WFGY/ProblemMap/GlobalFixMap/RAG_VectorDB/chunking_to_embedding_contract.md

Chunking to Embedding Contract — Guardrails and Fix Pattern

🧭 Quick Return to Map

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

• RAG_VectorDB — vector databases for retrieval and grounding
• 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.

Use this page when retrieval fails because the chunk schema is not aligned with the embedding ingestion contract.
If the retriever expects fields that were never embedded, or chunks omit IDs/offsets/anchors, then citations drift and ΔS rises.

---

Open these first

• Visual map and recovery: RAG Architecture & Recovery
• Snippet and citation schema: data-contracts.md
• Retrieval traceability: retrieval-traceability.md
• Chunking checklist: chunking-checklist.md

---

Core acceptance

• Every chunk has chunk_id, section_id, source_url, offsets, tokens.
• Embedding index was built from the same schema as retrieval contract.
• ΔS(question, retrieved) ≤ 0.45 across 3 paraphrases.
• Coverage ≥ 0.70 to the target section.

---

Typical breakpoints and the right fix

• Missing fields in ingestion (e.g., no section_id)
  → Enforce data-contracts.md.

• Different schema for ingest vs retrieve
  → Corpus ingested raw text, retriever expects chunk JSON → rebuild with schema.

• Offsets not tracked
  → Cannot map back to original document → enforce offsets at ingest.

• Tokenizer drift
  → Chunk IDs differ between preprocessing runs → use chunking-checklist.md.

---

Fix in 60 seconds

1. Check ingestion schema
   Compare the fields stored in the index with the fields expected in retrieval.

2. Align contracts
   Define chunk = {chunk_id, section_id, source_url, offsets, tokens, text}.
   Enforce that this exact object is used both in ingestion and retrieval.

3. Rebuild index if misaligned
   If fields differ, re-ingest corpus with enforced schema.

---

Copy-paste schema

{
  "chunk_id": "uuid-v4",
  "section_id": "doc-23-sec-7",
  "source_url": "https://example.com/doc23",
  "offsets": [120, 320],
  "tokens": 512,
  "text": "...."
}

Target: retriever always returns this schema, LLM consumes directly.

---

Common gotchas

• Only text embedded, no IDs → cannot trace back → citations drift.
• Chunk boundaries not logged → hallucinations reappear.
• JSON schema updated mid-deploy → index mismatch.

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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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