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117 lines
6 KiB
Markdown
117 lines
6 KiB
Markdown
# Retention Policy — Enterprise Knowledge Governance
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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 **Enterprise_Knowledge_Gov**.
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> To reorient, go back here:
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>
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> - [**Enterprise_Knowledge_Gov** — corporate knowledge management and governance](./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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Guardrails and fix patterns for enterprise knowledge retention. Use this page when AI systems over-retain, delete too early, or mix expired data with active knowledge.
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---
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## When to use this page
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- AI responses reference documents that should have been deleted per policy.
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- Retained snippets do not respect jurisdictional time limits (e.g., GDPR 3 years).
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- Knowledge base or embeddings store does not purge revisions.
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- RAG answers mix archived with active content.
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---
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## Core acceptance targets
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- ΔS(question, expired_snippet) ≥ 0.70 → expired content must not surface.
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- All snippets carry `{expiry_date, retention_scope, audit_hash}` fields.
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- Coverage ≥ 0.70 within *active* retention window only.
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- λ remains convergent across three paraphrases and two seeds.
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---
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## Typical retention problems → exact fix
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| Symptom | Likely cause | Open this |
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|---------|--------------|-----------|
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| Expired docs still retrieved | Store never purged embeddings | [vectorstore-fragmentation.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/vectorstore-fragmentation.md) |
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| Wrong answer mixes expired + active | Snippets missing `expiry_date` field | [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) |
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| AI cites “archived only” docs as live | Retrieval trace missing retention scope | [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) |
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---
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## Fix in 60 seconds
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1. **Check ΔS to expired content**: run probe with expired snippets, expect ΔS ≥ 0.70.
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2. **Schema enforcement**: require `expiry_date` and `retention_scope` in every snippet.
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3. **Index purge**: remove expired embeddings before next RAG run.
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4. **Audit λ**: if λ flips when expired vs active co-exist, clamp with BBAM and enforce contracts.
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---
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## Copy-paste schema (JSON)
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```json
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{
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"snippet_id": "KB-5532",
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"expiry_date": "2025-12-31",
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"retention_scope": "eu-3y",
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"audit_hash": "sha256:...",
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"text": "..."
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}
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````
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---
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## Escalate when
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* Expired content continues to surface after purge.
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* ΔS < 0.70 against expired content → embeddings contamination.
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* Audit requires full deletion trace and cannot be reproduced.
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Use [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) for deep purge testing and [eval\_rag\_precision\_recall.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_rag_precision_recall.md) to validate coverage.
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---
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### 🔗 Quick-Start Downloads
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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 based tension engine |
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| Engine | [WFGY 2.0](/core/README.md) | Production tension kernel and math engine for RAG and agents |
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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 checklist and fix map |
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| Map | [Problem Map 2.0](/ProblemMap/rag-architecture-and-recovery.md) | RAG focused recovery pipeline |
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| Map | [Problem Map 3.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card, image as a debug protocol layer |
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| Map | [Semantic Clinic](/ProblemMap/SemanticClinicIndex.md) | Symptom to family to exact fix |
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| Map | [Grandma’s Clinic](/ProblemMap/GrandmaClinic/README.md) | Plain language stories mapped to Problem Map 1.0 |
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| Onboarding | [Starter Village](/StarterVillage/README.md) | Guided tour for newcomers |
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| App | [TXT OS](/OS/README.md) | TXT semantic OS, fast boot |
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| App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q and A built on TXT OS |
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| App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image with semantic control |
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| App | [Blow Blow Blow](/OS/BlowBlowBlow/README.md) | Reasoning game engine and memory demo |
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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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要不要直接衝刺？
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