# Dimension Mismatch and Projection — Guardrails and Fix Pattern
🧭 Quick Return to Map
> You are in a sub-page of **RAG_VectorDB**.
> To reorient, go back here:
>
> - [**RAG_VectorDB** — vector databases for retrieval and grounding](./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 **embeddings break because vector dimensions do not match the store or runtime index**.
This happens if you switch models (e.g. 1536 → 1024 dims) or if the store silently coerces vectors.
---
## Open these first
- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
- Embedding drift vs semantic mismatch: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
- Chunking and index alignment: [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
- Retrieval knobs: [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
---
## Core acceptance
- All embeddings in a store share identical dimension length.
- ΔS(question, retrieved) ≤ 0.45 after dimension fix.
- Coverage ≥ 0.70 across three paraphrases.
- λ remains convergent when switching embedding models.
---
## Typical breakpoints and the right fix
- **Store rejects insert** with `dimension mismatch` error.
→ Rebuild index with correct `dim` parameter.
- **Store accepts but pads/truncates silently**.
→ Causes random retrieval drift.
→ Explicitly validate vector length on every ingestion.
- **Multiple models used** → Some 1024-d, some 1536-d vectors.
→ Project to common dimension space with PCA/linear map.
- **Migration between providers** (e.g. OpenAI → Cohere).
→ Use adapter layer: re-embed corpus or apply projection matrix.
---
## Fix in 60 seconds
1. **Probe corpus**
Sample 100 embeddings, assert uniform `len(vec)`.
2. **Detect hidden coercion**
Compute L2 norm variance. If unusually high, store is truncating.
3. **Apply projection**
If mixing models, fit PCA/linear map on overlap dataset.
4. **Rebuild index**
Always reset store with explicit `dim=…` before production.
---
## Example projection (Python, pseudo)
```python
from sklearn.decomposition import PCA
import numpy as np
# Fit projection from 1536-d → 1024-d
pca = PCA(n_components=1024)
pca.fit(corpus_vecs_1536)
projected = pca.transform(new_vecs_1536)
````
Target: after projection, ΔS variance ≤ 0.05 vs original gold set.
---
## Common gotchas
* Store CLI defaults to wrong dimension (FAISS index built at 768, model outputs 1024).
* Silent fallback in wrappers (LangChain auto-pads zeros).
* Mixing sparse + dense without explicit projection weights.
---
### 🔗 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 & 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) |
---
> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
> Engineers, hackers, and open source builders who supported WFGY from day one.
> 
⭐ [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).
[](https://github.com/onestardao/WFGY)
[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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