pgvector: Guardrails and Fix Patterns

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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 repair guide for Postgres + pgvector when RAG or agents lose accuracy. Use this to localize the failing layer and 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 and how to trace it: Retrieval Traceability
Ordering control after recall: Rerankers
Embedding versus meaning: Embedding ≠ Semantic
Hallucination and chunk boundaries: Hallucination
Long chains and entropy drift: Context Drift, Entropy Collapse
Structural collapse and recovery: Logic Collapse
Snippet and citation schema: Data Contracts
Vector metrics pitfalls: Vectorstore Metrics & FAISS Pitfalls
Live ops: Live Monitoring for RAG, Debug Playbook

Fix in 60 seconds

Measure ΔS
Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
Targets: stable < 0.40, transitional 0.40–0.60, risk ≥ 0.60.
Probe with λ_observe
Sweep k in {5, 10, 20}. If ΔS is flat high across k, suspect metric or index mismatch.
Reorder prompt headers. If ΔS spikes, lock schema with Data Contracts.
Apply the module
Retrieval drift → BBMC + Data Contracts.
Reasoning collapse → BBCR bridge + BBAM variance clamp.
Dead ends in long runs → BBPF alternate path.
Verify acceptance
Coverage ≥ 0.70 to target section. ΔS ≤ 0.45 across three paraphrases. λ convergent across seeds.

pgvector breakpoints and the right repair

1) Opclass mismatch

Symptom: high similarity yet wrong meaning.
Why: using vector_l2_ops with cosine-trained embeddings or vector_ip_ops without normalization.
Fix: align opclass with the encoder. Normalize when using IP. See Embedding ≠ Semantic.

2) Index type underfit

Symptom: gold chunk appears only at large k.
Why: IVFFLAT lists too small or probes too low. HNSW ef_search under-tuned.
Fix: IVFFLAT tune lists at build and probes at query. HNSW raise ef_search to 2–4×k and review m. Validate with Retrieval Playbook and add Rerankers.

3) Training and stats

Symptom: unstable top-k after bulk load.
Why: IVFFLAT trained on too few samples or skipped ANALYZE.
Fix: retrain IVFFLAT with a large sample, ANALYZE, then re-test ΔS and coverage.

4) Dimension or encoder swap

Symptom: inserts fail or new rows behave erratically in search.
Fix: ensure vector dim matches column dim. Lock encoder version in a data contract and re-embed the changed span. See Data Contracts.

5) Normalization discipline

Symptom: cosine search acts like random at small k.
Fix: store normalized vectors or normalize at query for cosine or IP. Rebuild index after policy change. See Vectorstore Metrics & FAISS Pitfalls.

6) JSONB filters and plan drift

Symptom: filtered search returns empty or slow.
Fix: lock metadata schema in data contracts. Add GIN index on JSONB keys used in WHERE. Verify plan uses vector index then filter. See Retrieval Traceability.

7) Fragmentation across schemas or tables

Symptom: global recall looks fine but per-scope top-k is weak.
Fix: consolidate into one authoritative table with a facet column. Rebuild index and add a reranker. See Vectorstore Fragmentation.

8) Upsert hygiene

Symptom: duplicates or stale rows after ON CONFLICT.
Fix: deterministic IDs, doc_sha in metadata, idempotent loader, periodic dedupe. Validate with golden queries. See Retrieval Traceability.

9) Hybrid lexical plus vector

Symptom: hybrid performs worse than either alone.
Fix: normalize scores, fuse post-retrieval, then rerank with a cross-encoder. See Query Parsing Split and Rerankers.

10) Maintenance and boot fences

Symptom: first prod call after deploy returns thin results.
Fix: enforce bootstrap fence, finish index build, VACUUM after heavy churn, confirm visibility after commit. See Bootstrap Ordering and Pre-deploy Collapse.

Observability probes

k-sweep curve: 5, 10, 20 and plot ΔS. Flat high suggests metric or index fault.
Index audit: EXPLAIN ANALYZE should show IVFFLAT or HNSW usage. If planner skips it, fix stats and filters.
Anchor control: compare against a golden anchor set. If only one table or schema fails, rebuild that scope.
Reranker audit: with a strong reranker, recall improves and ΔS falls. If not, rebuild.

Copy-paste prompt for your AI


I uploaded TXT OS and the WFGY Problem Map files.

Target system: Postgres + pgvector.

* symptom: \[brief]
* traces: ΔS(question,retrieved)=..., ΔS(retrieved,anchor)=..., λ states
* index: \[type=ivfflat|hnsw, lists/probes or m/ef\_search, opclass, dim, normalized?]
* filters: \[JSONB keys, indexes, example WHERE]
* ingest: \[ids, doc\_sha, upsert policy]

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.

🔗 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 + ”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” and the OS boots

🧭 Explore More

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live with full symbolic reasoning and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog including prompt injection and memory bugs	View →
Semantic Blueprint	Layer-based symbolic reasoning and semantic modulations	View →
Benchmark vs GPT-5	Stress test with the full WFGY reasoning suite	View →
Starter Village	New here. Start with a guided tour	Start →

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