haiku.rag/evaluations
Yiorgis Gozadinos 721acbcf38
Address review on PR #524
- _budget_notice no longer names the cite tool after prepare_tools has
  withdrawn it; the post-grace state gets the plain no-tools text back.
- Split search-budget rejections from any failed tool call: the code tool
  raises ToolFailed for every error in model-written Python, so
  budget_spent was true for a ZeroDivisionError.
- docs/capabilities/rag.md described the old single-turn removal.
- Drop the rationale clause from the CHANGELOG entry.
2026-07-30 15:50:06 +03:00
..
configs replace haiku.skills with native Pydantic AI capabilities 2026-07-24 15:26:17 +03:00
evaluations Address review on PR #524 2026-07-30 15:50:06 +03:00
scripts Add T²-RAGBench leaderboard submission exporter 2026-06-08 11:51:02 +03:00
tests Address review on PR #524 2026-07-30 15:50:06 +03:00
LICENSE Restructure into uv workspace to support minimal and full installations 2025-11-04 17:59:12 +02:00
pyproject.toml vb 2026-07-29 09:06:55 +03:00
README.md fix capability execution limits and chat loading 2026-07-24 15:26:17 +03:00

Haiku RAG - Evaluations

Internal benchmarking and evaluation scripts for haiku.rag.

This package is not published to PyPI and is only used for development and testing purposes.

Overview

Contains evaluation scripts for benchmarking RAG retrieval and QA performance. Available datasets:

  • WiX (wix)
  • HotpotQA (hotpotqa) — multi-hop QA over Wikipedia paragraphs (distractor validation split, 7,405 questions, two gold documents per question)
  • OpenRAG Bench, two variants:
    • orb_text — text embedder (qwen3-embedding:4b, 2560-dim) with VLM picture descriptions baked into chunk content at ingest. Use for text-only retrieval/QA against figure-rich corpora.
    • orb_multimodal — multimodal embedder (qwen3-vl-embedding-8b, 4096-dim) with picture vectors in the same space as text. Use for cross-modal retrieval (text-as-query → figure hits, image-as-query) and vision QA where the figure itself is the answer.

Usage

After installing the package, you can run evaluations using the evaluations command:

# Run retrieval + QA benchmarks
evaluations run wix
evaluations run orb_text

# Use a custom config file
evaluations run wix --config /path/to/haiku.rag.yaml

# Override the database path
evaluations run wix --db /path/to/custom.lancedb

# Skip database population and run only benchmarks
evaluations run wix --skip-db

# Skip specific benchmarks
evaluations run wix --skip-retrieval
evaluations run wix --skip-qa

# Limit the number of test cases
evaluations run wix --limit 100

Choosing the target

evaluations run benchmarks --target rag-capability by default. Use --target analysis-capability to benchmark the analysis capability against the same datasets and judge:

evaluations run wix --target rag-capability
evaluations run wix --target analysis-capability --capability-model ollama:gpt-oss

--capability-model "provider:name" overrides the capability model independently from the judge (defaults to qa.model, or analysis.model when set for the analysis-capability target). A citation retrieval metric (cited_map) is computed alongside QA accuracy from the URIs the capability registered via the cite tool.

Debugging runs in Logfire

With LOGFIRE_TOKEN set, runs ship spans under service_name = 'evals'. The debug-evals skill in .claude/skills/ turns these into ready-made Logfire queries (recent runs, per-case pass rate and cited_map, failing and slowest cases) for use from Claude Code.

Pre-built Databases

Download pre-built evaluation databases from HuggingFace:

evaluations download wix
evaluations download all
evaluations download wix --force

Upload databases (maintainer only):

evaluations upload wix
evaluations upload all

Database Storage

By default, evaluation databases are stored in the haiku.rag data directory:

  • Linux: ~/.local/share/haiku.rag/evaluations/dbs/
  • macOS: ~/Library/Application Support/haiku.rag/evaluations/dbs/
  • Windows: C:/Users/<USER>/AppData/Roaming/haiku.rag/evaluations/dbs/

You can override this with the --db option.