🧭 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
- 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 vector similarity is unstable because embeddings are not normalized or scaling factors differ between training and retrieval.
This failure often appears when cosine distance is requested but vectors are stored raw, or when IP/dot metrics exaggerate magnitude.
- Visual map and recovery: RAG Architecture & Recovery
- Embedding vs meaning: embedding-vs-semantic.md
- Metric mismatch: metric_mismatch.md
- Chunking checklist: chunking-checklist.md
- Vectors are L2-normalized when using cosine similarity.
- ΔS(question, retrieved) ≤ 0.45, stable across three paraphrases.
- Coverage ≥ 0.70 on the target section.
- λ remains convergent across seeds.
-
Cosine similarity reported but vectors not normalized
→ metric_mismatch.md -
Dot product used without rescaling (large norm vectors dominate retrieval)
→ Normalize or rescale embeddings before indexing. -
Cross-model mixing (embeddings from different checkpoints with different norms)
→ Re-normalize the corpus and queries to unit length. -
Hybrid dense + sparse weighting unstable (scale mismatch between BM25 scores and vector norms)
→ Apply explicit min-max or z-score scaling before weighted sum.
-
Check norms
Sample 100 embeddings. Compute mean L2 norm. If not ~1.0 under cosine, normalization missing. -
Normalize queries
Ensurequery_vector = vector / ||vector||before retrieval when using cosine. -
Corpus re-index
Drop and rebuild index with normalized vectors if store does not enforce it. -
Hybrid scaling
Normalize dense similarity scores into the same 0–1 range as BM25 before combining.
import numpy as np
def check_norms(vectors):
norms = np.linalg.norm(vectors, axis=1)
return norms.mean(), norms.std()
mean_norm, std_norm = check_norms(sample_vectors)
print("Mean norm:", mean_norm, "Std:", std_norm)Target: mean ≈ 1.0, std ≤ 0.05 for cosine retrieval.
| 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 |
| Layer | Page | What it’s for |
|---|---|---|
| ⭐ Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | WFGY 1.0 | Original PDF tension engine and early logic sketch (legacy reference) |
| ⚙️ 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 |
| 🗺️ Map | Problem Map 2.0 | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | Problem Map 3.0 | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | TXT OS | .txt semantic OS with fast bootstrap |
| 🧰 App | Blah Blah Blah | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | Blur Blur Blur | Text to image generation with semantic control |
| 🏡 Onboarding | Starter Village | Guided entry point for new users |
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