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.
Third-Party Agent Skill: Review the code before installing. Agent skills execute in your AI assistant's environment and can access your files. Learn more about security
Installation for Agentic Skill
View all platforms →skilz install giuseppe-trisciuoglio/developer-kit/langchain4j-vector-stores-configuration skilz install giuseppe-trisciuoglio/developer-kit/langchain4j-vector-stores-configuration --agent opencode skilz install giuseppe-trisciuoglio/developer-kit/langchain4j-vector-stores-configuration --agent codex skilz install giuseppe-trisciuoglio/developer-kit/langchain4j-vector-stores-configuration --agent gemini
First time? Install Skilz: pip install skilz
Works with 14 AI coding assistants
Cursor, Aider, Copilot, Windsurf, Qwen, Kimi, and more...
Extract and copy to ~/.claude/skills/ then restart Claude Desktop
git clone https://github.com/giuseppe-trisciuoglio/developer-kit cp -r developer-kit/skills/langchain4j/langchain4j-vector-stores-configuration ~/.claude/skills/ Need detailed installation help? Check our platform-specific guides:
Related Agentic Skills
google-gemini-embeddings
by jezweb| Build RAG systems, semantic search, and document clustering with Gemini embeddings API (gemini-embedding-001). Generate 768-3072 dimension embedding...
langchain4j-rag-implementation-patterns
by giuseppe-trisciuoglioImplement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategie...
"AgentDB Vector Search"
by ruvnet"Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building ...
"AgentDB Vector Search"
by proffesor-for-testing"Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building ...
Agentic Skill Details
- Repository
- developer-kit
- Type
- Non-Technical
- Meta-Domain
- general
- Primary Domain
- general
- Sub-Domain
- search vector
- Market Score
- 24.5
Browse Category
More general Agentic SkillsReport Security Issue
Found a security vulnerability in this agent skill?