Merge pull request 'gemini: minimal thinking on correlation, token accounting per call' (#180) from feat/gemini-cost into main

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

@@ -0,0 +1,94 @@
+"""
+One place every Gemini call goes through: JSON-mode generation, an explicit
+thinking level, and token accounting.
+
+Why it exists: a day of replay runs (server-26#170) cost ~$5 of Gemini for
+~7 two-hour windows — roughly $0.70 per 290 calls, which projects to several
+dollars a day per live deployment for correlation alone — and nothing in DRB
+could say where it went (server-26#45). Gemini 3.x models "think" by default
+and bill that as output; the old google-generativeai SDK these calls used
+cannot even set a thinking level. A link/new/orphan choice or a transcript
+cleanup does not need extended reasoning.
+
+Every call logs its token counts, and inside a replay run they are also added
+to the run's own usage sink (see app/internal/replay.py), so a run reports
+what it actually spent instead of an estimate.
+"""
+import json
+import threading
+from contextvars import ContextVar
+from typing import Optional
+
+from app.config import settings
+from app.internal.logger import logger
+
+_client = None
+_client_lock = threading.Lock()
+# Models that rejected a thinking level: retried without one from then on.
+_no_thinking_level: set[str] = set()
+
+_usage_sink: ContextVar[Optional[dict]] = ContextVar("drb_gemini_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 _get_client():
+    global _client
+    with _client_lock:
+        if _client is None:
+            from google import genai  # lazy — only when a Gemini call is made
+            _client = genai.Client(api_key=settings.gemini_api_key)
+        return _client
+
+
+def _config(thinking_level: Optional[str]):
+    from google.genai import types
+    kwargs = {"response_mime_type": "application/json"}
+    if thinking_level:
+        kwargs["thinking_config"] = types.ThinkingConfig(thinking_level=thinking_level)
+    return types.GenerateContentConfig(**kwargs)
+
+
+def _record(purpose: str, model: str, usage) -> None:
+    prompt = getattr(usage, "prompt_token_count", None) or 0
+    output = getattr(usage, "candidates_token_count", None) or 0
+    thoughts = getattr(usage, "thoughts_token_count", None) or 0
+    logger.info(f"gemini usage {purpose} {model}: in={prompt} out={output} thinking={thoughts}")
+    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"] += thoughts
+
+
+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, exactly like the old per-module helpers, so callers'
+    ai_health classification (billing / dead model / transient) is unchanged.
+    """
+    client = _get_client()
+    level = None if model in _no_thinking_level else thinking_level
+    try:
+        resp = client.models.generate_content(model=model, contents=prompt, config=_config(level))
+    except Exception as e:
+        # A model that doesn't accept this thinking level answers 400 for
+        # every call; drop the setting for that model rather than lose the tier.
+        if level and "thinking" in str(e).lower():
+            logger.warning(f"gemini: {model} rejected thinking_level={level!r} ({e}); retrying without it")
+            _no_thinking_level.add(model)
+            resp = client.models.generate_content(model=model, contents=prompt, config=_config(None))
+        else:
+            raise
+    _record(purpose, model, getattr(resp, "usage_metadata", None))
+    return json.loads(resp.text)

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

@@ -20,7 +20,6 @@ Error handling: any Gemini failure returns None from decide() and the
 rules_decision from tiebreak() so the pipeline never stalls.
 """
 import asyncio
-import json
 from datetime import datetime, timezone
 from typing import Optional
 from app.internal.logger import logger
@@ -190,15 +189,8 @@ def _build_tiebreak_prompt(rules_decision: dict, llm_decision: dict, ctx: dict)
 # ─────────────────────────────────────────────────────────────────────────────
 
 def _sync_gemini(model_name: str, prompt: str) -> dict:
-    import google.generativeai as genai  # lazy import — only when needed
-
-    genai.configure(api_key=settings.gemini_api_key)
-    model = genai.GenerativeModel(
-        model_name,
-        generation_config={"response_mime_type": "application/json"},
-    )
-    response = model.generate_content(prompt)
-    return json.loads(response.text)
+    from app.internal import gemini
+    return gemini.generate_json(model_name, prompt, purpose="correlation")
 
 
 # ─────────────────────────────────────────────────────────────────────────────

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

@@ -469,6 +469,9 @@ async def _run(run_id: str, org_id: str, calls: list[dict], mode: str,
     fl_token = force_flags(_flags_for(mode))
     ai_failures: list = []
     ai_token = ai_health.collect_sandbox_failures(ai_failures)
+    from app.internal import gemini
+    usage: dict = {}
+    usage_token = gemini.collect_usage(usage)
     try:
         sem = asyncio.Semaphore(PREFETCH)
 
@@ -561,6 +564,7 @@ async def _run(run_id: str, org_id: str, calls: list[dict], mode: str,
         metrics = compute_metrics(incidents, sb_calls)
         metrics["est_cost_usd"] = _running_cost(progress, metrics, mode)
         metrics["ai_failures"] = dict(Counter(f"{f['tier']}: {f['problem']}" for f in ai_failures))
+        metrics["gemini_usage"] = usage
     except Exception as e:
         status = "failed"
         errors.append(f"run: {type(e).__name__}: {e}"[:300])
@@ -568,6 +572,7 @@ async def _run(run_id: str, org_id: str, calls: list[dict], mode: str,
         logger.error(f"Replay {run_id} failed: {e}")
     finally:
         ai_health._sandbox_failures.reset(ai_token)
+        gemini.reset_usage(usage_token)
         unforce_flags(fl_token)
         fstore.exit_sandbox(sb_token)
         _cancel.discard(run_id)

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

@@ -43,7 +43,6 @@ another equally plausible word.
 """
 
 import asyncio
-import json
 import re
 from typing import Any, Optional
 
@@ -228,14 +227,11 @@ def build_context_block(context: dict, talkgroup_name: Optional[str]) -> str:
 
 
 def _sync_gemini(model_name: str, prompt: str) -> dict:
-    import google.generativeai as genai  # lazy import — only when needed
-
-    genai.configure(api_key=settings.gemini_api_key)
-    model = genai.GenerativeModel(
-        model_name,
-        generation_config={"response_mime_type": "application/json"},
-    )
-    return json.loads(model.generate_content(prompt).text)
+    from app.internal import gemini
+    # Correction rewrites text against vocabulary; keep a little reasoning
+    # ("low") rather than the correlator's "minimal" until a replay shows
+    # minimal doesn't hurt it.
+    return gemini.generate_json(model_name, prompt, purpose="correction", thinking_level="low")
 
 
 async def correct(

drb-c2-core/requirements.txt

@@ -6,6 +6,7 @@ firebase-admin
 google-cloud-storage
 openai
 google-generativeai
+google-genai
 numpy
 httpx
 python-multipart

drb-c2-core/tests/test_gemini_helper.py

@@ -0,0 +1,70 @@
+"""
+app/internal/gemini.py — thinking level, fallback when a model rejects it,
+and token accounting into a replay's usage sink (server-26#170 cost finding).
+"""
+from types import SimpleNamespace
+from unittest.mock import patch
+
+from app.internal import gemini
+
+
+class _FakeModels:
+    def __init__(self, reject_thinking=False):
+        self.reject_thinking = reject_thinking
+        self.configs = []
+
+    def generate_content(self, model, contents, config):
+        self.configs.append(config)
+        if self.reject_thinking and config.get("thinking_level"):
+            raise RuntimeError("400 INVALID_ARGUMENT: thinking_level is not supported for this model")
+        return SimpleNamespace(
+            text='{"action": "link"}',
+            usage_metadata=SimpleNamespace(prompt_token_count=1200, candidates_token_count=30,
+                                           thoughts_token_count=0),
+        )
+
+
+def _patched(models):
+    client = SimpleNamespace(models=models)
+    return (patch.object(gemini, "_get_client", return_value=client),
+            patch.object(gemini, "_config", lambda level: {"thinking_level": level}))
+
+
+def test_minimal_thinking_by_default_and_usage_lands_in_the_sink():
+    models = _FakeModels()
+    a, b = _patched(models)
+    sink = {}
+    tok = gemini.collect_usage(sink)
+    try:
+        with a, b:
+            assert gemini.generate_json("m1", "p", purpose="correlation") == {"action": "link"}
+    finally:
+        gemini.reset_usage(tok)
+    assert models.configs == [{"thinking_level": "minimal"}]
+    assert sink == {"correlation:m1": {"calls": 1, "in": 1200, "out": 30, "thinking": 0}}
+
+
+def test_model_that_rejects_thinking_level_falls_back_once():
+    models = _FakeModels(reject_thinking=True)
+    a, b = _patched(models)
+    gemini._no_thinking_level.discard("m2")
+    with a, b:
+        gemini.generate_json("m2", "p", purpose="correlation")
+        gemini.generate_json("m2", "p", purpose="correlation")
+    # first call: tried minimal, retried without; second call: straight without
+    assert models.configs == [{"thinking_level": "minimal"}, {"thinking_level": None}, {"thinking_level": None}]
+    gemini._no_thinking_level.discard("m2")
+
+
+def test_other_failures_still_raise_for_ai_health():
+    class Boom(_FakeModels):
+        def generate_content(self, **kw):
+            raise RuntimeError("429 insufficient_quota")
+    a, b = _patched(Boom())
+    with a, b:
+        try:
+            gemini.generate_json("m3", "p", purpose="correlation")
+        except RuntimeError as e:
+            assert "insufficient_quota" in str(e)
+        else:
+            raise AssertionError("should raise")

drb-frontend/components/admin/ReplayTab.tsx

@@ -361,6 +361,14 @@ function RunDetail({ run }: { run: ReplayRun }) {
           paths: {Object.entries(m.corr_path).map(([k, v]) => `${k} ${v}`).join(" · ")}
         </p>
       )}
+      {m?.gemini_usage && Object.keys(m.gemini_usage).length > 0 && (
+        <p className="text-xs font-mono text-gray-500">
+          Gemini tokens:{" "}
+          {Object.entries(m.gemini_usage)
+            .map(([k, u]) => `${k} ${u.calls} calls, in ${u.in.toLocaleString()} / out ${u.out.toLocaleString()} / thinking ${u.thinking.toLocaleString()}`)
+            .join(" · ")}
+        </p>
+      )}
       {m?.ai_failures && Object.keys(m.ai_failures).length > 0 && (
         <p className="text-xs font-mono text-amber-400">
           AI failures: {Object.entries(m.ai_failures).map(([k, v]) => `${k} ×${v}`).join(" · ")}

drb-frontend/lib/types.ts

@@ -340,6 +340,7 @@ export interface ReplayMetrics {
   llm_decisions: number;
   est_cost_usd: number;
   ai_failures?: Record<string, number>;
+  gemini_usage?: Record<string, { calls: number; in: number; out: number; thinking: number }>;
 }
 
 export interface ReplayRun {