haiku.rag

Debug haiku.rag evaluation runs in Logfire. Use when asked to look at Logfire for an eval run, find failing or low-scoring eval cases, compare runs, check citation quality (cited_map) or judge pass rate (answer_equivalent), or explain why an eval case failed. Drives the Logfire MCP against the `evals` service.

Debug haiku.rag ingestion in Logfire. Use when asked to look at Logfire for ingestion, find failed or dead ingestion jobs, investigate retries or circuit-breaker events, trace a document through convert/chunk/embed/store, find which docling-serve instance served a request, spot slow conversions, or tell concurrent ingesters apart. Drives the Logfire MCP against the `haiku-ingester` service.

Computational analysis of the knowledge base via code execution in a sandboxed Python interpreter. Use for questions requiring counting, aggregation, statistics, data traversal, comparison across documents, or any task best answered by writing Python code. Examples: "how many pages?", "compare table 3 across documents", "calculate average word count", "extract all email addresses".

Search, retrieve and analyze documents using RAG (Retrieval Augmented Generation).