rag

Builds RAG pipelines that process documents, generate embeddings, store vectors, retrieve context, and ground AI responses.

When to use it

Use when building document Q&A, semantic search, knowledge-based chatbots, documentation assistants, or other systems grounded in domain-specific information.

Give it a RAG implementation goal; it builds a pipeline that processes documents, generates embeddings, and stores them for retrieval.

No additional actions listed in the analysis.