# Dimension Mismatch and Projection — Guardrails and Fix Patterns
🧭 Quick Return to Map
> You are in a sub-page of **Embeddings**.
> To reorient, go back here:
>
> - [**Embeddings** — vector representations and semantic search](./README.md)
> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
>
> 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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
* End to end retrieval knobs: [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
* Schema and audits: [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
* Wrong meaning despite high similarity: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
* FAISS metric and index traps: [vectorstore-metrics-and-faiss-pitfalls.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/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
---
## 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.
---
## 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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/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.
---
## 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
```json
{
"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
---
## 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`
---
## 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.
```
---
### 🔗 Quick-Start Downloads (60 sec)
| Tool | Link | 3-Step Setup |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \” |
| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
---
### 🧭 Explore More
| Module | Description | Link |
| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| WFGY Core | WFGY 2.0 engine is live. full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
| Problem Map 1.0 | Initial 16 mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| Problem Map 2.0 | RAG focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
| Semantic Clinic Index | Expanded failure catalog. prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
| Semantic Blueprint | Layer based symbolic reasoning and semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
| 🧙♂️ Starter Village 🏡 | New here. lost in symbols. click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
---
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> 
⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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