WFGY/ProblemMap/GlobalFixMap/Chunking

Chunking — Global Fix Map

🏥 Quick Return to Emergency Room

You are in a specialist desk.
For full triage and doctors on duty, return here:

• 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 sub-room.
If you want full consultation and prescriptions, go back to the Emergency Room lobby.

A compact hub to stabilize document chunking across formats, pipelines, and retrieval systems.
This folder routes chunk-related bugs to structural fixes and provides checklists, schema, and live recipes.
No infra change required.

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Orientation: what each page does

Page	What it solves	Typical symptom
Chunk ID Schema	Unique ID + schema for each chunk	Duplicate or drifting chunks across runs
Chunking Checklist	Minimal audit list for validity	Chunks too long, too short, or incomplete
Code / Tables / Blocks	Preserve structure for code, tables, blocks	Retrieval drops formatting or logic
Section Detection	Detect paragraph and section anchors	Anchors missing, snippets cut mid-thought
Title Hierarchy	Maintain document heading hierarchy	Only partial or meaningless sub-sections retrieved
PDF Layouts & OCR	Repair PDF/OCR-specific chunking	Citations collapse after parsing
Reindex & Migration	Safe chunk migration during reindex	Index rebuilt but old refs mismatch
Eval RAG Precision & Recall	Deterministic evaluation recipes	“Better” chunking cannot be proven
Live Monitoring (RAG)	Online health checks for chunking	Sudden drift or collapse after deploy

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When to use this folder

• Your chunks look fine by eye but retrieval skips important sections.
• PDF / OCR parsing collapses headers, math, or tables.
• Hybrid retrievers underperform due to inconsistent chunk boundaries.
• Reindexing breaks old citations.
• Context flips between runs with same corpus.

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Acceptance targets

• Chunk boundaries align with semantic windows
• ΔS(question, retrieved) ≤ 0.45
• Coverage of target section ≥ 0.70
• λ_observe convergent across 3 paraphrases and 2 seeds
• Traceability contract fields always present: {snippet_id, section_id, source_url, offsets, tokens}

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60-second fix checklist

1. Check chunk IDs
   Apply chunk_id_schema. Ensure unique + stable across reindex.

2. Audit with checklist
   Run the chunking-checklist before ingest.

3. Preserve structure
   Use code_tables_blocks for code, tables, blocks.

4. Validate anchors
   Confirm section and title detection. Apply title_hierarchy.

5. Reindex safely
   Use reindex_migration with hash/version lock.

6. Monitor live
   Apply live_monitoring_rag to catch collapse early.

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Minimal probe pack

Context: I loaded TXT OS and the WFGY pages.

Task:
- Given doc corpus D, log ΔS(question, retrieved) and λ across 3 paraphrases.
- Validate chunk IDs and section anchors.
- If ΔS ≥ 0.60 or λ flips, propose the smallest structural change:
  chunk schema, checklist, or reindex.
- Verify coverage ≥ 0.70 after fix.

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

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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 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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