13 docs tagged with "AI"

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

Pair raw search results with related evidence, summary metrics, and traversal paths so users and agents can understand why a result was returned.

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

Combine structured where-clause filtering with vector semantic search to narrow candidates by business constraints, then rank by relevance.

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.

Model persistent facts, episodic interactions, and linked reference material as a graph so agents and applications can retrieve and reason over connected context.

Combine structured filters, semantic ranking, and contextual fields to build explainable, user-facing search experiences on top of RushDB.

Build tenant-safe semantic retrieval using RushDB's project-scoped prefilter and exact cosine similarity ranking — without global index assumptions.

Create embedding indexes, wait for backfill, and run your first semantic search query in TypeScript, Python, or REST.

End-to-end guide — connect RushDB to OpenClaw, install the RushDB skills pack, and let your AI assistant query, store, and model structured data without writing a single line of code.