haiku.rag/evaluations

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:

• HotpotQA (hotpotqa) — multi-hop QA over Wikipedia paragraphs (distractor validation split, 7,405 questions, two gold documents per question)
• MTRAG ClapNQ (mtrag_clapnq, mtrag_clapnq_rewrite) — IBM's multi-turn RAG benchmark, ClapNQ (Wikipedia) domain: 183,408 passages, 208 retrieval queries with binary qrels, 224 generation tasks. The base key retrieves with the raw last user turn; the _rewrite variant uses the human standalone rewrites (both share one database). Retrieval reports Recall@5/@10, nDCG@5/@10, and MAP against IBM's published setup. QA replays each task's reference conversation prefix as message history and answers the final turn; the judge sees the conversation as a transcript, citation MAP is scored only on turns with gold passages, and refusal precision/recall is reported against the answerability labels. Generation scores are internal (our judge and rubric), not comparable with IBM's published generation numbers. The mtrag_clapnq_live key replays whole conversations (one case per conversation, --limit counts conversations) through a single capability session, carrying the model's own answers and tool history across turns; it reports the same outcomes per turn plus micro (per-turn) and macro (per-conversation) aggregates.
• FRAMES (frames) — multi-hop QA (822 questions, 2-23 gold Wikipedia articles per question; 2 of the original 824 questions are excluded because a linked article has been deleted from Wikipedia). The corpus is the union of the 2,521 linked articles, fetched from the Wikipedia REST API at current revision (revision id and fetch date recorded in the article cache) with navigation chrome stripped. There is no official FRAMES evaluation setup; numbers here correspond to the paper's multi-step retrieval setting (fixed corpus, agentic retrieval, judged accuracy) and are not comparable to its closed-book, oracle-prompt, or web-search settings. Answers were authored against ~2024 revisions and may have drifted with article content.
• 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 hotpotqa
evaluations run orb_text

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

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

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

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

# Limit the number of test cases
evaluations run hotpotqa --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 hotpotqa --target rag-capability
evaluations run hotpotqa --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 hotpotqa
evaluations download all
evaluations download hotpotqa --force

Upload databases (maintainer only):

evaluations upload hotpotqa
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.

Evaluating over Multiple Databases

With lancedb.databases configured, evaluations run <dataset> --skip-db benchmarks the full set. Retrieval, QA, and live conversations preserve the database name on results and citations. A configured set of one follows the same path and retains its name.

Population writes one database and therefore requires --db:

evaluations run hotpotqa --db /path/to/one.lancedb   # populate, then benchmark
evaluations run hotpotqa --skip-db                   # benchmark the configured set

--db overrides the configured set for both population and benchmarks.