Does it handle document chunking?

Yes, it provides implementation patterns for EmbeddingStoreIngestor and recursive document splitters to optimize text segments for specific LLM token limits.

Can I use this for Spring Boot projects?

Yes, it includes specific patterns for Spring @Bean configurations, service-layer integration, and health indicators compatible with Spring Boot Actuator.

What vector databases does this skill support?

This skill supports all major LangChain4j-compatible providers including PostgreSQL (pgvector), Pinecone, MongoDB Atlas, Milvus, and Neo4j.

Is there support for hybrid search?

Absolutely, the skill includes instructions for setting up EmbeddingSearchRequest with metadata-based filtering to combine vector similarity with structured data queries.

LangChain4j Vector Stores Configuration

Name: LangChain4j Vector Stores Configuration
Author: giuseppe-trisciuoglio

bygiuseppe-trisciuoglio

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データサイエンスとML

Configures and optimizes LangChain4j vector stores for RAG applications, enabling seamless semantic search and embedding storage in Java environments.

概要

This skill provides specialized guidance for implementing vector databases and embedding storage using the LangChain4j framework within Java applications. It assists developers in setting up RAG (Retrieval-Augmented Generation) pipelines, configuring providers like pgvector, Pinecone, and MongoDB, and implementing advanced features like hybrid search, document chunking, and metadata filtering. Whether you are building a semantic search engine or an AI-powered recommendation system, this skill ensures production-ready configurations with built-in best practices for performance monitoring, connection pooling, and health checks.

主な機能

Multi-provider vector store integration including PostgreSQL, MongoDB, and Pinecone
Production-grade optimization with HikariCP connection pooling and monitoring
34 GitHub stars
Automated document ingestion with recursive splitting and embedding pipelines
Advanced metadata filtering and hybrid search capability configuration
Pre-built health check indicators for vector database connectivity

ユースケース

Developing context-aware RAG agents that require persistent long-term memory
Building semantic search capabilities into existing Java or Spring Boot applications
Migrating between different vector database providers with minimal code changes

概要

主な機能

Multi-provider vector store integration including PostgreSQL, MongoDB, and Pinecone
Production-grade optimization with HikariCP connection pooling and monitoring
34 GitHub stars
Automated document ingestion with recursive splitting and embedding pipelines
Advanced metadata filtering and hybrid search capability configuration
Pre-built health check indicators for vector database connectivity

ユースケース

Developing context-aware RAG agents that require persistent long-term memory
Building semantic search capabilities into existing Java or Spring Boot applications
Migrating between different vector database providers with minimal code changes