When embeddings are inserted or updated across time without a consistent chunking, normalization, or merge strategy, the vectorstore becomes fragmented.
This creates “holes” where semantically related text lives in different shards, versions, or duplicate vectors, leading to unstable recall.
| Sign | What You See |
|---|
| Retrieval drops | Facts exist in DB but never show up |
| Duplicate chunks | Nearly identical snippets appear multiple times |
| Version skew | Old vectors mix with new encoders |
| Query instability | Same query → different answers each run |
| Hybrid failure | BM25 beats hybrid retriever that should win |
| Weakness | Result |
|---|
| Mixed encoders | Same corpus stored under incompatible embeddings |
| No chunk contract | Sentence vs paragraph vs sliding window → fractured recall |
| No dedupe layer | Near-duplicate vectors inflate noise |
| No update strategy | Old vectors never pruned, drift builds up |
| Shard misalignment | Different stores or partitions hold overlapping data |
| Problem | Module | Remedy |
|---|
| Metric mismatch | ΔS checks + BBMC | Compare across seeds, enforce unified metric |
| Chunk drift | Chunking Contract | Standardize window, overlap, anchor rules |
| Duplicate noise | BBPF fork + collapse | Collapse near-dupes before index write |
| Update skew | BBCR re-index | Wipe and rebuild with normalized schema |
| Store fragmentation | Semantic Tree | Trace lineage, merge shards consistently |
Query:
"Who approved the compliance waiver for dataset X?"
Before:
• Top-3 results: duplicate sentences from old version
• Actual approval record missing
After WFGY:
• ΔS(question,retrieved) = 0.38
• Coverage = 0.78 for target section
• Single, authoritative snippet retrieved
Stable recall restored once fragmented vectors were collapsed and re-indexed.
| Module | Role |
|---|
| ΔS Metric | Detects fragmentation via semantic drift |
| BBMC | Checks consistency across seeds/encoders |
| BBPF | Collapses near-duplicate embeddings |
| BBCR | Forces clean rebuild when skew detected |
| Semantic Tree | Tracks provenance across shards/versions |
| Feature | State |
|---|
| Chunking contract enforcement | ✅ Active |
| Duplicate collapse | ✅ Stable |
| Encoder version check | ✅ Stable |
| Shard merge & lineage tracking | 🔜 Planned |
- Always record encoder version in metadata.
- Run ΔS probe on 3 paraphrases before/after re-index.
- Use semantic contract: same chunk size, stride, and normalization across all updates.
- If >15% duplicate rate detected, wipe and rebuild index.
| Tool | Link | 3-Step Setup |
|---|
| WFGY 1.0 PDF | Engine Paper | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ” |
| 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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