Building AI-Powered Search with RAG & LangChain in Java

RAG (Retrieval-Augmented Generation) combines semantic search with LLMs to ground answers in your data.

How RAG Works

1. Embed user query to vector
2. Search vector database for similar documents
3. Build prompt: "Context: [documents]\nQuestion: [query]"
4. Send to LLM
5. Get answer grounded in YOUR data, not hallucinations

Why RAG?

• GPT-4 has April 2024 cutoff
• Your Q3 revenue: not in training data
• RAG lets LLM answer "What's our Q3 revenue?" by searching your reports

Implementation with LangChain4j

ConversationalRetrievalChain chain = ConversationalRetrievalChain.builder()
    .chatLanguageModel(gpt4Model)
    .retriever(semanticRetriever)
    .chatMemory(conversationMemory)
    .build();

String answer = chain.execute("How many users signed up last month?");

Production Patterns

• Cache retrieved documents (reduce API calls)
• Implement graceful fallbacks
• Monitor query quality & latency
• Use proper prompt engineering
• Split large documents smartly

Use Cases

• Customer support chatbot (index FAQ)
• Internal documentation assistant
• Code search for engineers
• Sales assistant with product specs
• Legal document Q&A

Next Steps

1. Start with LangChain4j + OpenAI
2. Index your documentation
3. Experiment with prompts
4. Monitor accuracy
5. Scale to vector database (Milvus, Pinecone)

Build RAG systems that are accurate, grounded, and production-ready.