Building RAG Applications with LangChain and Vector Databases

Retrieval-Augmented Generation (RAG) is revolutionizing how we build AI applications. Instead of relying solely on a model's training data, RAG allows us to ground responses in specific, up-to-date documents.

What is RAG?

RAG combines two powerful concepts:

1. Retrieval: Finding relevant documents from a knowledge base
2. Generation: Using an LLM to generate responses based on retrieved context

1from langchain.embeddings import OpenAIEmbeddings
2from langchain.vectorstores import Chroma
3from langchain.chat_models import ChatOpenAI
4from langchain.chains import RetrievalQA
5
6# Initialize components
7embeddings = OpenAIEmbeddings()
8vectorstore = Chroma(persist_directory="./db", embedding_function=embeddings)
9
10# Create retrieval chain
11llm = ChatOpenAI(model="gpt-4", temperature=0)
12qa_chain = RetrievalQA.from_chain_type(
13    llm=llm,
14    chain_type="stuff",
15    retriever=vectorstore.as_retriever(search_kwargs={"k": 3})
16)
17
18# Query the system
19response = qa_chain.run("What are the key features of our product?")

Vector Databases

The heart of RAG is the vector database. It stores document embeddings and enables semantic search:

• Chroma: Great for development and small-scale production
• Pinecone: Managed solution with excellent scaling
• Weaviate: Open-source with rich querying capabilities
• Milvus: High-performance for large-scale applications

Chunking Strategies

How you split documents matters. Consider:

1. Fixed-size chunks: Simple but may break context
2. Semantic chunking: Uses NLP to find natural boundaries
3. Recursive chunking: Hierarchical splitting for better context

Best Practices

1. Chunk overlap: Use 10-20% overlap to maintain context
2. Metadata filtering: Add metadata for more precise retrieval
3. Hybrid search: Combine semantic and keyword search
4. Re-ranking: Use a re-ranker model to improve relevance

Conclusion

RAG is a game-changer for building intelligent applications. Start simple, measure quality, and iterate based on real user feedback.

Interested in AI engineering? Check out my projects on GitHub.