rag-implementation - Build Retrieval-Augmented Generation systems for LLMs.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
Tags
Updated: 2026-09-23Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Store document embeddings
- Retrieve document embeddings
- Convert text to numerical vectors
- Split text into chunks
- Perform hybrid search
- Rerank search results
- Filter retrieval using metadata
- Evaluate RAG system performance
Inputs
- Documents
- Natural language queries
- API keys
- Vector store configurations
Outputs
- Generated answers with source citations
- Retrieved source document chunks
- Persisted vector store directory
- System evaluation metrics
Requirements
- Python environment
- LangChain library
- Vector database service or library
- LLM API access
- Embedding model access