LangChain RAG 检索增强生成

RAG (Retrieval-Augmented Generation) 是让 LLM 基于你的私有数据回答问题的技术，是构建知识库问答系统的核心。

#什么是 RAG？

RAG 的工作流程：

1. 文档预处理
   文档 → 分割 → 向量化 → 存储到向量数据库

2. 查询时
   问题 → 向量化 → 检索相关文档 → 结合上下文 → LLM 生成答案

#快速开始

#最简单的 RAG

python
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

# 1. 加载文档
loader = TextLoader("./knowledge.txt", encoding="utf-8")
documents = loader.load()

# 2. 分割文档
splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=50
)
chunks = splitter.split_documents(documents)

# 3. 创建向量存储
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)

# 4. 创建检索器
retriever = vectorstore.as_retriever()

# 5. 创建 RAG Chain
prompt = ChatPromptTemplate.from_template("""
基于以下上下文回答问题。如果上下文中没有相关信息，请说"我不知道"。

上下文：
{context}

问题：{question}

回答：
""")

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | ChatOpenAI()
    | StrOutputParser()
)

# 6. 使用
answer = rag_chain.invoke("这个项目是做什么的？")
print(answer)

#文档加载

#常用加载器

python
# 文本文件
from langchain_community.document_loaders import TextLoader
loader = TextLoader("./doc.txt")

# PDF
from langchain_community.document_loaders import PyPDFLoader
loader = PyPDFLoader("./doc.pdf")

# Word
from langchain_community.document_loaders import Docx2txtLoader
loader = Docx2txtLoader("./doc.docx")

# Markdown
from langchain_community.document_loaders import UnstructuredMarkdownLoader
loader = UnstructuredMarkdownLoader("./doc.md")

# HTML
from langchain_community.document_loaders import UnstructuredHTMLLoader
loader = UnstructuredHTMLLoader("./page.html")

# 网页
from langchain_community.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://example.com")

# CSV
from langchain_community.document_loaders.csv_loader import CSVLoader
loader = CSVLoader("./data.csv")

# 目录批量加载
from langchain_community.document_loaders import DirectoryLoader
loader = DirectoryLoader("./docs/", glob="**/*.md")

#加载文档

python
documents = loader.load()

# 查看文档内容
for doc in documents:
    print(f"内容: {doc.page_content[:100]}...")
    print(f"元数据: {doc.metadata}")

#文本分割

#RecursiveCharacterTextSplitter (推荐)

python
from langchain_text_splitters import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,       # 块大小
    chunk_overlap=200,     # 重叠大小
    length_function=len,
    separators=["\n\n", "\n", " ", ""]  # 分割顺序
)

chunks = splitter.split_documents(documents)
print(f"分割为 {len(chunks)} 个块")

#代码分割

python
from langchain_text_splitters import (
    RecursiveCharacterTextSplitter,
    Language
)

python_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.PYTHON,
    chunk_size=2000,
    chunk_overlap=200
)

# 支持的语言
# Language.PYTHON, Language.JS, Language.TS, Language.JAVA, Language.GO, ...

#Markdown 分割

python
from langchain_text_splitters import MarkdownHeaderTextSplitter

headers_to_split_on = [
    ("#", "Header 1"),
    ("##", "Header 2"),
    ("###", "Header 3"),
]

splitter = MarkdownHeaderTextSplitter(
    headers_to_split_on=headers_to_split_on
)

splits = splitter.split_text(markdown_document)

#向量存储

#Chroma (本地)

python
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

# 创建
vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=embeddings,
    persist_directory="./chroma_db"  # 持久化
)

# 加载已有
vectorstore = Chroma(
    persist_directory="./chroma_db",
    embedding_function=embeddings
)

#Pinecone (云端)

python
from langchain_pinecone import PineconeVectorStore
from pinecone import Pinecone

pc = Pinecone(api_key="your-api-key")

vectorstore = PineconeVectorStore.from_documents(
    documents=chunks,
    embedding=embeddings,
    index_name="my-index"
)

#FAISS (高性能本地)

python
from langchain_community.vectorstores import FAISS

vectorstore = FAISS.from_documents(chunks, embeddings)

# 保存
vectorstore.save_local("./faiss_index")

# 加载
vectorstore = FAISS.load_local("./faiss_index", embeddings)

#检索器

#基础检索

python
retriever = vectorstore.as_retriever(
    search_type="similarity",  # 相似度搜索
    search_kwargs={"k": 5}     # 返回 5 个结果
)

docs = retriever.invoke("问题")

#MMR 检索 (多样性)

python
retriever = vectorstore.as_retriever(
    search_type="mmr",  # Maximum Marginal Relevance
    search_kwargs={
        "k": 5,
        "fetch_k": 20,  # 先检索 20 个，再筛选 5 个
        "lambda_mult": 0.5  # 多样性参数
    }
)

#带阈值的检索

python
retriever = vectorstore.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={
        "score_threshold": 0.8,  # 只返回相似度 > 0.8 的
        "k": 5
    }
)

#混合检索

python
from langchain.retrievers import BM25Retriever, EnsembleRetriever

# 向量检索
vector_retriever = vectorstore.as_retriever()

# 关键词检索 (BM25)
bm25_retriever = BM25Retriever.from_documents(chunks)

# 混合检索
ensemble_retriever = EnsembleRetriever(
    retrievers=[vector_retriever, bm25_retriever],
    weights=[0.5, 0.5]  # 权重
)

#完整 RAG Pipeline

python
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_community.document_loaders import DirectoryLoader, TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough, RunnableParallel

class RAGSystem:
    def __init__(self, docs_path: str):
        self.embeddings = OpenAIEmbeddings()
        self.llm = ChatOpenAI(model="gpt-4o")
        self.vectorstore = None
        self._load_documents(docs_path)
        self._create_chain()

    def _load_documents(self, path: str):
        # 加载文档
        loader = DirectoryLoader(path, glob="**/*.txt", loader_cls=TextLoader)
        documents = loader.load()

        # 分割
        splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200
        )
        chunks = splitter.split_documents(documents)

        # 创建向量存储
        self.vectorstore = Chroma.from_documents(
            chunks,
            self.embeddings,
            persist_directory="./rag_db"
        )

    def _create_chain(self):
        retriever = self.vectorstore.as_retriever(
            search_kwargs={"k": 5}
        )

        prompt = ChatPromptTemplate.from_template("""
你是一个专业的问答助手。请基于以下上下文回答问题。

上下文：
{context}

问题：{question}

要求：
1. 只基于上下文回答，不要编造
2. 如果上下文不包含答案，请说"我在文档中没有找到相关信息"
3. 回答要简洁明了

回答：
""")

        def format_docs(docs):
            return "\n\n---\n\n".join([
                f"[来源: {doc.metadata.get('source', '未知')}]\n{doc.page_content}"
                for doc in docs
            ])

        self.chain = (
            RunnableParallel(
                context=retriever | format_docs,
                question=RunnablePassthrough()
            )
            | prompt
            | self.llm
            | StrOutputParser()
        )

    def query(self, question: str) -> str:
        return self.chain.invoke(question)

    def add_documents(self, texts: list[str]):
        """添加新文档"""
        self.vectorstore.add_texts(texts)

# 使用
rag = RAGSystem("./documents/")
answer = rag.query("这个项目的主要功能是什么？")
print(answer)

#优化技巧

#1. 优化分块

python
# 针对不同内容调整分块大小
# 技术文档：较大块 (1000-2000)
# 对话/问答：较小块 (200-500)
# 代码：按函数/类分割

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,  # 重叠保持上下文
)

#2. 添加元数据

python
# 在分割后添加元数据
for chunk in chunks:
    chunk.metadata["source"] = "user_manual"
    chunk.metadata["category"] = "tutorial"
    chunk.metadata["date"] = "2024-01-01"

# 检索时可以过滤
retriever = vectorstore.as_retriever(
    search_kwargs={
        "k": 5,
        "filter": {"category": "tutorial"}
    }
)

#3. 重排序

python
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank

# 使用 Cohere 重排序
compressor = CohereRerank()
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor,
    base_retriever=retriever
)

#4. 查询转换

python
from langchain.retrievers.multi_query import MultiQueryRetriever

# 将一个问题转换为多个查询
multi_query_retriever = MultiQueryRetriever.from_llm(
    retriever=retriever,
    llm=llm
)

#下一步

LangGraph - 复杂工作流
ChromaDB - 深入向量数据库
Pinecone - 云端向量数据库

提示：RAG 的效果很大程度取决于分块策略和检索质量，需要根据具体场景调优。

LangChain 框架指南

LangChain RAG 检索增强生成

#什么是 RAG？

#快速开始

#最简单的 RAG

#文档加载

#常用加载器

#加载文档

#文本分割

#RecursiveCharacterTextSplitter (推荐)

#代码分割

#Markdown 分割

#向量存储

#Chroma (本地)

#Pinecone (云端)

#FAISS (高性能本地)

#检索器

#基础检索

#MMR 检索 (多样性)

#带阈值的检索

#混合检索

#完整 RAG Pipeline

#优化技巧

#1. 优化分块

#2. 添加元数据

#3. 重排序

#4. 查询转换

#下一步

相关指南