haiku.rag/docs/benchmarks.md

Benchmarks

We evaluate haiku.rag on a small set of datasets that exercise different parts of the pipeline. Wix and OpenRAG Bench (ORB) are the two we currently track. Retrieval, QA accuracy, and citation retrieval are scored end-to-end through the rag and rag-analysis skills.

Running Evaluations

You can run evaluations with the evaluations CLI:

evaluations run wix
evaluations run orb_text

The evaluation flow is orchestrated with pydantic-evals, which we leverage for dataset management, scoring, and report generation.

Pre-built Databases

Building evaluation databases from scratch can take a long time, especially for large datasets like OpenRAG Bench. Pre-built databases are available on HuggingFace:

# Download a specific dataset
evaluations download wix

# Download all datasets
evaluations download all

# Force re-download (overwrite existing)
evaluations download wix --force

Active datasets:

Dataset	Size
wix	~511MB
orb_text — OpenRAG Bench, text embedder (qwen3-embedding:4b) with VLM picture descriptions baked into chunk content	~18 GB
orb_multimodal — OpenRAG Bench, multimodal embedder (qwen3-vl-embedding-8b); picture vectors live in the same space as text for cross-modal retrieval	~16 GB

Inactive (kept downloadable, not currently maintained):

Dataset	Size
repliqa	~30MB
hotpotqa	~331MB

After downloading, run benchmarks with --skip-db to use the pre-built database:

evaluations run wix --skip-db

Configuration

The benchmark script accepts several options:

evaluations run wix --config /path/to/haiku.rag.yaml --db /path/to/custom.lancedb

Options:

• --config PATH - Specify a custom haiku.rag.yaml configuration file
• --db PATH - Override the database path (default: platform-specific user data directory)
• --skip-db - Skip updating the evaluation database
• --skip-retrieval - Skip retrieval benchmark
• --skip-qa - Skip QA benchmark
• --limit N - Limit number of test cases
• --name NAME - Override the evaluation name
• --target {rag-skill,analysis-skill} - Choose which skill to benchmark end-to-end (default: rag-skill).
• --skill-model PROVIDER:NAME - Override the skill model independently from the judge (default: config.qa.model, or config.analysis.model when set for --target analysis-skill).

If no config file is specified, the script searches standard locations: ./haiku.rag.yaml, user config directory, then falls back to defaults.

To pin the LLM judge in YAML (rather than the default ollama:qwen3.6):

evaluations:
  judge:
    provider: openai
    name: gpt-4o-mini
    base_url: http://localhost:8000/v1   # optional, for OpenAI-compatible servers (vLLM, LM Studio, etc.)

Methodology

Retrieval Metrics

Mean Reciprocal Rank (MRR) - Used when each query has exactly one relevant document.

• For each query, find the rank (position) of the first relevant document in top-K results
• Reciprocal rank = 1/rank (e.g., rank 3 → 1/3 ≈ 0.333)
• If not found in top-K, score is 0
• MRR is the mean across all queries
• Range: 0 (never found) to 1 (always at rank 1)

Mean Average Precision (MAP) - Used when queries have multiple relevant documents.

• For each relevant document at position k, calculate precision@k = (relevant docs in top k) / k
• Average Precision (AP) = mean of these precision values / total relevant documents
• MAP is the mean of AP scores across all queries
• Range: 0 to 1; rewards ranking relevant documents higher

QA Accuracy

pydantic-evals coordinates an LLM judge to determine whether the skill's answer is correct. The default judge is ollama:qwen3.6 — pinned so changes to the skill model don't change the judge underneath. Set evaluations.judge in haiku.rag.yaml to override (including a custom base_url for any OpenAI-compatible endpoint). Accuracy is the fraction of correctly answered questions.

We picked qwen3.6 over the previously-pinned gpt-oss after a 4-cell calibration (gpt-oss / qwen3.6 as both answerer and judge, with Claude Opus 4.7 as a reference). qwen3.6 had κ ≥ 0.66 vs the reference on both same-family and cross-family answerers (vs ~0.39–0.55 for gpt-oss) and showed no measurable self-preference bias, while gpt-oss was ~10 pp more lenient on its own outputs.

Citation Retrieval

Alongside QA accuracy, a second metric scores the URIs the skill registered via the cite tool against each dataset's gold expected_uris, using the same MRR / MAP math as raw retrieval. The score key is cited_mrr for single-doc datasets and cited_map for multi-doc. Console output also includes the cite rate (% of cases with at least one citation) and the mean number of citations per case.

This is computed alongside QA accuracy from the same skill run — no extra invocations. The signal complements raw retrieval: where raw retrieval measures whether the retriever surfaced the gold document at any rank, citation retrieval measures whether the skill grounded its answer on it.

Current results

Numbers measured under the current pinned judge (ollama:qwen3.6) on a recent haiku.rag version.

Wix

WixQA — real customer support questions paired with curated answers. 200 cases.

evaluations run wix --target rag-skill runs the RAG skill end-to-end and produces both QA accuracy and a citation retrieval metric (cited_map) computed from the URIs the skill registered via the cite tool against the gold expected_uris.

Skill model	Reranker	QA accuracy	Mean cited_map
vllm:Gemma-4-26B-A4B-NVFP4	mxbai-rerank-base-v2	0.87	0.38

Measured on haiku.rag v0.48.0 with qwen3-embedding:4b (vLLM, dim 2560), chunk_size=256, search.limit=5. Judged by vllm:Qwen3.6-35B-A3B-NVFP4 (qwen3.6 family, NVFP4 quant served via vLLM rather than the default Ollama). 172 / 198 completed cases (2 errored).

OpenRAG Bench (ORB)

OpenRAG Bench contains ArXiv research papers with multimodal question-answering pairs. Queries include both text-based and image-based questions, testing retrieval and reasoning over visual content like figures, charts, and diagrams. Each query maps to one relevant document.

Two approaches are benchmarked separately:

• Multimodal embedder (Qwen/Qwen3-VL-Embedding-8B, served via vLLM): picture bytes and text live in a shared vector space, no VLM is run at ingest.
• Text embedder + VLM picture descriptions (qwen3-embedding:4b + ollama/ministral-3): pictures are described at ingest and the descriptions are woven into chunk text; retrieval runs over text only. See Picture handling configuration.

Multimodal embedder

Retrieval (MAP)

Embedding Model	Source bucket	Cases	MAP
Qwen/Qwen3-VL-Embedding-8B	text only	1914	0.9801
Qwen/Qwen3-VL-Embedding-8B	text + image	763	0.9720
Qwen/Qwen3-VL-Embedding-8B	text + table	148	0.9786
Qwen/Qwen3-VL-Embedding-8B	text + table+image	220	0.9720
Qwen/Qwen3-VL-Embedding-8B	all	3045	0.9774

QA Accuracy

Embedding Model	Skill model	Reranker	Source bucket	Cases	Accuracy
Qwen/Qwen3-VL-Embedding-8B	ollama:qwen3.6 (vision)	none	text only	682	96.9 %
Qwen/Qwen3-VL-Embedding-8B	ollama:qwen3.6 (vision)	none	with image	299	91.3 %
Qwen/Qwen3-VL-Embedding-8B	vllm:Gemma-4-26B-A4B-NVFP4	mxbai-rerank-base-v2	text	894	88.5 %
Qwen/Qwen3-VL-Embedding-8B	vllm:Gemma-4-26B-A4B-NVFP4	mxbai-rerank-base-v2	text+image	341	88.0 %
Qwen/Qwen3-VL-Embedding-8B	vllm:Gemma-4-26B-A4B-NVFP4	mxbai-rerank-base-v2	text+table	72	88.9 %
Qwen/Qwen3-VL-Embedding-8B	vllm:Gemma-4-26B-A4B-NVFP4	mxbai-rerank-base-v2	text+table+image	102	93.1 %
Qwen/Qwen3-VL-Embedding-8B	vllm:Gemma-4-26B-A4B-NVFP4	mxbai-rerank-base-v2	all	1409	88.7 %

Text embedder + VLM picture descriptions

Retrieval (MAP)

Embedding Model	VLM	Source bucket	Cases	MAP
qwen3-embedding:4b	Ollama / ministral-3	all	3045	0.9722

Measured on haiku.rag v0.45.0.

QA accuracy + citation retrieval

Embedding Model	VLM	Skill model	QA accuracy	Mean cited_map
qwen3-embedding:4b	Ollama / ministral-3	ollama:gpt-oss	0.94	0.86
qwen3-embedding:4b	Ollama / ministral-3	vllm:Gemma-4-26B-A4B-NVFP4	0.90	0.89

ollama:gpt-oss row measured on haiku.rag v0.44.0, on 2992 of 3044 completed cases. vllm:Gemma-4-26B-A4B-NVFP4 row measured on haiku.rag v0.47.0, with mxbai-rerank-base-v2, stopped at 674 of 3045 cases (cumulative means stable from case ~200). Both judged by ollama:qwen3.6 (current default).

Inactive datasets

The benchmarks below are not currently maintained. Numbers were measured against earlier haiku.rag versions and an older pinned judge (ollama:gpt-oss), before the skill workflow became the only path. Retrieval tables don't depend on the judge, but the QA tables aren't reproducible against the current skill-only setup. We may revive them.

RepliQA

RepliQA contains synthetic news stories with question-answer pairs. We use News Stories from repliqa_3 (1035 documents). Each question has exactly one relevant document, so we use MRR for retrieval evaluation.

Retrieval (MRR)

Embedding Model	MRR	Reranker
Ollama / qwen3-embedding:8b	0.91	-

Measured on haiku.rag v0.19.6.

QA Accuracy

Embedding Model	QA Model	Accuracy	Reranker
Ollama / qwen3-embedding:4b	Ollama / gpt-oss - no thinking	0.82	None
Ollama / qwen3-embedding:8b	Ollama / gpt-oss - thinking	0.89	None
Ollama / mxbai-embed-large	Ollama / qwen3 - thinking	0.85	None
Ollama / mxbai-embed-large	Ollama / qwen3 - thinking	0.87	mxbai-rerank-base-v2
Ollama / mxbai-embed-large	Ollama / qwen3:0.6b	0.28	None

Measured on haiku.rag v0.19.6, judged by ollama:gpt-oss.

Note the significant degradation when very small models are used such as qwen3:0.6b.

HotpotQA

HotpotQA is a multi-hop question answering dataset requiring reasoning over multiple Wikipedia paragraphs. Each question requires evidence from 2+ documents, making it ideal for testing retrieval and reasoning capabilities. We use MAP for retrieval evaluation since queries have multiple relevant documents.

Retrieval (MAP)

Embedding Model	MAP	Reranker
qwen3-embedding:4b	0.69	none

Measured on haiku.rag v0.20.2.

QA Accuracy

Embedding Model	QA Model	Accuracy
qwen3-embedding:4b	gpt-oss:20b - thinking	0.86

Measured on haiku.rag v0.20.2, judged by ollama:gpt-oss.

Wix (historical, plain text and HTML)

Earlier Wix runs measured under different chunk settings and reranker combinations, against the older gpt-oss judge.

Retrieval (MAP)

Embedding Model	Chunk size	MAP	Reranker	Notes
qwen3-embedding:4b	256	0.34	None	html, chunk-radius=2
qwen3-embedding:4b	256	0.39	mxbai-rerank-base-v2	html, chunk-radius=2
qwen3-embedding:4b	256	0.43	None	plain text, chunk-radius=0
qwen3-embedding:4b	512	0.45	None	plain text, chunk-radius=0

Measured on haiku.rag v0.27.2.

QA Accuracy

Embedding Model	Chunk size	QA Model	Accuracy	Notes
qwen3-embedding:4b	256	gpt-oss:20b - no thinking	0.80	html, chunk-radius=2
qwen3-embedding:4b	256	gpt-oss:20b - no thinking	0.83	html, chunk-radius=2, jinaai/jina-reranker-v3

Measured on haiku.rag v0.27.2, judged by ollama:gpt-oss.