Dimension Mismatch and Projection

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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.

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Open these first

• Visual map and recovery: RAG Architecture & Recovery
• Embedding drift vs semantic mismatch: embedding-vs-semantic.md
• Chunking and index alignment: chunking-checklist.md
• Retrieval knobs: retrieval-playbook.md

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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.

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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.

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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.

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Example projection (Python, pseudo)

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.

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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.

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🔗 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 + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

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⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
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