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

Mixed metrics or analyzers after deploy Fix: rebuild with a single metric and analyzer. See Retrieval playbook
Fragmented store, anchors missing Fix: re-chunk with anchor tests. See Chunking checklist · Vectorstore fragmentation

🔗 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 tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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