haiku.rag/docs/benchmarks.md

Benchmarks

We evaluate haiku.rag on several datasets to measure both retrieval quality and question-answering accuracy.

Running Evaluations

You can run evaluations with the evaluations CLI:

evaluations run repliqa
evaluations run wix

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 repliqa

# Download all datasets
evaluations download all

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

Available datasets:

Dataset	Size
repliqa	~30MB
hotpotqa	~331MB
wix	~511MB
open_rag_bench	~14GB

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

evaluations run repliqa --skip-db

Configuration

The benchmark script accepts several options:

evaluations run repliqa --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
• --judge-model PROVIDER:NAME - Override the LLM judge model. Defaults to ollama:qwen3.6 so the judge stays stable when the QA / skill model changes.
• --target {qa,rag-skill,analysis-skill} - Choose what to benchmark (default: qa). rag-skill and analysis-skill run the corresponding skill end-to-end against the same datasets and judge as the QA agent.
• --skill-model PROVIDER:NAME - Override the skill model independently from the judge (default: config.qa.model). Only valid with skill targets.

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

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

For question-answering evaluation, pydantic-evals coordinates an LLM judge to determine whether answers are correct. The default judge is ollama:qwen3.6 — pinned so changes to the QA or skill model don't change the judge underneath. Override per run with --judge-model provider:name. 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

When benchmarking a skill (--target rag-skill or --target analysis-skill), 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.

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.

Results from v0.19.6

Retrieval (MRR)

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

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

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

Wix

WixQA contains real customer support questions paired with curated answers from Wix. The benchmark follows the evaluation protocol from the WixQA paper. Each query can have multiple relevant passages, so we use MAP for retrieval evaluation.

We benchmark both the plain text version (HTML stripped, no structure) and HTML version. Since HTML chunks are small (typically a phrase), we use chunk_radius=2 to expand context.

Results from v0.27.2

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

QA Accuracy

Embedding Model	Chunk size	QA Model	Accuracy	Notes
qwen3-embedding:4b	256	gpt-oss:20b - thinking	0.82	html, chunk-radius=2
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

Skill QA + citation retrieval

evaluations run wix --target rag-skill benchmarks 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	QA accuracy	Cite rate	Mean cited_map
ollama:gpt-oss	0.78	0.96	0.48

35 % of cases produce a perfect citation (cited_map = 1.0). 1 % of correct answers come back without a citation — the rest are grounded.

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.

Results from v0.20.2

Retrieval (MAP)

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

QA Accuracy

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

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 over visual content like figures, charts, and diagrams. We use MAP for retrieval evaluation since each query maps to one relevant document.

Multimodal processing: Picture descriptions are generated using a Vision Language Model (VLM) during document conversion, making embedded images searchable via text queries. See Picture Description configuration.

Results from v0.26.8

Retrieval (MAP)

Embedding Model	MAP	VLM
qwen3-embedding:4b	0.9626	Ollama / ministral-3

QA Accuracy

Embedding Model	QA Model	Accuracy	VLM
qwen3-embedding:4b	gpt-oss:20b - no thinking	0.912	Ollama / ministral-3