FAISS: Guardrails and Fix Patterns

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• 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

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A compact repair guide for FAISS retrieval stacks. Use this when recall looks fine but meaning drifts, or when IVF/HNSW tuning flips answers across seeds. The checks below route you to the exact WFGY fix pages and give a minimal recipe you can paste into a runbook.

Open these first

• Visual map and recovery: RAG Architecture & Recovery
• End-to-end retrieval knobs: Retrieval Playbook
• Traceability schema: Retrieval Traceability
• Embedding ≠ meaning: Embedding vs Semantic
• Vectorstore health: Vectorstore Fragmentation
• Query split (HyDE vs BM25): Query Parsing Split
• Ordering control: Rerankers
• Snippet and citation schema: Data Contracts
• Evaluating RAG: RAG Precision/Recall
• FAISS specifics: Vectorstore Metrics & FAISS Pitfalls
• Live checks: Live Monitoring

Fix in 60 seconds

1. Measure ΔS

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

2. Probe with λ_observe

   • Sweep k ∈ {5, 10, 20} and for IVF sweep nprobe ∈ {1, 4, 8, 16}.
   • For HNSW, sweep efSearch ∈ {32, 64, 128}.
   • If ΔS flattens high across k, suspect metric/index mismatch.

3. Apply the module

   • Retrieval drift → BBMC + Data Contracts.
   • Reasoning collapse after retrieval → BBCR bridge + BBAM variance clamp, see Logic Collapse.
   • Dead ends in long runs → BBPF alternate path + Context Drift.

4. Verify

   • Coverage to target section ≥ 0.70, ΔS ≤ 0.45 on three paraphrases, λ stays convergent across seeds.

Typical breakpoints and the right fix

Symptom	Likely cause	Open this	Minimal fix
High cosine similarity but wrong meaning	IP vs L2 mixup, un-normalized embeddings	Embedding vs Semantic	Normalize vectors; match metric to embedder; re-index
Good recall, messy top-k order	Rerank missing or weak	Rerankers	Add cross-encoder rerank, k=50→top-10
Some facts never show up	Shards or label fragmentation	Vectorstore Fragmentation	Merge shards; rebuild IVF lists; verify dim
Answers flip between runs	IVF nlist/nprobe underfit, PQ over-aggressive	FAISS Pitfalls	Raise nprobe, enlarge training set, reduce PQ
Hybrid gets worse than single retriever	Query split and prompt coupling	Query Parsing Split	Split semantic vs lexical prompts; fuse post-retrieval

FAISS quick checklist

• Confirm dimension matches the embedding model output exactly.
• Confirm metric: IP with normalized vectors, or L2 with raw vectors. Do not mix.
• For IVF, set nlist based on corpus size, train with at least $100 \times \text{nlist}$ examples.
• Start with nprobe ≈ sqrt(nlist) and tune upward until ΔS stabilizes.
• For HNSW, raise efConstruction and efSearch until ΔS stops improving.
• Rebuild the index after changing normalization or metric.
• Lock the snippet schema and citations using Data Contracts.

Copy-paste repair prompt

audit FAISS retrieval with ΔS and λ\_observe.
report: metric choice (IP/L2), normalization, dim, index type, nlist/nprobe or HNSW ef.
run three paraphrases, k in {5,10,20}. if ΔS stays >0.45, switch to normalized IP and rebuild.
apply BBMC + Data Contracts; add reranker for top-50→10. show before/after ΔS table.

Acceptance targets

• Coverage ≥ 0.70 to the target section.
• ΔS ≤ 0.45 across three paraphrases.
• λ remains convergent across seeds.
• E_resonance flat under long windows.

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