WFGY/ProblemMap/GlobalFixMap/VectorDBs_and_Stores

Vector DBs & Stores — Global Fix Map

🏥 Quick Return to Emergency Room

You are in a specialist desk.
For full triage and doctors on duty, return here:

• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a sub-room.
If you want full consultation and prescriptions, go back to the Emergency Room lobby.

This page is your hub to stabilize retrieval pipelines across popular vector stores.
If your results look similar but the answer is wrong, start here. Each store page gives guardrails, fix steps, and the same acceptance targets so you can verify without changing infra.

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Quick routes to per-store pages

Store	Best for	Why choose	Link
FAISS	local development, labs	fast, widely used, you manage it	faiss.md
Chroma	quick demos, notebooks	simple API, easy to start	chroma.md
Qdrant	production and multitenant	Rust core, good scaling, persistence	qdrant.md
Weaviate	hybrid search and schemas	first class filters, hybrid pipelines	weaviate.md
Milvus	enterprise ANN at scale	mature ecosystem and performance	milvus.md
pgvector	teams already on Postgres	keep data in the same DB, simple ops	pgvector.md
Redis (Search/Vec)	caches and small hybrid sets	key value plus vectors, low latency	redis.md
Elasticsearch (ANN)	text plus vector in one stack	reuse analyzers and infra you already have	elasticsearch.md
Pinecone	zero ops SaaS	managed reliability and steady API	pinecone.md
Typesense	simple full text plus vectors	friendly setup, good defaults	typesense.md
Vespa	large scale search and recsys	query routing and ranking at scale	vespa.md

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When to use this folder

• High similarity but wrong meaning.
• Citations do not match the retrieved section.
• Hybrid retrieval performs worse than a single retriever.
• After deploy, query casing or analyzer or metric does not line up.
• Index looks healthy but coverage stays low.

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Acceptance targets for any store

• ΔS(question, retrieved) ≤ 0.45
• Coverage of target section ≥ 0.70
• λ_observe convergent across three paraphrases
• E_resonance flat on long windows

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Map symptoms to structural fixes

• Embedding ≠ Semantic
  Wrong meaning despite high similarity.
  → embedding-vs-semantic.md

• Retrieval traceability
  Snippet or section mismatch, unverifiable citations.
  → retrieval-traceability.md
  Payload contract → data-contracts.md

• Ordering or version skew
  Runtime loads the wrong index or analyzer.
  → bootstrap-ordering.md · predeploy-collapse.md

• Hybrid collapse or query split
  HyDE and BM25 disagree, reranker blind spots.
  → Pattern → pattern_query_parsing_split.md
  → Knobs → rerankers.md

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60 second fix checklist

1. Lock metrics and analyzers
   One embedding model per field. One distance function. Same analyzer for write and read.

2. Contract the snippet
   Require {snippet_id, section_id, source_url, offsets, tokens} and enforce cite then explain.
   → data-contracts.md

3. Add deterministic reranking
   Keep candidate lists from BM25 and ANN. Detect query split.
   → rerankers.md

4. Cold start and deploy fences
   Block traffic until index hash, analyzer, and model versions match.
   → bootstrap-ordering.md

5. Observability
   Log ΔS and λ across retrieve, rerank, reason. Alert when ΔS ≥ 0.60.

6. Regression gate
   Require coverage ≥ 0.70 and ΔS ≤ 0.45 before publish.

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Copy paste audit prompt

I uploaded TXT OS and the WFGY Problem Map pages.
Store: <name>. Retrieval: <bm25|ann|hybrid> with <distance>.

Audit this query and return:

- ΔS(question,retrieved) and λ across retrieve → rerank → reason.
- If ΔS ≥ 0.60, choose one minimal structural fix and name the page:
  embedding-vs-semantic, retrieval-traceability, data-contracts, rerankers.
- JSON only:
  { "citations":[...], "ΔS":0.xx, "λ":"→|←|<>|×", "next_fix":"..." }

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Quick Start Downloads

Tool	Link	3 step setup
WFGY 1.0 PDF	Engine Paper	1) Download 2) Upload to your LLM 3) Ask “Answer using WFGY + ”
TXT OS (plain text OS)	TXTOS.txt	1) Download 2) Paste into any LLM chat 3) Type “hello world” to boot

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Explore More

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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