rag-implementation - Build grounded RAG applications with semantic search
Build Retrieval-Augmented Generation systems that retrieve external knowledge and generate grounded answers for LLM applications.
Tags
Updated: 2026-10-02Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Split documents into chunks
- Generate document embeddings
- Store vectors in databases
- Retrieve relevant documents
- Combine dense and sparse search
- Rerank retrieval results
- Generate grounded answers
Inputs
- Source documents
- Natural-language questions
- Vector database
- Embedding model
- Large language model
- Service API credentials
Outputs
- Retrieved document context
- Generated answers
- Stored document embeddings
- Compiled RAG pipelines
Requirements
- Python environment
- LangGraph and LangChain libraries
- Vector database
- Embedding model
- Large language model
- API access for selected services