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bootcamp/tutorials/integration/integrate_with_phidata.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Integrate Milvus with Phidata\n", | ||
| "\n", | ||
| "[Phidata](https://github.com/phidatahq/phidata/tree/main) is a powerful framework for building intelligent agents and workflows. It allows you to create multi-modal agents that can understand text, images, audio, and video, and leverage various tools and knowledge sources to accomplish complex tasks. Phidata supports multi-agent orchestration, enabling teams of agents to collaborate and solve problems together. It also provides a beautiful Agent UI for interacting with your agents.\n", | ||
| "\n", | ||
| "Milvus vector database enable efficient storage and retrieval of information as embeddings. With Milvus and Phidata, you can easily integrate your knowledge into your Agent workflows. This document is a basic guide on how to use Milvus integration with Phidata." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Preparation\n", | ||
| "Install the necessary dependencies:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": { | ||
| "vscode": { | ||
| "languageId": "shellscript" | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "! pip install --upgrade phidata pymilvus openai" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "> If you are using Google Colab, to enable dependencies just installed, you may need to **restart the runtime** (click on the \"Runtime\" menu at the top of the screen, and select \"Restart session\" from the dropdown menu)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We will use OpenAI as the LLM in this example. You should prepare the [api key](https://platform.openai.com/docs/quickstart) `OPENAI_API_KEY` as an environment variable." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "\n", | ||
| "os.environ[\"OPENAI_API_KEY\"] = \"sk-***********\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Initalize Milvus\n", | ||
| "\n", | ||
| "Import the packages and initialize the Milvus vector database instance." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from phi.agent import Agent\n", | ||
| "from phi.knowledge.pdf import PDFUrlKnowledgeBase\n", | ||
| "from phi.vectordb.milvus import Milvus\n", | ||
| "\n", | ||
| "# Initialize Milvus\n", | ||
| "vector_db = Milvus(\n", | ||
| " collection=\"recipes\",\n", | ||
| " uri=\"./milvus.db\",\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Specify the collection name and the uri and token(optinal) for your Milvus server.\n", | ||
| "\n", | ||
| "Here is how to set the uri and token:\n", | ||
| "\n", | ||
| "> - If you only need a local vector database for small scale data or prototyping, setting the uri as a local file, e.g.`./milvus.db`, is the most convenient method, as it automatically utilizes [Milvus Lite](https://milvus.io/docs/milvus_lite.md) to store all data in this file.\n", | ||
| "> - If you have large scale of data, say more than a million vectors, you can set up a more performant Milvus server on [Docker or Kubernetes](https://milvus.io/docs/quickstart.md). In this setup, please use the server address and port as your uri, e.g.`http://localhost:19530`. If you enable the authentication feature on Milvus, use \"<your_username>:<your_password>\" as the token, otherwise don't set the token.\n", | ||
| "> - If you use [Zilliz Cloud](https://zilliz.com/cloud), the fully managed cloud service for Milvus, adjust the `uri` and `token`, which correspond to the [Public Endpoint and API key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#cluster-details) in Zilliz Cloud.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Load data\n", | ||
| "\n", | ||
| "Create a PDF url knowledage base instance and load the data into the instance. We use a public recipe pdf data as an example." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO </span> Creating collection \n", | ||
| "</pre>\n" | ||
| ], | ||
| "text/plain": [ | ||
| "\u001b[34mINFO \u001b[0m Creating collection \n" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO </span> Loading knowledge base \n", | ||
| "</pre>\n" | ||
| ], | ||
| "text/plain": [ | ||
| "\u001b[34mINFO \u001b[0m Loading knowledge base \n" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO </span> Reading: <span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf</span> \n", | ||
| "</pre>\n" | ||
| ], | ||
| "text/plain": [ | ||
| "\u001b[34mINFO \u001b[0m Reading: \u001b[4;94mhttps://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf\u001b[0m \n" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO </span> Added <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> documents to knowledge base \n", | ||
| "</pre>\n" | ||
| ], | ||
| "text/plain": [ | ||
| "\u001b[34mINFO \u001b[0m Added \u001b[1;36m0\u001b[0m documents to knowledge base \n" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Create knowledge base\n", | ||
| "knowledge_base = PDFUrlKnowledgeBase(\n", | ||
| " urls=[\"https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf\"],\n", | ||
| " vector_db=vector_db,\n", | ||
| ")\n", | ||
| "\n", | ||
| "knowledge_base.load(recreate=False) # Comment out after first run" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Use agent to response to a question\n", | ||
| "Integrate the knowledge base into an agent, then we can ask the agent a question and get a response." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "application/vnd.jupyter.widget-view+json": { | ||
| "model_id": "4b528ef9435248688320a110c3aab0e4", | ||
| "version_major": 2, | ||
| "version_minor": 0 | ||
| }, | ||
| "text/plain": [ | ||
| "Output()" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/html": [ | ||
| "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n" | ||
| ], | ||
| "text/plain": [] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Create and use the agent\n", | ||
| "agent = Agent(knowledge_base=knowledge_base, use_tools=True, show_tool_calls=True)\n", | ||
| "\n", | ||
| "# Query the agent\n", | ||
| "agent.print_response(\"How to make Tom Kha Gai\", markdown=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", | ||
| "┃ ┃\n", | ||
| "┃ How to make Tom Kha Gai ┃\n", | ||
| "┃ ┃\n", | ||
| "┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\n", | ||
| "┏━ Response (6.9s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", | ||
| "┃ ┃\n", | ||
| "┃ Running: ┃\n", | ||
| "┃ ┃\n", | ||
| "┃ • search_knowledge_base(query=Tom Kha Gai recipe) ┃\n", | ||
| "┃ ┃\n", | ||
| "┃ Here's a recipe for Tom Kha Gai, a delicious Thai chicken and galangal soup made with coconut milk: ┃\n", | ||
| "┃ ┃\n", | ||
| "┃ Ingredients (One serving): ┃\n", | ||
| "┃ ┃\n", | ||
| "┃ • 150 grams chicken, cut into bite-size pieces ┃\n", | ||
| "┃ • 50 grams sliced young galangal ┃\n", | ||
| "┃ • 100 grams lightly crushed lemongrass, julienned ┃\n", | ||
| "┃ • 100 grams straw mushrooms ┃\n", | ||
| "┃ • 250 grams coconut milk ┃\n", | ||
| "┃ • 100 grams chicken stock ┃\n", | ||
| "┃ • 3 tbsp lime juice ┃\n", | ||
| "┃ • 3 tbsp fish sauce ┃\n", | ||
| "┃ • 2 leaves kaffir lime, shredded ┃\n", | ||
| "┃ • 1-2 bird’s eye chilies, pounded ┃\n", | ||
| "┃ • 3 leaves coriander ┃\n", | ||
| "┃ ┃\n", | ||
| "┃ Directions: ┃\n", | ||
| "┃ ┃\n", | ||
| "┃ 1 Bring the chicken stock and coconut milk to a slow boil. ┃\n", | ||
| "┃ 2 Add galangal, lemongrass, chicken, and mushrooms. Once the soup returns to a boil, season it with fish sauce. ┃\n", | ||
| "┃ 3 Wait until the chicken is cooked, then add the kaffir lime leaves and bird’s eye chilies. ┃\n", | ||
| "┃ 4 Remove the pot from heat and add lime juice. ┃\n", | ||
| "┃ 5 Garnish with coriander leaves. ┃\n", | ||
| "┃ ┃\n", | ||
| "┃ Tips: ┃\n", | ||
| "┃ ┃\n", | ||
| "┃ • Keep the heat low throughout the cooking process to prevent the oil in the coconut milk from separating. ┃\n", | ||
| "┃ • If using mature galangal, reduce the amount. ┃\n", | ||
| "┃ • Adding lime juice after removing the pot from heat makes it more aromatic. ┃\n", | ||
| "┃ • Reduce the number of chilies for a milder taste. ┃\n", | ||
| "┃ ┃\n", | ||
| "┃ Enjoy making and savoring this flavorful Thai soup! ┃\n", | ||
| "┃ ┃\n", | ||
| "┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Congratulations, you have learned the basics of using Milvus in Phidata. If you want to know more about how to use Phidata, please refer to the [official documentation](https://docs.phidata.com/introduction).\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "py310", | ||
| "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", | ||
| "version": "3.10.13" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |