ) -> RetrieverEvent | None:\n",
" \"Entry point for RAG, triggered by a StartEvent with `query`.\"\n",
" query = ev.get(\"query\")\n",
+ " index = ev.get(\"index\")\n",
+ "\n",
" if not query:\n",
" return None\n",
"\n",
" print(f\"Query the database with: {query}\")\n",
"\n",
" # store the query in the global context\n",
- " ctx.data[\"query\"] = query\n",
+ " await ctx.set(\"query\", query)\n",
"\n",
" # get the index from the global context\n",
- " index = ctx.data.get(\"index\")\n",
" if index is None:\n",
" print(\"Index is empty, load some documents before querying!\")\n",
" return None\n",
"\n",
" retriever = index.as_retriever(similarity_top_k=2)\n",
- " nodes = retriever.retrieve(query)\n",
+ " nodes = await retriever.aretrieve(query)\n",
" print(f\"Retrieved {len(nodes)} nodes.\")\n",
" return RetrieverEvent(nodes=nodes)\n",
"\n",
@@ -227,9 +228,9 @@
" ranker = LLMRerank(\n",
" choice_batch_size=5, top_n=3, llm=OpenAI(model=\"gpt-4o-mini\")\n",
" )\n",
- " print(ctx.data.get(\"query\"), flush=True)\n",
+ " print(await ctx.get(\"query\", default=None), flush=True)\n",
" new_nodes = ranker.postprocess_nodes(\n",
- " ev.nodes, query_str=ctx.data.get(\"query\")\n",
+ " ev.nodes, query_str=await ctx.get(\"query\", default=None)\n",
" )\n",
" print(f\"Reranked nodes to {len(new_nodes)}\")\n",
" return RerankEvent(nodes=new_nodes)\n",
@@ -239,7 +240,7 @@
" \"\"\"Return a streaming response using reranked nodes.\"\"\"\n",
" llm = OpenAI(model=\"gpt-4o-mini\")\n",
" summarizer = CompactAndRefine(llm=llm, streaming=True, verbose=True)\n",
- " query = ctx.data.get(\"query\")\n",
+ " query = await ctx.get(\"query\", default=None)\n",
"\n",
" response = await summarizer.asynthesize(query, nodes=ev.nodes)\n",
" return StopEvent(result=response)"
@@ -268,23 +269,12 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'Indexed 77 documents.'"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"w = RAGWorkflow()\n",
"\n",
"# Ingest the documents\n",
- "await w.run(dirname=\"data\")"
+ "index = await w.run(dirname=\"data\")"
]
},
{
@@ -298,17 +288,19 @@
"text": [
"Query the database with: How was Llama2 trained?\n",
"Retrieved 2 nodes.\n",
- "Llama 2 was trained through a multi-step process that began with pretraining using publicly available online sources. This was followed by the creation of an initial version of Llama 2-Chat through supervised fine-tuning. The model was then iteratively refined using Reinforcement Learning with Human Feedback (RLHF) methodologies, which included techniques like rejection sampling and Proximal Policy Optimization (PPO). \n",
+ "How was Llama2 trained?\n",
+ "Reranked nodes to 2\n",
+ "Llama 2 was trained through a multi-step process that began with pretraining using publicly available online sources. This was followed by the creation of an initial version of Llama 2-Chat through supervised fine-tuning. The model was then iteratively refined using Reinforcement Learning with Human Feedback (RLHF) methodologies, which included rejection sampling and Proximal Policy Optimization (PPO). \n",
"\n",
- "During pretraining, the model utilized an optimized auto-regressive transformer architecture, incorporating robust data cleaning, updated data mixes, and training on a significantly larger dataset of 2 trillion tokens. The training process also involved increased context length and the use of grouped-query attention (GQA) to enhance inference scalability. \n",
+ "During pretraining, the model utilized an optimized auto-regressive transformer architecture, incorporating robust data cleaning, updated data mixes, and training on a significantly larger dataset of 2 trillion tokens. The training process also involved increased context length and the use of grouped-query attention (GQA) to enhance inference scalability.\n",
"\n",
- "The training employed the AdamW optimizer with specific hyperparameters, a cosine learning rate schedule, and gradient clipping. The models were pretrained on Meta’s Research SuperCluster and internal production clusters, utilizing NVIDIA A100 GPUs for the training process."
+ "The training employed the AdamW optimizer with specific hyperparameters, including a cosine learning rate schedule and gradient clipping. The models were pretrained on Meta’s Research SuperCluster and internal production clusters, utilizing NVIDIA A100 GPUs."
]
}
],
"source": [
"# Run a query\n",
- "result = await w.run(query=\"How was Llama2 trained?\")\n",
+ "result = await w.run(query=\"How was Llama2 trained?\", index=index)\n",
"async for chunk in result.async_response_gen():\n",
" print(chunk, end=\"\", flush=True)"
]
diff --git a/docs/docs/examples/workflow/react_agent.ipynb b/docs/docs/examples/workflow/react_agent.ipynb
index c633c836ff7fb..49ba102995d72 100644
--- a/docs/docs/examples/workflow/react_agent.ipynb
+++ b/docs/docs/examples/workflow/react_agent.ipynb
@@ -30,7 +30,7 @@
"source": [
"import os\n",
"\n",
- "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\""
+ "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj--...\""
]
},
{
@@ -218,7 +218,7 @@
" self.memory.put(user_msg)\n",
"\n",
" # clear current reasoning\n",
- " ctx.data[\"current_reasoning\"] = []\n",
+ " await ctx.set(\"current_reasoning\", [])\n",
"\n",
" return PrepEvent()\n",
"\n",
@@ -228,7 +228,7 @@
" ) -> InputEvent:\n",
" # get chat history\n",
" chat_history = self.memory.get()\n",
- " current_reasoning = ctx.data.get(\"current_reasoning\", [])\n",
+ " current_reasoning = await ctx.get(\"current_reasoning\", default=[])\n",
" llm_input = self.formatter.format(\n",
" self.tools, chat_history, current_reasoning=current_reasoning\n",
" )\n",
@@ -244,7 +244,9 @@
"\n",
" try:\n",
" reasoning_step = self.output_parser.parse(response.message.content)\n",
- " ctx.data.get(\"current_reasoning\", []).append(reasoning_step)\n",
+ " (await ctx.get(\"current_reasoning\", default=[])).append(\n",
+ " reasoning_step\n",
+ " )\n",
" if reasoning_step.is_done:\n",
" self.memory.put(\n",
" ChatMessage(\n",
@@ -255,7 +257,9 @@
" result={\n",
" \"response\": reasoning_step.response,\n",
" \"sources\": [*self.sources],\n",
- " \"reasoning\": ctx.data.get(\"current_reasoning\", []),\n",
+ " \"reasoning\": await ctx.get(\n",
+ " \"current_reasoning\", default=[]\n",
+ " ),\n",
" }\n",
" )\n",
" elif isinstance(reasoning_step, ActionReasoningStep):\n",
@@ -271,7 +275,7 @@
" ]\n",
" )\n",
" except Exception as e:\n",
- " ctx.data.get(\"current_reasoning\", []).append(\n",
+ " (await ctx.get(\"current_reasoning\", default=[])).append(\n",
" ObservationReasoningStep(\n",
" observation=f\"There was an error in parsing my reasoning: {e}\"\n",
" )\n",
@@ -291,7 +295,7 @@
" for tool_call in tool_calls:\n",
" tool = tools_by_name.get(tool_call.tool_name)\n",
" if not tool:\n",
- " ctx.data.get(\"current_reasoning\", []).append(\n",
+ " (await ctx.get(\"current_reasoning\", default=[])).append(\n",
" ObservationReasoningStep(\n",
" observation=f\"Tool {tool_call.tool_name} does not exist\"\n",
" )\n",
@@ -301,11 +305,11 @@
" try:\n",
" tool_output = tool(**tool_call.tool_kwargs)\n",
" self.sources.append(tool_output)\n",
- " ctx.data.get(\"current_reasoning\", []).append(\n",
+ " (await ctx.get(\"current_reasoning\", default=[])).append(\n",
" ObservationReasoningStep(observation=tool_output.content)\n",
" )\n",
" except Exception as e:\n",
- " ctx.data.get(\"current_reasoning\", []).append(\n",
+ " (await ctx.get(\"current_reasoning\", default=[])).append(\n",
" ObservationReasoningStep(\n",
" observation=f\"Error calling tool {tool.metadata.get_name()}: {e}\"\n",
" )\n",
@@ -419,7 +423,13 @@
"Step new_user_msg produced event PrepEvent\n",
"Running step prepare_chat_history\n",
"Step prepare_chat_history produced event InputEvent\n",
- "Running step handle_llm_input\n",
+ "Running step handle_llm_input\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"Step handle_llm_input produced event ToolCallEvent\n",
"Running step handle_tool_calls\n",
"Step handle_tool_calls produced event PrepEvent\n",
diff --git a/docs/docs/examples/workflow/reflection.ipynb b/docs/docs/examples/workflow/reflection.ipynb
index 618ba9798a8fd..1dae796bc8559 100644
--- a/docs/docs/examples/workflow/reflection.ipynb
+++ b/docs/docs/examples/workflow/reflection.ipynb
@@ -165,11 +165,11 @@
" async def extract(\n",
" self, ctx: Context, ev: StartEvent | ValidationErrorEvent\n",
" ) -> StopEvent | ExtractionDone:\n",
- " current_retries = ctx.data.get(\"retries\", 0)\n",
+ " current_retries = await ctx.get(\"retries\", default=0)\n",
" if current_retries >= self.max_retries:\n",
" return StopEvent(result=\"Max retries reached\")\n",
" else:\n",
- " ctx.data[\"retries\"] = current_retries + 1\n",
+ " await ctx.set(\"retries\", current_retries + 1)\n",
"\n",
" if isinstance(ev, StartEvent):\n",
" passage = ev.get(\"passage\")\n",
diff --git a/docs/docs/examples/workflow/router_query_engine.ipynb b/docs/docs/examples/workflow/router_query_engine.ipynb
index 90c76569f6639..1727683f54ba3 100644
--- a/docs/docs/examples/workflow/router_query_engine.ipynb
+++ b/docs/docs/examples/workflow/router_query_engine.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -152,10 +152,10 @@
" Selects a single/ multiple query engines based on the query.\n",
" \"\"\"\n",
"\n",
- " ctx.data[\"query\"] = ev.get(\"query\")\n",
- " ctx.data[\"llm\"] = ev.get(\"llm\")\n",
- " ctx.data[\"query_engine_tools\"] = ev.get(\"query_engine_tools\")\n",
- " ctx.data[\"summarizer\"] = ev.get(\"summarizer\")\n",
+ " await ctx.set(\"query\", ev.get(\"query\"))\n",
+ " await ctx.set(\"llm\", ev.get(\"llm\"))\n",
+ " await ctx.set(\"query_engine_tools\", ev.get(\"query_engine_tools\"))\n",
+ " await ctx.set(\"summarizer\", ev.get(\"summarizer\"))\n",
"\n",
" llm = Settings.llm\n",
" select_multiple_query_engines = ev.get(\"select_multi\")\n",
@@ -184,9 +184,9 @@
" ) -> SynthesizeEvent:\n",
" \"\"\"Generate the responses from the selected query engines.\"\"\"\n",
"\n",
- " query = ctx.data.get(\"query\")\n",
+ " query = await ctx.get(\"query\", default=None)\n",
" selected_query_engines = ev.selected_query_engines\n",
- " query_engine_tools = ctx.data[\"query_engine_tools\"]\n",
+ " query_engine_tools = await ctx.get(\"query_engine_tools\")\n",
"\n",
" query_engines = [engine.query_engine for engine in query_engine_tools]\n",
"\n",
@@ -265,8 +265,8 @@
" \"\"\"Synthesizes the responses from the generated responses.\"\"\"\n",
"\n",
" response_generated = ev.result\n",
- " query = ctx.data.get(\"query\")\n",
- " summarizer = ctx.data[\"summarizer\"]\n",
+ " query = await ctx.get(\"query\", default=None)\n",
+ " summarizer = await ctx.get(\"summarizer\")\n",
" selected_query_engines = ev.selected_query_engines\n",
"\n",
" if len(response_generated) > 1:\n",
diff --git a/docs/docs/examples/workflow/self_discover_workflow.ipynb b/docs/docs/examples/workflow/self_discover_workflow.ipynb
index cd4e3e57b6e8d..c38e05bf897b9 100644
--- a/docs/docs/examples/workflow/self_discover_workflow.ipynb
+++ b/docs/docs/examples/workflow/self_discover_workflow.ipynb
@@ -230,7 +230,7 @@
" if task is None or llm is None:\n",
" raise ValueError(\"'task' and 'llm' arguments are required.\")\n",
"\n",
- " ctx.data[\"llm\"] = llm\n",
+ " await ctx.set(\"llm\", llm)\n",
"\n",
" # format prompt and get result from LLM\n",
" prompt = SELECT_PRMOPT_TEMPLATE.format(\n",
@@ -247,7 +247,7 @@
" \"\"\"Refine modules step.\"\"\"\n",
" task = ev.task\n",
" modules = ev.modules\n",
- " llm: LLM = ctx.data[\"llm\"]\n",
+ " llm: LLM = await ctx.get(\"llm\")\n",
"\n",
" # format prompt and get result\n",
" prompt = ADAPT_PROMPT_TEMPLATE.format(\n",
@@ -264,7 +264,7 @@
" \"\"\"Create reasoning structures step.\"\"\"\n",
" task = ev.task\n",
" refined_modules = ev.refined_modules\n",
- " llm: LLM = ctx.data[\"llm\"]\n",
+ " llm: LLM = await ctx.get(\"llm\")\n",
"\n",
" # format prompt, get result\n",
" prompt = IMPLEMENT_PROMPT_TEMPLATE.format(\n",
@@ -283,7 +283,7 @@
" \"\"\"Gets final result from reasoning structure event.\"\"\"\n",
" task = ev.task\n",
" reasoning_structure = ev.reasoning_structure\n",
- " llm: LLM = ctx.data[\"llm\"]\n",
+ " llm: LLM = await ctx.get(\"llm\")\n",
"\n",
" # format prompt, get res\n",
" prompt = REASONING_PROMPT_TEMPLATE.format(\n",
diff --git a/docs/docs/examples/workflow/sub_question_query_engine.ipynb b/docs/docs/examples/workflow/sub_question_query_engine.ipynb
index c40387e3d1ca4..b236e4b003af3 100644
--- a/docs/docs/examples/workflow/sub_question_query_engine.ipynb
+++ b/docs/docs/examples/workflow/sub_question_query_engine.ipynb
@@ -104,16 +104,16 @@
" @step\n",
" async def query(self, ctx: Context, ev: StartEvent) -> QueryEvent:\n",
" if hasattr(ev, \"query\"):\n",
- " ctx.data[\"original_query\"] = ev.query\n",
- " print(f\"Query is {ctx.data['original_query']}\")\n",
+ " await ctx.set(\"original_query\", ev.query)\n",
+ " print(f\"Query is {await ctx.get('original_query')}\")\n",
"\n",
" if hasattr(ev, \"llm\"):\n",
- " ctx.data[\"llm\"] = ev.llm\n",
+ " await ctx.set(\"llm\", ev.llm)\n",
"\n",
" if hasattr(ev, \"tools\"):\n",
- " ctx.data[\"tools\"] = ev.tools\n",
+ " await ctx.set(\"tools\", ev.tools)\n",
"\n",
- " response = ctx.data[\"llm\"].complete(\n",
+ " response = (await ctx.get(\"llm\")).complete(\n",
" f\"\"\"\n",
" Given a user question, and a list of tools, output a list of\n",
" relevant sub-questions, such that the answers to all the\n",
@@ -126,9 +126,9 @@
" \"What is the GDP of San Francisco?\"\n",
" ]\n",
" }}\n",
- " Here is the user question: {ctx.data['original_query']}\n",
+ " Here is the user question: {await ctx.get('original_query')}\n",
"\n",
- " And here is the list of tools: {ctx.data['tools']}\n",
+ " And here is the list of tools: {await ctx.get('tools')}\n",
" \"\"\"\n",
" )\n",
"\n",
@@ -137,7 +137,7 @@
" response_obj = json.loads(str(response))\n",
" sub_questions = response_obj[\"sub_questions\"]\n",
"\n",
- " ctx.data[\"sub_question_count\"] = len(sub_questions)\n",
+ " await ctx.set(\"sub_question_count\", len(sub_questions))\n",
"\n",
" for question in sub_questions:\n",
" self.send_event(QueryEvent(question=question))\n",
@@ -149,7 +149,7 @@
" print(f\"Sub-question is {ev.question}\")\n",
"\n",
" agent = ReActAgent.from_tools(\n",
- " ctx.data[\"tools\"], llm=ctx.data[\"llm\"], verbose=True\n",
+ " await ctx.get(\"tools\"), llm=await ctx.get(\"llm\"), verbose=True\n",
" )\n",
" response = agent.chat(ev.question)\n",
"\n",
@@ -160,7 +160,7 @@
" self, ctx: Context, ev: AnswerEvent\n",
" ) -> StopEvent | None:\n",
" ready = ctx.collect_events(\n",
- " ev, [AnswerEvent] * ctx.data[\"sub_question_count\"]\n",
+ " ev, [AnswerEvent] * await ctx.get(\"sub_question_count\")\n",
" )\n",
" if ready is None:\n",
" return None\n",
@@ -177,7 +177,7 @@
" each of which has been answered. Combine the answers to all the sub-questions\n",
" into a single answer to the original question.\n",
"\n",
- " Original question: {ctx.data['original_query']}\n",
+ " Original question: {await ctx.get('original_query')}\n",
"\n",
" Sub-questions and answers:\n",
" {answers}\n",
@@ -185,7 +185,7 @@
"\n",
" print(f\"Final prompt is {prompt}\")\n",
"\n",
- " response = ctx.data[\"llm\"].complete(prompt)\n",
+ " response = (await ctx.get(\"llm\")).complete(prompt)\n",
"\n",
" print(\"Final response is\", response)\n",
"\n",
diff --git a/docs/docs/examples/workflow/workflows_cookbook.ipynb b/docs/docs/examples/workflow/workflows_cookbook.ipynb
index 4a5a35be9c089..a9af8212f6dee 100644
--- a/docs/docs/examples/workflow/workflows_cookbook.ipynb
+++ b/docs/docs/examples/workflow/workflows_cookbook.ipynb
@@ -59,9 +59,8 @@
"outputs": [],
"source": [
"import os\n",
- "from google.colab import userdata\n",
"\n",
- "os.environ[\"OPENAI_API_KEY\"] = userdata.get(\"openai-key\")"
+ "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\""
]
},
{
@@ -82,7 +81,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "LlamaIndex, previously known as GPT Index, is a data framework designed to facilitate the connection between large language models (LLMs) and external data sources. It provides a suite of tools that enable LLMs to ingest, structure, and query data from various sources such as documents, databases, and APIs. This framework is particularly useful for applications that require LLMs to interact with and retrieve information from large and diverse datasets, enhancing their ability to generate accurate and contextually relevant responses.\n"
+ "LlamaIndex, formerly known as GPT Index, is a data framework designed to facilitate the connection between large language models (LLMs) and external data sources. It provides tools to index various data types, such as documents, databases, and APIs, enabling LLMs to interact with and retrieve information from these sources more effectively. The framework supports the creation of indices that can be queried by LLMs, enhancing their ability to access and utilize external data in a structured manner. This capability is particularly useful for applications requiring the integration of LLMs with specific datasets or knowledge bases.\n"
]
}
],
@@ -114,15 +113,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "trivial_workflow.html\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"draw_all_possible_flows(OpenAIGenerator, filename=\"trivial_workflow.html\")"
]
@@ -247,14 +238,8 @@
"Running step answer_query\n",
"Step answer_query produced event FailedEvent\n",
"Running step improve_query\n",
- "Step improve_query produced event QueryEvent\n",
- "Running step answer_query\n",
- "Step answer_query produced event FailedEvent\n",
- "Running step improve_query\n",
- "Step improve_query produced event QueryEvent\n",
- "Running step answer_query\n",
- "Step answer_query produced event StopEvent\n",
- "The answer to your query\n"
+ "Step improve_query produced event StopEvent\n",
+ "Your query can't be fixed.\n"
]
}
],
@@ -283,14 +268,14 @@
" @step\n",
" async def setup(self, ctx: Context, ev: StartEvent) -> QueryEvent:\n",
" # load our data here\n",
- " ctx.data[\"some_database\"] = [\"value1\", \"value2\", \"value3\"]\n",
+ " await ctx.set(\"some_database\", [\"value1\", \"value2\", \"value3\"])\n",
"\n",
" return QueryEvent(query=ev.query)\n",
"\n",
" @step\n",
" async def query(self, ctx: Context, ev: QueryEvent) -> StopEvent:\n",
" # use our data with our query\n",
- " data = ctx.data[\"some_database\"]\n",
+ " data = await ctx.get(\"some_database\")\n",
"\n",
" result = f\"The answer to your query is {data[1]}\"\n",
" return StopEvent(result=result)"
@@ -336,7 +321,7 @@
" @step\n",
" async def setup(self, ctx: Context, ev: StartEvent) -> StopEvent:\n",
" if hasattr(ev, \"data\"):\n",
- " ctx.data[\"some_database\"] = ev.data\n",
+ " await ctx.set(\"data\", ev.data)\n",
"\n",
" return StopEvent(result=None)\n",
"\n",
@@ -344,8 +329,8 @@
" async def query(self, ctx: Context, ev: StartEvent) -> StopEvent:\n",
" if hasattr(ev, \"query\"):\n",
" # do we have any data?\n",
- " if \"some_database\" in ctx.data:\n",
- " data = ctx.data[\"some_database\"]\n",
+ " if hasattr(self, \"data\"):\n",
+ " data = await ctx.get(\"data\")\n",
" return StopEvent(result=f\"Got the data {data}\")\n",
" else:\n",
" # there's non data yet\n",
@@ -457,10 +442,10 @@
" @step\n",
" async def setup(self, ctx: Context, ev: StartEvent) -> SetupEvent:\n",
" # generically start everything up\n",
- " if \"setup\" not in ctx.data:\n",
- " ctx.data[\"setup\"] = True\n",
+ " if not hasattr(self, \"setup\") or not self.setup:\n",
+ " self.setup = True\n",
" print(\"I got set up\")\n",
- " return SetupEvent(error=False)\n",
+ " return SetupEvent(error=False)\n",
"\n",
" @step\n",
" async def collect_input(self, ev: StartEvent) -> InputEvent:\n",
@@ -479,18 +464,18 @@
" @step\n",
" async def run_query(\n",
" self, ctx: Context, ev: InputEvent | SetupEvent | QueryEvent\n",
- " ) -> StopEvent:\n",
+ " ) -> StopEvent | None:\n",
" ready = ctx.collect_events(ev, [QueryEvent, InputEvent, SetupEvent])\n",
" if ready is None:\n",
" print(\"Not enough events yet\")\n",
- " return StopEvent()\n",
+ " return None\n",
"\n",
" # run the query\n",
" print(\"Now I have all the events\")\n",
" print(ready)\n",
- " return StopEvent(\n",
- " result=f\"Ran query {ready[0].query} on input {ready[1].input}\"\n",
- " )"
+ "\n",
+ " result = f\"Ran query '{ready[0].query}' on input '{ready[1].input}'\"\n",
+ " return StopEvent(result=result)"
]
},
{
@@ -502,26 +487,19 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "I got set up\n",
- "Not enough events yet\n",
- "---\n",
"I got some input\n",
- "Not enough events yet\n",
- "---\n",
"I got a query\n",
+ "Not enough events yet\n",
+ "Not enough events yet\n",
"Now I have all the events\n",
"[QueryEvent(query=\"Here's my question\"), InputEvent(input=\"Here's some input\"), SetupEvent(error=False)]\n",
- "Ran query Here's my question on input Here's some input\n"
+ "Ran query 'Here's my question' on input 'Here's some input'\n"
]
}
],
"source": [
"c = CollectExampleFlow()\n",
- "result = await c.run()\n",
- "print(\"---\")\n",
- "result = await c.run(input=\"Here's some input\")\n",
- "print(\"---\")\n",
- "result = await c.run(query=\"Here's my question\")\n",
+ "result = await c.run(input=\"Here's some input\", query=\"Here's my question\")\n",
"print(result)"
]
},
@@ -556,211 +534,6 @@
""
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can also collect multiple events of the same kind, for instance to monitor failures:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "class FailureEvent(Event):\n",
- " error: str\n",
- "\n",
- "\n",
- "class FailureExampleFlow(Workflow):\n",
- " @step\n",
- " async def try_to_do_thing(\n",
- " self, ev: StartEvent\n",
- " ) -> StopEvent | FailureEvent:\n",
- " random_number = random.randint(0, 10)\n",
- " if random_number < 5:\n",
- " print(\"There was a failure\")\n",
- " return FailureEvent(\n",
- " error=f\"This is the bad place: {random_number}\"\n",
- " )\n",
- " else:\n",
- " print(\"There was success\")\n",
- " return StopEvent(result=\"Success\")\n",
- "\n",
- " @step\n",
- " async def monitor_failures(\n",
- " self, ctx: Context, ev: FailureEvent\n",
- " ) -> StopEvent | None:\n",
- " failed = ctx.collect_events(\n",
- " ev, [FailureEvent, FailureEvent, FailureEvent]\n",
- " )\n",
- " if failed is not None:\n",
- " print(failed)\n",
- " print(\"The simulation has failed!\")\n",
- " return StopEvent(result=\"Failure\")\n",
- "\n",
- " return StopEvent()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "There was a failure\n",
- "==== None\n",
- "There was success\n",
- "==== Success\n",
- "There was success\n",
- "==== Success\n",
- "There was success\n",
- "==== Success\n",
- "There was a failure\n",
- "==== None\n",
- "There was a failure\n",
- "[FailureEvent(error='This is the bad place: 2'), FailureEvent(error='This is the bad place: 3'), FailureEvent(error='This is the bad place: 0')]\n",
- "The simulation has failed!\n"
- ]
- }
- ],
- "source": [
- "f = FailureExampleFlow(verbose=False)\n",
- "result = await f.run()\n",
- "print(\"====\", result)\n",
- "result = await f.run()\n",
- "print(\"====\", result)\n",
- "result = await f.run()\n",
- "print(\"====\", result)\n",
- "result = await f.run()\n",
- "print(\"====\", result)\n",
- "result = await f.run()\n",
- "print(\"====\", result)\n",
- "result = await f.run()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As you can see, it didn't trigger system failure until the third failure event. The visualization here is again pretty simple."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "failure_workflow.html\n"
- ]
- }
- ],
- "source": [
- "draw_all_possible_flows(FailureExampleFlow, \"failure_workflow.html\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Stepping through workflows\n",
- "\n",
- "You can also walk through a workflow step-by-step:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Kick off the workflow\n",
- "f3 = FailureExampleFlow(verbose=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Running step try_to_do_thing\n",
- "There was a failure\n",
- "Step try_to_do_thing produced event FailureEvent\n"
- ]
- }
- ],
- "source": [
- "await f3.run_step()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Running step monitor_failures\n",
- "Step monitor_failures produced event StopEvent\n"
- ]
- }
- ],
- "source": [
- "await f3.run_step()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can also use visualization to show just the most recent execution rather than all possible flows, even when the flow is mid-execution:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "last_execution.html\n"
- ]
- }
- ],
- "source": [
- "draw_most_recent_execution(f3, \"last_execution.html\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -778,7 +551,15 @@
"name": "python3"
},
"language_info": {
- "name": "python"
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
}
},
"nbformat": 4,
diff --git a/docs/docs/getting_started/concepts.md b/docs/docs/getting_started/concepts.md
index 09a2375c5a342..cfca32f608ef8 100644
--- a/docs/docs/getting_started/concepts.md
+++ b/docs/docs/getting_started/concepts.md
@@ -6,7 +6,7 @@ This is a quick guide to the high-level concepts you'll encounter frequently whe
There are endless use cases for data-backed LLM applications but they can be roughly grouped into four categories:
-[**Structured Data Extraction**](../use_cases/extraction/)
+[**Structured Data Extraction**](../use_cases/extraction.md/)
Pydantic extractors allow you to specify a precise data structure to extract from your data and use LLMs to fill in the missing pieces in a type-safe way. This is useful for extracting structured data from unstructured sources like PDFs, websites, and more, and is key to automating workflows.
[**Query Engines**](../module_guides/deploying/query_engine/index.md):
diff --git a/docs/docs/index.md b/docs/docs/index.md
index 78c7cf2cdd29e..e76155f250bc6 100644
--- a/docs/docs/index.md
+++ b/docs/docs/index.md
@@ -2,7 +2,7 @@
# Welcome to LlamaIndex 🦙 !
-LlamaIndex is a framework for building context-augmented generative AI applications with [LLMs](https://en.wikipedia.org/wiki/Large_language_model) including [agents](./understanding/agent/basic_agent/) and workflows.
+LlamaIndex is a framework for building context-augmented generative AI applications with [LLMs](https://en.wikipedia.org/wiki/Large_language_model) including [agents](./understanding/agent/basic_agent/) and [workflows](./understanding/workflows/).
"
+ ""
]
},
{
diff --git a/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/image/base.py b/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/image/base.py
index b414c1964022e..ff175e6eef105 100644
--- a/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/image/base.py
+++ b/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/image/base.py
@@ -82,7 +82,7 @@ def load_data(
# load document image
if fs:
with fs.open(path=file) as f:
- image = Image.open(f.read())
+ image = Image.open(BytesIO(f.read()))
else:
image = Image.open(file)
diff --git a/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml
index 87617d5196262..816cfc838c1ce 100644
--- a/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml
+++ b/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml
@@ -51,7 +51,7 @@ license = "MIT"
maintainers = ["FarisHijazi", "Haowjy", "ephe-meral", "hursh-desai", "iamarunbrahma", "jon-chuang", "mmaatouk", "ravi03071991", "sangwongenip", "thejessezhang"]
name = "llama-index-readers-file"
readme = "README.md"
-version = "0.2.0"
+version = "0.2.1"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
diff --git a/llama-index-integrations/readers/llama-index-readers-llama-parse/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-llama-parse/pyproject.toml
index e367ef9afc939..d6c2371a71827 100644
--- a/llama-index-integrations/readers/llama-index-readers-llama-parse/pyproject.toml
+++ b/llama-index-integrations/readers/llama-index-readers-llama-parse/pyproject.toml
@@ -28,11 +28,11 @@ keywords = ["PDF", "llama", "llama-parse", "parse"]
license = "MIT"
name = "llama-index-readers-llama-parse"
readme = "README.md"
-version = "0.2.0"
+version = "0.3.0"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
-llama-parse = ">=0.4.0"
+llama-parse = ">=0.5.0"
llama-index-core = "^0.11.0"
[tool.poetry.group.dev.dependencies]
diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py
index 48910873fdfc4..f2b4e4c16fc74 100644
--- a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py
+++ b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py
@@ -116,12 +116,17 @@ def _get_access_token(self) -> str:
data=payload,
)
- if response.status_code == 200 and "access_token" in response.json():
- return response.json()["access_token"]
+ json_response = response.json()
+
+ if response.status_code == 200 and "access_token" in json_response:
+ return json_response["access_token"]
else:
- logger.error(response.json()["error"])
- raise ValueError(response.json()["error_description"])
+ error_message = json_response.get("error_description") or json_response.get(
+ "error"
+ )
+ logger.error("Error retrieving access token: %s", json_response["error"])
+ raise ValueError(f"Error retrieving access token: {error_message}")
def _get_site_id_with_host_name(
self, access_token, sharepoint_site_name: Optional[str]
@@ -178,10 +183,11 @@ def _get_site_id_with_host_name(
f"The specified sharepoint site {sharepoint_site_name} is not found."
)
else:
- if "error_description" in json_response:
- logger.error(json_response["error"])
- raise ValueError(json_response["error_description"])
- raise ValueError(json_response["error"])
+ error_message = json_response.get(
+ "error_description"
+ ) or json_response.get("error")
+ logger.error("Error retrieving site ID: %s", json_response["error"])
+ raise ValueError(f"Error retrieving site ID: {error_message}")
raise ValueError(
f"The specified sharepoint site {sharepoint_site_name} is not found."
@@ -209,26 +215,27 @@ def _get_drive_id(self) -> str:
url=self._drive_id_endpoint,
headers=self._authorization_headers,
)
+ json_response = response.json()
- if response.status_code == 200 and "value" in response.json():
- if len(response.json()["value"]) > 0 and self.drive_name is not None:
- for drive in response.json()["value"]:
+ if response.status_code == 200 and "value" in json_response:
+ if len(json_response["value"]) > 0 and self.drive_name is not None:
+ for drive in json_response["value"]:
if drive["name"].lower() == self.drive_name.lower():
return drive["id"]
raise ValueError(f"The specified drive {self.drive_name} is not found.")
- if (
- len(response.json()["value"]) > 0
- and "id" in response.json()["value"][0]
- ):
- return response.json()["value"][0]["id"]
+ if len(json_response["value"]) > 0 and "id" in json_response["value"][0]:
+ return json_response["value"][0]["id"]
else:
raise ValueError(
"Error occurred while fetching the drives for the sharepoint site."
)
else:
- logger.error(response.json()["error"])
- raise ValueError(response.json()["error_description"])
+ error_message = json_response.get("error_description") or json_response.get(
+ "error"
+ )
+ logger.error("Error retrieving drive ID: %s", json_response["error"])
+ raise ValueError(f"Error retrieving drive ID: {error_message}")
def _get_sharepoint_folder_id(self, folder_path: str) -> str:
"""
@@ -252,7 +259,9 @@ def _get_sharepoint_folder_id(self, folder_path: str) -> str:
if response.status_code == 200 and "id" in response.json():
return response.json()["id"]
else:
- raise ValueError(response.json()["error"])
+ error_message = response.json().get("error", "Unknown error")
+ logger.error("Error retrieving folder ID: %s", error_message)
+ raise ValueError(f"Error retrieving folder ID: {error_message}")
def _download_files_and_extract_metadata(
self,
@@ -300,9 +309,14 @@ def _get_file_content_by_url(self, item: Dict[str, Any]) -> bytes:
"""
file_download_url = item["@microsoft.graph.downloadUrl"]
response = requests.get(file_download_url)
+
if response.status_code != 200:
- logger.error(response.json()["error"])
- raise ValueError(response.json()["error_description"])
+ json_response = response.json()
+ error_message = json_response.get("error_description") or json_response.get(
+ "error"
+ )
+ logger.error("Error downloading file content: %s", json_response["error"])
+ raise ValueError(f"Error downloading file content: {error_message}")
return response.content
@@ -788,10 +802,8 @@ def load_resource(self, resource_id: str, **kwargs) -> List[Document]:
item = self._get_item_from_path(path)
- input_file_dir = path.parent
-
with tempfile.TemporaryDirectory() as temp_dir:
- metadata = self._download_file(item, temp_dir, input_file_dir)
+ metadata = self._download_file(item, temp_dir)
return self._load_documents_with_metadata(
metadata, temp_dir, recursive=False
)
diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml
index b4ab9d40b29ca..2565ce2196900 100644
--- a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml
+++ b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml
@@ -29,7 +29,7 @@ license = "MIT"
maintainers = ["arun-soliton"]
name = "llama-index-readers-microsoft-sharepoint"
readme = "README.md"
-version = "0.3.0"
+version = "0.3.1"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
diff --git a/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml
index 1d3c095dd740c..e578c84a84338 100644
--- a/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml
+++ b/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml
@@ -12,7 +12,7 @@ contains_example = false
import_path = "llama_index.readers.minio"
[tool.llamahub.class_authors]
-BotoMinioReader = "llama-index"
+BotoMinioReader = "webcoderz"
MinioReader = "llama-index"
[tool.mypy]
diff --git a/llama-index-integrations/readers/llama-index-readers-mongodb/README.md b/llama-index-integrations/readers/llama-index-readers-mongodb/README.md
index 385fa0d23c8de..9533946d476d0 100644
--- a/llama-index-integrations/readers/llama-index-readers-mongodb/README.md
+++ b/llama-index-integrations/readers/llama-index-readers-mongodb/README.md
@@ -15,7 +15,7 @@ pip install llama-index-readers-mongodb
### Usage
```python
-from llama_index.readers.mongo import SimpleMongoReader
+from llama_index.readers.mongodb import SimpleMongoReader
# Initialize SimpleMongoReader
reader = SimpleMongoReader(
diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/firecrawl_web/base.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/firecrawl_web/base.py
index ebdc4d98cf69a..7367dc57e3e5a 100644
--- a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/firecrawl_web/base.py
+++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/firecrawl_web/base.py
@@ -87,6 +87,7 @@ def load_data(
)
elif self.mode == "crawl":
firecrawl_docs = self.firecrawl.crawl_url(url, params=self.params)
+ firecrawl_docs = firecrawl_docs.get("data", [])
for doc in firecrawl_docs:
documents.append(
Document(
diff --git a/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml
index 18d8e29a5958f..eb38ea0f97036 100644
--- a/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml
+++ b/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml
@@ -45,7 +45,7 @@ license = "MIT"
maintainers = ["HawkClaws", "Hironsan", "NA", "an-bluecat", "bborn", "jasonwcfan", "kravetsmic", "pandazki", "ruze00", "selamanse", "thejessezhang"]
name = "llama-index-readers-web"
readme = "README.md"
-version = "0.2.1"
+version = "0.2.2"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/.gitignore b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/.gitignore
new file mode 100644
index 0000000000000..990c18de22908
--- /dev/null
+++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/.gitignore
@@ -0,0 +1,153 @@
+llama_index/_static
+.DS_Store
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+bin/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+etc/
+include/
+lib/
+lib64/
+parts/
+sdist/
+share/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+.ruff_cache
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+notebooks/
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+pyvenv.cfg
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Jetbrains
+.idea
+modules/
+*.swp
+
+# VsCode
+.vscode
+
+# pipenv
+Pipfile
+Pipfile.lock
+
+# pyright
+pyrightconfig.json
diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/BUILD b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/BUILD
new file mode 100644
index 0000000000000..0896ca890d8bf
--- /dev/null
+++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/BUILD
@@ -0,0 +1,3 @@
+poetry_requirements(
+ name="poetry",
+)
diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/Makefile b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/Makefile
new file mode 100644
index 0000000000000..b9eab05aa3706
--- /dev/null
+++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/Makefile
@@ -0,0 +1,17 @@
+GIT_ROOT ?= $(shell git rev-parse --show-toplevel)
+
+help: ## Show all Makefile targets.
+ @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}'
+
+format: ## Run code autoformatters (black).
+ pre-commit install
+ git ls-files | xargs pre-commit run black --files
+
+lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy
+ pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files
+
+test: ## Run tests via pytest.
+ pytest tests
+
+watch-docs: ## Build and watch documentation.
+ sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/
diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/README.md b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/README.md
new file mode 100644
index 0000000000000..50ad75f53b45b
--- /dev/null
+++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/README.md
@@ -0,0 +1,99 @@
+# LlamaIndex Selectors Integration: NotDiamond
+
+[Not Diamond](https://notdiamond.ai) offers an AI-powered model router that automatically determines which LLM is best suited to respond to any query, improving LLM output quality by combining multiple LLMs into a **meta-model** that learns when to call each LLM.
+
+Not Diamond supports cost and latency tradeoffs, customized router training, and real-time router personalization. Learn more via [the documentation](https://notdiamond.readme.io/).
+
+## Installation
+
+```shell
+pip install llama-index-selectors-notdiamond
+```
+
+## Configuration
+
+- API keys: You need API keys from [Not Diamond](https://app.notdiamond.ai/keys) and any LLM you want to use.
+
+## Quick Start
+
+```python
+import os
+from typing import List
+
+from llama_index.core import (
+ SimpleDirectoryReader,
+ VectorStoreIndex,
+ SummaryIndex,
+ Settings,
+)
+from llama_index.core.query_engine import RouterQueryEngine
+from llama_index.core.tools import QueryEngineTool
+from llama_index.selectors.notdiamond.base import NotDiamondSelector
+
+from notdiamond import NotDiamond
+
+
+# Set up your API keys
+os.environ["OPENAI_API_KEY"] = "sk-..."
+os.environ["ANTHROPIC_API_KEY"] = "sk-ant-..."
+os.environ["NOTDIAMOND_API_KEY"] = "sk-..."
+
+
+# Create indexes
+documents = SimpleDirectoryReader("data/paul_graham").load_data()
+nodes = Settings.node_parser.get_nodes_from_documents(documents)
+
+vector_index = VectorStoreIndex.from_documents(documents)
+summary_index = SummaryIndex.from_documents(documents)
+query_text = "What was Paul Graham's role at Yahoo?"
+
+
+# Set up Tools for the QueryEngine
+list_query_engine = summary_index.as_query_engine(
+ response_mode="tree_summarize",
+ use_async=True,
+)
+vector_query_engine = vector_index.as_query_engine()
+
+list_tool = QueryEngineTool.from_defaults(
+ query_engine=list_query_engine,
+ description=(
+ "Useful for summarization questions related to Paul Graham eassy on"
+ " What I Worked On."
+ ),
+)
+
+vector_tool = QueryEngineTool.from_defaults(
+ query_engine=vector_query_engine,
+ description=(
+ "Useful for retrieving specific context from Paul Graham essay on What"
+ " I Worked On."
+ ),
+)
+
+
+# Create a NotDiamondSelector and RouterQueryEngine
+client = NotDiamond(
+ api_key=os.environ["NOTDIAMOND_API_KEY"],
+ llm_configs=["openai/gpt-4o", "anthropic/claude-3-5-sonnet-20240620"],
+)
+preference_id = client.create_preference_id()
+client.preference_id = preference_id
+
+nd_selector = NotDiamondSelector(client=client)
+
+query_engine = RouterQueryEngine(
+ selector=nd_selector,
+ query_engine_tools=[
+ list_tool,
+ vector_tool,
+ ],
+)
+
+
+# Use Not Diamond to Query Indexes
+response = query_engine.query(
+ "Please summarize Paul Graham's working experience."
+)
+print(str(response))
+```
diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/BUILD b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/BUILD
new file mode 100644
index 0000000000000..db46e8d6c978c
--- /dev/null
+++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/BUILD
@@ -0,0 +1 @@
+python_sources()
diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/__init__.py b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/__init__.py
new file mode 100644
index 0000000000000..6f2a506585d89
--- /dev/null
+++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/__init__.py
@@ -0,0 +1,4 @@
+from llama_index.selectors.notdiamond.base import NotDiamondSelector
+
+
+__all__ = ["NotDiamondSelector"]
diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/base.py b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/base.py
new file mode 100644
index 0000000000000..083858c54baeb
--- /dev/null
+++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/base.py
@@ -0,0 +1,178 @@
+import logging
+import os
+from typing import Sequence
+
+from llama_index.core.llms.llm import LLM
+from llama_index.core.schema import QueryBundle
+from llama_index.core.tools.types import ToolMetadata
+from llama_index.core.base.base_selector import SelectorResult
+from llama_index.core.selectors import LLMSingleSelector
+from llama_index.core.selectors.llm_selectors import _build_choices_text
+
+from notdiamond import NotDiamond, LLMConfig, Metric
+
+LOGGER = logging.getLogger(__name__)
+LOGGER.setLevel(logging.WARNING)
+
+
+class NotDiamondSelectorResult(SelectorResult):
+ """A single selection of a choice provided by Not Diamond."""
+
+ class Config:
+ arbitrary_types_allowed = True
+
+ session_id: str
+ llm: LLMConfig
+
+ @classmethod
+ def from_selector_result(
+ cls, selector_result: SelectorResult, session_id: str, best_llm: LLMConfig
+ ) -> "NotDiamondSelectorResult":
+ return cls(session_id=session_id, llm=best_llm, **selector_result.dict())
+
+
+class NotDiamondSelector(LLMSingleSelector):
+ def __init__(
+ self,
+ client: NotDiamond,
+ metric: Metric = None,
+ timeout: int = 10,
+ api_key: str = None,
+ *args,
+ **kwargs,
+ ):
+ """
+ Initialize a NotDiamondSelector. Users should instantiate and configure a NotDiamond client as needed before
+ creating this selector. The constructor will raise errors re: required client fields.
+ """
+ # Not needed - we will route using our own client based on the query prompt
+ # Add @property for _llm here
+ _encap_selector = LLMSingleSelector.from_defaults()
+ self._llm = None
+ self._prompt = _encap_selector._prompt
+
+ if not getattr(client, "llm_configs", None):
+ raise ValueError(
+ "NotDiamond client must have llm_configs before creating a NotDiamondSelector."
+ )
+
+ if metric and not isinstance(metric, Metric):
+ raise ValueError(f"Invalid metric - needed type Metric but got {metric}")
+ self._metric = metric or Metric("accuracy")
+
+ self._client = client
+ self._llms = [
+ self._llm_config_to_client(llm_config)
+ for llm_config in self._client.llm_configs
+ ]
+ self._timeout = timeout
+ super().__init__(_encap_selector._llm, _encap_selector._prompt, *args, **kwargs)
+
+ def _llm_config_to_client(self, llm_config: LLMConfig | str) -> LLM:
+ """
+ For the selected LLMConfig dynamically create an LLM instance. NotDiamondSelector will
+ assign this to self._llm to help select the best index.
+ """
+ if isinstance(llm_config, str):
+ llm_config = LLMConfig.from_string(llm_config)
+ provider, model = llm_config.provider, llm_config.model
+
+ output = None
+ if provider == "openai":
+ from llama_index.llms.openai import OpenAI
+
+ output = OpenAI(model=model, api_key=os.getenv("OPENAI_API_KEY"))
+ elif provider == "anthropic":
+ from llama_index.llms.anthropic import Anthropic
+
+ output = Anthropic(model=model, api_key=os.getenv("ANTHROPIC_API_KEY"))
+ elif provider == "cohere":
+ from llama_index.llms.cohere import Cohere
+
+ output = Cohere(model=model, api_key=os.getenv("COHERE_API_KEY"))
+ elif provider == "mistral":
+ from llama_index.llms.mistralai import MistralAI
+
+ output = MistralAI(model=model, api_key=os.getenv("MISTRALAI_API_KEY"))
+ elif provider == "togetherai":
+ from llama_index.llms.together import TogetherLLM
+
+ output = TogetherLLM(model=model, api_key=os.getenv("TOGETHERAI_API_KEY"))
+ else:
+ raise ValueError(f"Unsupported provider for NotDiamondSelector: {provider}")
+
+ return output
+
+ def _select(
+ self, choices: Sequence[ToolMetadata], query: QueryBundle, timeout: int = None
+ ) -> SelectorResult:
+ """
+ Call Not Diamond to select the best LLM for the given prompt, then have the LLM select the best tool.
+ """
+ messages = [
+ {"role": "system", "content": self._format_prompt(choices, query)},
+ {"role": "user", "content": query.query_str},
+ ]
+
+ session_id, best_llm = self._client.model_select(
+ messages=messages,
+ llm_configs=self._client.llm_configs,
+ metric=self._metric,
+ notdiamond_api_key=self._client.api_key,
+ max_model_depth=self._client.max_model_depth,
+ hash_content=self._client.hash_content,
+ tradeoff=self._client.tradeoff,
+ preference_id=self._client.preference_id,
+ tools=self._client.tools,
+ timeout=timeout or self._timeout,
+ )
+
+ self._llm = self._llm_config_to_client(best_llm)
+
+ return NotDiamondSelectorResult.from_selector_result(
+ super()._select(choices, query), session_id, best_llm
+ )
+
+ async def _aselect(
+ self, choices: Sequence[ToolMetadata], query: QueryBundle, timeout: int = None
+ ) -> SelectorResult:
+ """
+ Call Not Diamond asynchronously to select the best LLM for the given prompt, then have the LLM select the best tool.
+ """
+ messages = [
+ {"role": "system", "content": self._format_prompt(choices, query)},
+ {"role": "user", "content": query.query_str},
+ ]
+
+ session_id, best_llm = await self._client.amodel_select(
+ messages=messages,
+ llm_configs=self._client.llm_configs,
+ metric=self._metric,
+ notdiamond_api_key=self._client.api_key,
+ max_model_depth=self._client.max_model_depth,
+ hash_content=self._client.hash_content,
+ tradeoff=self._client.tradeoff,
+ preference_id=self._client.preference_id,
+ tools=self._client.tools,
+ timeout=timeout or self._timeout,
+ )
+
+ self._llm = self._llm_config_to_client(best_llm)
+
+ return NotDiamondSelectorResult.from_selector_result(
+ await super()._aselect(choices, query), session_id, best_llm
+ )
+
+ def _format_prompt(
+ self, choices: Sequence[ToolMetadata], query: QueryBundle
+ ) -> str:
+ """
+ A system prompt for selection is created when instantiating the parent LLMSingleSelector class.
+ This method formats the prompt into a str so that it can be serialized for the NotDiamond API.
+ """
+ context_list = _build_choices_text(choices)
+ return self._prompt.format(
+ num_choices=len(choices),
+ context_list=context_list,
+ query_str=query.query_str,
+ )
diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/pyproject.toml b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/pyproject.toml
new file mode 100644
index 0000000000000..7cfb12f5b3205
--- /dev/null
+++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/pyproject.toml
@@ -0,0 +1,56 @@
+[build-system]
+build-backend = "poetry.core.masonry.api"
+requires = ["poetry-core"]
+
+[tool.codespell]
+check-filenames = true
+check-hidden = true
+# Feel free to un-skip examples, and experimental, you will just need to
+# work through many typos (--write-changes and --interactive will help)
+skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb"
+
+[tool.llamahub]
+contains_example = true
+import_path = "llama_index.selectors.notdiamond"
+
+[tool.llamahub.class_authors]
+NotDiamondSelector = "acompa"
+
+[tool.mypy]
+disallow_untyped_defs = true
+# Remove venv skip when integrated with pre-commit
+exclude = ["_static", "build", "examples", "notebooks", "venv"]
+ignore_missing_imports = true
+python_version = "3.11"
+
+[tool.poetry]
+authors = ["Not Diamond \n",
+ "\n",
"\n",
"This tutorial walks through using the LLM tools provided by the [Exa API](https://exa.ai) to allow LLMs to use semantic queries to search for and retrieve rich web content from the internet.\n",
"\n",
diff --git a/llama-index-integrations/tools/llama-index-tools-tavily-research/examples/tavily.ipynb b/llama-index-integrations/tools/llama-index-tools-tavily-research/examples/tavily.ipynb
index 587088f27e472..d42ecb0286f8f 100644
--- a/llama-index-integrations/tools/llama-index-tools-tavily-research/examples/tavily.ipynb
+++ b/llama-index-integrations/tools/llama-index-tools-tavily-research/examples/tavily.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Building a Tavily Data Agent\n",
"\n",
- "\n",
+ "\n",
"\n",
"This tutorial walks through using the LLM tools provided by the [Tavily API](https://app.tavily.com/) to allow LLMs to easily search and retrieve relevant content from the Internet.\n",
"\n",
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/.gitignore b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/.gitignore
new file mode 100644
index 0000000000000..990c18de22908
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/.gitignore
@@ -0,0 +1,153 @@
+llama_index/_static
+.DS_Store
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+bin/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+etc/
+include/
+lib/
+lib64/
+parts/
+sdist/
+share/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+.ruff_cache
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+notebooks/
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+pyvenv.cfg
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Jetbrains
+.idea
+modules/
+*.swp
+
+# VsCode
+.vscode
+
+# pipenv
+Pipfile
+Pipfile.lock
+
+# pyright
+pyrightconfig.json
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/BUILD
new file mode 100644
index 0000000000000..0896ca890d8bf
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/BUILD
@@ -0,0 +1,3 @@
+poetry_requirements(
+ name="poetry",
+)
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/Makefile b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/Makefile
new file mode 100644
index 0000000000000..b9eab05aa3706
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/Makefile
@@ -0,0 +1,17 @@
+GIT_ROOT ?= $(shell git rev-parse --show-toplevel)
+
+help: ## Show all Makefile targets.
+ @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}'
+
+format: ## Run code autoformatters (black).
+ pre-commit install
+ git ls-files | xargs pre-commit run black --files
+
+lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy
+ pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files
+
+test: ## Run tests via pytest.
+ pytest tests
+
+watch-docs: ## Build and watch documentation.
+ sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/README.md b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/README.md
new file mode 100644
index 0000000000000..ae0f783f8498e
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/README.md
@@ -0,0 +1,65 @@
+# Azure Cosmos DB for NoSQL Vector Store
+
+This integration makes possible to use [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/en-us/azure/cosmos-db/nosql/)
+as a vector store in LlamaIndex.
+
+## Quick start
+
+Install the integration with:
+
+```sh
+pip install llama-index-vector-stores-azurecosmosnosql
+```
+
+Create the CosmosDB client:
+
+```python
+URI = "AZURE_COSMOSDB_URI"
+KEY = "AZURE_COSMOSDB_KEY"
+client = CosmosClient(URI, credential=KEY)
+```
+
+Specify the vector store properties:
+
+```python
+indexing_policy = {
+ "indexingMode": "consistent",
+ "includedPaths": [{"path": "/*"}],
+ "excludedPaths": [{"path": '/"_etag"/?'}],
+ "vectorIndexes": [{"path": "/embedding", "type": "quantizedFlat"}],
+}
+
+vector_embedding_policy = {
+ "vectorEmbeddings": [
+ {
+ "path": "/embedding",
+ "dataType": "float32",
+ "distanceFunction": "cosine",
+ "dimensions": 3072,
+ }
+ ]
+}
+```
+
+Create the vector store:
+
+```python
+store = AzureCosmosDBNoSqlVectorSearch(
+ cosmos_client=client,
+ vector_embedding_policy=vector_embedding_policy,
+ indexing_policy=indexing_policy,
+ cosmos_container_properties={"partition_key": PartitionKey(path="/id")},
+ cosmos_database_properties={},
+ create_container=True,
+)
+```
+
+Finally, create the index from a list containing documents:
+
+```python
+storage_context = StorageContext.from_defaults(vector_store=store)
+
+index = VectorStoreIndex.from_documents(
+ documents, storage_context=storage_context
+)
+```
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/BUILD
new file mode 100644
index 0000000000000..db46e8d6c978c
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/BUILD
@@ -0,0 +1 @@
+python_sources()
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/__init__.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/__init__.py
new file mode 100644
index 0000000000000..74da620f48e9d
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/__init__.py
@@ -0,0 +1,5 @@
+from llama_index.vector_stores.azurecosmosnosql.base import (
+ AzureCosmosDBNoSqlVectorSearch,
+)
+
+__all__ = ["AzureCosmosDBNoSqlVectorSearch"]
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/base.py
new file mode 100644
index 0000000000000..7bb209946c723
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/base.py
@@ -0,0 +1,251 @@
+"""Azure CosmosDB NoSQL vCore Vector store index.
+
+An index that is built on top of an existing vector store.
+
+"""
+import logging
+from typing import Any, Optional, Dict, cast, List
+
+from azure.cosmos import CosmosClient
+from llama_index.core.bridge.pydantic import PrivateAttr
+from llama_index.core.schema import BaseNode, MetadataMode
+from llama_index.core.vector_stores.types import (
+ BasePydanticVectorStore,
+ VectorStoreQuery,
+ VectorStoreQueryResult,
+)
+from llama_index.core.vector_stores.utils import (
+ metadata_dict_to_node,
+ node_to_metadata_dict,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class AzureCosmosDBNoSqlVectorSearch(BasePydanticVectorStore):
+ """Azure CosmosDB NoSQL vCore Vector Store.
+
+ To use, you should have both:
+ -the ``azure-cosmos`` python package installed
+ -from llama_index.vector_stores.azurecosmosnosql import AzureCosmosDBNoSqlVectorSearch
+ """
+
+ stores_text: bool = True
+ flat_metadata: bool = True
+
+ _cosmos_client: Any = PrivateAttr()
+ _database_name: Any = PrivateAttr()
+ _container_name: Any = PrivateAttr()
+ _embedding_key: Any = PrivateAttr()
+ _vector_embedding_policy: Any = PrivateAttr()
+ _indexing_policy: Any = PrivateAttr()
+ _cosmos_container_properties: Any = PrivateAttr()
+ _cosmos_database_properties: Any = PrivateAttr()
+ _create_container: Any = PrivateAttr()
+ _database: Any = PrivateAttr()
+ _container: Any = PrivateAttr()
+ _id_key: Any = PrivateAttr()
+ _text_key: Any = PrivateAttr()
+ _metadata_key: Any = PrivateAttr()
+
+ def __init__(
+ self,
+ cosmos_client: CosmosClient,
+ vector_embedding_policy: Dict[str, Any],
+ indexing_policy: Dict[str, Any],
+ cosmos_container_properties: Dict[str, Any],
+ cosmos_database_properties: Optional[Dict[str, Any]] = None,
+ database_name: str = "vectorSearchDB",
+ container_name: str = "vectorSearchContainer",
+ create_container: bool = True,
+ id_key: str = "id",
+ text_key: str = "text",
+ metadata_key: str = "metadata",
+ **kwargs: Any,
+ ) -> None:
+ """Initialize the vector store.
+
+ Args:
+ cosmos_client: Client used to connect to azure cosmosdb no sql account.
+ database_name: Name of the database to be created.
+ container_name: Name of the container to be created.
+ embedding: Text embedding model to use.
+ vector_embedding_policy: Vector Embedding Policy for the container.
+ indexing_policy: Indexing Policy for the container.
+ cosmos_container_properties: Container Properties for the container.
+ cosmos_database_properties: Database Properties for the container.
+ """
+ super().__init__()
+
+ if cosmos_client is not None:
+ self._cosmos_client = cast(CosmosClient, cosmos_client)
+
+ if create_container:
+ if (
+ indexing_policy["vectorIndexes"] is None
+ or len(indexing_policy["vectorIndexes"]) == 0
+ ):
+ raise ValueError(
+ "vectorIndexes cannot be null or empty in the indexing_policy."
+ )
+ if (
+ vector_embedding_policy is None
+ or len(vector_embedding_policy["vectorEmbeddings"]) == 0
+ ):
+ raise ValueError(
+ "vectorEmbeddings cannot be null "
+ "or empty in the vector_embedding_policy."
+ )
+ if (
+ cosmos_container_properties is None
+ or cosmos_container_properties["partition_key"] is None
+ ):
+ raise ValueError(
+ "partition_key cannot be null " "or empty for a container."
+ )
+
+ self._database_name = database_name
+ self._container_name = container_name
+ self._vector_embedding_policy = vector_embedding_policy
+ self._indexing_policy = indexing_policy
+ self._cosmos_container_properties = cosmos_container_properties
+ self._cosmos_database_properties = cosmos_database_properties
+ self._id_key = id_key
+ self._text_key = text_key
+ self._metadata_key = metadata_key
+ self._embedding_key = self._vector_embedding_policy["vectorEmbeddings"][0][
+ "path"
+ ][1:]
+
+ self._database = self._cosmos_client.create_database_if_not_exists(
+ id=self._database_name,
+ offer_throughput=self._cosmos_database_properties.get("offer_throughput"),
+ session_token=self._cosmos_database_properties.get("session_token"),
+ initial_headers=self._cosmos_database_properties.get("initial_headers"),
+ etag=self._cosmos_database_properties.get("etag"),
+ match_condition=self._cosmos_database_properties.get("match_condition"),
+ )
+
+ # Create the collection if it already doesn't exist
+ self._container = self._database.create_container_if_not_exists(
+ id=self._container_name,
+ partition_key=self._cosmos_container_properties["partition_key"],
+ indexing_policy=self._indexing_policy,
+ default_ttl=self._cosmos_container_properties.get("default_ttl"),
+ offer_throughput=self._cosmos_container_properties.get("offer_throughput"),
+ unique_key_policy=self._cosmos_container_properties.get(
+ "unique_key_policy"
+ ),
+ conflict_resolution_policy=self._cosmos_container_properties.get(
+ "conflict_resolution_policy"
+ ),
+ analytical_storage_ttl=self._cosmos_container_properties.get(
+ "analytical_storage_ttl"
+ ),
+ computed_properties=self._cosmos_container_properties.get(
+ "computed_properties"
+ ),
+ etag=self._cosmos_container_properties.get("etag"),
+ match_condition=self._cosmos_container_properties.get("match_condition"),
+ session_token=self._cosmos_container_properties.get("session_token"),
+ initial_headers=self._cosmos_container_properties.get("initial_headers"),
+ vector_embedding_policy=self._vector_embedding_policy,
+ )
+
+ def add(
+ self,
+ nodes: List[BaseNode],
+ **add_kwargs: Any,
+ ) -> List[str]:
+ """Add nodes to index.
+
+ Args:
+ nodes: List[BaseNode]: list of nodes with embeddings
+
+ Returns:
+ A List of ids for successfully added nodes.
+
+ """
+ ids = []
+ data_to_insert = []
+
+ if not nodes:
+ raise Exception("Texts can not be null or empty")
+
+ for node in nodes:
+ metadata = node_to_metadata_dict(
+ node, remove_text=True, flat_metadata=self.flat_metadata
+ )
+
+ entry = {
+ self._id_key: node.node_id,
+ self._embedding_key: node.get_embedding(),
+ self._text_key: node.get_content(metadata_mode=MetadataMode.NONE) or "",
+ self._metadata_key: metadata,
+ }
+ data_to_insert.append(entry)
+ ids.append(node.node_id)
+
+ for item in data_to_insert:
+ self._container.upsert_item(item)
+
+ return ids
+
+ def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
+ """
+ Delete nodes using with ref_doc_id.
+
+ Args:
+ ref_doc_id (str): The doc_id of the document to delete.
+
+ """
+ self._container.delete_item(ref_doc_id, partition_key=ref_doc_id)
+
+ @property
+ def client(self) -> Any:
+ """Return CosmosDB client."""
+ return self._cosmos_client
+
+ def _query(self, query: VectorStoreQuery) -> VectorStoreQueryResult:
+ params: Dict[str, Any] = {
+ "vector": query.query_embedding,
+ "path": self._embedding_key,
+ "k": query.similarity_top_k,
+ }
+
+ top_k_nodes = []
+ top_k_ids = []
+ top_k_scores = []
+
+ for item in self._container.query_items(
+ query="SELECT TOP @k c.id, c.embedding, c.text, c.metadata, VectorDistance(c.embedding,@embedding) AS SimilarityScore FROM c ORDER BY VectorDistance(c.embedding,@embedding)",
+ parameters=[
+ {"name": "@k", "value": params["k"]},
+ {"name": "@embedding", "value": params["vector"]},
+ ],
+ enable_cross_partition_query=True,
+ ):
+ node = metadata_dict_to_node(item[self._metadata_key])
+ node.set_content(item[self._text_key])
+
+ node_id = item[self._id_key]
+ node_score = item["SimilarityScore"]
+
+ top_k_ids.append(node_id)
+ top_k_nodes.append(node)
+ top_k_scores.append(node_score)
+
+ return VectorStoreQueryResult(
+ nodes=top_k_nodes, similarities=top_k_scores, ids=top_k_ids
+ )
+
+ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
+ """Query index for top k most similar nodes.
+
+ Args:
+ query: a VectorStoreQuery object.
+
+ Returns:
+ A VectorStoreQueryResult containing the results of the query.
+ """
+ return self._query(query)
diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/pyproject.toml
new file mode 100644
index 0000000000000..2d830de9de68b
--- /dev/null
+++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/pyproject.toml
@@ -0,0 +1,63 @@
+[build-system]
+build-backend = "poetry.core.masonry.api"
+requires = ["poetry-core"]
+
+[tool.codespell]
+check-filenames = true
+check-hidden = true
+skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb"
+
+[tool.llamahub]
+contains_example = false
+import_path = "llama_index.vector_stores.azurecosmosnosql"
+
+[tool.llamahub.class_authors]
+AzureCosmosDBMongoDBVectorSearch = "llama-index"
+
+[tool.mypy]
+disallow_untyped_defs = true
+exclude = ["_static", "build", "examples", "notebooks", "venv"]
+ignore_missing_imports = true
+python_version = "3.8"
+
+[tool.poetry]
+authors = ["Your Name \n",
+ "\n",
"\n",
"**NOTE**: Full credits to the [source repo for LLMCompiler](https://github.com/SqueezeAILab/LLMCompiler). A lot of our implementation was lifted from this repo (and adapted with LlamaIndex modules).\n",
"\n",
diff --git a/llama-index-packs/llama-index-packs-infer-retrieve-rerank/examples/infer_retrieve_rerank.ipynb b/llama-index-packs/llama-index-packs-infer-retrieve-rerank/examples/infer_retrieve_rerank.ipynb
index 05de7db0a95fc..32bbe34a181c5 100644
--- a/llama-index-packs/llama-index-packs-infer-retrieve-rerank/examples/infer_retrieve_rerank.ipynb
+++ b/llama-index-packs/llama-index-packs-infer-retrieve-rerank/examples/infer_retrieve_rerank.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Infer-Retrieve-Rerank Llama Pack\n",
"\n",
- "\n",
+ "\n",
"\n",
"This is our implementation of the paper [\"In-Context Learning for Extreme Multi-Label Classification](https://arxiv.org/pdf/2401.12178.pdf) by Oosterlinck et al.\n",
"\n",
diff --git a/llama-index-packs/llama-index-packs-llama-guard-moderator/examples/rag_moderator_llama_guard_pack.ipynb b/llama-index-packs/llama-index-packs-llama-guard-moderator/examples/rag_moderator_llama_guard_pack.ipynb
index 27526e043a6d9..6ea3557f3c20f 100644
--- a/llama-index-packs/llama-index-packs-llama-guard-moderator/examples/rag_moderator_llama_guard_pack.ipynb
+++ b/llama-index-packs/llama-index-packs-llama-guard-moderator/examples/rag_moderator_llama_guard_pack.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Safeguarding Your RAG Pipeline with LlamaGuardModeratorPack\n",
"\n",
- "\n",
+ "\n",
"\n",
"\n",
"This notebook shows how we can use LlamaGuardModeratorPack to safeguard the LLM inputs and outputs of a RAG pipeline using [Llama Guard](https://huggingface.co/meta-llama/LlamaGuard-7b). The RAG pipeline uses the following models:\n",
diff --git a/llama-index-packs/llama-index-packs-multi-tenancy-rag/examples/multi_tenancy_rag.ipynb b/llama-index-packs/llama-index-packs-multi-tenancy-rag/examples/multi_tenancy_rag.ipynb
index a9b1f3e5e5032..300bbd6522f55 100644
--- a/llama-index-packs/llama-index-packs-multi-tenancy-rag/examples/multi_tenancy_rag.ipynb
+++ b/llama-index-packs/llama-index-packs-multi-tenancy-rag/examples/multi_tenancy_rag.ipynb
@@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/llama-index-packs/llama-index-packs-neo4j-query-engine/examples/llama_packs_neo4j.ipynb b/llama-index-packs/llama-index-packs-neo4j-query-engine/examples/llama_packs_neo4j.ipynb
index 447770d357d34..c99cec59bc823 100644
--- a/llama-index-packs/llama-index-packs-neo4j-query-engine/examples/llama_packs_neo4j.ipynb
+++ b/llama-index-packs/llama-index-packs-neo4j-query-engine/examples/llama_packs_neo4j.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Llama Pack - Neo4j Query Engine\n",
"\n",
- "\n",
+ "\n",
"\n",
"This Llama Pack creates a Neo4j knowledge graph query engine, and executes its `query` function. This pack offers the option of creating multiple types of query engines for Neo4j knowledge graphs, namely:\n",
"\n",
diff --git a/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/examples/semantic_chunking.ipynb b/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/examples/semantic_chunking.ipynb
index 583bc71430f74..f24e7ff5b4b22 100644
--- a/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/examples/semantic_chunking.ipynb
+++ b/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/examples/semantic_chunking.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Semantic Chunker\n",
"\n",
- "\n",
+ "\n",
"\n",
"\"Semantic chunking\" is a new concept proposed Greg Kamradt in his video tutorial on 5 levels of embedding chunking: https://youtu.be/8OJC21T2SL4?t=1933.\n",
"\n",
diff --git a/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/examples/rag_fusion_pipeline.ipynb b/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/examples/rag_fusion_pipeline.ipynb
index d16ae9e60663b..3715d16f32154 100644
--- a/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/examples/rag_fusion_pipeline.ipynb
+++ b/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/examples/rag_fusion_pipeline.ipynb
@@ -7,7 +7,7 @@
"source": [
"# RAG Fusion Query Pipeline\n",
"\n",
- "\n",
+ "\n",
"\n",
"This notebook shows how to implement RAG Fusion using the LlamaIndex Query Pipeline syntax."
]
diff --git a/llama-index-packs/llama-index-packs-self-rag/examples/self_rag.ipynb b/llama-index-packs/llama-index-packs-self-rag/examples/self_rag.ipynb
index 5b576f10c2e48..bf539dfd30a90 100644
--- a/llama-index-packs/llama-index-packs-self-rag/examples/self_rag.ipynb
+++ b/llama-index-packs/llama-index-packs-self-rag/examples/self_rag.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Simple Self RAG Notebook\n",
"\n",
- "\n",
+ "\n",
"\n",
"This LlamaPack implements short form the [self-RAG paper by Akari et al.](https://arxiv.org/pdf/2310.11511.pdf).\n",
"\n",
diff --git a/llama-index-packs/llama-index-packs-tables/examples/chain_of_table.ipynb b/llama-index-packs/llama-index-packs-tables/examples/chain_of_table.ipynb
index c60006af85962..0612a2940d58d 100644
--- a/llama-index-packs/llama-index-packs-tables/examples/chain_of_table.ipynb
+++ b/llama-index-packs/llama-index-packs-tables/examples/chain_of_table.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Chain of Table Notebook\n",
"\n",
- "\n",
+ "\n",
"\n",
"In this notebook we highlight our implementation of the [\"Chain of Table\" paper by Wang et al.](https://arxiv.org/pdf/2401.04398v1.pdf).\n",
"\n",
diff --git a/llama-index-packs/llama-index-packs-tables/examples/mix_self_consistency.ipynb b/llama-index-packs/llama-index-packs-tables/examples/mix_self_consistency.ipynb
index edfd6fc6a3c69..f30bf22b1df91 100644
--- a/llama-index-packs/llama-index-packs-tables/examples/mix_self_consistency.ipynb
+++ b/llama-index-packs/llama-index-packs-tables/examples/mix_self_consistency.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Mix Self-Consistency Notebook \n",
"\n",
- "\n",
+ "\n",
"\n",
"\n",
"In this notebook, we highlight the mix self-consistency method proposed in [\"Rethinking Tabular Data Understanding with Large Language Models\"](https://arxiv.org/pdf/2312.16702v1.pdf) paper by Liu et al.\n",
diff --git a/llama-index-utils/llama-index-utils-workflow/llama_index/utils/workflow/draw.py b/llama-index-utils/llama-index-utils-workflow/llama_index/utils/workflow/draw.py
index c0305e1522786..d9313af484777 100644
--- a/llama-index-utils/llama-index-utils-workflow/llama_index/utils/workflow/draw.py
+++ b/llama-index-utils/llama-index-utils-workflow/llama_index/utils/workflow/draw.py
@@ -1,4 +1,3 @@
-from pyvis.network import Network
from typing import Optional
from llama_index.core.workflow.events import StartEvent, StopEvent
@@ -7,14 +6,17 @@
get_steps_from_class,
get_steps_from_instance,
)
+from llama_index.core.workflow.workflow import Workflow
def draw_all_possible_flows(
- workflow,
+ workflow: Workflow,
filename: str = "workflow_all_flows.html",
notebook: bool = False,
) -> None:
"""Draws all possible flows of the workflow."""
+ from pyvis.network import Network
+
net = Network(directed=True, height="750px", width="100%")
# Add the nodes + edge for stop events
@@ -33,8 +35,9 @@ def draw_all_possible_flows(
# If no steps are defined in the class, try to get them from the instance
steps = get_steps_from_instance(workflow)
- for step_name, step_func in get_steps_from_class(workflow).items():
- step_config: Optional[StepConfig] = getattr(step_func, "__step_config", None)
+ step_config: Optional[StepConfig] = None
+ for step_name, step_func in steps.items():
+ step_config = getattr(step_func, "__step_config", None)
if step_config is None:
continue
@@ -51,8 +54,8 @@ def draw_all_possible_flows(
) # Light green for events
# Add edges from all steps
- for step_name, step_func in get_steps_from_class(workflow).items():
- step_config: Optional[StepConfig] = getattr(step_func, "__step_config", None)
+ for step_name, step_func in steps.items():
+ step_config = getattr(step_func, "__step_config", None)
if step_config is None:
continue
@@ -68,15 +71,21 @@ def draw_all_possible_flows(
def draw_most_recent_execution(
- workflow,
+ workflow: Workflow,
filename: str = "workflow_recent_execution.html",
notebook: bool = False,
) -> None:
"""Draws the most recent execution of the workflow."""
+ from pyvis.network import Network
+
net = Network(directed=True, height="750px", width="100%")
# Add nodes and edges based on execution history
- for i, (step, event) in enumerate(workflow._accepted_events):
+ existing_context = next(iter(workflow._contexts), None)
+ if existing_context is None:
+ raise ValueError("No runs found in workflow")
+
+ for i, (step, event) in enumerate(existing_context._accepted_events):
event_node = f"{event}_{i}"
step_node = f"{step}_{i}"
net.add_node(
@@ -88,7 +97,7 @@ def draw_most_recent_execution(
net.add_edge(event_node, step_node)
if i > 0:
- prev_step_node = f"{workflow._accepted_events[i - 1][0]}_{i - 1}"
+ prev_step_node = f"{existing_context._accepted_events[i - 1][0]}_{i - 1}"
net.add_edge(prev_step_node, event_node)
net.show(filename, notebook=notebook)
diff --git a/llama-index-utils/llama-index-utils-workflow/poetry.lock b/llama-index-utils/llama-index-utils-workflow/poetry.lock
deleted file mode 100644
index de3d74d429c4e..0000000000000
--- a/llama-index-utils/llama-index-utils-workflow/poetry.lock
+++ /dev/null
@@ -1,4492 +0,0 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
-
-[[package]]
-name = "aiohappyeyeballs"
-version = "2.4.0"
-description = "Happy Eyeballs for asyncio"
-optional = false
-python-versions = ">=3.8"
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-]
-
-[[package]]
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diff --git a/llama-index-utils/llama-index-utils-workflow/pyproject.toml b/llama-index-utils/llama-index-utils-workflow/pyproject.toml
index 2bd5703dd8138..3efbfa4bf1ccd 100644
--- a/llama-index-utils/llama-index-utils-workflow/pyproject.toml
+++ b/llama-index-utils/llama-index-utils-workflow/pyproject.toml
@@ -24,7 +24,7 @@ exclude = ["**/BUILD"]
license = "MIT"
name = "llama-index-utils-workflow"
readme = "README.md"
-version = "0.2.0"
+version = "0.2.1"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
diff --git a/pants.toml b/pants.toml
index fd2d071fcf9ef..12c7194790441 100644
--- a/pants.toml
+++ b/pants.toml
@@ -19,7 +19,7 @@ unmatched_build_file_globs = "error"
config = "./pyproject.toml"
[python]
-interpreter_constraints = ["==3.10.*", "==3.8.*", "==3.9.*"]
+interpreter_constraints = ["==3.10.*", "==3.11.*", "==3.12.*", "==3.9.*"]
[python-infer]
ambiguity_resolution = "by_source_root"
diff --git a/poetry.lock b/poetry.lock
index da26d4df56663..47b427ca90793 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
[[package]]
name = "aiohappyeyeballs"
@@ -383,89 +383,89 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
[[package]]
name = "certifi"
-version = "2024.7.4"
+version = "2024.8.30"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
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- {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
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+ {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
]
[[package]]
name = "cffi"
-version = "1.17.0"
+version = "1.17.1"
description = "Foreign Function Interface for Python calling C code."
optional = false
python-versions = ">=3.8"
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[[package]]
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python-versions = ">=3.8"
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http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
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name = "identify"
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python-versions = "<4,>=3.8"
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]
[package.dependencies]
@@ -4621,4 +4614,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
-content-hash = "d50a2e23d473f9acf7dfc9fcacdaae05dfc85a9eb6f473e0d2d79b201781659a"
+content-hash = "d9e288ecee5c65d0808fb002ae08194a74a38d416b4fb110a4efc470573452b6"
diff --git a/pyproject.toml b/pyproject.toml
index 4ff2362fb6a75..b70900f8dbae3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,7 @@ explicit_package_bases = true
ignore_missing_imports = true
mypy_path = "llama_index"
namespace_packages = true
+plugins = "pydantic.mypy"
python_version = "3.8"
[tool.poetry]
@@ -44,22 +45,22 @@ name = "llama-index"
packages = [{from = "_llama-index", include = "llama_index"}]
readme = "README.md"
repository = "https://github.com/run-llama/llama_index"
-version = "0.11.1"
+version = "0.11.9"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
llama-index-legacy = "^0.9.48"
-llama-index-llms-openai = "^0.2.0"
-llama-index-embeddings-openai = "^0.2.0"
+llama-index-llms-openai = "^0.2.3"
+llama-index-embeddings-openai = "^0.2.4"
llama-index-program-openai = "^0.2.0"
llama-index-question-gen-openai = "^0.2.0"
-llama-index-agent-openai = "^0.3.0"
+llama-index-agent-openai = "^0.3.1"
llama-index-readers-file = "^0.2.0"
-llama-index-readers-llama-parse = ">=0.2.0"
+llama-index-readers-llama-parse = ">=0.3.0"
llama-index-indices-managed-llama-cloud = ">=0.3.0"
-llama-index-core = "^0.11.1"
+llama-index-core = "^0.11.9"
llama-index-multi-modal-llms-openai = "^0.2.0"
-llama-index-cli = "^0.3.0"
+llama-index-cli = "^0.3.1"
nltk = ">3.8.1" # avoids a CVE, temp until next release, should be in llama-index-core
[tool.poetry.group.dev.dependencies]