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266 lines
14 KiB
Markdown
266 lines
14 KiB
Markdown
# RAG precision/recall evaluation
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<details>
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<summary><strong>🧭 Quick Return to Map</strong></summary>
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<br>
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> You are in a sub-page of **Chunking**.
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> To reorient, go back here:
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>
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> - [**Chunking** — text segmentation and context window management](./README.md)
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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>
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> Think of this page as a desk within a ward.
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> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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</details>
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A compact, repeatable harness to measure retrieval precision, recall, and coverage after you change chunking, OCR, or indexing. This page also defines ΔS and λ probes so you can gate rollouts with hard numbers.
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## Open these first
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- Chunk ids and stability: [chunk_id_schema.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/chunk_id_schema.md)
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- Title tree numbering: [title_hierarchy.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/title_hierarchy.md)
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- Section boundary rules: [section_detection.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/section_detection.md)
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- Typed blocks (code, tables, figures): [code_tables_blocks.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/code_tables_blocks.md)
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- PDF, layout, OCR normalization: [pdf_layouts_and_ocr.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/pdf_layouts_and_ocr.md)
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- Traceable results and cite schema: [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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- Payload contracts for RAG: [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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- Visual recovery map and ops: [rag-architecture-and-recovery.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
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## What this measures
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- **Precision@k**: fraction of retrieved snippets among top-k that truly answer the question.
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- **Recall@k**: fraction of all relevant snippets that appear in top-k.
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- **Coverage**: proportion of questions whose final answer can be justified by at least one cited snippet.
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- **Citation accuracy**: percentage of answers where `section_id` and offsets match the gold.
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- **ΔS(question, retrieved)**: semantic distance. Stable ≤ 0.45, transitional 0.40–0.60, risk ≥ 0.60.
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- **λ_observe**: convergence state across paraphrases and seeds.
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These metrics tell you if a chunking or index change helps retrieval without breaking traceability.
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## Acceptance targets
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- Coverage ≥ 0.70 on the project’s gold set.
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- ΔS(question, retrieved) ≤ 0.45 for the cited snippet of each answered item.
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- Citation accuracy ≥ 0.95 for `section_id` + offsets.
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- λ remains convergent on three paraphrases and two seeds.
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- No drop in Recall@k compared with the previous index beyond 2 percent absolute.
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---
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## Gold set construction
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1) **Scope 200–400 items** that span headings, code regions, tables, and prose.
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2) **Write three paraphrases** per question with identical intent.
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3) **Annotate relevant blocks** using canonical ids from [chunk_id_schema.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/chunk_id_schema.md).
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4) **Mark hard negatives** near the true section to test boundary quality.
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5) **Freeze** the canonical text and store byte offsets after normalization from [pdf_layouts_and_ocr.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/pdf_layouts_and_ocr.md).
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Gold rows should look like:
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```json
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{
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"qid": "Q-0137",
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"paraphrases": [
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"How does SCU unlock safety refusals?",
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"Explain symbolic constraint unlock.",
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"SCU: what is it and when to use?"
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],
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"relevant": ["S.4.2.p.Bk011a", "S.4.2.p.Bk011b"],
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"anchor_section": "S.4.2",
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"negatives": ["S.4.1.p.Bk010", "S.4.3.p.Bk014"]
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}
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````
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---
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## Logging schema for evaluation
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Your retriever must emit a trace per query. Use the fields defined in [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) and [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md).
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```json
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{
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"qid": "Q-0137",
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"query": "Explain symbolic constraint unlock.",
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"topk": [
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{"id": "S.4.2.p.Bk011a", "score": 0.83, "offsets": [204611,205279], "type": "prose"},
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{"id": "S.4.1.p.Bk010", "score": 0.79, "offsets": [198002,199112], "type": "prose"}
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],
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"ΔS": [0.31, 0.59],
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"λ_state": "→",
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"anchor": "S.4.2",
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"index_hash": "faiss:v3:hnsw:cos",
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"ts": "2025-08-27T12:30:22Z"
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}
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```
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---
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## Offline evaluation (index only)
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1. Run each paraphrase against the **shadow index**.
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2. For each qid, compute:
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* **P\@k**: relevant ids ∩ top-k over k ∈ {1, 3, 5, 10}.
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* **R\@k**: relevant ids covered by top-k.
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* **Anchor hit**: any retrieved id with `section_id == anchor_section`.
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* **ΔS probes** for each retrieved item.
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3. Aggregate by content type using `type ∈ {prose, code, table, figure}`.
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4. Compare with the **live index** as a baseline and record deltas.
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---
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## Online shadow evaluation
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* Mirror live questions to the shadow index.
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* Require **cite-first** answers with the schema from [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md).
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* For each answer, verify that at least one citation matches a gold `relevant` id or the `anchor_section`.
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* Log ΔS for the chosen citation and the final λ state after reasoning.
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---
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## Metrics definitions
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Let `G(q)` be the set of relevant ids for q. Let `R_k(q)` be the ids in top-k.
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* **Precision\@k** = |G(q) ∩ R\_k(q)| / |R\_k(q)|
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* **Recall\@k** = |G(q) ∩ R\_k(q)| / |G(q)|
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* **Coverage** = fraction of questions where the answer cites at least one element in `G(q)` or any block within `anchor_section`.
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* **Citation accuracy** = fraction where both `section_id` and byte offsets overlap the gold within a 30-byte window.
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* **Anchor proximity** = average path distance in the title tree from the cited `section_id` to `anchor_section` using rules in [title\_hierarchy.md](https://github.com/github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/title_hierarchy.md).
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---
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## Pass and fail gates
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A shadow index is **eligible for canary** if:
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* Coverage ≥ 0.70 on gold.
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* Citation accuracy ≥ 0.95.
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* ΔS median ≤ 0.40 and 90-pct ≤ 0.55.
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* Recall\@5 does not drop more than 2 points absolute vs live.
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* λ convergent on ≥ 95 percent of paraphrase triplets.
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If any fail, return to chunk boundary checks in [section\_detection.md](https://github.com/github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/section_detection.md) and typed block lifting in [code\_tables\_blocks.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/code_tables_blocks.md).
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---
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## Diagnosis map
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* **High similarity yet wrong meaning** → [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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* **Order flips across runs** → [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
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* **Boundary leaks or mixed topics in a chunk** → revisit [section\_detection.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/section_detection.md)
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* **Tables or code referenced as plain text** → [code\_tables\_blocks.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/code_tables_blocks.md)
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* **OCR drift and offset mismatch** → [pdf\_layouts\_and\_ocr.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/pdf_layouts_and_ocr.md)
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* **Index rebuilt, citations break** → [reindex\_migration.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/reindex_migration.md)
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---
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## Minimal evaluator pseudocode
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```python
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def score_run(gold, logs, k=5):
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p_hits, r_hits, cov_hits, cite_ok = [], [], 0, 0
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ds_med, ds_90 = [], []
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for q in gold: # q has qid, paraphrases, relevant, anchor_section
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items = logs[q.qid]["topk"][:k]
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got = {it["id"] for it in items}
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rel = set(q.relevant)
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prec = len(got & rel) / max(1, len(items))
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rec = len(got & rel) / max(1, len(rel))
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p_hits.append(prec); r_hits.append(rec)
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ds = logs[q.qid]["ΔS"][:k]
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if ds: ds_med.append(median(ds)); ds_90.append(percentile(ds, 90))
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# coverage and citation accuracy from the final answer's first citation
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ans = logs[q.qid].get("answer_citations", [])
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if ans:
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cited = ans[0]["id"]
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off = ans[0]["offsets"]
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if cited in rel or section_of(cited) == q.anchor_section:
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cov_hits += 1
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if cited in rel and overlaps(off, gold_offsets(cited)):
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cite_ok += 1
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return {
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"P@k": mean(p_hits),
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"R@k": mean(r_hits),
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"coverage": cov_hits / len(gold),
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"citation_accuracy": cite_ok / len(gold),
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"ΔS_med": median(ds_med) if ds_med else None,
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"ΔS_p90": median(ds_90) if ds_90 else None
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}
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```
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---
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## Common pitfalls
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* **Evaluating answers without enforcing cite-first**. You cannot measure coverage reliably. Fix the contract in [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md).
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* **Mixing normalizers between builds**. Offsets will not compare. Lock the same whitespace and hyphen rules as in [pdf\_layouts\_and\_ocr.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Chunking/pdf_layouts_and_ocr.md).
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* **Ignoring content types**. Aggregates hide failures in code or tables. Segment metrics by `type`.
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* **k too small for long documents**. Use k ∈ {5, 10} when sections are dense.
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* **Comparing across different rerankers**. Pin rerank during offline runs, then test rerankers separately in a controlled A/B.
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---
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## Copy-paste prompt for LLM-assisted scoring
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```
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You have TXT OS and the WFGY Problem Map.
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Given:
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- gold.json: gold questions with {qid, paraphrases[], relevant[], anchor_section}
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- logs.jsonl: retriever traces with topk ids, ΔS per item, and answer_citations
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Do:
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1) Compute P@5, R@5, coverage, citation accuracy.
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2) Report ΔS median and p90 for the cited snippet per question.
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3) Flag any questions with coverage==0 or ΔS>0.60 and return their qids.
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4) Summarize per-type breakdown for {prose, code, table, figure}.
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Return compact JSON:
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{ "P@5": 0.xx, "R@5": 0.xx, "coverage": 0.xx, "citation_accuracy": 0.xx,
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"ΔS_med": 0.xx, "ΔS_p90": 0.xx, "bad_qids": ["Q-..."], "by_type": {...} }
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```
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
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| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \<your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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<!-- WFGY_FOOTER_START -->
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### Explore More
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| Layer | Page | What it’s for |
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| --- | --- | --- |
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| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
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| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
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| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
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| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
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| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
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| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
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| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
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| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
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| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
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| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
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If this repository helped, starring it improves discovery so more builders can find the docs and tools.
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[](https://github.com/onestardao/WFGY)
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<!-- WFGY_FOOTER_END -->
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要我繼續下一頁就說：**GO live\_monitoring\_rag.md** 或指定別的檔名。
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