AI providers: correlation decision and transcript correction move to OpenAI gpt-4o-mini

One funded account (OpenAI) now covers extraction, embeddings, summaries,
the ambiguous-scene correlation call and transcript correction; Groq stays
optional for STT. New app/internal/llm.py picks the provider from the model
name, so setting either model back to a gemini-* ID needs no code change.
Usage accounting for replay runs now covers both providers.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

drb-c2-core/app/internal/llm.py

@@ -0,0 +1,92 @@
+"""
+One entry point for the two small JSON-mode LLM jobs: the correlation decision
+on ambiguous scenes and transcript correction.
+
+The provider follows the model name: a `gemini-*` model goes through
+app/internal/gemini.py, anything else is an OpenAI chat model. Since the
+2026-11-08 provider consolidation both defaults are OpenAI (gpt-4o-mini), so
+one funded account (OpenAI) covers extraction, embeddings, summaries and these
+two jobs; Groq stays optional for transcription. Pointing either model setting
+back at a gemini-* ID needs no code change.
+
+Token counts from both providers land in the same usage sink, so a replay run
+(app/internal/replay.py) reports what it spent whichever provider served it.
+"""
+import json
+from contextvars import ContextVar
+from typing import Optional
+
+from app.config import settings
+from app.internal.logger import logger
+
+_usage_sink: ContextVar[Optional[dict]] = ContextVar("drb_llm_usage", default=None)
+
+
+def collect_usage(sink: Optional[dict]):
+    """Route token counts for the current context into `sink` (a replay run). Returns a reset token."""
+    return _usage_sink.set(sink)
+
+
+def reset_usage(token) -> None:
+    _usage_sink.reset(token)
+
+
+def record_usage(purpose: str, model: str, prompt: int, output: int, thinking: int = 0) -> None:
+    logger.info(f"llm usage {purpose} {model}: in={prompt} out={output} thinking={thinking}")
+    sink = _usage_sink.get()
+    if sink is not None:
+        row = sink.setdefault(f"{purpose}:{model}", {"calls": 0, "in": 0, "out": 0, "thinking": 0})
+        row["calls"] += 1
+        row["in"] += prompt
+        row["out"] += output
+        row["thinking"] += thinking
+
+
+def provider_for(model: str) -> str:
+    return "gemini" if (model or "").startswith("gemini") else "openai"
+
+
+def available(model: str) -> bool:
+    """Whether the provider serving `model` has a key configured."""
+    key = settings.gemini_api_key if provider_for(model) == "gemini" else settings.openai_api_key
+    return bool(key)
+
+
+def timeout_seconds(model: str) -> float:
+    if provider_for(model) == "gemini":
+        return settings.gemini_timeout_seconds
+    return (1 + max(0, settings.openai_max_retries)) * settings.openai_timeout_seconds
+
+
+def billing_fix(model: str) -> str:
+    if provider_for(model) == "gemini":
+        return "Top up billing at https://ai.studio/projects"
+    return "Top up billing at https://platform.openai.com/settings/organization/billing"
+
+
+def generate_json(model: str, prompt: str, *, purpose: str,
+                  thinking_level: Optional[str] = "minimal") -> dict:
+    """
+    Synchronous (run it via asyncio.to_thread). Returns the parsed JSON body.
+    Raises on API failure so callers' ai_health classification (billing /
+    dead model / transient) is unchanged. `thinking_level` only applies to
+    Gemini 3.x; OpenAI's gpt-4o family has no such setting.
+    """
+    if provider_for(model) == "gemini":
+        from app.internal import gemini
+        return gemini.generate_json(model, prompt, purpose=purpose, thinking_level=thinking_level)
+
+    from app.internal.ai_clients import openai_client
+    # JSON mode requires the word "JSON" in the prompt; both prompts say
+    # "Return JSON" / "Respond with JSON only".
+    resp = openai_client().chat.completions.create(
+        model=model,
+        messages=[{"role": "user", "content": prompt}],
+        response_format={"type": "json_object"},
+        temperature=0,
+    )
+    usage = getattr(resp, "usage", None)
+    record_usage(purpose, model,
+                 getattr(usage, "prompt_tokens", 0) or 0,
+                 getattr(usage, "completion_tokens", 0) or 0)
+    return json.loads(resp.choices[0].message.content or "{}")