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133 lines
5.2 KiB
Markdown
133 lines
5.2 KiB
Markdown
# Chunking Checklist — Stability at Joins
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<details>
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<summary><strong>🧭 Quick Return to Map</strong></summary>
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<br>
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> You are in a sub-page of **MemoryLongContext**.
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> To reorient, go back here:
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>
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> - [**MemoryLongContext** — extended context windows and memory retention](./README.md)
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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>
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> Think of this page as a desk within a ward.
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> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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</details>
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Long-context retrieval often fails not at the level of whole documents but at the **joins between chunks**.
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This checklist enforces stable, reproducible chunking so citations line up and entropy does not melt across boundaries.
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---
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## When to use
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- Citations drift by a few lines between runs.
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- Long transcripts lose alignment after OCR or parsing.
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- Model answers cover the right fact but cite the wrong block.
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- ΔS spikes exactly at chunk joins.
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- Different agents disagree on chunk IDs.
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---
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## Core acceptance targets
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- Each join ΔS ≤ **0.50**.
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- Overall ΔS(question, retrieved) ≤ **0.45**.
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- Coverage ≥ **0.70** of intended section.
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- λ remains convergent across 3 paraphrases.
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- Each chunk has immutable `chunk_id`, `start_line`, `end_line`.
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---
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## Checklist for stable chunking
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- **Deterministic boundaries**
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Split on semantic units (sections, paragraphs, headings). Never by raw token count alone.
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- **Overlap fence**
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Add 10–15% overlap at joins. Enforce consistent overlap across every run.
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- **Immutable IDs**
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Generate `chunk_id = sha256(doc_id + start_line + end_line)`. Store and reuse.
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- **Audit trail**
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Store `{chunk_id, start_line, end_line, source_url, tokens}` for every chunk.
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- **Normalization**
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Apply Unicode NFC, collapse whitespace, unify casing.
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- **Confidence gating**
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Drop OCR or parsing lines with low confidence before chunking.
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---
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## Fix in 60 seconds
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1. Re-chunk corpus using semantic units.
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2. Apply overlap fence and store immutable chunk IDs.
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3. Run ΔS probes at joins. If ΔS > 0.50, re-check boundaries.
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4. Store all chunk metadata in trace logs.
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5. Require cite-then-answer. Reject any orphan chunk references.
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---
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## Copy-paste prompt
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```
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You have TXT OS and the WFGY Problem Map.
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Task: enforce stable chunking.
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Protocol:
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1. Verify each snippet has {chunk\_id, start\_line, end\_line, section\_id, source\_url}.
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2. Reject orphans: if citation lacks chunk\_id, stop and request fix.
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3. Require cite-then-answer.
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4. Probe ΔS across joins, keep ≤ 0.50.
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5. Report ΔS(question,retrieved), ΔS(joins), and λ state.
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```
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---
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## Common failure signals
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- Answers cite correct fact but wrong block → chunk IDs not stable.
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- ΔS spikes exactly at joins → overlap missing.
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- OCR transcripts break alignment → normalization skipped.
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- Multi-agent systems cite different chunk IDs → contract drift.
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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|------|------|--------------|
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| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \<your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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<!-- WFGY_FOOTER_START -->
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### Explore More
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| Layer | Page | What it’s for |
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| --- | --- | --- |
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| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
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| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
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| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
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| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
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| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
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| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
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| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
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| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
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| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
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| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
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If this repository helped, starring it improves discovery so more builders can find the docs and tools.
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[](https://github.com/onestardao/WFGY)
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<!-- WFGY_FOOTER_END -->
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