LLM APIs (OpenAI-Compatible + OSS Fallback)

# LLM APIs (OpenAI-Compatible + OSS Fallback) Call one chat-completions API and one OSS fallback; log cost and latency for both. ## 1. The OpenAI-compatible shape Nearly every provider (OpenAI, OSS servers, gateways) accepts the same POST body: `model`, `messages` (system/user/tool), `temperature`, `max_tokens`, `response_format`. Learn this shape once; switching providers is a base-URL + key change. ## 2. Wrapper skeleton (starter) ```python def chat(messages, model, temperature=0.2, max_tokens=500): # primary -> fallback on timeout/5xx; time both calls; log USD per 1K tokens ``` Rules: temperature 0-0.3 for extraction; always set `max_tokens`; retry once on 429/5xx with backoff, then fall back to the OSS endpoint and flag the row. ## 3. Cost/latency log Per call record: model, input/output tokens, USD cost, TTFT + total latency, fallback-used flag. The capstone cost dashboard is built from this exact log. ## 4. Grounding discipline Every factual claim in the final answer must trace to either the retrieved context or an explicit "unsupported" flag. A fluent answer with no citation is a failure, not a partial pass — this is what the 20Q golden refusal suite checks.