Easy RAG with LangChain4J and Docker by Julien Dubois

LangChain4J is a Java version of LangChain that provides abstractions and tooling for working with LLMs, vector databases, and RAG implementations

RAG (Retrieval Augmented Generation) helps solve two main LLM limitations:

• Outdated training data
• Lack of access to private/company data

Key components of a RAG implementation:

• Document ingestion and cleaning
• Text splitting into appropriate segments (300-1000 characters recommended)
• Embedding model to convert text to vectors
• Vector database for storage (QDrant recommended for Docker deployments)
• Prompt engineering to combine context with user questions

Best practices for RAG:

• Don’t send too much data to the LLM
• Test different embedding models and segment sizes
• Use metadata and source references
• Be prepared to re-index frequently as new data arrives
• Consider GPU requirements for production

Infrastructure considerations:

• Docker support makes deployment easier
• GPU acceleration important for production performance
• Test containers useful for integration testing
• Cloud GPUs may be needed for larger models

Model selection tradeoffs:

• Smaller models like TinyLLAMA work well for testing
• Larger models like GPT-4 provide better results but cost more
• Consider model size vs speed vs accuracy for your use case
• Test different models for specific domain knowledge

Vector stores should maintain:

• Text segments
• Vector embeddings
• Metadata and source references
• Appropriate segment overlap (30-200 characters recommended)