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ProblemMap/GlobalFixMap/VectorStore/README.md
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# Vector Store — Global Fix Map
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Make your store consistent, populated, and explainable.
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Use this when FAISS/Qdrant/Chroma/Elastic “works” but retrieval still feels wrong or inconsistent.
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## What this page is
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- A concise checklist to validate population, metrics, and read/write symmetry.
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- Structural fixes for empty/fragmented stores and stale or misconfigured indices.
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- Steps you can verify with ΔS curves and citation tables.
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## When to use
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- Answers look unrelated even though the store is “full”.
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- First queries after boot return nothing or random snippets.
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- Some facts never appear although indexed.
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- Hybrid retrieval becomes worse than a single retriever.
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- After a deploy, results change wildly with the same query.
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## Open these first
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- Why vectors ≠ meaning: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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- Fragmented / partially empty collections: [Vectorstore Fragmentation](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_vectorstore_fragmentation.md)
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- End-to-end retrieval knobs: [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
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- Ordering after recall (keep it measurable): [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
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- Why this snippet (trace schema): [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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- Visual pipeline & recovery path: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
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- Eval targets: [RAG Precision/Recall](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_rag_precision_recall.md)
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## Fix in 60 seconds
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1) **Probe ΔS**
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- Chart `ΔS(question, retrieved)` vs `k ∈ {5,10,20}`.
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- Flat-high curve → index/metric/normalization mismatch or partial population.
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2) **Population sanity**
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- Count vectors per collection and compare to docs/chunks.
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- Ensure no silent failures in batch ingestion or concurrency during build.
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3) **Read/write symmetry**
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- Same embedding model id on write and read.
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- Same distance metric (cosine vs inner product) and dimensionality.
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- If cosine, confirm unit normalization on both sides.
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4) **Index configuration**
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- FAISS: confirm index type (IVF/HNSW/PQ), nprobe/efSearch, and that the trained index file is persisted + reloaded.
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- Qdrant/Chroma/Elastic: verify exact metric flags, shard/replica consistency, warm-up finished.
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5) **Rebuild once with explicit metadata**
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- Persist: model_id, dim, metric, normalizer, tokenizer, build_params.
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- After rebuild, re-probe ΔS and store acceptance plots with traceability.
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6) **Rank after recall**
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- If recall is good but ordering is noisy, add a light reranker from the playbook.
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- Keep citation schema to audit the change.
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## Copy-paste prompt
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```
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I uploaded TXT OS and the WFGY ProblemMap pages.
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My vector store bug:
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* symptom: \[brief]
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* ΔS traces: vs k = {...}, current ΔS(question, retrieved)=..., anchor ΔS=...
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* write: model=\[...], metric=\[cosine|ip], dim=\[...], norm=\[on|off], index=\[IVF|HNSW|PQ], params=\[...]
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* read: model=\[...], metric=\[...], dim=\[...], norm=\[...]
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* population: vectors=\[count], docs=\[count], ingestion logs=\[summary]
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Tell me:
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1. what mismatch or population issue explains it,
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2. which exact WFGY pages to open,
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3. the minimal rebuild/rescore steps to push ΔS ≤ 0.45,
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4. how to verify with ΔS-vs-k, precision/recall, and a snippet↔citation table.
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Use BBMC alignment if anchors are stable; add a reranker only after recall is fixed.
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```
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## Minimal checklist
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- One embedding model per collection or store `model_id` with each vector.
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- Fix metric/normalization once and persist with the index.
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- Keep text pre-processing identical on write and read.
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- Validate `dim` and dtype; no truncation or hidden casts.
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- Log and compare vector count = sum(chunk count).
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- Disallow writes during index training; warm up after boot.
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- Snapshot + restore indexes atomically; avoid mixed versions.
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- Run fragmentation pattern if some facts never retrieve.
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## Acceptance targets
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- ΔS(question, retrieved) ≤ 0.45 across paraphrases.
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- ΔS-vs-k descends then flattens, not flat-high.
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- Precision/recall meet your eval sheet; top-k is explainable by traceability.
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- λ stays convergent at retrieval after rebuild.
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- Same results across restarts with deterministic warm-up.
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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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