WFGY/ProblemMap/GlobalFixMap/Embeddings/README.md

Embeddings — Global Fix Map

Make embedding space match real meaning, not just cosine tricks.
Use this when recall looks high yet answers point to the wrong idea, or when FAISS/Qdrant “works” but context is off.

What this page is

• A tight checklist to align models, metrics, and normalization.
• Structural fixes that do not require changing your LLM or infra.
• Steps you can verify with ΔS and small A/B probes.

When to use

• Similarity scores look strong but retrieved snippets are semantically wrong.
• Different pipelines write/read with different distance metrics.
• Mixed models created the index and now query it.
• Some facts never show up although definitely indexed.
• Cross-language corpus drifts or tokenizers don’t match.

Open these first

• Meaning vs vector score: Embedding ≠ Semantic
• Fragmented or half-empty index: Vectorstore Fragmentation
• End-to-end knobs: Retrieval Playbook
• Ordering layer after recall: Rerankers
• Trace why a snippet was picked: Retrieval Traceability
• Quality gates: RAG Precision/Recall · Latency vs Accuracy

Fix in 60 seconds

1. Measure ΔS

   • Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
   • Triggers: ΔS ≥ 0.60 or flat-high ΔS when you vary k ∈ {5,10,20}.

2. Check metric + normalization agreement

   • The model that built vectors must match the model used at query time.
   • Confirm cosine vs inner-product flags on both write and read.
   • Unit-normalize on both sides if you use cosine.

3. Verify dimensionality and truncation

   • Same vector length everywhere.
   • No hidden cast, dtype mismatch, or silent truncation.

4. Rebuild once with explicit config

   • Persist metric, normalizer, and model id with the index file.
   • After rebuild, probe ΔS again and compare the ΔS-vs-k curve.

5. Patch recall before ranking

   • If ΔS drops yet ordering still looks noisy, enable a light reranker from the playbook.
   • Keep citation schema from traceability to audit the change.

Copy-paste prompt

I uploaded TXT OS and the WFGY ProblemMap files.

My embedding bug:

* symptom: \[brief]
* traces: ΔS(question, retrieved)=..., ΔS(retrieved, anchor)=..., curve vs k=...
* context: write-model=\[...], read-model=\[...], metric=\[cosine|ip], norm=\[on|off]

Tell me:

1. which mismatch explains the failure,
2. which exact pages to open from this repo,
3. the minimal steps to rebuild or rescore to push ΔS ≤ 0.45,
4. how to verify with a reproducible ΔS-vs-k chart and a citation table.
   Use BBMC alignment if anchors are stable, then add a lightweight reranker if needed.

Minimal checklist

• One embedding model per corpus or store the model id with each vector.
• Fix the metric flag once and persist it with the index.
• Enforce unit normalization for cosine, never mix with raw dot product.
• Keep text pre-processing identical on write and read.
• Log vector counts per collection; compare to document counts.
• Run the fragmentation pattern if some facts vanish from results.

Acceptance targets

• ΔS(question, retrieved) ≤ 0.45 across three paraphrases.
• ΔS-vs-k curve descends then flattens, not flat-high.
• Recall/precision meet your eval sheet thresholds.
• λ stays convergent at the retrieval layer after the rebuild.
• Traceability explains why each snippet was selected.

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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

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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