langchain4j-vector-stores-configuration
Configure LangChain4J vector stores for 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.
npx skills add majiayu000/claude-skill-registry --skill langchain4j-vector-stores-configuration-giuseppe-trisciuogli-dev --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LangChain4J Vector Stores Configuration Configure vector stores for Retrieval-Augmented Generation applications with LangChain4J. ## When to Use To configure vector stores when: - Building RAG applications requiring embedding storage and retrieval - Implementing semantic search in Java applications - Integrating LLMs with vector databases for context-aware responses - Configuring multi-modal embedding storage for text, images, or other data - Setting up hybrid search combining vector similarity and full-text search - Migrating between different vector store providers - Optimizing vector database performance for production workloads - Building AI-powered applications with memory and persistence - Implementing document chunking and embedding pipelines - Creating recommendation systems based on vector similarity ## Instructions ### Set Up Basic Vector Store Configure an embedding store for vector operations: ```java @Bean public EmbeddingStore<TextSegment> embeddingStore() { return PgVectorEmbeddingStore.builder() .host("localhost") .port(5432) .database("vectordb") .user("username") .password("password") .table("embeddings") .dimension(1536) // OpenAI embedding dimension .createTab
- When to Use
- Instructions
- Set Up Basic Vector Store
- Configure Multiple Vector Stores
- Implement Document Ingestion
- Set Up Metadata Filtering
- Configure Production Settings
- Implement Health Checks
- Examples
- Basic RAG Application Setup
- Semantic Search Service
- Production Setup with Monitoring
- Best Practices
- Choose the Right Vector Store
What does the langchain4j-vector-stores-configuration skill do?
Configure LangChain4J vector stores for 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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill langchain4j-vector-stores-configuration-giuseppe-trisciuogli-dev --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.