Does this skill help with document chunking?

Yes, it includes implementation details for EmbeddingStoreIngestor which handles document splitting, tokenization (such as OpenAI's tokenizer), and automated embedding generation.

Can I use this for production Java applications?

Absolutely. The skill includes advanced configurations for connection pooling with HikariCP, performance monitoring with MeterRegistry, and robust health check indicators.

Which vector databases are supported by this skill?

This skill provides configuration patterns for a wide range of databases including PostgreSQL (pgvector), Pinecone, MongoDB, Milvus, Neo4j, and local InMemory stores for development.

How does it handle metadata-based filtering?

The skill provides code examples for mapping metadata fields (like author, category, or date) and executing filtered searches to narrow down vector retrieval results.

LangChain4J Vector Stores Configuration

Name: LangChain4J Vector Stores Configuration
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Configures and optimizes vector databases for Retrieval-Augmented Generation (RAG) applications using the LangChain4J framework.

About

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.

Key Features

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

Use Cases

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

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pur3v4d3r/pur3-pkb-codebase langchain4j-vector-stores-configuration

For use in Claude.ai and ChatGPT

Download Skill

GitHub