LangChain 集成指南

基础配置

LangChain 的 ChatOpenAI 类支持自定义 baseURL，接入 ChinaWHAPI 只需修改参数。

from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage

llm = ChatOpenAI(
    model="qwen3.6-plus",
    openai_api_key="your_chinawhapi_key",
    openai_api_base="https://chinawhapi.com/v1",
    temperature=0.7,
)

Simple Chain

用 LLMChain 构建最简单的问答 Chain。

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

prompt = PromptTemplate(
    input_variables=["question"],
    template="请用简洁的方式回答：{question}",
)

chain = LLMChain(llm=llm, prompt=prompt)
response = chain.run("什么是 RAG？")
print(response)

Conversation Chain（多轮对话）

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

llm = ChatOpenAI(
    model="qwen3.6-plus",
    openai_api_key="your_chinawhapi_key",
    openai_api_base="https://chinawhapi.com/v1",
    temperature=0.7,
)

memory = ConversationBufferMemory()
conversation = ConversationChain(llm=llm, memory=memory)

print(conversation.predict(input="我叫小明"))
print(conversation.predict(input="我叫什么名字？"))

ReAct Agent

用 LangChain Agents 构建自主规划型 AI Agent，支持工具调用和推理循环。

from langchain.agents import initialize_agent, Tool
from langchain.tools import WikipediaQueryRun
from langchain.utilities import SerpAPIWrapper

tools = [
    Tool(name="Search", func=SerpAPIWrapper().run, description="搜索网络信息"),
    Tool(name="Wikipedia", func=WikipediaQueryRun().run, description="查询维基百科"),
]

agent = initialize_agent(
    tools, llm,
    agent="zero-shot-react-description",
    verbose=True,
)

agent.run("量子计算的基本原理是什么？")

RAG with LangChain

用 LangChain 的 RetrievalQA 链结合 ChinaWHAPI 模型构建知识库问答。

from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import TextLoader

# 加载文档
loader = TextLoader("./docs.txt")
documents = loader.load()
splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
texts = splitter.split_documents(documents)

# 向量化
vectorstore = Chroma.from_documents(texts, OpenAIEmbeddings(
    openai_api_base="https://chinawhapi.com/v1",
    openai_api_key="your_chinawhapi_key",
))

# 构建 RAG QA 链
qa = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
)

result = qa.run("这份文档的核心观点是什么？")

多模型路由 Agent

根据任务类型自动选择最优模型，在 LangChain 中实现成本优化。

def get_llm_by_task(task_type: str):
    if task_type == "reasoning":
        return ChatOpenAI(model="deepseek-r1", openai_api_base="...", temperature=0.3)
    elif task_type == "code":
        return ChatOpenAI(model="qwen3-coder-plus", openai_api_base="...", temperature=0.3)
    else:
        return ChatOpenAI(model="qwen3.5-flash", openai_api_base="...", temperature=0.7)

llm = get_llm_by_task("code")