客户端代码：
from langserve import RemoteRunnable
from langchain_core.messages import BaseMessage
chat_history = [HumanMessage(content=“Hello”), AIMessage(content=“Hi”)]
remote_chain = RemoteRunnable(“http://127.0.0.1:8000/agent/”)
remote_chain.invoke({
“input”: “how can langsmith help with testing?”,
“chat_history”: chat_history
})
服务端代码：
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_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
-1 配置langsmith
import os
from uuid import uuid4
unique_id = uuid4().hex[0:8]
os.environ[“LANGCHAIN_PROJECT”] = f"Tracing Walkthrough - {unique_id}"
os.environ[“LANGCHAIN_ENDPOINT”] = “https://api.smith.langchain.com”
0 Load API keys
from dotenv import load_dotenv
import os
load_dotenv()
openai_api_key = os.getenv(“openai_api_key”)
tavily_api_key = os.getenv(“tavily_api_key”)
1. Load Retriever
loader = WebBaseLoader(“LangSmith User Guide | 🦜️🛠️ LangSmith”)
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(tavily_api_key=tavily_api_key)
tools = [retriever_tool, search]
3. Create Agent
prompt = hub.pull(“hwchase17/openai-functions-agent”)
llm = ChatOpenAI(model=“gpt-3.5-turbo”, temperature=0, openai_api_key=openai_api_key)
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
class Input(BaseModel):
input: str
# chat_history: List[BaseMessage] = []
chat_history: List[BaseMessage] = Field(
…,
extra={“widget”: {“type”: “chat”, “input”: “location”}},
)
class Output(BaseModel):
output: str
add_routes(
app,
llm,
path=“/try”,
)
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)
# print(agent_executor.with_types(input_type=Input, output_type=Output).with_config(config).input_schema)