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#!/usr/bin/env python
from typing import List
from fastapi import FastAPI
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_community.document_loaders import WebBaseLoader
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.tools.retriever import create_retriever_tool
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai import ChatOpenAI
from langchain import hub
from langchain.agents import create_openai_functions_agent
from langchain.agents import AgentExecutor
from langchain.pydantic_v1 import BaseModel, Field
from langchain_core.messages import BaseMessage
from langserve import add_routes
from langchain.utilities import SerpAPIWrapper
# 1. Load Retriever
loader = WebBaseLoader("https://docs.smith.langchain.com")
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter()
documents = text_splitter.split_documents(docs)
embeddings = OpenAIEmbeddings()
vector = FAISS.from_documents(documents, embeddings)
retriever = vector.as_retriever()
# 2. Create Tools
retriever_tool = create_retriever_tool(
retriever,
"langsmith_search",
"Search for information about LangSmith. For any questions about LangSmith, you must use this tool!",
)
search = SerpAPIWrapper()
tools = [retriever_tool, search]
# 3. Create Agent
prompt = hub.pull("hwchase17/openai-functions-agent")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
# 4. App definition
app = FastAPI(
title="LangChain Server",
version="1.0",
description="A simple API server using LangChain's Runnable interfaces",
)
# 5. Adding chain route
# We need to add these input/output schemas because the current AgentExecutor
# is lacking in schemas.
class Input(BaseModel):
input: str
chat_history: List[BaseMessage] = Field(
...,
extra={"widget": {"type": "chat", "input": "location"}},
)
class Output(BaseModel):
output: str
add_routes(
app,
agent_executor.with_types(input_type=Input, output_type=Output),
path="/agent",
)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="localhost", port=8000) |