add retrieval agent (#13317)

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__pycache__

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MIT License
Copyright (c) 2023 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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# retrieval-agent
This package uses Azure OpenAI to do retrieval using an agent architecture.
By default, this does retrieval over Arxiv.
## Environment Setup
Since we are using Azure OpenAI, we will need to set the following environment variables:
```shell
export AZURE_OPENAI_API_BASE=...
export AZURE_OPENAI_API_VERSION=...
export AZURE_OPENAI_API_KEY=...
export AZURE_OPENAI_DEPLOYMENT_NAME=...
```
## Usage
To use this package, you should first have the LangChain CLI installed:
```shell
pip install -U langchain-cli
```
To create a new LangChain project and install this as the only package, you can do:
```shell
langchain app new my-app --package retrieval-agent
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add retrieval-agent
```
And add the following code to your `server.py` file:
```python
from retrieval_agent import chain as retrieval_agent_chain
add_routes(app, retrieval_agent_chain, path="/retrieval-agent")
```
(Optional) Let's now configure LangSmith.
LangSmith will help us trace, monitor and debug LangChain applications.
LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
If you don't have access, you can skip this section
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
```
If you are inside this directory, then you can spin up a LangServe instance directly by:
```shell
langchain serve
```
This will start the FastAPI app with a server is running locally at
[http://localhost:8000](http://localhost:8000)
We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
We can access the playground at [http://127.0.0.1:8000/retrieval-agent/playground](http://127.0.0.1:8000/retrieval-agent/playground)
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/retrieval-agent")
```

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[tool.poetry]
name = "retrieval-agent"
version = "0.0.1"
description = ""
authors = []
readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain = ">=0.0.313, <0.1"
openai = "^0.28.1"
arxiv = "^2.0.0"
[tool.poetry.group.dev.dependencies]
langchain-cli = ">=0.0.4"
fastapi = "^0.104.0"
sse-starlette = "^1.6.5"
[tool.langserve]
export_module = "retrieval_agent"
export_attr = "agent_executor"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

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from retrieval_agent.chain import agent_executor
__all__ = ["agent_executor"]

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import os
from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.chat_models import AzureChatOpenAI
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.pydantic_v1 import BaseModel, Field
from langchain.schema.messages import AIMessage, HumanMessage
from langchain.tools import ArxivQueryRun
from langchain.tools.render import format_tool_to_openai_function
from langchain.utilities import ArxivAPIWrapper
class ArxivInput(BaseModel):
query: str = Field(description="search query to look up")
# Create the tool
arxiv_tool = ArxivQueryRun(api_wrapper=ArxivAPIWrapper(), args_schema=ArxivInput)
tools = [arxiv_tool]
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"],
)
assistant_system_message = """You are a helpful research assistant. \
Lookup relevant information as needed."""
prompt = ChatPromptTemplate.from_messages(
[
("system", assistant_system_message),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
def _format_chat_history(chat_history: List[Tuple[str, str]]):
buffer = []
for human, ai in chat_history:
buffer.append(HumanMessage(content=human))
buffer.append(AIMessage(content=ai))
return buffer
agent = (
{
"input": lambda x: x["input"],
"chat_history": lambda x: _format_chat_history(x["chat_history"]),
"agent_scratchpad": lambda x: format_to_openai_function_messages(
x["intermediate_steps"]
),
}
| prompt
| llm_with_tools
| OpenAIFunctionsAgentOutputParser()
)
class AgentInput(BaseModel):
input: str
chat_history: List[Tuple[str, str]] = Field(
..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
input_type=AgentInput
)