# Update and Index Skew — 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](./README.md)
> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
>
> 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 **recall flips or citations drift after a data or model update**.
Skew appears when writers and readers see different corpus revisions, or when the index was rebuilt with changed params without a cutover plan.
---
## Open these first
- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
- Retrieval knobs: [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
- Vector store fragmentation: [vectorstore_fragmentation.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/vectorstore_fragmentation.md)
- Metric mismatch: [metric_mismatch.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/metric_mismatch.md)
- Normalization and scaling: [normalization_and_scaling.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/normalization_and_scaling.md)
- Tokenization and casing: [tokenization_and_casing.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/tokenization_and_casing.md)
- Chunking to embedding contract: [chunking_to_embedding_contract.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/chunking_to_embedding_contract.md)
---
## Core acceptance
- Single **INDEX_HASH** is identical for writer, retriever, reranker, and LLM side prompts.
- ΔS(question, retrieved) ≤ 0.45 on 3 paraphrases and 2 seeds after the update.
- Coverage ≥ 0.70 to the target section, stable across shards and regions.
- λ remains convergent during the cutover window, no flip states at header reorder.
---
## Symptoms → likely cause → open this
- Results differ between two identical queries minutes apart
→ mixed corpus revisions or warm readers on stale index
→ [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
- Top-k looks similar in distance yet meaning is off after upgrade
→ metric or normalization changed during rebuild
→ [metric_mismatch.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/metric_mismatch.md), [normalization_and_scaling.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/normalization_and_scaling.md)
- Some shards return old docs, others new ones
→ partial index redeploy or cache warmup skew
→ [vectorstore_fragmentation.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/vectorstore_fragmentation.md)
- After model switch, recall falls only on certain languages or code blocks
→ tokenizer or casing schema diverged
→ [tokenization_and_casing.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/tokenization_and_casing.md)
- Large jump in ΔS on long windows, citations no longer align
→ chunk schema changed but old vectors remain
→ [chunking_to_embedding_contract.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/chunking_to_embedding_contract.md)
---
## Fix in 60 seconds
1) **Pin the contract**
Compute `INDEX_HASH = sha256(model_id + tokenizer_ver + chunk_schema + metric + dim + store_params + corpus_rev)`.
Log it on writer, retriever, reranker, and in the LLM prompt header.
2) **Shadow read**
Run a gold set against current index and a rebuilt index in parallel.
Alert if ΔS variance > 0.05 or coverage drops below 0.70.
3) **Freeze and rebuild**
Stop writes. Re-embed and rebuild offline with explicit `dim`, `metric`, and `normalization`.
Verify tokenizer and casing are identical to the previous contract.
4) **Cutover with warmup**
Warm the new index. Switch read traffic via percentage ramp.
Abort if λ flips or ΔS exceeds 0.60 on any guardrail probe.
---
## Minimal checks you must script
- **Contract echo**
Every query path must log `INDEX_HASH`, `MODEL_ID`, `TOKENIZER_VER`, `CHUNK_SCHEMA_VER`.
- **Shard parity probe**
Run the same 25 queries to each shard or region.
Flag if Jaccard(top-k) < 0.6 against the reference shard.
- **Cache invalidation**
Clear reranker and query embedding caches when `INDEX_HASH` changes.
- **Reader staleness**
Reject queries if `reader_index_hash != router_index_hash`. Fail fast, do not serve stale.
---
## Common gotchas
- Silent analyzer change in a search backend re-tokenizes text while vectors are unchanged.
- HNSW or IVF params differ between shards, causing order instability at k=10 but not at k=3.
- APM dashboards show healthy ingestion yet the retriever reads from a lagging replica.
- Reranker model upgraded without re-baselining acceptance targets.
- Partial re-embed of only new docs creates a semantic seam at time T.
---
## Verification
- Gold set of 100 questions, 3 paraphrases each.
- Require ΔS ≤ 0.45 and coverage ≥ 0.70 on both old and new indexes before cutover.
- After cutover, repeat on two seeds. If λ remains convergent and ΔS does not spike, close the change.
---
### 🔗 Quick-Start Downloads (60 sec)
| Tool | Link | 3-Step Setup |
|------|------|--------------|
| **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 + \” |
| **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 |
---
### 🧭 Explore More
| Module | Description | Link |
|-----------------------|----------------------------------------------------------|----------|
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
---
> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
> Engineers, hackers, and open source builders who supported WFGY from day one.
> 
⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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