# Pattern — Vector Store Fragmentation (No.3 Schema/Index Drift)
**Scope**
Your index “works,” but retrieval is unstable across runs and environments. Scores don’t compare, top-k sets flicker, and relevant chunks disappear or get outranked by junk. The store is **fragmented**: mismatched embeddings, dimensions, normalization, or mixed chunkers across rebuilds.
**Why it matters**
Fragmentation silently degrades recall and skews reranking. You’ll chase “model hallucinations” that are actually **index incompatibilities**.
> Quick nav: [Patterns Index](./README.md) · Examples:
> [Example 01](../examples/example_01_basic_fix.md) ·
> [Example 03](../examples/example_03_pipeline_patch.md) ·
> [Eval: Precision & CHR](../eval/eval_rag_precision_recall.md)
---
## 1) Signals & fast triage
**Likely symptoms**
- Swapping machines or deploying a new build flips results with no corpus change.
- Cosine/inner-product scores shift scale or sign; thresholds stop making sense.
- Recall@k drops only in **some** environments.
- Example 02 labels spike in `retrieval_drift` after an index rebuild.
**Deterministic checks (no LLM):**
- **Manifest mismatch** between runtime vs `index_out/manifest.json` (Example 05 validator).
- **Dimension drift**: query vector `d` ≠ index `d`.
- **Normalization drift**: `normalized=True` at build but **not** at query (or vice versa).
- **Chunker/version drift**: `chunker.version` differs; ids no longer align with text.
- **Score comparability**: Spearman ρ < 0.9 when only non-semantic knobs changed (Example 05 `score_corr.py`).
---
## 2) Minimal reproducible case
Prepare two small indexes from the same `chunks.json`:
- **Index A**: `all-MiniLM-L6-v2`, `normalized=True`, `faiss.IndexFlatIP`.
- **Index B**: same model but **normalized=False**.
**Observation**
Query with normalized vectors against A and raw vectors against B. You’ll see rank inversions and knee-cut thresholds drifting → classic fragmentation.
---
## 3) Root causes
- **Embedding pipeline skew** (tokenizer/model/normalization differ between build and query).
- **Metric mismatch** (index uses IP; query assumes cosine on unnormalized vectors).
- **Mixed chunkers** (window sizes or rules changed; ids collide).
- **Hotfix builds** that partially rebuild embeddings or ids without bumping versions.
- **Multi-index union** without harmonized manifests.
---
## 4) Standard fix (ordered, minimal, measurable)
**Step 1 — Enforce manifest gate (hard fail on mismatch)**
Use Example 05 validator **before** queries. Abort if any of the following differ:
`index_type, metric, embedding.model, embedding.dimension, embedding.normalized, chunker.version, text_preproc.*`
**Step 2 — Normalize on both sides**
- If using inner product (IP) as cosine, **L2-normalize** embeddings at **build** and **query**.
- Pin `normalized: true` in manifest and runtime config.
**Step 3 — One chunker, one id space**
- Bump `chunker.version` on **any** rule change (window length, overlap, headers).
- Rebuild the entire store; never append to an index built with a different chunker.
**Step 4 — Score comparability test**
- For non-semantic backend changes (e.g., FAISS params), run Example 05 `score_corr.py`.
- Require Spearman ρ ≥ 0.9 on a small query list before shipping.
**Step 5 — Quality re-baseline**
- Run Example 08. Require recall@5 and CHR within ε of baseline (e.g., ε ≤ 0.02).
---
## 5) “Good” vs “Bad” configurations
**Good (cosine via IP):**
```
index\_type: faiss.IndexFlatIP
metric: inner\_product
embedding:
model: sentence-transformers/all-MiniLM-L6-v2
dimension: 384
normalized: true
```
**Bad (fragmented):**
```
# Build:
metric: inner\_product
normalized: true
# Query:
metric treated as cosine but vectors NOT normalized
```
**Bad (mixed chunkers):**
```
chunker.version: 2025.07.01 (build)
chunker.version at runtime: 2025.08.12
```
---
## 6) Acceptance criteria (ship/no-ship)
A build **may ship** only if:
1) Manifest validator **PASS** (no drift).
2) Score comparability test **PASS** for non-semantic changes (ρ ≥ 0.9).
3) Eval gates **PASS** (Example 08 thresholds).
4) Readiness sentinel (Example 07) passes using the **same** query path as production.
If any fail → **REPAIR** (full rebuild), update baseline, and re-eval.
---
## 7) Prevention (contracts & defaults)
- **Contract**: Always write `index_out/manifest.json` with embedding/metric/normalization and `chunker.version`.
- **Runtime pinning**: Load a single `tools/runtime_config.json`; reject drift before first query.
- **Atomic deploy**: Replace index + manifest together; never hot-swap one file.
- **CI gate**: Include manifest validation + smoke recall@k on every PR.
- **Observability**: Log query vector norm stats; sudden norm shifts hint at normalization drift.
---
## 8) Debug workflow (10 minutes)
1) Run manifest validator (Example 05).
2) If mismatch → rebuild with the expected config.
3) If match → run score correlation between **old vs new** indices (Example 05).
4) If ρ < 0.9 but embedding model changed → treat as **semantic change**; rebuild baseline and update thresholds.
5) Re-run Example 08 and compare `eval/report.md`.
6) Flip ready only after sentinel + gates are green (Example 07).
---
## 9) Common traps & fixes
- **“It’s only dimension 768 vs 384”** → not “only”; that’s a semantic change. Rebuild and re-baseline.
- **Cosine in code, IP in index** → normalize both or switch to a cosine-aware index.
- **Appending to old ids** after chunker change → orphaned ids; nuke and rebuild.
- **Multi-tenant union** of indices with different manifests → create a **broker** that routes queries to a per-manifest pool; do **not** merge.
---
## 10) Minimal checklist (copy into PR)
- [ ] `manifest.json` written and committed with build artifacts.
- [ ] `tools/validate_index.py` (or Node variant) wired to fail fast.
- [ ] Embeddings normalized at build **and** query.
- [ ] Single chunker/version per corpus; full rebuild on change.
- [ ] Score correlation + eval gates pass before rollout.
---
## References to hands-on examples
- **Example 03** — Retrieval stabilization (reduces tail noise made worse by fragmentation)
- **Example 05** — Manifest, validator, repair & metrics
- **Example 07** — Readiness gate (prevents serving while fragmented)
- **Example 08** — Quality scoring & CI gates
---
### 🧭 Explore More
| Module | Description | Link |
| --------------------- | -------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| WFGY Core | Standalone semantic reasoning engine for any LLM | [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) |
---
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