WFGY/ProblemMap/GlobalFixMap/VectorDBs_and_Stores/vespa.md

Vespa: Guardrails and Fix Patterns

🧭 Quick Return to Map

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

• VectorDBs_and_Stores — vector indexes and storage backends
• 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 your retrieval stack runs on Vespa with vector or hybrid ranking. It routes common store-level failures to the right structural fixes in the Problem Map and gives a minimal checklist you can apply fast.

When to open this page

• High vector similarity yet wrong meaning
• Hybrid keyword + vector flips order between runs
• Citations land on the wrong section or offsets do not match
• Tensor dimensions or distance functions differ across write and read
• Coverage stays low on the intended section even though recall looks fine

Acceptance targets

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

---

Map symptoms → structural fixes

• Wrong-meaning hits despite high similarity.
  → embedding-vs-semantic.md

• Snippet or citation does not match the retrieved section.
  → retrieval-traceability.md
  → data-contracts.md

• Hybrid collapse or query split between BM25 and vector.
  → patterns/pattern_query_parsing_split.md
  → rerankers.md

• Version skew after deploy or wrong rank profile in use.
  → bootstrap-ordering.md
  → predeploy-collapse.md

• Long chains smear topics or capitalization across sessions.
  → rag-architecture-and-recovery.md

---

Minimal Vespa setup checklist

1. Pin tensor schema and metric
   Use one embedding model per vector field. Keep a single tensor dimension and a single distance function for the rank profile. Reject documents that do not match the expected dimension.

2. Contract the snippet
   Every hit must carry {snippet_id, section_id, source_url, offsets, tokens}. Enforce cite-then-explain in the answer layer.
   Spec: data-contracts.md

3. Hybrid ordering and reranking
   Combine keyword recall with nearestNeighbor recall, then run a deterministic rerank pass. Log both candidate lists to detect query split.
   Guide: rerankers.md

4. Analyzer and casing parity
   Freeze tokenization, stemming, and casing rules for all text fields used in BM25 or filters. Keep the same rules at write and read.

5. Observability probes
   Log ΔS(question, retrieved) and λ per step: retrieve → rerank → reason. Alert when ΔS ≥ 0.60 or λ diverges.

6. Cold start and deploy fences
   Block traffic until the new application package, schema hash, and rank profile version are active.
   See: bootstrap-ordering.md

---

60-second diagnosis

1. Run a 10-question smoke set on one target section.
2. Compute ΔS(question, retrieved) for each question.
3. If median ΔS ≥ 0.60, apply one structural fix in this order:
   a) normalize embeddings and pin one distance function
   b) enforce data contracts and traceability
   c) add deterministic reranking and align analyzers
4. Require coverage ≥ 0.70 before you publish.

---

Copy-paste audit prompt

I uploaded TXT OS and the WFGY Problem Map pages.
Store: Vespa. Retrieval: BM25 + nearestNeighbor with rerank.

Audit this query and return:

* ΔS(question, retrieved) and λ across retrieve → rerank → reason.
* If ΔS ≥ 0.60, choose exactly one minimal structural fix and name the page:
  embedding-vs-semantic, retrieval-traceability, data-contracts, rerankers.
* JSON only:
  { "citations":\[...], "ΔS":0.xx, "λ":"→|←|<>|×", "next\_fix":"..." }

---

Common Vespa gotchas

• Mixed embedding dimensions or distance functions across rank profiles
  Standardize on one and validate on write.

• Summary fields do not include offsets or token spans
  Add fields to the summary and verify the contract.

• Match-phase or targetHits tuned too low for the collection size
  Recall collapses and rerank cannot recover. Increase recall or shard-level limits.

• Filter mismatches due to analyzer differences
  Keep analyzer and casing identical across environments.

• Application package deployed but old profile still served
  Fence the cutover and verify the active profile hash before enabling traffic.

---

🔗 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 + ”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

---

Explore More

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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