langchain4j-vector-stores-configuration
LangChain4J vector store configuration for RAG applications with multiple database backends.
Works with
What it does
Supports PostgreSQL/pgvector, Pinecone, MongoDB Atlas, Milvus, Neo4j, and in-memory stores with unified abstraction
Includes document ingestion pipelines with configurable chunking, metadata filtering, and batch operations
Provides production patterns for connection pooling, health checks, monitoring, and index optimization
Covers semantic search implementation, multi-store setups, and dim
Installation Guide
How to use langchain4j-vector-stores-configuration on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
langchain4j-vector-stores-configuration
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches langchain4j-vector-stores-configuration from giuseppe-trisciuoglio/developer-kit and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate langchain4j-vector-stores-configuration. Access via /langchain4j-vector-stores-configuration in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort