Chunking to Embedding Contract

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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

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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.

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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.

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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.

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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.

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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 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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