5 docs tagged with "RAG"

Retrieve chunks semantically, then traverse the knowledge graph to assemble author, topic, and source provenance as richer LLM context.

Ingest PDFs, web pages, and database records as distinct labels, then search across all sources in a single vector query with source-aware citations.

Measure Precision@k and Recall@k for your retrieval pipeline, detect score drift after model updates, and add a CI regression gate that fails on quality drops.

Chunk Markdown files, store them in RushDB, and build a retrieval-augmented generation pipeline in TypeScript, Python, or REST.

Improve retrieval precision with two-stage search — over-fetch candidates with vector similarity, then rerank with LLM scoring or Reciprocal Rank Fusion before sending to the LLM.