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add retrieval agent (#13317)
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templates/retrieval-agent/.gitignore
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templates/retrieval-agent/.gitignore
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__pycache__
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templates/retrieval-agent/LICENSE
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templates/retrieval-agent/LICENSE
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MIT License
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Copyright (c) 2023 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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templates/retrieval-agent/README.md
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templates/retrieval-agent/README.md
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# retrieval-agent
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This package uses Azure OpenAI to do retrieval using an agent architecture.
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By default, this does retrieval over Arxiv.
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## Environment Setup
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Since we are using Azure OpenAI, we will need to set the following environment variables:
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```shell
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export AZURE_OPENAI_API_BASE=...
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export AZURE_OPENAI_API_VERSION=...
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export AZURE_OPENAI_API_KEY=...
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export AZURE_OPENAI_DEPLOYMENT_NAME=...
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```
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package retrieval-agent
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add retrieval-agent
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```
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And add the following code to your `server.py` file:
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```python
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from retrieval_agent import chain as retrieval_agent_chain
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add_routes(app, retrieval_agent_chain, path="/retrieval-agent")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/retrieval-agent/playground](http://127.0.0.1:8000/retrieval-agent/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/retrieval-agent")
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```
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templates/retrieval-agent/pyproject.toml
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templates/retrieval-agent/pyproject.toml
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[tool.poetry]
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name = "retrieval-agent"
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version = "0.0.1"
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description = ""
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authors = []
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readme = "README.md"
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[tool.poetry.dependencies]
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python = ">=3.8.1,<4.0"
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langchain = ">=0.0.313, <0.1"
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openai = "^0.28.1"
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arxiv = "^2.0.0"
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[tool.poetry.group.dev.dependencies]
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langchain-cli = ">=0.0.4"
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fastapi = "^0.104.0"
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sse-starlette = "^1.6.5"
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[tool.langserve]
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export_module = "retrieval_agent"
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export_attr = "agent_executor"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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templates/retrieval-agent/retrieval_agent/__init__.py
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templates/retrieval-agent/retrieval_agent/__init__.py
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from retrieval_agent.chain import agent_executor
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__all__ = ["agent_executor"]
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templates/retrieval-agent/retrieval_agent/chain.py
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templates/retrieval-agent/retrieval_agent/chain.py
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import os
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from typing import List, Tuple
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from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_to_openai_function_messages
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from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
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from langchain.chat_models import AzureChatOpenAI
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.pydantic_v1 import BaseModel, Field
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from langchain.schema.messages import AIMessage, HumanMessage
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from langchain.tools import ArxivQueryRun
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from langchain.tools.render import format_tool_to_openai_function
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from langchain.utilities import ArxivAPIWrapper
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class ArxivInput(BaseModel):
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query: str = Field(description="search query to look up")
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# Create the tool
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arxiv_tool = ArxivQueryRun(api_wrapper=ArxivAPIWrapper(), args_schema=ArxivInput)
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tools = [arxiv_tool]
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llm = AzureChatOpenAI(
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temperature=0,
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deployment_name=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
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openai_api_base=os.environ["AZURE_OPENAI_API_BASE"],
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openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
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openai_api_key=os.environ["AZURE_OPENAI_API_KEY"],
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)
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assistant_system_message = """You are a helpful research assistant. \
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Lookup relevant information as needed."""
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", assistant_system_message),
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MessagesPlaceholder(variable_name="chat_history"),
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("user", "{input}"),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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)
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llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
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def _format_chat_history(chat_history: List[Tuple[str, str]]):
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buffer = []
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for human, ai in chat_history:
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buffer.append(HumanMessage(content=human))
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buffer.append(AIMessage(content=ai))
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return buffer
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agent = (
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{
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"input": lambda x: x["input"],
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"chat_history": lambda x: _format_chat_history(x["chat_history"]),
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"agent_scratchpad": lambda x: format_to_openai_function_messages(
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x["intermediate_steps"]
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),
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}
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| prompt
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| llm_with_tools
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| OpenAIFunctionsAgentOutputParser()
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)
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class AgentInput(BaseModel):
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input: str
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chat_history: List[Tuple[str, str]] = Field(
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..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
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)
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
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input_type=AgentInput
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)
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templates/retrieval-agent/tests/__init__.py
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0
templates/retrieval-agent/tests/__init__.py
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