4 docs tagged with "Embeddings"

Use your own embedding model to generate vectors and store them in RushDB, then search with queryVector instead of query text.

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.

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.