langchain4j-rag-implementation-patterns
Complete Retrieval-Augmented Generation systems with LangChain4j for knowledge-enhanced AI applications.
- Document ingestion pipelines with configurable chunking, metadata management, and embedding generation using OpenAI or custom embedding models
- Vector search and content retrieval with filtering, re-ranking, and configurable similarity thresholds for precise context matching
- RAG-enabled AI services that automatically inject retrieved context into chat models, with support for multi-domain assistants and hierarchical retrieval patterns
- Hybrid search combining vector similarity with keyword matching, batch embedding operations, and production patterns for in-memory and persistent embedding stores
- Best practices for chunk sizing, metadata strategies, query processing, and troubleshooting common issues like poor retrieval quality and stale cached embeddings
LangChain4j RAG Implementation Patterns
Overview
Implements RAG systems with LangChain4j: document ingestion pipelines, embedding stores, and vector search for chat-with-documents and knowledge-enhanced AI applications.
When to Use This Skill
- Building chat-with-documents systems or document Q&A over PDFs, text files, or web pages
- Creating AI assistants with access to company knowledge bases or external sources
- Implementing semantic search or hybrid search over document repositories
- Building domain-specific AI with curated knowledge and source attribution
Instructions
Initialize RAG Project
Create a new Spring Boot project with required dependencies:
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