LangChain4J Vector Stores Configuration

概要

This skill provides specialized guidance for integrating and managing vector stores within Java applications powered by LangChain4J. It streamlines the setup of semantic search, embedding storage, and hybrid search capabilities across popular providers such as PostgreSQL (pgvector), Pinecone, MongoDB, and Milvus. By offering standardized patterns for document ingestion pipelines, metadata filtering, and production-ready performance tuning—including connection pooling and health monitoring—this skill enables developers to build scalable, context-aware AI applications with professional-grade persistence layers.

主な機能

• Automated document ingestion with recursive splitting and tokenization
• Production-grade optimization using HikariCP and connection pooling
• 1 GitHub stars
• Multi-provider integration for PostgreSQL, Pinecone, MongoDB, Milvus, and Neo4j
• Advanced metadata filtering and hybrid search implementation patterns
• Integrated health checks and monitoring for vector store connectivity

ユースケース

• Implementing high-performance semantic search within Java backend services
• Building RAG-based systems that require persistent embedding storage and retrieval
• Configuring batch processing pipelines for large-scale document embedding and indexing