langchain4j-vector-stores-configuration

name	langchain4j-vector-stores-configuration
description	Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
allowed-tools	Read, Write, Edit, Bash, Glob, Grep

LangChain4J Vector Stores Configuration

Configure vector stores for Retrieval-Augmented Generation applications with LangChain4J.

Overview

LangChain4J provides a unified abstraction for vector stores (PostgreSQL/pgvector, Pinecone, MongoDB Atlas, Milvus, Neo4j) with builder-based configuration, metadata filtering, and hybrid search support.

When to Use

• Configuring vector stores for semantic search and RAG applications
• Setting up embedding storage with metadata filtering and hybrid search
• Optimizing vector database performance for production AI workloads

Instructions

Set Up Basic Vector Store

Configure an embedding store for vector operations:

@Bean
public EmbeddingStore<TextSegment> embeddingStore() {
    return PgVectorEmbeddingStore.builder()
        .host("localhost")
        .port(5432)
        .database("vectordb")
        .user("username")
        .password()
        .table()
        .dimension() 
        .createTable()
        .useIndex()
        .build();
}