Vectorstore Fragmentation

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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
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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 retrieval recall drops because the vector index is fragmented.
This happens when multiple shards, partitions, or replicas return partial results and the top-k merge is unstable.

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Open these first

• Visual map and recovery: RAG Architecture & Recovery
• Deployment issues: deployment-deadlock.md
• Retrieval playbook: retrieval-playbook.md
• Traceability schema: retrieval-traceability.md

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Core acceptance

• Top-k results consistent across shards with variance ≤ 0.05.
• Coverage ≥ 0.70 on the target section.
• ΔS(question, retrieved) ≤ 0.45 across three paraphrases.
• λ remains convergent under shard fanout.

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Typical breakpoints and the right fix

• Shards not balanced → Some partitions miss updates, recall drops.
  → Re-index with balanced sharding and verify ingestion logs.

• Merge strategy unstable → Top-k from each shard merged without normalization.
  → Apply global reranker after merging, not local-only.

• Version skew between replicas → Old embeddings live in one shard.
  → Enforce deployment-deadlock.md checks and hash validation.

• Distributed query latency → Timeout before all shards return.
  → Add backpressure and enforce full quorum before top-k selection.

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Fix in 60 seconds

1. Run shard probe
   Fire the same query against each shard individually. Compare ΔS variance.

2. Align replicas
   Verify INDEX_HASH matches across partitions. If not, rebuild.

3. Global reranker
   Always normalize scores before merging. Rerank final list with semantic signal.

4. Quorum guard
   Require ≥80% shard response before producing result. If missing, retry.

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Copy-paste probe script (pseudo)

def shard_probe(query, shards):
    results = {}
    for shard in shards:
        hits = shard.search(query, k=10)
        ΔS_vals = [compute_deltaS(query, h) for h in hits]
        results[shard.id] = (np.mean(ΔS_vals), np.var(ΔS_vals))
    return results

Target: shard-to-shard ΔS variance ≤ 0.05.

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Common gotchas

• Shard IDs not logged → Cannot trace back retrieval → enforce retrieval-traceability.md.
• Hybrid retriever mixing BM25 + dense done locally per shard → breaks weighting.
• Replicas updated asynchronously → ingestion race.

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🔗 Quick-Start Downloads (60 sec)

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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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