Dimension Mismatch and Projection — Guardrails and Fix Pattern

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WFGY Global Fix Map — main Emergency Room, 300+ structured fixes

WFGY Problem Map 1.0 — 16 reproducible failure modes

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
Embedding drift vs semantic mismatch: embedding-vs-semantic.md
Chunking and index alignment: chunking-checklist.md
Retrieval knobs: 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

Probe corpus
Sample 100 embeddings, assert uniform len(vec).
Detect hidden coercion
Compute L2 norm variance. If unusually high, store is truncating.
Apply projection
If mixing models, fit PCA/linear map on overlap dataset.
Rebuild index
Always reset store with explicit dim=… before production.

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.

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	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

Explore More

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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