haiku.rag/evaluations/README.md

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# 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, plus GEPA-based prompt optimization. Available datasets:
- RepliQA (`repliqa`)
- WiX (`wix`)
- HotpotQA (`hotpotqa`)
- 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:
```bash
# Run retrieval + QA benchmarks
evaluations run repliqa
evaluations run wix
# Use a custom config file
evaluations run repliqa --config /path/to/haiku.rag.yaml
# Override the database path
evaluations run repliqa --db /path/to/custom.lancedb
# Skip database population and run only benchmarks
evaluations run repliqa --skip-db
# Skip specific benchmarks
evaluations run repliqa --skip-retrieval
evaluations run repliqa --skip-qa
# Limit the number of test cases
evaluations run repliqa --limit 100
```
### Benchmarking the skills
By default `evaluations run` benchmarks the QA agent. Pass `--target` to
benchmark the RAG or analysis skill instead, against the same datasets and judge:
```bash
evaluations run wix --target rag-skill
evaluations run wix --target analysis-skill --skill-model ollama:gpt-oss
```
`--skill-model "provider:name"` overrides the skill model independently from
the judge (defaults to `qa.model`). For skill targets, a citation retrieval
metric (`cited_mrr` / `cited_map`) is computed alongside QA accuracy from the
URIs the skill registered via the `cite` tool.
### Pre-built Databases
Download pre-built evaluation databases from HuggingFace:
```bash
evaluations download repliqa
evaluations download all
evaluations download repliqa --force
```
Upload databases (maintainer only):
```bash
evaluations upload repliqa
evaluations upload all
```
### Prompt Optimization
Optimize QA system prompts using GEPA (Generalized Evolutionary Prompt Algorithm):
```bash
evaluations optimize wix
evaluations optimize repliqa --limit 40 --num-candidates 30
evaluations optimize wix --output optimized_prompt.txt
```
See [Tuning docs](https://ggozad.github.io/haiku.rag/tuning/#prompt-optimization-gepa) for details on applying results.
## 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.