WFGY/ProblemMap/GlobalFixMap/VectorDBs_and_Stores/weaviate.md

Weaviate: Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of VectorDBs_and_Stores.
To reorient, go back here:

• VectorDBs_and_Stores — vector indexes and storage backends
• 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 desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

A compact field guide to stabilize Weaviate when your RAG or agent stack loses accuracy. Use the checks below to localize the failure, then jump to the exact WFGY fix page.

Open these first

• Visual map and recovery: RAG Architecture & Recovery
• End-to-end retrieval knobs: Retrieval Playbook
• Why this snippet was picked: Retrieval Traceability
• Ordering control after recall: Rerankers
• Embedding vs meaning: Embedding ≠ Semantic
• Hallucination and chunk boundaries: Hallucination
• Long chains and entropy: Context Drift, Entropy Collapse
• Structural collapse and recovery: Logic Collapse
• Snippet and citation schema: Data Contracts
• Patterns: Vectorstore Fragmentation, Query Parsing Split, Hallucination Re-entry
• Ops: Live Monitoring for RAG, Debug Playbook

Fix in 60 seconds

1. Measure ΔS

   • Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
   • Targets: stable < 0.45, transitional 0.40–0.60, risk ≥ 0.60.

2. Probe with λ_observe

   • Try k in {5, 10, 20}. Flat high curve suggests metric or index mismatch.
   • Reorder prompt headers. If ΔS spikes, fix schema or anchors.

3. Apply the module

   • Retrieval drift → BBMC plus Data Contracts.
   • Reasoning collapse → BBCR bridge plus BBAM variance clamp.
   • Dead ends in long runs → BBPF alternate path.

4. Verify

   • Coverage to target section ≥ 0.70.
   • λ convergent across three paraphrases and two seeds.

Typical breakpoints and the right fix

• Metric mismatch

  • Corpus built with cosine but class uses dot or L2. Normalization tests raise ΔS while recall looks fine.
  • Action: rebuild class with correct distance or normalize embeddings at write and query. See Embedding ≠ Semantic and Retrieval Playbook.

• Dimension or encoder swap

  • Import accepts new vectors then recall collapses for only the new span.
  • Action: lock encoder version in the schema via a data contract, re-index the affected classes. See Data Contracts.

• HNSW tuning traps

  • efSearch too low for your k, or M too small for dense corpora. Symptoms are plateaued recall and unstable top-k ordering.
  • Action: raise efSearch to 2–4×k, validate with reranker sandwiched on top. See Rerankers and Retrieval Playbook.

• Shard or replica consistency

  • Some queries never surface fresh writes. Multi-tenant classes or replicas returning stale reads.
  • Action: align consistency level during validation, confirm write-ack before eval. See Live Monitoring for RAG.

• Hybrid search weighting

  • BM25 plus vector performs worse than vector alone. Query template or HyDE text dominates vector term.
  • Action: run the split test. If the hybrid flip is the cause, re-balance weights and clean prompt glue. See Query Parsing Split.

• Vectorstore fragmentation

  • Multiple classes with near-duplicate schemas. Coverage drops while ΔS stays flat high across k.
  • Action: merge or route by class key, then rebuild a single authoritative index. See Vectorstore Fragmentation.

• Tokenization and filter mismatch

  • Filters on properties return empty or unstable results. Analyzer not aligned with corpus language or case rules.
  • Action: lock analyzers in a data contract and re-ingest with normalized fields. See Data Contracts.

• Batch import and boot order

  • First production call after deploy fails or returns zero results although objects exist.
  • Action: enforce bootstrap fence and idempotent batcher. See Debug Playbook.

Observability probes

• k-sweep curve: run k in 5, 10, 20 and plot ΔS. A flat high curve means metric or class routing fault.
• Anchor control: compare ΔS against a golden anchor set for one class. If only a class fails, route or rebuild.
• Hybrid toggle: run vector only and hybrid with equal weight. If hybrid degrades, fix query split or weight.
• Reranker audit: with a strong reranker, recall should improve monotonically while ΔS falls. If not, rebuild index.

Escalate when

• ΔS stays above 0.60 for the golden questions after metric and efSearch corrections.
• Coverage cannot reach 0.70 even with a reranker and clean anchors.
• Fresh writes are invisible for more than one minute under your consistency setting.

Open:

• Retrieval Playbook
• Data Contracts
• Live Monitoring for RAG

Copy-paste prompt for your AI

I uploaded TXT OS and the WFGY Problem Map files.

Target system: Weaviate.

* symptom: \[brief]
* traces: ΔS(question,retrieved)=..., ΔS(retrieved,anchor)=..., λ states
* index: \[class name, distance metric, efSearch, M, shards, replicas]
* encoder: \[model, dim, normalization, version]

Tell me:

1. which layer is failing and why,
2. which exact fix page to open from this repo,
3. minimal steps to push ΔS ≤ 0.45 and keep λ convergent,
4. how to verify with a reproducible test.

Use BBMC/BBPF/BBCR/BBAM when relevant.

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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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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