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RAG知识增强系统2 - 检索器retriever

news 2025/9/24 8:57:47

1.简介

读的环节，利用它获取最符合，最准的答案

2.使用样例

2.1 简单检索器实现

from langchain_community.document_loaders import TextLoader
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitterloader = TextLoader("test.txt",encoding="utf-8")documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings_model = OpenAIEmbeddings(model="BAAI/bge-m3",api_key='sk-zenbqykvzngjvzeoikvxdplnxbvrjsyntpiejwdefmigzjgb',base_url="https://api.siliconflow.cn/v1",
)vectorstore = InMemoryVectorStore.from_documents(texts, embeddings_model)
retriever = vectorstore.as_retriever()
docs = retriever.invoke("deepseek是什么？")
docs

输出：

1.这里首先load test.txt这个文件，文件中含有大量的deepseek的信息

2.切分和实例化嵌入模型后

3 用向量数据库 InMemoryVectorStore 存入资料

4 实例化检索器并匹配相关问题

2.2 查询重写

from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import WebBaseLoader
from langchain_openai import OpenAIEmbeddingsfrom langchain_text_splitters import RecursiveCharacterTextSplitter# Load blog post
loader = WebBaseLoader("https://python.langchain.com/docs/how_to/MultiQueryRetriever/")
data = loader.load()# Split
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
splits = text_splitter.split_documents(data)# VectorDB
vectordb = FAISS.from_documents(documents=splits, embedding=embeddings_model)

from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_deepseek import ChatDeepSeek
import os
from dotenv import load_dotenvload_dotenv()llm = ChatDeepSeek(model="deepseek-chat",temperature=0,api_key=os.getenv('DEEPSEEK_API_KEY'),api_base=os.getenv('DEEPSEEK_API_BASE')
)question = "如何让用户查询更准确？"
retriever_from_llm = MultiQueryRetriever.from_llm(retriever=vectordb.as_retriever(), llm=llm
)import logginglogging.basicConfig()
logging.getLogger("langchain.retrievers.multi_query").setLevel(logging.INFO)unique_docs = retriever_from_llm.invoke(question)
print(unique_docs)
len(unique_docs)

输出：