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# Vectorstore Fragmentation — Guardrails and Fix Pattern
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Use this page when **retrieval recall drops because the vector index is fragmented**.
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This happens when multiple shards, partitions, or replicas return partial results and the top-k merge is unstable.
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---
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## Open these first
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- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
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- Deployment issues: [deployment-deadlock.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/deployment-deadlock.md)
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- Retrieval playbook: [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
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- Traceability schema: [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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---
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## Core acceptance
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- Top-k results consistent across shards with variance ≤ 0.05.
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- Coverage ≥ 0.70 on the target section.
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- ΔS(question, retrieved) ≤ 0.45 across three paraphrases.
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- λ remains convergent under shard fanout.
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---
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## Typical breakpoints and the right fix
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- **Shards not balanced** → Some partitions miss updates, recall drops.
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→ Re-index with balanced sharding and verify ingestion logs.
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- **Merge strategy unstable** → Top-k from each shard merged without normalization.
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→ Apply global reranker after merging, not local-only.
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- **Version skew between replicas** → Old embeddings live in one shard.
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→ Enforce [deployment-deadlock.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/deployment-deadlock.md) checks and hash validation.
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- **Distributed query latency** → Timeout before all shards return.
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→ Add backpressure and enforce full quorum before top-k selection.
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---
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## Fix in 60 seconds
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1. **Run shard probe**
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Fire the same query against each shard individually. Compare ΔS variance.
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2. **Align replicas**
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Verify `INDEX_HASH` matches across partitions. If not, rebuild.
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3. **Global reranker**
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Always normalize scores before merging. Rerank final list with semantic signal.
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4. **Quorum guard**
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Require ≥80% shard response before producing result. If missing, retry.
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---
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## Copy-paste probe script (pseudo)
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```python
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def shard_probe(query, shards):
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results = {}
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for shard in shards:
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hits = shard.search(query, k=10)
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ΔS_vals = [compute_deltaS(query, h) for h in hits]
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results[shard.id] = (np.mean(ΔS_vals), np.var(ΔS_vals))
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return results
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````
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Target: shard-to-shard ΔS variance ≤ 0.05.
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---
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## Common gotchas
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* Shard IDs not logged → Cannot trace back retrieval → enforce [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md).
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* Hybrid retriever mixing BM25 + dense done locally per shard → breaks weighting.
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* Replicas updated asynchronously → ingestion race.
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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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### 🧭 Explore More
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| Module | Description | Link |
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| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
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| 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) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| 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) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| 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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| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
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> Engineers, hackers, and open source builders who supported WFGY from day one.
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> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars"> ⭐ [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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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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