diff --git a/backend/app/agent.py b/backend/app/agent.py
new file mode 100644
index 00000000..2bae07ad
--- /dev/null
+++ b/backend/app/agent.py
@@ -0,0 +1,149 @@
+from enum import Enum
+from typing import Any, Mapping, Optional, Sequence
+
+from langchain_core.messages import AnyMessage
+from langchain_core.runnables import (
+ ConfigurableField,
+ ConfigurableFieldMultiOption,
+ RunnableBinding,
+)
+
+from app.agent_types.google_agent import get_google_agent_executor
+from app.agent_types.openai_agent import get_openai_agent_executor
+from app.agent_types.xml_agent import get_xml_agent_executor
+from app.checkpoint import RedisCheckpoint
+from app.llms import get_anthropic_llm, get_google_llm, get_openai_llm
+from app.tools import (
+ RETRIEVAL_DESCRIPTION,
+ TOOL_OPTIONS,
+ TOOLS,
+ AvailableTools,
+ get_retrieval_tool,
+)
+
+
+class AgentType(str, Enum):
+ GPT_35_TURBO = "GPT 3.5 Turbo"
+ GPT_4 = "GPT 4"
+ AZURE_OPENAI = "GPT 4 (Azure OpenAI)"
+ CLAUDE2 = "Claude 2"
+ BEDROCK_CLAUDE2 = "Claude 2 (Amazon Bedrock)"
+ GEMINI = "GEMINI"
+
+
+DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant."
+
+
+def get_agent_executor(
+ tools: list,
+ agent: AgentType,
+ system_message: str,
+):
+ checkpointer = RedisCheckpoint()
+ if agent == AgentType.GPT_35_TURBO:
+ llm = get_openai_llm()
+ return get_openai_agent_executor(tools, llm, system_message, checkpointer)
+ elif agent == AgentType.GPT_4:
+ llm = get_openai_llm(gpt_4=True)
+ return get_openai_agent_executor(tools, llm, system_message, checkpointer)
+ elif agent == AgentType.AZURE_OPENAI:
+ llm = get_openai_llm(azure=True)
+ return get_openai_agent_executor(tools, llm, system_message, checkpointer)
+ elif agent == AgentType.CLAUDE2:
+ llm = get_anthropic_llm()
+ return get_xml_agent_executor(tools, llm, system_message, checkpointer)
+ elif agent == AgentType.BEDROCK_CLAUDE2:
+ llm = get_anthropic_llm(bedrock=True)
+ return get_xml_agent_executor(tools, llm, system_message, checkpointer)
+ elif agent == AgentType.GEMINI:
+ llm = get_google_llm()
+ return get_google_agent_executor(tools, llm, system_message, checkpointer)
+ else:
+ raise ValueError("Unexpected agent type")
+
+
+class ConfigurableAgent(RunnableBinding):
+ tools: Sequence[str]
+ agent: AgentType
+ system_message: str = DEFAULT_SYSTEM_MESSAGE
+ retrieval_description: str = RETRIEVAL_DESCRIPTION
+ assistant_id: Optional[str] = None
+ user_id: Optional[str] = None
+
+ def __init__(
+ self,
+ *,
+ tools: Sequence[str],
+ agent: AgentType = AgentType.GPT_35_TURBO,
+ system_message: str = DEFAULT_SYSTEM_MESSAGE,
+ assistant_id: Optional[str] = None,
+ retrieval_description: str = RETRIEVAL_DESCRIPTION,
+ kwargs: Optional[Mapping[str, Any]] = None,
+ config: Optional[Mapping[str, Any]] = None,
+ **others: Any,
+ ) -> None:
+ others.pop("bound", None)
+ _tools = []
+ for _tool in tools:
+ if _tool == AvailableTools.RETRIEVAL:
+ if assistant_id is None:
+ raise ValueError(
+ "assistant_id must be provided if Retrieval tool is used"
+ )
+ _tools.append(get_retrieval_tool(assistant_id, retrieval_description))
+ else:
+ _tools.append(TOOLS[_tool]())
+ _agent = get_agent_executor(_tools, agent, system_message)
+ agent_executor = _agent.with_config({"recursion_limit": 50})
+ super().__init__(
+ tools=tools,
+ agent=agent,
+ system_message=system_message,
+ retrieval_description=retrieval_description,
+ bound=agent_executor,
+ kwargs=kwargs or {},
+ config=config or {},
+ )
+
+
+agent = (
+ ConfigurableAgent(
+ agent=AgentType.GPT_35_TURBO,
+ tools=[],
+ system_message=DEFAULT_SYSTEM_MESSAGE,
+ retrieval_description=RETRIEVAL_DESCRIPTION,
+ assistant_id=None,
+ )
+ .configurable_fields(
+ agent=ConfigurableField(id="agent_type", name="Agent Type"),
+ system_message=ConfigurableField(id="system_message", name="System Message"),
+ assistant_id=ConfigurableField(
+ id="assistant_id", name="Assistant ID", is_shared=True
+ ),
+ tools=ConfigurableFieldMultiOption(
+ id="tools",
+ name="Tools",
+ options=TOOL_OPTIONS,
+ default=[],
+ ),
+ retrieval_description=ConfigurableField(
+ id="retrieval_description", name="Retrieval Description"
+ ),
+ )
+ .with_types(input_type=Sequence[AnyMessage], output_type=Sequence[AnyMessage])
+)
+
+if __name__ == "__main__":
+ import asyncio
+
+ from langchain.schema.messages import HumanMessage
+
+ async def run():
+ async for m in agent.astream_events(
+ HumanMessage(content="whats your name"),
+ config={"configurable": {"user_id": "2", "thread_id": "test1"}},
+ version="v1",
+ ):
+ print(m)
+
+ asyncio.run(run())
diff --git a/backend/packages/agent-executor/agent_executor/__init__.py b/backend/app/agent_types/__init__.py
similarity index 100%
rename from backend/packages/agent-executor/agent_executor/__init__.py
rename to backend/app/agent_types/__init__.py
diff --git a/backend/app/agent_types/google_agent.py b/backend/app/agent_types/google_agent.py
new file mode 100644
index 00000000..daf8752e
--- /dev/null
+++ b/backend/app/agent_types/google_agent.py
@@ -0,0 +1,97 @@
+import json
+
+from langchain.schema.messages import FunctionMessage
+from langchain.tools import BaseTool
+from langchain_core.language_models.base import LanguageModelLike
+from langchain_core.messages import SystemMessage
+from langgraph.checkpoint import BaseCheckpointSaver
+from langgraph.graph import END
+from langgraph.graph.message import MessageGraph
+from langgraph.prebuilt import ToolExecutor, ToolInvocation
+
+
+def get_google_agent_executor(
+ tools: list[BaseTool],
+ llm: LanguageModelLike,
+ system_message: str,
+ checkpoint: BaseCheckpointSaver,
+):
+ def _get_messages(messages):
+ return [SystemMessage(content=system_message)] + messages
+
+ if tools:
+ llm_with_tools = llm.bind(functions=tools)
+ else:
+ llm_with_tools = llm
+ agent = _get_messages | llm_with_tools
+ tool_executor = ToolExecutor(tools)
+
+ # Define the function that determines whether to continue or not
+ def should_continue(messages):
+ last_message = messages[-1]
+ # If there is no function call, then we finish
+ if "function_call" not in last_message.additional_kwargs:
+ return "end"
+ # Otherwise if there is, we continue
+ else:
+ return "continue"
+
+ # Define the function to execute tools
+ async def call_tool(messages):
+ # Based on the continue condition
+ # we know the last message involves a function call
+ last_message = messages[-1]
+ # We construct an ToolInvocation from the function_call
+ action = ToolInvocation(
+ tool=last_message.additional_kwargs["function_call"]["name"],
+ tool_input=json.loads(
+ last_message.additional_kwargs["function_call"]["arguments"]
+ ),
+ )
+ # We call the tool_executor and get back a response
+ response = await tool_executor.ainvoke(action)
+ # We use the response to create a FunctionMessage
+ function_message = FunctionMessage(content=str(response), name=action.tool)
+ # We return a list, because this will get added to the existing list
+ return function_message
+
+ workflow = MessageGraph()
+
+ # Define the two nodes we will cycle between
+ workflow.add_node("agent", agent)
+ workflow.add_node("action", call_tool)
+
+ # Set the entrypoint as `agent`
+ # This means that this node is the first one called
+ workflow.set_entry_point("agent")
+
+ # We now add a conditional edge
+ workflow.add_conditional_edges(
+ # First, we define the start node. We use `agent`.
+ # This means these are the edges taken after the `agent` node is called.
+ "agent",
+ # Next, we pass in the function that will determine which node is called next.
+ should_continue,
+ # Finally we pass in a mapping.
+ # The keys are strings, and the values are other nodes.
+ # END is a special node marking that the graph should finish.
+ # What will happen is we will call `should_continue`, and then the output of that
+ # will be matched against the keys in this mapping.
+ # Based on which one it matches, that node will then be called.
+ {
+ # If `tools`, then we call the tool node.
+ "continue": "action",
+ # Otherwise we finish.
+ "end": END,
+ },
+ )
+
+ # We now add a normal edge from `tools` to `agent`.
+ # This means that after `tools` is called, `agent` node is called next.
+ workflow.add_edge("action", "agent")
+
+ # Finally, we compile it!
+ # This compiles it into a LangChain Runnable,
+ # meaning you can use it as you would any other runnable
+ app = workflow.compile(checkpointer=checkpoint)
+ return app
diff --git a/backend/app/agent_types/openai_agent.py b/backend/app/agent_types/openai_agent.py
new file mode 100644
index 00000000..8ee9a645
--- /dev/null
+++ b/backend/app/agent_types/openai_agent.py
@@ -0,0 +1,100 @@
+import json
+
+from langchain.schema.messages import FunctionMessage
+from langchain.tools import BaseTool
+from langchain.tools.render import format_tool_to_openai_function
+from langchain_core.language_models.base import LanguageModelLike
+from langchain_core.messages import SystemMessage
+from langgraph.checkpoint import BaseCheckpointSaver
+from langgraph.graph import END
+from langgraph.graph.message import MessageGraph
+from langgraph.prebuilt import ToolExecutor, ToolInvocation
+
+
+def get_openai_agent_executor(
+ tools: list[BaseTool],
+ llm: LanguageModelLike,
+ system_message: str,
+ checkpoint: BaseCheckpointSaver,
+):
+ def _get_messages(messages):
+ return [SystemMessage(content=system_message)] + messages
+
+ if tools:
+ llm_with_tools = llm.bind(
+ functions=[format_tool_to_openai_function(t) for t in tools]
+ )
+ else:
+ llm_with_tools = llm
+ agent = _get_messages | llm_with_tools
+ tool_executor = ToolExecutor(tools)
+
+ # Define the function that determines whether to continue or not
+ def should_continue(messages):
+ last_message = messages[-1]
+ # If there is no function call, then we finish
+ if "function_call" not in last_message.additional_kwargs:
+ return "end"
+ # Otherwise if there is, we continue
+ else:
+ return "continue"
+
+ # Define the function to execute tools
+ async def call_tool(messages):
+ # Based on the continue condition
+ # we know the last message involves a function call
+ last_message = messages[-1]
+ # We construct an ToolInvocation from the function_call
+ action = ToolInvocation(
+ tool=last_message.additional_kwargs["function_call"]["name"],
+ tool_input=json.loads(
+ last_message.additional_kwargs["function_call"]["arguments"]
+ ),
+ )
+ # We call the tool_executor and get back a response
+ response = await tool_executor.ainvoke(action)
+ # We use the response to create a FunctionMessage
+ function_message = FunctionMessage(content=str(response), name=action.tool)
+ # We return a list, because this will get added to the existing list
+ return function_message
+
+ workflow = MessageGraph()
+
+ # Define the two nodes we will cycle between
+ workflow.add_node("agent", agent)
+ workflow.add_node("action", call_tool)
+
+ # Set the entrypoint as `agent`
+ # This means that this node is the first one called
+ workflow.set_entry_point("agent")
+
+ # We now add a conditional edge
+ workflow.add_conditional_edges(
+ # First, we define the start node. We use `agent`.
+ # This means these are the edges taken after the `agent` node is called.
+ "agent",
+ # Next, we pass in the function that will determine which node is called next.
+ should_continue,
+ # Finally we pass in a mapping.
+ # The keys are strings, and the values are other nodes.
+ # END is a special node marking that the graph should finish.
+ # What will happen is we will call `should_continue`, and then the output of that
+ # will be matched against the keys in this mapping.
+ # Based on which one it matches, that node will then be called.
+ {
+ # If `tools`, then we call the tool node.
+ "continue": "action",
+ # Otherwise we finish.
+ "end": END,
+ },
+ )
+
+ # We now add a normal edge from `tools` to `agent`.
+ # This means that after `tools` is called, `agent` node is called next.
+ workflow.add_edge("action", "agent")
+
+ # Finally, we compile it!
+ # This compiles it into a LangChain Runnable,
+ # meaning you can use it as you would any other runnable
+ app = workflow.compile(checkpointer=checkpoint)
+ return app
diff --git a/backend/app/agent_types/prompts.py b/backend/app/agent_types/prompts.py
new file mode 100644
index 00000000..125e6a80
--- /dev/null
+++ b/backend/app/agent_types/prompts.py
@@ -0,0 +1,27 @@
+xml_template = """{system_message}
+
+You have access to the following tools:
+
+{tools}
+
+In order to use a tool, you can use and tags. You will then get back a response in the form
+For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:
+
+searchweather in SF
+64 degrees
+
+When you are done, you can respond as normal to the user.
+
+Example 1:
+
+Human: Hi!
+
+Assistant: Hi! How are you?
+
+Human: What is the weather in SF?
+Assistant: searchweather in SF
+64 degrees
+It is 64 degrees in SF
+
+
+Begin!"""
diff --git a/backend/app/agent_types/xml_agent.py b/backend/app/agent_types/xml_agent.py
new file mode 100644
index 00000000..2a1654cc
--- /dev/null
+++ b/backend/app/agent_types/xml_agent.py
@@ -0,0 +1,143 @@
+from langchain.schema.messages import FunctionMessage
+from langchain.tools import BaseTool
+from langchain.tools.render import render_text_description
+from langchain_core.language_models.base import LanguageModelLike
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from langgraph.checkpoint import BaseCheckpointSaver
+from langgraph.graph import END
+from langgraph.graph.message import MessageGraph
+from langgraph.prebuilt import ToolExecutor, ToolInvocation
+
+from app.agent_types.prompts import xml_template
+
+
+def _collapse_messages(messages):
+ log = ""
+ if isinstance(messages[-1], AIMessage):
+ scratchpad = messages[:-1]
+ final = messages[-1]
+ else:
+ scratchpad = messages
+ final = None
+ if len(scratchpad) % 2 != 0:
+ raise ValueError("Unexpected")
+ for i in range(0, len(scratchpad), 2):
+ action = messages[i]
+ observation = messages[i + 1]
+ log += f"{action.content}{observation.content}"
+ if final is not None:
+ log += final.content
+ return AIMessage(content=log)
+
+
+def construct_chat_history(messages):
+ collapsed_messages = []
+ temp_messages = []
+ for message in messages:
+ if isinstance(message, HumanMessage):
+ if temp_messages:
+ collapsed_messages.append(_collapse_messages(temp_messages))
+ temp_messages = []
+ collapsed_messages.append(message)
+ else:
+ temp_messages.append(message)
+
+ # Don't forget to add the last non-human message if it exists
+ if temp_messages:
+ collapsed_messages.append(_collapse_messages(temp_messages))
+
+ return collapsed_messages
+
+
+def get_xml_agent_executor(
+ tools: list[BaseTool],
+ llm: LanguageModelLike,
+ system_message: str,
+ checkpoint: BaseCheckpointSaver,
+):
+ formatted_system_message = xml_template.format(
+ system_message=system_message,
+ tools=render_text_description(tools),
+ tool_names=", ".join([t.name for t in tools]),
+ )
+
+ llm_with_stop = llm.bind(stop=[""])
+
+ def _get_messages(messages):
+ return [
+ SystemMessage(content=formatted_system_message)
+ ] + construct_chat_history(messages)
+
+ agent = _get_messages | llm_with_stop
+ tool_executor = ToolExecutor(tools)
+
+ # Define the function that determines whether to continue or not
+ def should_continue(messages):
+ last_message = messages[-1]
+ if "" in last_message.content:
+ return "continue"
+ else:
+ return "end"
+
+ # Define the function to execute tools
+ async def call_tool(messages):
+ # Based on the continue condition
+ # we know the last message involves a function call
+ last_message = messages[-1]
+ # We construct an ToolInvocation from the function_call
+ tool, tool_input = last_message.content.split("")
+ _tool = tool.split("")[1]
+ _tool_input = tool_input.split("")[1]
+ if "" in _tool_input:
+ _tool_input = _tool_input.split("")[0]
+ action = ToolInvocation(
+ tool=_tool,
+ tool_input=_tool_input,
+ )
+ # We call the tool_executor and get back a response
+ response = await tool_executor.ainvoke(action)
+ # We use the response to create a FunctionMessage
+ function_message = FunctionMessage(content=str(response), name=action.tool)
+ # We return a list, because this will get added to the existing list
+ return function_message
+
+ workflow = MessageGraph()
+
+ # Define the two nodes we will cycle between
+ workflow.add_node("agent", agent)
+ workflow.add_node("action", call_tool)
+
+ # Set the entrypoint as `agent`
+ # This means that this node is the first one called
+ workflow.set_entry_point("agent")
+
+ # We now add a conditional edge
+ workflow.add_conditional_edges(
+ # First, we define the start node. We use `agent`.
+ # This means these are the edges taken after the `agent` node is called.
+ "agent",
+ # Next, we pass in the function that will determine which node is called next.
+ should_continue,
+ # Finally we pass in a mapping.
+ # The keys are strings, and the values are other nodes.
+ # END is a special node marking that the graph should finish.
+ # What will happen is we will call `should_continue`, and then the output of that
+ # will be matched against the keys in this mapping.
+ # Based on which one it matches, that node will then be called.
+ {
+ # If `tools`, then we call the tool node.
+ "continue": "action",
+ # Otherwise we finish.
+ "end": END,
+ },
+ )
+
+ # We now add a normal edge from `tools` to `agent`.
+ # This means that after `tools` is called, `agent` node is called next.
+ workflow.add_edge("action", "agent")
+
+ # Finally, we compile it!
+ # This compiles it into a LangChain Runnable,
+ # meaning you can use it as you would any other runnable
+ app = workflow.compile(checkpointer=checkpoint)
+ return app
diff --git a/backend/app/api/runs.py b/backend/app/api/runs.py
index abeada0c..35988ae6 100644
--- a/backend/app/api/runs.py
+++ b/backend/app/api/runs.py
@@ -1,47 +1,33 @@
import asyncio
import json
-from typing import AsyncIterator, Sequence
-from uuid import uuid4
+from typing import Sequence
import langsmith.client
-import orjson
from fastapi import APIRouter, BackgroundTasks, HTTPException, Request
from fastapi.exceptions import RequestValidationError
-from gizmo_agent import agent
from langchain.pydantic_v1 import ValidationError
-from langchain.schema.messages import AnyMessage, FunctionMessage
-from langchain.schema.output import ChatGeneration
-from langchain.schema.runnable import RunnableConfig
-from langserve.callbacks import AsyncEventAggregatorCallback
+from langchain_core.messages import AnyMessage
+from langchain_core.runnables import RunnableConfig
from langserve.schema import FeedbackCreateRequest
-from langserve.serialization import WellKnownLCSerializer
-from langserve.server import _get_base_run_id_as_str, _unpack_input
+from langserve.server import _unpack_input
from langsmith.utils import tracing_is_enabled
from pydantic import BaseModel, Field
from sse_starlette import EventSourceResponse
+from app.agent import agent
from app.schema import OpengptsUserId
-from app.storage import get_assistant, get_thread_messages, public_user_id
-from app.stream import StreamMessagesHandler
+from app.storage import get_assistant, public_user_id
+from app.stream import astream_messages, to_sse
router = APIRouter()
-_serializer = WellKnownLCSerializer()
-
-
-class AgentInput(BaseModel):
- """An input into an agent."""
-
- messages: Sequence[AnyMessage] = Field(default_factory=list)
-
-
class CreateRunPayload(BaseModel):
"""Payload for creating a run."""
assistant_id: str
thread_id: str
- input: AgentInput = Field(default_factory=AgentInput)
+ input: Sequence[AnyMessage] = Field(default_factory=list)
async def _run_input_and_config(request: Request, opengpts_user_id: OpengptsUserId):
@@ -49,16 +35,13 @@ async def _run_input_and_config(request: Request, opengpts_user_id: OpengptsUser
body = await request.json()
except json.JSONDecodeError:
raise RequestValidationError(errors=["Invalid JSON body"])
- assistant, public_assistant, state = await asyncio.gather(
+ assistant, public_assistant = await asyncio.gather(
asyncio.get_running_loop().run_in_executor(
None, get_assistant, opengpts_user_id, body["assistant_id"]
),
asyncio.get_running_loop().run_in_executor(
None, get_assistant, public_user_id, body["assistant_id"]
),
- asyncio.get_running_loop().run_in_executor(
- None, get_thread_messages, opengpts_user_id, body["thread_id"]
- ),
)
assistant = assistant or public_assistant
if not assistant:
@@ -77,94 +60,32 @@ async def _run_input_and_config(request: Request, opengpts_user_id: OpengptsUser
except ValidationError as e:
raise RequestValidationError(e.errors(), body=body)
- return input_, config, state["messages"]
+ return input_, config
@router.post("")
async def create_run(
- request: Request,
payload: CreateRunPayload, # for openapi docs
+ request: Request,
opengpts_user_id: OpengptsUserId,
background_tasks: BackgroundTasks,
):
"""Create a run."""
- input_, config, messages = await _run_input_and_config(request, opengpts_user_id)
+ input_, config = await _run_input_and_config(request, opengpts_user_id)
background_tasks.add_task(agent.ainvoke, input_, config)
return {"status": "ok"} # TODO add a run id
@router.post("/stream")
async def stream_run(
- request: Request,
payload: CreateRunPayload, # for openapi docs
+ request: Request,
opengpts_user_id: OpengptsUserId,
):
"""Create a run."""
- input_, config, messages = await _run_input_and_config(request, opengpts_user_id)
- streamer = StreamMessagesHandler(messages + input_["messages"])
- event_aggregator = AsyncEventAggregatorCallback()
- config["callbacks"] = [streamer, event_aggregator]
-
- # Call the runnable in streaming mode,
- # add each chunk to the output stream
- async def consume_astream() -> None:
- try:
- async for chunk in agent.astream(input_, config):
- await streamer.send_stream.send(chunk)
- # hack: function messages aren't generated by chat model
- # so the callback handler doesn't know about them
- if chunk["messages"]:
- message = chunk["messages"][-1]
- if isinstance(message, FunctionMessage):
- streamer.output[uuid4()] = ChatGeneration(message=message)
- except Exception as e:
- await streamer.send_stream.send(e)
- finally:
- await streamer.send_stream.aclose()
-
- # Start the runnable in the background
- task = asyncio.create_task(consume_astream())
-
- # Consume the stream into an EventSourceResponse
- async def _stream() -> AsyncIterator[dict]:
- has_sent_metadata = False
-
- async for chunk in streamer.receive_stream:
- if isinstance(chunk, BaseException):
- yield {
- "event": "error",
- # Do not expose the error message to the client since
- # the message may contain sensitive information.
- # We'll add client side errors for validation as well.
- "data": orjson.dumps(
- {"status_code": 500, "message": "Internal Server Error"}
- ).decode(),
- }
- raise chunk
- else:
- if not has_sent_metadata and event_aggregator.callback_events:
- yield {
- "event": "metadata",
- "data": orjson.dumps(
- {"run_id": _get_base_run_id_as_str(event_aggregator)}
- ).decode(),
- }
- has_sent_metadata = True
-
- yield {
- # EventSourceResponse expects a string for data
- # so after serializing into bytes, we decode into utf-8
- # to get a string.
- "data": _serializer.dumps(chunk).decode("utf-8"),
- "event": "data",
- }
-
- # Send an end event to signal the end of the stream
- yield {"event": "end"}
- # Wait for the runnable to finish
- await task
-
- return EventSourceResponse(_stream())
+ input_, config = await _run_input_and_config(request, opengpts_user_id)
+
+ return EventSourceResponse(to_sse(astream_messages(agent, input_, config)))
@router.get("/input_schema")
diff --git a/backend/packages/agent-executor/agent_executor/checkpoint.py b/backend/app/checkpoint.py
similarity index 65%
rename from backend/packages/agent-executor/agent_executor/checkpoint.py
rename to backend/app/checkpoint.py
index a2c5ddca..5013b3b6 100644
--- a/backend/packages/agent-executor/agent_executor/checkpoint.py
+++ b/backend/app/checkpoint.py
@@ -1,13 +1,14 @@
import os
import pickle
from functools import partial
-from typing import Any, Mapping
+from typing import Any
from langchain.pydantic_v1 import Field
from langchain.schema.runnable import RunnableConfig
from langchain.schema.runnable.utils import ConfigurableFieldSpec
from langchain.utilities.redis import get_client
-from permchain.checkpoint.base import BaseCheckpointAdapter
+from langgraph.checkpoint import BaseCheckpointSaver
+from langgraph.checkpoint.base import Checkpoint, empty_checkpoint
from redis.client import Redis as RedisType
@@ -26,7 +27,7 @@ def _load(mapping: dict[bytes, bytes]) -> dict:
}
-class RedisCheckpoint(BaseCheckpointAdapter):
+class RedisCheckpoint(BaseCheckpointSaver):
client: RedisType = Field(
default_factory=partial(get_client, os.environ.get("REDIS_URL"))
)
@@ -60,8 +61,23 @@ def _hash_key(self, config: RunnableConfig) -> str:
config["configurable"]["user_id"], config["configurable"]["thread_id"]
)
- def get(self, config: RunnableConfig) -> Mapping[str, Any] | None:
- return _load(self.client.hgetall(self._hash_key(config)))
+ def get(self, config: RunnableConfig) -> Checkpoint | None:
+ value = _load(self.client.hgetall(self._hash_key(config)))
+ if value.get("v") == 1:
+ # langgraph version 1
+ return value
+ elif value.get("__pregel_version") == 1:
+ # permchain version 1
+ value.pop("__pregel_version")
+ value.pop("__pregel_ts")
+ checkpoint = empty_checkpoint()
+ checkpoint["channel_values"] = value
+ for key in value:
+ checkpoint["channel_versions"][key] = 1
+ return checkpoint
+ else:
+ # unknown version
+ return None
- def put(self, config: RunnableConfig, checkpoint: Mapping[str, Any]) -> None:
+ def put(self, config: RunnableConfig, checkpoint: Checkpoint) -> None:
return self.client.hmset(self._hash_key(config), _dump(checkpoint))
diff --git a/backend/packages/agent-executor/agent_executor/ingest.py b/backend/app/ingest.py
similarity index 85%
rename from backend/packages/agent-executor/agent_executor/ingest.py
rename to backend/app/ingest.py
index 9570fd09..58dee61d 100644
--- a/backend/packages/agent-executor/agent_executor/ingest.py
+++ b/backend/app/ingest.py
@@ -8,11 +8,11 @@
"""
from typing import List
-from langchain.document_loaders import Blob
-from langchain.document_loaders.base import BaseBlobParser
-from langchain.schema import Document
-from langchain.schema.vectorstore import VectorStore
from langchain.text_splitter import TextSplitter
+from langchain_community.document_loaders import Blob
+from langchain_community.document_loaders.base import BaseBlobParser
+from langchain_core.documents import Document
+from langchain_core.vectorstores import VectorStore
def _update_document_metadata(document: Document, namespace: str) -> None:
diff --git a/backend/app/llms.py b/backend/app/llms.py
new file mode 100644
index 00000000..f7de5da2
--- /dev/null
+++ b/backend/app/llms.py
@@ -0,0 +1,44 @@
+import os
+
+import boto3
+from langchain_community.chat_models import BedrockChat, ChatAnthropic
+from langchain_google_vertexai import ChatVertexAI
+from langchain_openai import AzureChatOpenAI, ChatOpenAI
+
+
+def get_openai_llm(gpt_4: bool = False, azure: bool = False):
+ if not azure:
+ if gpt_4:
+ llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0, streaming=True)
+ else:
+ llm = ChatOpenAI(model="gpt-3.5-turbo-1106", temperature=0, streaming=True)
+ else:
+ llm = AzureChatOpenAI(
+ temperature=0,
+ deployment_name=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+ openai_api_base=os.environ["AZURE_OPENAI_API_BASE"],
+ openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+ openai_api_key=os.environ["AZURE_OPENAI_API_KEY"],
+ streaming=True,
+ )
+ return llm
+
+
+def get_anthropic_llm(bedrock: bool = False):
+ if bedrock:
+ client = boto3.client(
+ "bedrock-runtime",
+ region_name="us-west-2",
+ aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"),
+ aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY"),
+ )
+ model = BedrockChat(model_id="anthropic.claude-v2", client=client)
+ else:
+ model = ChatAnthropic(temperature=0, max_tokens_to_sample=2000)
+ return model
+
+
+def get_google_llm():
+ return ChatVertexAI(
+ model_name="gemini-pro", convert_system_message_to_human=True, streaming=True
+ )
diff --git a/backend/packages/agent-executor/agent_executor/parsing.py b/backend/app/parsing.py
similarity index 100%
rename from backend/packages/agent-executor/agent_executor/parsing.py
rename to backend/app/parsing.py
diff --git a/backend/app/server.py b/backend/app/server.py
index 2fa5e436..82fdd920 100644
--- a/backend/app/server.py
+++ b/backend/app/server.py
@@ -3,9 +3,9 @@
import orjson
from fastapi import FastAPI, Form, UploadFile
from fastapi.staticfiles import StaticFiles
-from gizmo_agent import ingest_runnable
from app.api import router as api_router
+from app.upload import ingest_runnable
app = FastAPI(title="OpenGPTs API")
diff --git a/backend/app/storage.py b/backend/app/storage.py
index 10b0b559..ad56a9f5 100644
--- a/backend/app/storage.py
+++ b/backend/app/storage.py
@@ -1,15 +1,16 @@
import os
from datetime import datetime
from typing import List, Sequence
+from app.stream import map_chunk_to_msg
import orjson
-from agent_executor.checkpoint import RedisCheckpoint
from langchain.schema.messages import AnyMessage
from langchain.utilities.redis import get_client
-from permchain.channels import Topic
-from permchain.channels.base import ChannelsManager, create_checkpoint
+from langgraph.channels.base import ChannelsManager
+from langgraph.checkpoint.base import empty_checkpoint
from redis.client import Redis as RedisType
+from app.agent import AgentType, get_agent_executor
from app.schema import Assistant, AssistantWithoutUserId, Thread, ThreadWithoutUserId
@@ -145,35 +146,34 @@ def get_thread(user_id: str, thread_id: str) -> Thread | None:
return load(thread_hash_keys, values) if any(values) else None
+# TODO remove hardcoded channel name
+MESSAGES_CHANNEL_NAME = "__root__"
+
+
def get_thread_messages(user_id: str, thread_id: str):
"""Get all messages for a thread."""
- client = RedisCheckpoint()
- checkpoint = client.get(
- {"configurable": {"user_id": user_id, "thread_id": thread_id}}
- )
- # TODO replace hardcoded messages channel with
- # channel extracted from agent
- with ChannelsManager(
- {"messages": Topic(AnyMessage, accumulate=True)}, checkpoint
- ) as channels:
- return {k: v.get() for k, v in channels.items()}
+ config = {"configurable": {"user_id": user_id, "thread_id": thread_id}}
+ app = get_agent_executor([], AgentType.GPT_35_TURBO, "")
+ checkpoint = app.checkpointer.get(config) or empty_checkpoint()
+ with ChannelsManager(app.channels, checkpoint) as channels:
+ return {
+ "messages": [
+ map_chunk_to_msg(msg) for msg in channels[MESSAGES_CHANNEL_NAME].get()
+ ]
+ }
def post_thread_messages(user_id: str, thread_id: str, messages: Sequence[AnyMessage]):
"""Add messages to a thread."""
- client = RedisCheckpoint()
config = {"configurable": {"user_id": user_id, "thread_id": thread_id}}
- checkpoint = client.get(config)
- # TODO replace hardcoded messages channel with
- # channel extracted from agent
- with ChannelsManager(
- {"messages": Topic(AnyMessage, accumulate=True)}, checkpoint
- ) as channels:
- channels["messages"].update(messages)
- checkpoint = {
- k: v for k, v in create_checkpoint(channels).items() if k == "messages"
- }
- client.put(config, checkpoint)
+ app = get_agent_executor([], AgentType.GPT_35_TURBO, "")
+ checkpoint = app.checkpointer.get(config) or empty_checkpoint()
+ with ChannelsManager(app.channels, checkpoint) as channels:
+ channel = channels[MESSAGES_CHANNEL_NAME]
+ channel.update([messages])
+ checkpoint["channel_values"][MESSAGES_CHANNEL_NAME] = channel.checkpoint()
+ checkpoint["channel_versions"][MESSAGES_CHANNEL_NAME] += 1
+ app.checkpointer.put(config, checkpoint)
def put_thread(user_id: str, thread_id: str, *, assistant_id: str, name: str) -> Thread:
diff --git a/backend/app/stream.py b/backend/app/stream.py
index 98ed43aa..e6526037 100644
--- a/backend/app/stream.py
+++ b/backend/app/stream.py
@@ -1,10 +1,6 @@
-import math
-from typing import Any, Dict, Optional, Sequence, Union
-from uuid import UUID
+from typing import AsyncIterator, Optional, Sequence, Union
-from anyio import create_memory_object_stream
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema.messages import (
+from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
@@ -15,55 +11,50 @@
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
+ AnyMessage,
)
-from langchain.schema.output import ChatGenerationChunk, GenerationChunk
+from langchain_core.runnables import Runnable, RunnableConfig
+from langserve.serialization import WellKnownLCSerializer
+import orjson
+MessagesStream = AsyncIterator[Union[list[AnyMessage], str]]
-class StreamMessagesHandler(BaseCallbackHandler):
- def __init__(self, messages: Sequence[BaseMessage]) -> None:
- self.messages = messages
- self.output: Dict[UUID, ChatGenerationChunk] = {}
- send_stream, receive_stream = create_memory_object_stream(math.inf)
- self.send_stream = send_stream
- self.receive_stream = receive_stream
- def on_llm_new_token(
- self,
- token: str,
- *,
- chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None,
- run_id: UUID,
- **kwargs: Any,
- ) -> Any:
- # If this is being called for a non-Chat Model run, convert to AIMessage
- if chunk is None:
- chunk = ChatGenerationChunk(message=AIMessageChunk(content=token))
- # If we get something we don't know how to handle, ignore it
- if not (
- isinstance(chunk, ChatGenerationChunk)
- or isinstance(chunk, BaseMessageChunk)
+async def astream_messages(
+ app: Runnable, input: Sequence[AnyMessage], config: RunnableConfig
+) -> MessagesStream:
+ """Stream messages from the runnable."""
+ root_run_id: Optional[str] = None
+ last_messages_list: Optional[list[AnyMessage]] = None
+ last_stream_run_id: Optional[str] = None
+
+ async for event in app.astream_events(
+ input, config, version="v1", output_keys=["__root__"]
+ ):
+ if event["event"] == "on_chain_start" and not root_run_id:
+ root_run_id = event["run_id"]
+
+ yield root_run_id
+ elif event["event"] == "on_chain_stream" and event["run_id"] == root_run_id:
+ last_messages_list = event["data"]["chunk"]["__root__"]
+
+ yield last_messages_list
+ elif (
+ event["event"] == "on_chat_model_stream" and last_messages_list is not None
):
- return
- # Convert messages to ChatGenerationChunks (workaround for old langchahin)
- if isinstance(chunk, BaseMessageChunk):
- chunk = ChatGenerationChunk(message=chunk)
- # Accumulate the output (ChatGenerationChunk implements __add__)
- if not self.output.get(run_id):
- self.output[run_id] = chunk
- else:
- self.output[run_id] += chunk
- # Send the messages to the stream
- self.send_stream.send_nowait(
- {
- "messages": (
- self.messages
- + [
- map_chunk_to_msg(chunk.message)
- for chunk in self.output.values()
- ]
- )
- }
- )
+ is_new_stream_run = (
+ last_stream_run_id is None or last_stream_run_id != event["run_id"]
+ )
+ is_diff_msg_type = last_messages_list and type( # noqa: E721
+ last_messages_list[-1]
+ ) != type(event["data"]["chunk"])
+ if is_new_stream_run or is_diff_msg_type:
+ last_stream_run_id = event["run_id"]
+ last_messages_list.append(event["data"]["chunk"])
+ else:
+ last_messages_list[-1] = last_messages_list[-1] + event["data"]["chunk"]
+
+ yield last_messages_list
def map_chunk_to_msg(chunk: BaseMessageChunk) -> BaseMessage:
@@ -80,3 +71,40 @@ def map_chunk_to_msg(chunk: BaseMessageChunk) -> BaseMessage:
return ChatMessage(**args)
else:
raise ValueError(f"Unknown chunk type: {chunk}")
+
+
+_serializer = WellKnownLCSerializer()
+
+
+async def to_sse(messages_stream: MessagesStream) -> AsyncIterator[dict]:
+ """Consume the stream into an EventSourceResponse"""
+ try:
+ async for chunk in messages_stream:
+ # EventSourceResponse expects a string for data
+ # so after serializing into bytes, we decode into utf-8
+ # to get a string.
+ if isinstance(chunk, str):
+ yield {
+ "event": "metadata",
+ "data": orjson.dumps({"run_id": chunk}).decode(),
+ }
+ else:
+ yield {
+ "event": "data",
+ "data": _serializer.dumps(
+ [map_chunk_to_msg(msg) for msg in chunk]
+ ).decode(),
+ }
+ except Exception:
+ yield {
+ "event": "error",
+ # Do not expose the error message to the client since
+ # the message may contain sensitive information.
+ # We'll add client side errors for validation as well.
+ "data": orjson.dumps(
+ {"status_code": 500, "message": "Internal Server Error"}
+ ).decode(),
+ }
+
+ # Send an end event to signal the end of the stream
+ yield {"event": "end"}
diff --git a/backend/packages/gizmo-agent/gizmo_agent/tools.py b/backend/app/tools.py
similarity index 85%
rename from backend/packages/gizmo-agent/gizmo_agent/tools.py
rename to backend/app/tools.py
index 29525dce..74e0eecf 100644
--- a/backend/packages/gizmo-agent/gizmo_agent/tools.py
+++ b/backend/app/tools.py
@@ -1,16 +1,20 @@
from enum import Enum
from langchain.pydantic_v1 import BaseModel, Field
-from langchain.retrievers import KayAiRetriever, PubMedRetriever, WikipediaRetriever
-from langchain.retrievers.you import YouRetriever
-from langchain.tools import ArxivQueryRun, DuckDuckGoSearchRun
from langchain.tools.retriever import create_retriever_tool
-from langchain.tools.tavily_search import TavilyAnswer, TavilySearchResults
-from langchain.utilities import ArxivAPIWrapper
-from langchain.utilities.tavily_search import TavilySearchAPIWrapper
-from langchain.vectorstores.redis import RedisFilter
-
-from gizmo_agent.ingest import vstore
+from langchain_community.retrievers import (
+ KayAiRetriever,
+ PubMedRetriever,
+ WikipediaRetriever,
+)
+from langchain_community.retrievers.you import YouRetriever
+from langchain_community.tools import ArxivQueryRun, DuckDuckGoSearchRun
+from langchain_community.tools.tavily_search import TavilyAnswer, TavilySearchResults
+from langchain_community.utilities.arxiv import ArxivAPIWrapper
+from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
+from langchain_community.vectorstores.redis import RedisFilter
+
+from app.upload import vstore
class DDGInput(BaseModel):
diff --git a/backend/packages/agent-executor/agent_executor/upload.py b/backend/app/upload.py
similarity index 70%
rename from backend/packages/agent-executor/agent_executor/upload.py
rename to backend/app/upload.py
index 515548bd..87168003 100644
--- a/backend/packages/agent-executor/agent_executor/upload.py
+++ b/backend/app/upload.py
@@ -8,25 +8,32 @@
"""
from __future__ import annotations
+import os
from typing import Any, BinaryIO, List, Optional
-from langchain.document_loaders.blob_loaders.schema import Blob
-from langchain.schema.runnable import RunnableConfig, RunnableSerializable
-from langchain.schema.vectorstore import VectorStore
-from langchain.text_splitter import TextSplitter
+from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
+from langchain_community.document_loaders.blob_loaders import Blob
+from langchain_community.vectorstores.redis import Redis
+from langchain_core.runnables import (
+ ConfigurableField,
+ RunnableConfig,
+ RunnableSerializable,
+)
+from langchain_core.vectorstores import VectorStore
+from langchain_openai import OpenAIEmbeddings
-from agent_executor.ingest import ingest_blob
-from agent_executor.parsing import MIMETYPE_BASED_PARSER
+from app.ingest import ingest_blob
+from app.parsing import MIMETYPE_BASED_PARSER
def _guess_mimetype(file_bytes: bytes) -> str:
"""Guess the mime-type of a file."""
try:
import magic
- except ImportError:
+ except ImportError as e:
raise ImportError(
"magic package not found, please install it with `pip install python-magic`"
- )
+ ) from e
mime = magic.Magic(mime=True)
mime_type = mime.from_buffer(file_bytes)
@@ -94,3 +101,26 @@ def batch(
)
)
return ids
+
+
+index_schema = {
+ "tag": [{"name": "namespace"}],
+}
+vstore = Redis(
+ redis_url=os.environ["REDIS_URL"],
+ index_name="opengpts",
+ embedding=OpenAIEmbeddings(),
+ index_schema=index_schema,
+)
+
+
+ingest_runnable = IngestRunnable(
+ text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200),
+ vectorstore=vstore,
+).configurable_fields(
+ assistant_id=ConfigurableField(
+ id="assistant_id",
+ annotation=str,
+ name="Assistant ID",
+ ),
+)
diff --git a/backend/packages/README.md b/backend/packages/README.md
deleted file mode 100644
index e69de29b..00000000
diff --git a/backend/packages/agent-executor/README.md b/backend/packages/agent-executor/README.md
deleted file mode 100644
index 3db94e99..00000000
--- a/backend/packages/agent-executor/README.md
+++ /dev/null
@@ -1 +0,0 @@
-# agent-executor
diff --git a/backend/packages/agent-executor/agent_executor/dnd.py b/backend/packages/agent-executor/agent_executor/dnd.py
deleted file mode 100644
index 4fd39502..00000000
--- a/backend/packages/agent-executor/agent_executor/dnd.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import json
-
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.pydantic_v1 import BaseModel, Field
-from langchain.utils.openai_functions import convert_pydantic_to_openai_function
-from langchain_core.language_models import BaseChatModel
-from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
-from permchain import BaseCheckpointAdapter, Channel, Pregel
-from permchain.channels import LastValue, Topic
-
-character_system_msg = """You are a dungeon master for a game of dungeons and dragons.
-
-You are interacting with the first (and only) player in the game. \
-Your job is to collect all needed information about their character. This will be used in the quest. \
-Feel free to ask them as many questions as needed to get to the relevant information.
-The relevant information is:
-- Character's name
-- Character's race (or species)
-- Character's class
-- Character's alignment
-
-Once you have gathered enough information, write that info to `notebook`."""
-
-
-class CharacterNotebook(BaseModel):
- """Notebook to write information to"""
-
- player_info: str = Field(
- description="Information about a player that you will remember over time"
- )
-
-
-character_prompt = ChatPromptTemplate.from_messages(
- [("system", character_system_msg), MessagesPlaceholder(variable_name="messages")]
-)
-
-gameplay_system_msg = """You are a dungeon master for a game of dungeons and dragons.
-
-You are leading a quest of one person. Their character description is here:
-
-{character}
-
-A summary of the game state is here:
-
-{state}"""
-
-game_prompt = ChatPromptTemplate.from_messages(
- [("system", gameplay_system_msg), MessagesPlaceholder(variable_name="messages")]
-)
-
-
-class StateNotebook(BaseModel):
- """Notebook to write information to"""
-
- state: str = Field(description="Information about the current game state")
-
-
-state_prompt = ChatPromptTemplate.from_messages(
- [
- ("system", gameplay_system_msg),
- MessagesPlaceholder(variable_name="messages"),
- (
- "human",
- "If any updates to the game state are neccessary, please update the state notebook. If none are, just say no.",
- ),
- ]
-)
-
-
-def _maybe_update_state(message: AnyMessage):
- if "function_call" in message.additional_kwargs:
- return Channel.write_to(
- "messages",
- state=json.loads(message.additional_kwargs["function_call"]["arguments"])[
- "state"
- ],
- )
-
-
-def _maybe_update_character(message: AnyMessage):
- if "function_call" in message.additional_kwargs:
- args = json.loads(message.additional_kwargs["function_call"]["arguments"])
- return Channel.write_to(
- messages=AIMessage(content="Ready for the quest?"),
- character=args["player_info"],
- )
-
-
-def create_dnd_bot(llm: BaseChatModel, checkpoint: BaseCheckpointAdapter):
- character_model = llm.bind(
- functions=[convert_pydantic_to_openai_function(CharacterNotebook)],
- )
- game_chain = game_prompt | llm | Channel.write_to("messages", check_update=True)
- state_model = llm.bind(
- functions=[convert_pydantic_to_openai_function(StateNotebook)],
- stream=False,
- )
- state_chain = (
- Channel.subscribe_to(["check_update"]).join(["messages", "character", "state"])
- | state_prompt
- | state_model
- | _maybe_update_state
- )
- character_chain = (
- character_prompt
- | character_model
- | Channel.write_to("messages")
- | _maybe_update_character
- )
-
- def _route_to_chain(_input):
- messages = _input["messages"]
- if not messages:
- return
- if not _input["character"] and isinstance(messages[-1], HumanMessage):
- return character_chain
- elif isinstance(messages[-1], HumanMessage):
- return game_chain
-
- executor = (
- Channel.subscribe_to(["messages"]).join(["character", "state"])
- | _route_to_chain
- )
- dnd = Pregel(
- chains={"executor": executor, "update_state": state_chain},
- channels={
- "messages": Topic(AnyMessage, accumulate=True),
- "character": LastValue(str),
- "state": LastValue(str),
- "check_update": LastValue(bool),
- },
- input=["messages"],
- output=["messages"],
- checkpoint=checkpoint,
- )
- return dnd
diff --git a/backend/packages/agent-executor/agent_executor/permchain.py b/backend/packages/agent-executor/agent_executor/permchain.py
deleted file mode 100644
index bbf7d8e9..00000000
--- a/backend/packages/agent-executor/agent_executor/permchain.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import json
-from operator import itemgetter
-from typing import Sequence
-
-from langchain.schema.agent import AgentAction, AgentActionMessageLog, AgentFinish
-from langchain.schema.messages import AIMessage, AnyMessage, FunctionMessage
-from langchain.schema.runnable import (
- Runnable,
- RunnableConfig,
- RunnableLambda,
- RunnablePassthrough,
-)
-from langchain.tools import BaseTool
-from permchain import Channel, Pregel, ReservedChannels
-from permchain.channels import Topic
-from permchain.checkpoint.base import BaseCheckpointAdapter
-
-
-def _create_agent_message(
- output: AgentAction | AgentFinish
-) -> list[AnyMessage] | AnyMessage:
- if isinstance(output, AgentAction):
- if isinstance(output, AgentActionMessageLog):
- output.message_log[-1].additional_kwargs["agent"] = output
- messages = output.message_log
- output.message_log = [] # avoid circular reference for json dumps
- return messages
- else:
- return AIMessage(
- content=output.log,
- additional_kwargs={"agent": output},
- )
- else:
- return AIMessage(
- content=output.return_values["output"],
- additional_kwargs={"agent": output},
- )
-
-
-def _create_function_message(
- agent_action: AgentAction, observation: str
-) -> FunctionMessage:
- if not isinstance(observation, str):
- try:
- content = json.dumps(observation, ensure_ascii=False)
- except Exception:
- content = str(observation)
- else:
- content = observation
- return FunctionMessage(
- name=agent_action.tool,
- content=content,
- )
-
-
-def _run_tool(
- messages: list[AnyMessage], config: RunnableConfig, *, tools: dict[str, BaseTool]
-) -> FunctionMessage:
- action: AgentAction = messages[-1].additional_kwargs["agent"]
- tool = tools[action.tool]
- result = tool.invoke(action.tool_input, config)
- return _create_function_message(action, result)
-
-
-async def _arun_tool(
- messages: list[AnyMessage], config: RunnableConfig, *, tools: dict[str, BaseTool]
-) -> FunctionMessage:
- action: AgentAction = messages[-1].additional_kwargs["agent"]
- tool = tools[action.tool]
- result = await tool.ainvoke(action.tool_input, config)
- return _create_function_message(action, result)
-
-
-def get_agent_executor(
- tools: list[BaseTool],
- agent: Runnable[dict[str, list[AnyMessage]], AgentAction | AgentFinish],
- checkpoint: BaseCheckpointAdapter,
-) -> Pregel:
- tool_map = {tool.name: tool for tool in tools}
- tool_lambda = RunnableLambda(_run_tool, _arun_tool).bind(tools=tool_map)
-
- tool_chain = itemgetter("messages") | tool_lambda | Channel.write_to("messages")
- agent_chain = agent | _create_agent_message | Channel.write_to("messages")
-
- def route_last_message(input: dict[str, bool | Sequence[AnyMessage]]) -> Runnable:
- if not input["messages"]:
- # no messages, do nothing
- return agent_chain
-
- message: AnyMessage = input["messages"][-1]
- if isinstance(message.additional_kwargs.get("agent"), AgentFinish):
- # finished, do nothing
- return RunnablePassthrough()
-
- if input[ReservedChannels.is_last_step]:
- # exhausted iterations without finishing, return stop message
- return Channel.write_to(
- messages=_create_agent_message(
- AgentFinish(
- {
- "output": "Agent stopped due to iteration limit or time limit."
- },
- "",
- )
- )
- )
-
- if isinstance(message.additional_kwargs.get("agent"), AgentAction):
- # agent action, run it
- return tool_chain
-
- # otherwise, run the agent
- return agent_chain
-
- executor = (
- Channel.subscribe_to(["messages"]).join([ReservedChannels.is_last_step])
- | route_last_message
- )
-
- return Pregel(
- chains={"executor": executor},
- channels={"messages": Topic(AnyMessage, accumulate=True)},
- input=["messages"],
- output=["messages"],
- checkpoint=checkpoint,
- )
diff --git a/backend/packages/agent-executor/poetry.lock b/backend/packages/agent-executor/poetry.lock
deleted file mode 100644
index cd0b272c..00000000
--- a/backend/packages/agent-executor/poetry.lock
+++ /dev/null
@@ -1,1615 +0,0 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
-
-[[package]]
-name = "aiohttp"
-version = "3.8.6"
-description = "Async http client/server framework (asyncio)"
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-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
-[metadata]
-lock-version = "2.0"
-python-versions = "^3.8.1"
-content-hash = "bbe092011f59709f0594e2ab445ee1ce4751ba2124c2c1b202541a9b07c19566"
diff --git a/backend/packages/agent-executor/pyproject.toml b/backend/packages/agent-executor/pyproject.toml
deleted file mode 100644
index e37fd516..00000000
--- a/backend/packages/agent-executor/pyproject.toml
+++ /dev/null
@@ -1,22 +0,0 @@
-[tool.poetry]
-name = "agent-executor"
-version = "0.1.0"
-description = ""
-authors = [
- "Lance Martin ",
-]
-readme = "README.md"
-
-[tool.poetry.dependencies]
-python = "^3.8.1"
-langchain = ">=0.0.333"
-python-magic = "^0.4.27"
-
-[tool.poetry.group.dev.dependencies]
-langchain-cli = ">=0.0.15"
-
-[build-system]
-requires = [
- "poetry-core",
-]
-build-backend = "poetry.core.masonry.api"
diff --git a/backend/packages/gizmo-agent/README.md b/backend/packages/gizmo-agent/README.md
deleted file mode 100644
index 1e5b0d15..00000000
--- a/backend/packages/gizmo-agent/README.md
+++ /dev/null
@@ -1 +0,0 @@
-# gizmo-agent
diff --git a/backend/packages/gizmo-agent/gizmo_agent/__init__.py b/backend/packages/gizmo-agent/gizmo_agent/__init__.py
deleted file mode 100644
index ad49400b..00000000
--- a/backend/packages/gizmo-agent/gizmo_agent/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from gizmo_agent.ingest import ingest_runnable
-from gizmo_agent.main import agent
-
-__all__ = ["agent", "ingest_runnable"]
diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/__init__.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/__init__.py
deleted file mode 100644
index e9b139f6..00000000
--- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from enum import Enum
-
-from .openai import get_openai_function_agent
-from .xml.agent import get_xml_agent
-
-
-class GizmoAgentType(str, Enum):
- GPT_35_TURBO = "GPT 3.5 Turbo"
- # GPT_4 = "GPT 4"
- # AZURE_OPENAI = "GPT 4 (Azure OpenAI)"
- # CLAUDE2 = "Claude 2"
- # BEDROCK_CLAUDE2 = "Claude 2 (Amazon Bedrock)"
-
-
-__all__ = [
- "get_openai_function_agent",
- "get_xml_agent",
- "GizmoAgentType",
-]
diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py
deleted file mode 100644
index 0e6c2a98..00000000
--- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/openai.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import os
-
-from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
-from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.tools.render import format_tool_to_openai_function
-
-
-def get_openai_function_agent(
- tools, system_message, gpt_4: bool = False, azure: bool = False
-):
- if not azure:
- if gpt_4:
- llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0, streaming=True)
- else:
- llm = ChatOpenAI(model="gpt-3.5-turbo-1106", temperature=0, streaming=True)
- else:
- llm = AzureChatOpenAI(
- temperature=0,
- deployment_name=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
- openai_api_base=os.environ["AZURE_OPENAI_API_BASE"],
- openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
- openai_api_key=os.environ["AZURE_OPENAI_API_KEY"],
- streaming=True,
- )
- prompt = ChatPromptTemplate.from_messages(
- [
- ("system", system_message),
- MessagesPlaceholder(variable_name="messages"),
- ]
- )
- if tools:
- llm_with_tools = llm.bind(
- functions=[format_tool_to_openai_function(t) for t in tools]
- )
- else:
- llm_with_tools = llm
- agent = prompt | llm_with_tools | OpenAIFunctionsAgentOutputParser()
- return agent
diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/__init__.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py
deleted file mode 100644
index 8ddea31d..00000000
--- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/agent.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import os
-
-import boto3
-from langchain.chat_models import BedrockChat, ChatAnthropic
-from langchain.schema.messages import AIMessage, HumanMessage
-from langchain.tools.render import render_text_description
-
-from .prompts import conversational_prompt, parse_output
-
-
-def _collapse_messages(messages):
- log = ""
- if isinstance(messages[-1], AIMessage):
- scratchpad = messages[:-1]
- final = messages[-1]
- else:
- scratchpad = messages
- final = None
- if len(scratchpad) % 2 != 0:
- raise ValueError("Unexpected")
- for i in range(0, len(scratchpad), 2):
- action = messages[i]
- observation = messages[i + 1]
- log += f"{action.content}{observation.content}"
- if final is not None:
- log += final.content
- return AIMessage(content=log)
-
-
-def construct_chat_history(messages):
- collapsed_messages = []
- temp_messages = []
- for message in messages:
- if isinstance(message, HumanMessage):
- if temp_messages:
- collapsed_messages.append(_collapse_messages(temp_messages))
- temp_messages = []
- collapsed_messages.append(message)
- else:
- temp_messages.append(message)
-
- # Don't forget to add the last non-human message if it exists
- if temp_messages:
- collapsed_messages.append(_collapse_messages(temp_messages))
-
- return collapsed_messages
-
-
-def get_xml_agent(tools, system_message, bedrock=False):
- if bedrock:
- client = boto3.client(
- "bedrock-runtime",
- region_name="us-west-2",
- aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"),
- aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY"),
- )
- model = BedrockChat(model_id="anthropic.claude-v2", client=client)
- else:
- model = ChatAnthropic(temperature=0, max_tokens_to_sample=2000)
- prompt = conversational_prompt.partial(
- tools=render_text_description(tools),
- tool_names=", ".join([t.name for t in tools]),
- system_message=system_message,
- )
- llm_with_stop = model.bind(stop=[""])
-
- agent = (
- {"messages": lambda x: construct_chat_history(x["messages"])}
- | prompt
- | llm_with_stop
- | parse_output
- )
- return agent
diff --git a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/prompts.py b/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/prompts.py
deleted file mode 100644
index b16dcbff..00000000
--- a/backend/packages/gizmo-agent/gizmo_agent/agent_types/xml/prompts.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.schema import AgentAction, AgentFinish
-
-template = """{system_message}
-
-You have access to the following tools:
-
-{tools}
-
-In order to use a tool, you can use and tags. You will then get back a response in the form
-For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:
-
-searchweather in SF
-64 degrees
-
-When you are done, you can respond as normal to the user.
-
-Example 1:
-
-Human: Hi!
-
-Assistant: Hi! How are you?
-
-Human: What is the weather in SF?
-Assistant: searchweather in SF
-64 degrees
-It is 64 degrees in SF
-
-
-Begin!""" # noqa: E501
-
-conversational_prompt = ChatPromptTemplate.from_messages(
- [
- ("system", template),
- MessagesPlaceholder(variable_name="messages"),
- ]
-)
-
-
-def parse_output(message):
- text = message.content
- if "" in text:
- tool, tool_input = text.split("")
- _tool = tool.split("")[1]
- _tool_input = tool_input.split("")[1]
- if "" in _tool_input:
- _tool_input = _tool_input.split("")[0]
- return AgentAction(tool=_tool, tool_input=_tool_input, log=text)
- else:
- return AgentFinish(return_values={"output": text}, log=text)
diff --git a/backend/packages/gizmo-agent/gizmo_agent/ingest.py b/backend/packages/gizmo-agent/gizmo_agent/ingest.py
deleted file mode 100644
index e1e41c01..00000000
--- a/backend/packages/gizmo-agent/gizmo_agent/ingest.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import os
-
-from agent_executor.upload import IngestRunnable
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.schema.runnable import ConfigurableField
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores.redis import Redis
-
-index_schema = {
- "tag": [{"name": "namespace"}],
-}
-vstore = Redis(
- redis_url=os.environ["REDIS_URL"],
- index_name="opengpts",
- embedding=OpenAIEmbeddings(),
- index_schema=index_schema,
-)
-
-
-ingest_runnable = IngestRunnable(
- text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200),
- vectorstore=vstore,
-).configurable_fields(
- assistant_id=ConfigurableField(
- id="assistant_id",
- annotation=str,
- name="Assistant ID",
- ),
-)
diff --git a/backend/packages/gizmo-agent/gizmo_agent/main.py b/backend/packages/gizmo-agent/gizmo_agent/main.py
deleted file mode 100644
index 647e34ce..00000000
--- a/backend/packages/gizmo-agent/gizmo_agent/main.py
+++ /dev/null
@@ -1,162 +0,0 @@
-from typing import Any, Mapping, Optional, Sequence
-
-from agent_executor.checkpoint import RedisCheckpoint
-from agent_executor.dnd import create_dnd_bot
-from agent_executor.permchain import get_agent_executor
-from langchain.chat_models import ChatOpenAI
-from langchain.pydantic_v1 import BaseModel, Field
-from langchain.schema.messages import AnyMessage
-from langchain.schema.runnable import (
- ConfigurableField,
- ConfigurableFieldMultiOption,
- RunnableBinding,
-)
-
-from gizmo_agent.agent_types import (
- GizmoAgentType,
- get_openai_function_agent,
- # get_xml_agent,
-)
-from gizmo_agent.tools import (
- RETRIEVAL_DESCRIPTION,
- TOOL_OPTIONS,
- TOOLS,
- AvailableTools,
- get_retrieval_tool,
-)
-
-DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant."
-
-
-class ConfigurableAgent(RunnableBinding):
- tools: Sequence[str]
- agent: GizmoAgentType
- system_message: str = DEFAULT_SYSTEM_MESSAGE
- retrieval_description: str = RETRIEVAL_DESCRIPTION
- assistant_id: Optional[str] = None
- user_id: Optional[str] = None
-
- def __init__(
- self,
- *,
- tools: Sequence[str],
- agent: GizmoAgentType = GizmoAgentType.GPT_35_TURBO,
- system_message: str = DEFAULT_SYSTEM_MESSAGE,
- assistant_id: Optional[str] = None,
- retrieval_description: str = RETRIEVAL_DESCRIPTION,
- kwargs: Optional[Mapping[str, Any]] = None,
- config: Optional[Mapping[str, Any]] = None,
- **others: Any,
- ) -> None:
- others.pop("bound", None)
- _tools = []
- for _tool in tools:
- if _tool == AvailableTools.RETRIEVAL:
- if assistant_id is None:
- raise ValueError(
- "assistant_id must be provided if Retrieval tool is used"
- )
- _tools.append(get_retrieval_tool(assistant_id, retrieval_description))
- else:
- _tools.append(TOOLS[_tool]())
- if agent == GizmoAgentType.GPT_35_TURBO:
- _agent = get_openai_function_agent(_tools, system_message)
- # elif agent == GizmoAgentType.GPT_4:
- # _agent = get_openai_function_agent(_tools, system_message, gpt_4=True)
- # elif agent == GizmoAgentType.AZURE_OPENAI:
- # _agent = get_openai_function_agent(_tools, system_message, azure=True)
- # elif agent == GizmoAgentType.CLAUDE2:
- # _agent = get_xml_agent(_tools, system_message)
- # elif agent == GizmoAgentType.BEDROCK_CLAUDE2:
- # _agent = get_xml_agent(_tools, system_message, bedrock=True)
- else:
- raise ValueError("Unexpected agent type")
- agent_executor = get_agent_executor(
- tools=_tools, agent=_agent, checkpoint=RedisCheckpoint()
- ).with_config({"recursion_limit": 10})
- super().__init__(
- tools=tools,
- agent=agent,
- system_message=system_message,
- retrieval_description=retrieval_description,
- bound=agent_executor,
- kwargs=kwargs or {},
- config=config or {},
- )
-
-
-class AgentInput(BaseModel):
- messages: Sequence[AnyMessage] = Field(default_factory=list)
-
-
-class AgentOutput(BaseModel):
- messages: Sequence[AnyMessage] = Field(..., extra={"widget": {"type": "chat"}})
-
-
-dnd_llm = ChatOpenAI(
- model="gpt-3.5-turbo-1106", temperature=0, streaming=True
-).configurable_alternatives(
- ConfigurableField(id="llm", name="LLM"),
- default_key="gpt-35-turbo",
- # azure_openai=AzureChatOpenAI(
- # temperature=0,
- # deployment_name=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
- # openai_api_base=os.environ["AZURE_OPENAI_API_BASE"],
- # openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
- # openai_api_key=os.environ["AZURE_OPENAI_API_KEY"],
- # streaming=True,
- # ),
-)
-
-
-dnd_bot = create_dnd_bot(dnd_llm, checkpoint=RedisCheckpoint()).with_types(
- input_type=AgentInput, output_type=AgentOutput
-)
-
-
-agent = (
- ConfigurableAgent(
- agent=GizmoAgentType.GPT_35_TURBO,
- tools=[],
- system_message=DEFAULT_SYSTEM_MESSAGE,
- retrieval_description=RETRIEVAL_DESCRIPTION,
- assistant_id=None,
- )
- .configurable_fields(
- agent=ConfigurableField(id="agent_type", name="Agent Type"),
- system_message=ConfigurableField(id="system_message", name="System Message"),
- assistant_id=ConfigurableField(
- id="assistant_id", name="Assistant ID", is_shared=True
- ),
- tools=ConfigurableFieldMultiOption(
- id="tools",
- name="Tools",
- options=TOOL_OPTIONS,
- default=[],
- ),
- retrieval_description=ConfigurableField(
- id="retrieval_description", name="Retrieval Description"
- ),
- )
- .configurable_alternatives(
- ConfigurableField(id="type", name="Bot Type"),
- default_key="agent",
- prefix_keys=True,
- dungeons_and_dragons=dnd_bot,
- )
- .with_types(input_type=AgentInput, output_type=AgentOutput)
-)
-
-if __name__ == "__main__":
- import asyncio
-
- from langchain.schema.messages import HumanMessage
-
- async def run():
- async for m in agent.astream_log(
- {"messages": HumanMessage(content="whats your name")},
- config={"configurable": {"user_id": "1", "thread_id": "test1"}},
- ):
- print(m)
-
- asyncio.run(run())
diff --git a/backend/packages/gizmo-agent/poetry.lock b/backend/packages/gizmo-agent/poetry.lock
deleted file mode 100644
index b32d12b8..00000000
--- a/backend/packages/gizmo-agent/poetry.lock
+++ /dev/null
@@ -1,3029 +0,0 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
-
-[[package]]
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- {file = "yarl-1.9.2.tar.gz", hash = "sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"},
-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
-[metadata]
-lock-version = "2.0"
-python-versions = "^3.8.1"
-content-hash = "48303886f04de80d0f73a8819eb1e64274821f8bf664e43aa3942e2632b1538b"
diff --git a/backend/packages/gizmo-agent/pyproject.toml b/backend/packages/gizmo-agent/pyproject.toml
deleted file mode 100644
index ebb0e7f7..00000000
--- a/backend/packages/gizmo-agent/pyproject.toml
+++ /dev/null
@@ -1,39 +0,0 @@
-[tool.poetry]
-name = "gizmo-agent"
-version = "0.1.0"
-description = ""
-authors = [
- "Lance Martin ",
-]
-readme = "README.md"
-
-[tool.poetry.dependencies]
-python = "^3.8.1"
-langchain = ">=0.0.333"
-openai = ">=0.5.0,<1.0"
-anthropic = "^0.3.11"
-langchain-experimental = "^0.0.37"
-duckduckgo-search = "^3.9.4"
-python-magic = "^0.4.27"
-bs4 = "^0.0.1"
-unstructured = {extras = ["doc", "docx"], version = "^0.10.29"}
-pdfminer-six = "^20221105"
-wikipedia = "^1.4.0"
-boto3 = "^1.28.84"
-redis = "^5.0.1"
-arxiv = "^2.0.0"
-kay = "^0.1.2"
-xmltodict = "^0.13.0"
-
-[tool.poetry.group.dev.dependencies]
-langchain-cli = ">=0.0.15"
-
-[tool.langserve]
-export_module = "gizmo_agent"
-export_attr = "agent_executor"
-
-[build-system]
-requires = [
- "poetry-core",
-]
-build-backend = "poetry.core.masonry.api"
diff --git a/backend/packages/gizmo-agent/tests/__init__.py b/backend/packages/gizmo-agent/tests/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/backend/poetry.lock b/backend/poetry.lock
index 2c7a9a99..ab13ff29 100644
--- a/backend/poetry.lock
+++ b/backend/poetry.lock
@@ -1,33 +1,5 @@
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
-[[package]]
-name = "agent-executor"
-version = "0.1.0"
-description = ""
-optional = false
-python-versions = "^3.8.1"
-files = []
-develop = true
-
-[package.dependencies]
-langchain = ">=0.0.333"
-python-magic = "^0.4.27"
-
-[package.source]
-type = "directory"
-url = "packages/agent-executor"
-
-[[package]]
-name = "aiofiles"
-version = "23.2.1"
-description = "File support for asyncio."
-optional = false
-python-versions = ">=3.7"
-files = [
- {file = "aiofiles-23.2.1-py3-none-any.whl", hash = "sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107"},
- {file = "aiofiles-23.2.1.tar.gz", hash = "sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a"},
-]
-
[[package]]
name = "aiohttp"
version = "3.9.1"
@@ -138,25 +110,6 @@ files = [
[package.dependencies]
frozenlist = ">=1.1.0"
-[[package]]
-name = "anthropic"
-version = "0.3.11"
-description = "Client library for the anthropic API"
-optional = false
-python-versions = ">=3.7,<4.0"
-files = [
- {file = "anthropic-0.3.11-py3-none-any.whl", hash = "sha256:5c81105cd9ee7388bff3fdb739aaddedc83bbae9b95d51c2d50c13b1ad106138"},
- {file = "anthropic-0.3.11.tar.gz", hash = "sha256:2e0fa5351c9b368cbed0bbd7217deaa9409b82b56afaf244e2196e99eb4fe20e"},
-]
-
-[package.dependencies]
-anyio = ">=3.5.0,<4"
-distro = ">=1.7.0,<2"
-httpx = ">=0.23.0,<1"
-pydantic = ">=1.9.0,<3"
-tokenizers = ">=0.13.0"
-typing-extensions = ">=4.5,<5"
-
[[package]]
name = "anyio"
version = "3.7.1"
@@ -180,13 +133,13 @@ trio = ["trio (<0.22)"]
[[package]]
name = "arxiv"
-version = "2.0.0"
+version = "2.1.0"
description = "Python wrapper for the arXiv API: https://arxiv.org/help/api/"
optional = false
python-versions = ">=3.7"
files = [
- {file = "arxiv-2.0.0-py3-none-any.whl", hash = "sha256:d64dd4b020782b91c267bcd3b98a1b9542250ab135fb54a1e298e8364722210c"},
- {file = "arxiv-2.0.0.tar.gz", hash = "sha256:97f910dadf97169793ab3fcf12e75d23cb9dac4dc7c16de8b09492cde179da9f"},
+ {file = "arxiv-2.1.0-py3-none-any.whl", hash = "sha256:d634a0a59c9f05baf524eaa65563bb0a4532d2b4727a1162a1a9ba7e1e6e48cc"},
+ {file = "arxiv-2.1.0.tar.gz", hash = "sha256:eb4b1d5ab9dfd66027c344bb324c20be21d56fe15f6ce216ed5b209df747dea8"},
]
[package.dependencies]
@@ -222,63 +175,55 @@ docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-
tests = ["attrs[tests-no-zope]", "zope-interface"]
tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-[[package]]
-name = "backoff"
-version = "2.2.1"
-description = "Function decoration for backoff and retry"
-optional = false
-python-versions = ">=3.7,<4.0"
-files = [
- {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"},
- {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
-]
-
[[package]]
name = "beautifulsoup4"
-version = "4.12.2"
+version = "4.12.3"
description = "Screen-scraping library"
optional = false
python-versions = ">=3.6.0"
files = [
- {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
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+ {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
]
[package.dependencies]
soupsieve = ">1.2"
[package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
html5lib = ["html5lib"]
lxml = ["lxml"]
[[package]]
name = "boto3"
-version = "1.33.2"
+version = "1.34.28"
description = "The AWS SDK for Python"
optional = false
-python-versions = ">= 3.7"
+python-versions = ">= 3.8"
files = [
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- {file = "boto3-1.33.2.tar.gz", hash = "sha256:70626598dd6698d6da8f2854a1ae5010f175572e2a465b2aa86685c745c1013c"},
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]
[package.dependencies]
-botocore = ">=1.33.2,<1.34.0"
+botocore = ">=1.34.28,<1.35.0"
jmespath = ">=0.7.1,<2.0.0"
-s3transfer = ">=0.8.0,<0.9.0"
+s3transfer = ">=0.10.0,<0.11.0"
[package.extras]
crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
-version = "1.33.2"
+version = "1.34.28"
description = "Low-level, data-driven core of boto 3."
optional = false
-python-versions = ">= 3.7"
+python-versions = ">= 3.8"
files = [
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- {file = "botocore-1.33.2.tar.gz", hash = "sha256:16a30faac6e6f17961c009defb74ab1a3508b8abc58fab98e7cf96af0d91ea84"},
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]
[package.dependencies]
@@ -290,152 +235,19 @@ urllib3 = [
]
[package.extras]
-crt = ["awscrt (==0.19.17)"]
+crt = ["awscrt (==0.19.19)"]
[[package]]
-name = "brotli"
-version = "1.1.0"
-description = "Python bindings for the Brotli compression library"
-optional = false
-python-versions = "*"
-files = [
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name = "dataclasses-json"
version = "0.6.3"
@@ -809,13 +633,13 @@ dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
[[package]]
name = "distro"
-version = "1.8.0"
+version = "1.9.0"
description = "Distro - an OS platform information API"
optional = false
python-versions = ">=3.6"
files = [
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@@ -830,38 +654,23 @@ files = [
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name = "duckduckgo-search"
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+version = "4.2"
description = "Search for words, documents, images, news, maps and text translation using the DuckDuckGo.com search engine."
optional = false
python-versions = ">=3.8"
files = [
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click = ">=8.1.7"
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lxml = ">=4.9.3"
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dev = ["pytest (>=7.4.2)", "pytest-asyncio (>=0.21.1)", "ruff (>=0.1.6)"]
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@@ -910,33 +719,6 @@ files = [
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sgmllib3k = "*"
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@@ -1008,69 +790,300 @@ files = [
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optional = false
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optional = false
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@@ -1213,14 +1298,10 @@ files = [
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httpcore = "==1.*"
idna = "*"
sniffio = "*"
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brotli = ["brotli", "brotlicffi"]
@@ -1228,50 +1309,6 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
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- {file = "tokenizers-0.15.0-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:a8da7533dbe66b88afd430c56a2f2ce1fd82e2681868f857da38eeb3191d7498"},
- {file = "tokenizers-0.15.0-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:fa8eb4584fc6cbe6a84d7a7864be3ed28e23e9fd2146aa8ef1814d579df91958"},
- {file = "tokenizers-0.15.0.tar.gz", hash = "sha256:10c7e6e7b4cabd757da59e93f5f8d1126291d16f8b54f28510825ef56a3e5d0e"},
-]
-
-[package.dependencies]
-huggingface_hub = ">=0.16.4,<1.0"
-
-[package.extras]
-dev = ["tokenizers[testing]"]
-docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
-testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
-
[[package]]
name = "tomli"
version = "2.0.1"
@@ -2966,6 +2876,56 @@ notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"]
telegram = ["requests"]
+[[package]]
+name = "types-protobuf"
+version = "4.24.0.20240106"
+description = "Typing stubs for protobuf"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "types-protobuf-4.24.0.20240106.tar.gz", hash = "sha256:024f034f3b5e2bb2bbff55ebc4d591ed0d2280d90faceedcb148b9e714a3f3ee"},
+ {file = "types_protobuf-4.24.0.20240106-py3-none-any.whl", hash = "sha256:0612ef3156bd80567460a15ac7c109b313f6022f1fee04b4d922ab2789baab79"},
+]
+
+[[package]]
+name = "types-requests"
+version = "2.31.0.6"
+description = "Typing stubs for requests"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "types-requests-2.31.0.6.tar.gz", hash = "sha256:cd74ce3b53c461f1228a9b783929ac73a666658f223e28ed29753771477b3bd0"},
+ {file = "types_requests-2.31.0.6-py3-none-any.whl", hash = "sha256:a2db9cb228a81da8348b49ad6db3f5519452dd20a9c1e1a868c83c5fe88fd1a9"},
+]
+
+[package.dependencies]
+types-urllib3 = "*"
+
+[[package]]
+name = "types-requests"
+version = "2.31.0.20240125"
+description = "Typing stubs for requests"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "types-requests-2.31.0.20240125.tar.gz", hash = "sha256:03a28ce1d7cd54199148e043b2079cdded22d6795d19a2c2a6791a4b2b5e2eb5"},
+ {file = "types_requests-2.31.0.20240125-py3-none-any.whl", hash = "sha256:9592a9a4cb92d6d75d9b491a41477272b710e021011a2a3061157e2fb1f1a5d1"},
+]
+
+[package.dependencies]
+urllib3 = ">=2"
+
+[[package]]
+name = "types-urllib3"
+version = "1.26.25.14"
+description = "Typing stubs for urllib3"
+optional = false
+python-versions = "*"
+files = [
+ {file = "types-urllib3-1.26.25.14.tar.gz", hash = "sha256:229b7f577c951b8c1b92c1bc2b2fdb0b49847bd2af6d1cc2a2e3dd340f3bda8f"},
+ {file = "types_urllib3-1.26.25.14-py3-none-any.whl", hash = "sha256:9683bbb7fb72e32bfe9d2be6e04875fbe1b3eeec3cbb4ea231435aa7fd6b4f0e"},
+]
+
[[package]]
name = "typing-extensions"
version = "4.8.0"
@@ -2992,85 +2952,6 @@ files = [
mypy-extensions = ">=0.3.0"
typing-extensions = ">=3.7.4"
-[[package]]
-name = "unstructured"
-version = "0.10.30"
-description = "A library that prepares raw documents for downstream ML tasks."
-optional = false
-python-versions = ">=3.7.0"
-files = [
- {file = "unstructured-0.10.30-py3-none-any.whl", hash = "sha256:0615f14daa37450e9c0fcf3c3fd178c3a06b6b8d006a36d1a5e54dbe487aa6b6"},
- {file = "unstructured-0.10.30.tar.gz", hash = "sha256:a86c3d15c572a28322d83cb5ecf0ac7a24f1c36864fb7c68df096de8a1acc106"},
-]
-
-[package.dependencies]
-backoff = "*"
-beautifulsoup4 = "*"
-chardet = "*"
-dataclasses-json = "*"
-emoji = "*"
-filetype = "*"
-langdetect = "*"
-lxml = "*"
-nltk = "*"
-numpy = "*"
-python-docx = {version = ">=1.1.0", optional = true, markers = "extra == \"doc\" or extra == \"docx\""}
-python-iso639 = "*"
-python-magic = "*"
-rapidfuzz = "*"
-requests = "*"
-tabulate = "*"
-typing-extensions = "*"
-
-[package.extras]
-airtable = ["pyairtable"]
-all-docs = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pypandoc", "python-docx (>=1.1.0)", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.11)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
-azure = ["adlfs", "fsspec (==2023.9.1)"]
-azure-cognitive-search = ["azure-search-documents"]
-bedrock = ["boto3", "langchain"]
-biomed = ["bs4"]
-box = ["boxfs", "fsspec (==2023.9.1)"]
-confluence = ["atlassian-python-api"]
-csv = ["pandas"]
-delta-table = ["deltalake", "fsspec (==2023.9.1)"]
-discord = ["discord-py"]
-doc = ["python-docx (>=1.1.0)"]
-docx = ["python-docx (>=1.1.0)"]
-dropbox = ["dropboxdrivefs", "fsspec (==2023.9.1)"]
-elasticsearch = ["elasticsearch", "jq"]
-embed-huggingface = ["huggingface", "langchain", "sentence-transformers"]
-epub = ["pypandoc"]
-gcs = ["bs4", "fsspec (==2023.9.1)", "gcsfs"]
-github = ["pygithub (>1.58.0)"]
-gitlab = ["python-gitlab"]
-google-drive = ["google-api-python-client"]
-huggingface = ["langdetect", "sacremoses", "sentencepiece", "torch", "transformers"]
-image = ["onnx", "pdf2image", "pdfminer.six", "unstructured-inference (==0.7.11)", "unstructured.pytesseract (>=0.3.12)"]
-jira = ["atlassian-python-api"]
-local-inference = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pypandoc", "python-docx (>=1.1.0)", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.11)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
-md = ["markdown"]
-msg = ["msg-parser"]
-notion = ["htmlBuilder", "notion-client"]
-odt = ["pypandoc", "python-docx (>=1.1.0)"]
-onedrive = ["Office365-REST-Python-Client (<2.4.3)", "bs4", "msal"]
-openai = ["langchain", "openai", "tiktoken"]
-org = ["pypandoc"]
-outlook = ["Office365-REST-Python-Client (<2.4.3)", "msal"]
-paddleocr = ["unstructured.paddleocr (==2.6.1.3)"]
-pdf = ["onnx", "pdf2image", "pdfminer.six", "unstructured-inference (==0.7.11)", "unstructured.pytesseract (>=0.3.12)"]
-ppt = ["python-pptx (<=0.6.23)"]
-pptx = ["python-pptx (<=0.6.23)"]
-reddit = ["praw"]
-rst = ["pypandoc"]
-rtf = ["pypandoc"]
-s3 = ["fsspec (==2023.9.1)", "s3fs"]
-salesforce = ["simple-salesforce"]
-sharepoint = ["Office365-REST-Python-Client (<2.4.3)", "msal"]
-slack = ["slack-sdk"]
-tsv = ["pandas"]
-wikipedia = ["wikipedia"]
-xlsx = ["networkx", "openpyxl", "pandas", "xlrd"]
-
[[package]]
name = "urllib3"
version = "1.26.18"
@@ -3371,5 +3252,5 @@ multidict = ">=4.0"
[metadata]
lock-version = "2.0"
-python-versions = "^3.8.1"
-content-hash = "60304f12bed53e0d58baec54df54e79e1b5936dc651cc50ec76802c0cf40860c"
+python-versions = "^3.9.0"
+content-hash = "87914c875a6cfe86785734dd5a0cb33d14700ffb050e1186a935579def43c612"
diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 1bcbbacf..b3806ae7 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -7,7 +7,7 @@ readme = "README.md"
packages = [{include = "app"}]
[tool.poetry.dependencies]
-python = "^3.8.1"
+python = "^3.9.0"
sse-starlette = "^1.6.5"
tomli-w = "^1.0.0"
uvicorn = "^0.23.2"
@@ -16,16 +16,25 @@ langserve = "0.0.32"
# Uncomment if you need to work from a development branch
# This will only work for local development though!
# langchain = { git = "git@github.com:langchain-ai/langchain.git/", branch = "nc/subclass-runnable-binding" , subdirectory = "libs/langchain"}
-gizmo-agent = {path = "packages/gizmo-agent", develop = true}
-agent-executor = {path = "packages/agent-executor", develop = true}
orjson = "^3.9.10"
redis = "^5.0.1"
python-multipart = "^0.0.6"
tiktoken = "^0.5.1"
langchain = ">=0.0.338"
-permchain = ">=0.0.8"
+langgraph = "^0.0.19"
pydantic = "<2.0"
python-magic = "^0.4.27"
+langchain-openai = "^0.0.5"
+beautifulsoup4 = "^4.12.3"
+boto3 = "^1.34.28"
+duckduckgo-search = "^4.2"
+arxiv = "^2.1.0"
+kay = "^0.1.2"
+xmltodict = "^0.13.0"
+wikipedia = "^1.4.0"
+langchain-google-vertexai = "^0.0.3"
+setuptools = "^69.0.3"
+pdfminer-six = "^20231228"
[tool.poetry.group.dev.dependencies]
uvicorn = "^0.23.2"
diff --git a/backend/requirements.txt b/backend/requirements.txt
index 2e3e3e08..9c832356 100644
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -1,7 +1,7 @@
langchain-cli
-e packages/gizmo-agent
-e packages/agent-executor
-langchain>=0.0.331
+langchain>=0.1.0
langserve>=0.0.23
-permchain
+langgraph
python-multipart
diff --git a/backend/tests/unit_tests/agent_executor/test_parsing.py b/backend/tests/unit_tests/agent_executor/test_parsing.py
index b3d1cc46..ff75336a 100644
--- a/backend/tests/unit_tests/agent_executor/test_parsing.py
+++ b/backend/tests/unit_tests/agent_executor/test_parsing.py
@@ -1,9 +1,9 @@
"""Test parsing logic."""
import mimetypes
-from agent_executor.parsing import MIMETYPE_BASED_PARSER, SUPPORTED_MIMETYPES
from langchain.document_loaders import Blob
+from app.parsing import MIMETYPE_BASED_PARSER, SUPPORTED_MIMETYPES
from tests.unit_tests.fixtures import get_sample_paths
diff --git a/backend/tests/unit_tests/agent_executor/test_upload.py b/backend/tests/unit_tests/agent_executor/test_upload.py
index 5b1f67fb..7a4b43c4 100644
--- a/backend/tests/unit_tests/agent_executor/test_upload.py
+++ b/backend/tests/unit_tests/agent_executor/test_upload.py
@@ -1,8 +1,8 @@
from io import BytesIO
-from agent_executor.upload import IngestRunnable, _guess_mimetype
from langchain.text_splitter import RecursiveCharacterTextSplitter
+from app.upload import IngestRunnable, _guess_mimetype
from tests.unit_tests.fixtures import get_sample_paths
from tests.unit_tests.utils import InMemoryVectorStore
diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx
index 06e3edf9..b6398bef 100644
--- a/frontend/src/App.tsx
+++ b/frontend/src/App.tsx
@@ -24,16 +24,14 @@ function App() {
)?.config;
if (!config) return;
await startStream(
- {
- messages: [
- {
- content: message,
- additional_kwargs: {},
- type: "human",
- example: false,
- },
- ],
- },
+ [
+ {
+ content: message,
+ additional_kwargs: {},
+ type: "human",
+ example: false,
+ },
+ ],
chat.assistant_id,
chat.thread_id
);
diff --git a/frontend/src/components/Config.tsx b/frontend/src/components/Config.tsx
index 9cf55bd4..4b33ce14 100644
--- a/frontend/src/components/Config.tsx
+++ b/frontend/src/components/Config.tsx
@@ -305,7 +305,7 @@ export function Config(props: {
readonly={readonly}
/>
);
- } else if (key === "type==agent/system_message") {
+ } else if (key === "system_message") {
return (
);
} else if (
- key === "type==agent/retrieval_description" &&
+ key === "retrieval_description" &&
(
- values?.configurable?.["type==agent/tools"] as
- | string[]
- | undefined
+ values?.configurable?.["tools"] as string[] | undefined
)?.includes("Retrieval")
) {
return (
@@ -346,7 +344,7 @@ export function Config(props: {
readonly={readonly}
/>
);
- } else if (key === "type==agent/tools") {
+ } else if (key === "tools") {
return (
Promise;
@@ -24,14 +24,10 @@ export function useStreamState(): StreamStateProps {
const [controller, setController] = useState(null);
const startStream = useCallback(
- async (
- input: { messages: Message[] },
- assistant_id: string,
- thread_id: string
- ) => {
+ async (input: Message[], assistant_id: string, thread_id: string) => {
const controller = new AbortController();
setController(controller);
- setCurrent({ status: "inflight", messages: input.messages, merge: true });
+ setCurrent({ status: "inflight", messages: input, merge: true });
await fetchEventSource("/runs/stream", {
signal: controller.signal,
@@ -41,7 +37,8 @@ export function useStreamState(): StreamStateProps {
openWhenHidden: true,
onmessage(msg) {
if (msg.event === "data") {
- const { messages } = JSON.parse(msg.data);
+ console.log(msg.data);
+ const messages = JSON.parse(msg.data);
setCurrent((current) => ({
status: "inflight",
messages,