# 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:

```bash
evaluations run repliqa
evaluations run wix
```

The evaluation flow is orchestrated with [`pydantic-evals`](https://github.com/pydantic/pydantic-ai/tree/main/libs/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:

```bash
# 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:

```bash
evaluations run repliqa --skip-db
```

### Configuration

The benchmark script accepts several options:

```bash
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 (default: `config.qa.model`)
- `--target {qa,rag-skill,analysis-skill}` - Choose what to benchmark (default: `qa`). `rag-skill` and `analysis-skill` run the corresponding [skill](skills/index.md) 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. By default the judge uses the same model as QA (`config.qa.model`); override with `--judge-model provider:name`. Accuracy is the fraction of correctly answered questions.

### 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](https://huggingface.co/datasets/ServiceNow/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](https://huggingface.co/datasets/Wix/WixQA) contains real customer support questions paired with curated answers from Wix. The benchmark follows the evaluation protocol from the [WixQA paper](https://arxiv.org/abs/2505.08643). 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` |

## HotpotQA

[HotpotQA](https://huggingface.co/datasets/hotpotqa/hotpot_qa) 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](https://huggingface.co/datasets/vectara/open_ragbench) 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](configuration/processing.md#picture-description-vlm).

*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 |