Azure OpenAI

🧭 Quick Return to Map

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

• LLM_Providers — model vendors and deployment options
• 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 failures look provider-specific on Azure OpenAI. Examples include wrong model alias vs deployment name, missing api-version, tool call payload shape drift, content safety blocks, or region throttling. Each fix maps back to WFGY pages with measurable targets.

Core acceptance

• ΔS(question, retrieved) ≤ 0.45
• coverage ≥ 0.70 for the target section
• λ remains convergent across 3 paraphrases

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Open these first

• Visual map and recovery: RAG Architecture & Recovery
• End-to-end retrieval knobs: Retrieval Playbook
• Why this snippet (traceability schema): Retrieval Traceability
• Ordering control: Rerankers
• Embedding vs meaning: Embedding ≠ Semantic
• Hallucination and chunk boundaries: Hallucination
• Long chains and entropy: Context Drift, Entropy Collapse
• Snippet and citation schema: Data Contracts
• Structural collapse and recovery: Logic Collapse
• First call after deploy fails: Bootstrap Ordering, Deployment Deadlock, Pre-deploy Collapse

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Fix in 60 seconds

1. Measure ΔS

• Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
• Thresholds: stable < 0.40, transitional 0.40–0.60, risk ≥ 0.60.

2. Probe with λ_observe

• Vary k (5, 10, 20). If ΔS stays high while recall is fine, suspect index or metric mismatch.
• Reorder prompt headers. If ΔS spikes, lock the schema.

3. Apply the module

• Retrieval drift → BBMC + Data Contracts.
• Reasoning collapse → BBCR bridge + BBAM variance clamp.
• Dead ends in long runs → BBPF alternate path.

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Azure-specific failure signatures and the right fix

Symptom	Likely cause on Azure	Open this fix
200 OK but empty or tool call ignored	Missing or wrong api-version for that model family	Data Contracts, Logic Collapse
“Deployment not found”, even though the model exists	Using model name instead of deployment name, or wrong resource/region	Retrieval Traceability
Output blocked, vague “content filtered”	Azure content safety layer different from OpenAI default	Hallucination, then clamp with BBAM
Tool call schema mismatch vs OpenAI	Response keys or enum names differ across api-version	Data Contracts
Works in one region, fails in another	Model availability and quotas are regional	Bootstrap Ordering
Latency spikes or 429 under load	Per-resource rate limits, private link, or vnet egress	Ops Live Monitoring, Debug Playbook
Function calls drop arguments	Old api-version truncates or renames fields	Data Contracts
Fine-tuned or staged deployment not picked	Wrong deployment alias bound in prod slot	Retrieval Traceability

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Minimal provider checklist

1. Endpoint correctness
   Resource url, region, and deployment name are consistent. Avoid mixing model name with deployment name in the same client.

2. Version pinning
   Pin an api-version known to support your features (tools, JSON mode, response format). Treat version bumps as schema migrations.

3. Schema lock
   Adopt the Problem Map Data Contracts snippet for tool payloads and citations. Reject partial responses. Require structured finish_reason.

4. Safety behavior
   Expect an extra content-safety layer. When blocked, route to BBPF alternate path and down-shift temperature, then retry with narrowed scope.

5. Observability
   Log λ state per step, ΔS per hop, and the exact deployment id used. Carry region and version in traces.

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Copy-paste prompt (safe to hand the AI)

I am using Azure OpenAI. Audit my run as follows:

* Check ΔS(question, retrieved) and ΔS(retrieved, anchor). Show the numbers.
* Confirm endpoint tuple: {resource, region, deployment}. Confirm `api-version` and tool schema.
* If tool/schema mismatch: apply the WFGY Data Contracts checklist and propose the exact fields to lock.
* If blocked by content safety: switch to a narrower prompt schema, reduce temperature, and route via BBPF.
* Keep λ convergent across 3 paraphrases. If it flips, apply BBCR + BBAM and show the before/after traces.

Link me to the exact Problem Map pages I should read next.

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Escalation

• Still unstable after schema lock → re-index and re-embed, verify with Retrieval Playbook and Embedding ≠ Semantic.
• Consistent provider errors → freeze api-version, roll back deployment alias, rerun with fixed seed, attach λ traces, then open Debug Playbook.
• First call after deploy fails → rebuild boot fence with Pre-deploy Collapse.

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Acceptance targets

• coverage to target section ≥ 0.70
• ΔS(question, retrieved) ≤ 0.45
• λ convergent across seeds and paraphrases
• repeatable traces and identical schema across regions

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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 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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