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