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WFGY/ProblemMap/GlobalFixMap/Retrieval/checklists/retrieval_readiness.md

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Retrieval Readiness Checklist

Purpose: confirm the pipeline is safe to run before any evaluation or go-live.
Applies to BM25, ANN, or hybrid stacks. Store agnostic.

Inputs are consistent

  • One embedding model per field, recorded in config.
  • Normalization rule set and saved with the index (L2 or cosine compatible).
  • Analyzer or tokenizer identical on write and read paths.
  • Stopword set and stemming rules fixed and versioned.

Refs:
Embedding ≠ Semantic · Store-agnostic guardrails

Index and data state

  • INDEX_HASH matches the current code revision that produced vectors.
  • Document count, chunk count, and vector count agree within 0.5 percent.
  • Ingestion job reported zero empty payloads and zero parser errors.
  • Cold caches warmed with ten representative queries.

Refs:
Bootstrap ordering · Pre-deploy collapse

Gold set and probes

  • Ten to fifty QA pairs with ground truth anchors prepared.
  • Each QA pair has at least one resolvable section_id and source_url.
  • ΔS probes ready for three paraphrases and two seeds.

Refs:
ΔS probes · Retrieval eval recipes

Acceptance targets

  • ΔS(question, retrieved) ≤ 0.45
  • Coverage of the target section ≥ 0.70
  • λ_observe convergent across 3 paraphrases and 2 seeds
  • E_resonance stable on long windows

Quick probe you can paste

I loaded TXT OS and WFGY pages.

Task:
- For question "Q", log ΔS(Q, retrieved) and λ across 3 paraphrases and 2 seeds.
- Enforce cite then explain with the traceability schema.
- If ΔS ≥ 0.60, return the smallest structural fix to reach ΔS ≤ 0.45 and coverage ≥ 0.70.

Return JSON:
{ "citations": [...], "ΔS": 0.xx, "λ_state": "<>", "coverage": 0.xx, "next_fix": "..." }

Common fails and minimal fixes

🔗 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

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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