WFGY/ProblemMap/GlobalFixMap/Embeddings/dimension_mismatch_and_projection.md

Dimension Mismatch and Projection — Guardrails and Fix Patterns

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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.

Use this page when vectors fail at write or retrieval due to mismatched dimensions or when a projection adapter silently degrades meaning. The goal is to align model output size, store configuration, and any projection layer, then verify with ΔS, coverage, and λ.

Open these first

• Visual map and recovery: rag-architecture-and-recovery.md
• End to end retrieval knobs: retrieval-playbook.md
• Schema and audits: data-contracts.md
• Wrong meaning despite high similarity: embedding-vs-semantic.md
• FAISS metric and index traps: vectorstore-metrics-and-faiss-pitfalls.md

When to use this page

• Client or batch embedder outputs 768 but the store is configured for 1024
• A projection layer or PCA was introduced and recall dropped
• Mixing two models with different dimensions caused invalid writes or runtime coercion
• ANN parameters trained on one dimension are reused after a dimension change
• Quantization artifacts after projection changed neighbor order

Acceptance targets

• ΔS(question, retrieved) ≤ 0.45
• Coverage of target section ≥ 0.70
• λ remains convergent across three paraphrases and two seeds
• E_resonance stays flat on long windows

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Symptom → likely cause

• Writes fail or vectors padded or truncated automatically Likely cause. Store dimension differs from embedder output or client uses a different model id than the index.

• Recall ok on a subset but anchor never ranks in top 3 Likely cause. Projection matrix or PCA learned on a different distribution. Mismatch between train corpus and live traffic.

• Top k changes after quantization or IVF training Likely cause. Product quantizer or HNSW graph trained before the dimension change. Requires retrain.

• Different tenants see different quality after a migration Likely cause. Some partitions still encode with the old dimension. Mixed collections without a union rerank.

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Fix in 60 seconds

1. Stop mixed writes Fail fast when vector_dim != store_dim. Never coerce with pad or slice.

2. Lock dimension in the contract Record dim, embed_model, embed_rev, projection_name, projection_rev, quantize=true|false. See data-contracts.md.

3. Rebuild the index If dimension changed or projection changed, re-embed. Retrain ANN and PQ on the new vectors. Do not reuse old graphs.

4. Verify Three paraphrases and two seeds. Require coverage ≥ 0.70 and ΔS ≤ 0.45 before cutover.

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Safe projection patterns

• No projection preferred Use a single model family per collection. Create another collection for a different model or dim.

• If projection is required

  • Learn a linear map on matched pairs. Solve W = argmin‖WX − Y‖² + λ‖W‖² on a representative corpus.
  • Normalize both spaces consistently. If the target uses cosine, L2 normalize after projection.
  • Validate with a held out gold set and ΔS thresholds. Reject if recall drops more than 3 percent.

• PCA or down projection

  • Fit PCA only on the target distribution. Fix the component count and version it.
  • Rebuild ANN structures with the projected vectors.

• Cross model blends

  • Do not mix dimensions in one store. Use a union retriever then a single deterministic reranker on top k.

---

Minimal probes

Probe A — hard dimension check
- Assert len(vec) == store_dim at write and at query. Abort otherwise.

Probe B — projection identity drift
- For N samples, compute ΔS(orig, projected). If median ΔS > 0.15, projection is too lossy for your task.

Probe C — ANN retrain necessity
- Compare recall@k before and after retraining ANN on projected vectors. If recall jumps only after retrain, previous graph was stale.

Probe D — quantization sanity
- Toggle quantization off for a 1k sample. If order stabilizes and ΔS drops, retrain PQ with the new dimension or disable for critical paths.

---

Contract fields to add

{
  "embed_model": "model-id",
  "embed_rev": "2025-08-01",
  "dim": 768,
  "projection_name": "linear_W_1024to768",
  "projection_rev": "v2",
  "normalize_l2": true,
  "ann_index": "hnsw",
  "ann_rev": "hnsw_v5",
  "quantize": false
}

---

Minimal rebuild playbook

• Freeze writers and export current contracts
• Re-embed with the target model and dimension
• Retrain ANN or PQ on the new vectors
• Dual read and union rerank for one week
• Cutover only if coverage and ΔS meet targets on the gold set

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Verification protocol

• Ten question gold set with exact anchors
• Three paraphrases and two seeds per question
• Pass if coverage ≥ 0.70 and ΔS ≤ 0.45 with λ convergent
• Store traces with dim, projection_name, ann_rev, and quantize

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Copy paste prompt for the LLM step

TXT OS and WFGY Problem Map are loaded.

My issue: dimension mismatch or projection degraded recall.
Traces:
- dim: source=..., store=...
- projection: name=..., rev=...
- ΔS(question,retrieved)=..., coverage=..., λ across 3 paraphrases

Tell me:
1) the failing layer and why,
2) the exact WFGY page to open,
3) the minimal structural fix to align dimensions or projection,
4) a verification plan to reach coverage ≥ 0.70 and ΔS ≤ 0.45.
Use BBMC, BBCR, BBPF, BBAM when relevant.

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