Run Piika Nanoknow

Run Piika NanoKnow

Run NanoKnow through generic installed manifests and adapters. Keep NanoKnow-specific paths and commands in this skill; do not add dataset-specific imports to Piika's TypeScript benchmark registry.

Select the condition

Choose one dataset:

• nanoknow-nq: aliases nq, nq-open; 3,021 ClimbMix-supported queries.
• nanoknow-squad: alias squad; 9,071 ClimbMix-supported queries.

Choose one evidence condition:

• Retrieval: start from the question and search climbmix-400b.
• Supplied documents: inject grouped documents per query, while retaining the backend for narrowly targeted gap filling.

Read references/end-to-end.md completely before executing either condition.

Guardrails

1. Work from the Piika repository root.
2. Inspect the required query, qrels, source-answer, and ground-truth paths.
3. Install the selected asset manifest with the generic installer; use --dry-run first.
4. Keep the retrieval and supplied-document conditions in distinct output directories.
5. Use the same model, thinking level, timeout, query population, and backend across a comparison.
6. Run a small --limit smoke test before a full dataset.
7. Verify benchmark_manifest_snapshot.json, run_setup.json, processed-query count, and failure count before evaluation.
8. Never treat missing backend access or provider usage limits as benchmark results.

Expected outputs

For each condition, produce:

• Native per-query run JSON.
• Retrieval evaluation from the benchmark qrels.
• Gold-answer judge evaluation using all acceptable answers.
• A Markdown report.

When comparing supplied documents against retrieval-only runs, record the supplied bundle path and hash outside ignored data directories if long-term provenance is required.