WFGY/ProblemMap/GlobalFixMap/Retrieval/store_agnostic_guardrails.md

Store-Agnostic Guardrails for Retrieval

🧭 Quick Return to Map

You are in a sub-page of Retrieval.
To reorient, go back here:

• Retrieval — information access and knowledge lookup
• 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 to harden retrieval quality without changing your vector store. The checks localize failure causes and route you to the exact structural fix so you can verify with measurable targets.

Acceptance targets

• ΔS(question, retrieved) ≤ 0.45
• Coverage of target section ≥ 0.70
• λ remains convergent across 3 paraphrases and 2 seeds
• E_resonance stays flat on long windows

---

15-minute triage checklist

1. Lock metrics and analyzers
   One analyzer for write and read. Verify distance metric and normalization.
   Open: Retrieval Playbook

2. Enforce the snippet contract
   Required fields: snippet_id, section_id, source_url, offsets, tokens.
   Open: Data Contracts

3. Trace why this snippet
   Add cite-then-explain and store the trace.
   Open: Retrieval Traceability

4. Probe ΔS and λ
   Three paraphrases and two seeds. If ΔS ≥ 0.60 or λ flips, clamp variance.
   Open: deltaS_probes.md

5. k sweep and rerankers
   k in {5, 10, 20}. Try a deterministic reranker when order matters.
   Open: Rerankers · hybrid_reranker_recipe.md

6. Check chunk boundaries and anchors
   If facts exist but never surface, realign chunking and anchors.
   Open: chunking-checklist.md · chunk_alignment.md

7. Detect fragmentation
   If coverage is low while index looks healthy, suspect store fragmentation.
   Open: pattern_vectorstore_fragmentation.md

8. Hybrid failure
   If hybrid underperforms a single retriever, split parsing and rebalance.
   Open: pattern_query_parsing_split.md

9. Embedding vs meaning
   High similarity yet wrong answer means metric or family mismatch.
   Open: embedding-vs-semantic.md

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Minimal instrumentation you can paste

# Pseudocode: keep these checkpoints store agnostic
def retrieve(q, k=10):
    # unified analyzer and explicit metric
    return retriever.invoke(q, k=k)

def trace_schema(snippet):
    assert {"snippet_id","section_id","source_url","offsets","tokens"} <= set(snippet.keys())

def observe(q, snippets, answer):
    # compute ΔS and λ, record probes
    log = probes.compute(q, snippets, answer)
    if log["ΔS"] >= 0.60 or log["λ_flip"]:
        raise Exception("High ΔS or λ flip. Apply variance clamp and rerankers.")
    return log

def pipeline(q):
    s = retrieve(q, k=10)
    for x in s: trace_schema(x)
    msg = prompt.cite_then_explain(q, s)
    ans = llm.invoke(msg)
    return observe(q, s, ans)

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Copy-paste LLM prompt

You have TXT OS and the WFGY pages loaded.

Task:
1) Enforce cite-then-explain with fields {snippet_id, section_id, source_url, offsets, tokens}.
2) Log ΔS(question, retrieved) and λ across 3 paraphrases and 2 seeds.
3) If ΔS ≥ 0.60 or λ flips, propose the smallest structural change referencing:
   retrieval-playbook, retrieval-traceability, data-contracts, rerankers, query-parsing-split.
4) Return JSON:
{ "citations": [...], "answer": "...", "ΔS": 0.xx, "λ_state": "<>", "coverage": 0.xx, "next_fix": "..." }

---

Symptoms → exact structural fix

Symptom	Likely cause	Open this
High similarity yet wrong meaning	metric or embedding family mismatch	embedding-vs-semantic.md
Facts exist but never retrieved	chunk drift or store fragmentation	chunking-checklist.md · pattern_vectorstore_fragmentation.md
Hybrid worse than single retriever	query parsing split, mis-weighted rerank	pattern_query_parsing_split.md · rerankers.md
Citations missing or unstable	schema not enforced, formatter renamed fields	retrieval-traceability.md · data-contracts.md
Answers flip between runs	prompt header reordering or variance	context-drift.md · rerankers.md

---

Rebuild order when numbers stay bad

Follow the store-agnostic sequence and re-measure after each step. Open: Retrieval Playbook

1. Lock analyzer and distance metric
2. Re-chunk with anchor checklist
3. Re-embed with a single family and normalization
4. Add deterministic reranker and stabilize order
5. Tighten data contracts and traceability
6. Evaluate with the gold set and ΔS probes Open: retrieval_eval_recipes.md

---

Ops monitors to keep on

• Index readiness fence and version hash Open: bootstrap-ordering.md

• Live ΔS and λ alerts on long windows Open: ops live monitoring

• Regression gate for coverage and ΔS Open: eval precision and recall

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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

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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