# 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 repliqa
evaluations 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.

### Configuration

The benchmark script accepts several options:

```bash
evaluations 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

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 (Ollama `qwen3`) to determine whether answers are correct. Accuracy is the fraction of correctly answered questions.

## 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:0.6b` | 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 v0.20.0*

### 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` - no thinking | 0.74     | plain text, `chunk-radius=0` |
| `qwen3-embedding:4b` | 256        | `gpt-oss:20b` - thinking    | 0.79     | html, `chunk-radius=2`       |
| `qwen3-embedding:4b` | 256        | `gpt-oss:20b` - thinking    | 0.80     | html, `chunk-radius=2`, reranker=`mxbai-rerank-base-v2` |

## 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*
QA accuracy is evaluated over 2000 "hard" questions from the validation dataset.

### 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     |