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pythonadvanced

RAG with FAISS and LangChain Python

Build a local RAG pipeline using FAISS vector store and LangChain for document Q&A.

python
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import TextLoader

loader = TextLoader('document.txt')
docs = loader.load()

splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(docs)

embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(chunks, embeddings)
vectorstore.save_local('faiss_index')

retriever = vectorstore.as_retriever(search_kwargs={'k': 4})
qa_chain = RetrievalQA.from_chain_type(llm=ChatOpenAI(model='gpt-4o-mini'), retriever=retriever)

result = qa_chain.invoke({'query': 'What is the main topic?'})
print(result['result'])

Use Cases

  • document Q&A
  • local RAG
  • knowledge base search

Tags

#rag#faiss#langchain#embeddings

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