WFGY/ProblemMap/GlobalFixMap/Retrieval/retrieval-playbook.md

Retrieval Playbook

🧭 Quick Return to Map

You are in a sub-page of Retrieval.
To reorient, go back here:

• Retrieval — information access and knowledge lookup
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

A practical, store-agnostic playbook to stabilize retrieval quality. Use this page to route symptoms to the right structural fix, apply measurable targets, and keep read/write parity across pipelines.

When to use

• High similarity yet wrong meaning
• Missing or unstable citations
• Hybrid retrieval performs worse than a single retriever
• Results flip across runs or paraphrases
• New deploy returns empty or partial context

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Acceptance targets

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70 for the intended section
• λ remains convergent across 3 paraphrases and 2 seeds
• E_resonance stays flat on long windows

Helpers:

• ΔS probes → deltaS_probes.md
• Eval recipes → retrieval_eval_recipes.md

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60-second fix path

1. Probe
   Run ΔS(question, retrieved) at k = 5, 10, 20. Log λ for each paraphrase.
   Tool: deltaS_probes.md

2. Lock schema
   Enforce cite-then-explain, and require snippet_id, section_id, source_url, offsets, tokens.
   Spec: Data Contracts

3. Repair the failing layer

   • Wrong meaning with high similarity → see Metric and analyzer parity below
   • Missing or shaky citations → install Traceability schema
   • Hybrid worse than single → run Hybrid weighting and Query parsing split
   • Flips across runs → clamp with Rerankers and parity checks

4. Verify
   Coverage ≥ 0.70 on 3 paraphrases; λ convergent on 2 seeds; ΔS ≤ 0.45.

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Root-cause map → exact fixes

1) Metric and analyzer parity

Symptoms: high similarity yet wrong meaning, language or casing skew, mixed punctuation behavior.

Actions

• Align dense and sparse analyzers. Keep lowercasing, accent fold, token boundaries consistent.
• Normalize vectors at write and read. Keep pooling identical.
• Rebuild with explicit metric and dimension logged in traces.

Open

• Wrong-meaning hits → Embedding ≠ Semantic
• Chunk window parity → chunk_alignment.md
• Store-agnostic fences → store_agnostic_guardrails.md

2) Traceability and citation locks

Symptoms: answer looks right but citations are missing, wrong section id, or not reproducible.

Actions

• Require snippet_id, section_id, source_url, offsets, tokens in every hop.
• Forbid cross-section reuse unless explicitly whitelisted.
• Enforce cite-then-explain in prompts.

Open

• Trace schema and audits → coming in this folder retrieval-traceability.md
• Contracts → Data Contracts

3) Hybrid retrieval that underperforms

Symptoms: BM25 + dense gives worse order than either alone; relevant docs appear far down; order flips.

Actions

• Separate query parsing from retrieval. Fix the parse.
• Weight dense and sparse explicitly. Add a deterministic tiebreak.
• Add a rerank step with a fixed cross-encoder and seed.

Open

• Hybrid knobs and recipes → coming in this folder hybrid_retrieval.md
• Query parsing split → coming in this folder query_parsing_split.md
• Rerankers and ordering control → rerankers.md

4) Fragmentation or contamination

Symptoms: facts exist but never show; duplicates or stale shards; inconsistent analyzers by batch.

Actions

• Rebuild a clean index with a single write path.
• Stamp index_hash, log embedding model id and normalization.
• Run a small gold set to verify recall.

Open

• Fragmentation pattern → Vectorstore Fragmentation
• Hallucination and chunk drift → Hallucination

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Guardrails to install in any pipeline

Write path

• One tokenizer and analyzer spec. Log it.
• One embedding model and pooling policy. Log it.
• Chunk window and overlap recorded in metadata.
• Field schema: doc_id, section_id, snippet_id, source_url, offsets, tokens, index_hash, embed_model, analyzer.

Read path

• Same analyzer, same normalization.
• k sweep at 5, 10, 20 for ΔS probes.
• Deterministic tiebreak on (score, section_id, snippet_id).

Prompt contract

• Cite first, then explain.
• Enforce JSON with citations and λ state.
• Forbid cross-section reuse unless allowed.

Specs

• DeltaS probes → deltaS_probes.md
• Contracts → Data Contracts

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Copy-paste prompt block for the reasoning step

You have TXTOS and the WFGY Problem Map loaded.

Retrieval inputs:
- question: "{Q}"
- k sweep results: {k5:..., k10:..., k20:...}
- citations: [{snippet_id, section_id, source_url, offsets, tokens}, ...]

Do:
1) Validate cite-then-explain. If any citation is missing or mismatched, return the failing field and stop.
2) Report ΔS(question, retrieved) and λ state. If ΔS ≥ 0.60 or λ divergent, return the minimal structural fix:
   - metric/analyzer parity
   - hybrid weighting and rerank
   - traceability schema
3) Output JSON:
   { "answer": "...", "citations": [...], "ΔS": 0.xx, "λ": "<state>", "next_fix": "<page to open>" }
Keep it auditable and short.

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Evaluation loop

• Gold questions per section: 3 to 5
• For each question: run 3 paraphrases, 2 seeds
• Metrics to log: coverage, ΔS, λ, recall@k, MAP@k, citation match rate
• Recipes → retrieval_eval_recipes.md

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Store-specific adapters

If a symptom points to a store quirk or feature gap, jump here:

• Vector DBs index → Vector DBs & Stores

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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