LangServe 帮助开发者部署langchain应用为REST API 服务

安装langserve服务依赖

pip install "langserve[all]"

正如我们之前做过的，依次构建

• 构建检索器
• 生成工具列表
• 生成智能体
• 定义fastapi应用
• 添加langchain 整体代码如下

#!/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_splitters import RecursiveCharacterTextSplitter
from langchain.tools.retriever import create_retriever_tool
from langchain_community.tools.tavily_search import TavilySearchResults
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

# 1. Load Retriever
loader = WebBaseLoader("https://docs.smith.langchain.com/user_guide")
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 = TavilySearchResults()
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)

用idea或命令行启动

python3 langserve.py

如果有类似的红色报错，需要配置host

127.0.0.1 localhost

浏览器UI访问

注意端口号是8000，不是8080 -_-!

http://localhost:8000/agent/playground/ 

继续之前的提问“how can langsmith help with testing?”,会正常得到输出。

用python客户端访问

问题

用浏览器访问，第二次点击start提问,目前有点问题，会报个这么个错，我这里先不主要关注了，有精力的同学可以继续看下写在评论讨论下。