update Gemini API : use thinking levels

Readme.org

@@ -174,6 +174,15 @@ ou éventuellement, la renseigner directement dans le fichier
 
 Copier `default_config.py` en `config.py`. Éventuellement le modifier.
 
+Les niveaux de réflexion Gemini se règlent indépendamment des modèles :
+=THINKING_LEVEL_LITE= (énoncés, ="low"=), =THINKING_LEVEL_FOR_LABEL=
+(labels, ="medium"=), =THINKING_LEVEL_FLASH= (correction courante,
+="medium"=) et =THINKING_LEVEL_PRO= (correction difficile, ="high"=).
+Ces réglages s'appliquent aussi aux requêtes batch. Utiliser =None= pour
+laisser le modèle choisir son défaut. Les niveaux disponibles dépendent
+du modèle : Gemini 3.8 Flash accepte ="low"=, ="medium"= et ="high"=,
+mais pas ="minimal"=. Avec un modèle Gemini 2.5, utiliser =None=.
+
 ** Correction d'un paquet de copies
 
  1. Créer un fichier =names= dans le dossier courant, avec les

copienator/commands/correction.py

@@ -20,6 +20,8 @@ from copienator import configuration as config
 from copienator.commands import grouping
 from copienator.answer_origins import save_origins
 from copienator import prompting
+from copienator.gemini_config import thinking_config
+from copienator import configuration as settings
 from copienator.feedback_boxes import valid_feedback_box
 from copienator.latex_json import repair_latex_json
 from copienator import (
@@ -279,6 +281,9 @@ def call_gemini_with_retries(model_id, contents, config,
         # Switch to fallback immediately if quota was exhausted by another thread
         if model_id == MODEL_ID_pro and pro_quota_exhausted and fallback_model_id:
             model_id = fallback_model_id
+            config = config.model_copy(update={"thinking_config": thinking_config(
+                settings.THINKING_LEVEL_FLASH
+            )})
 
         try:
             full_response_text = ""
@@ -312,6 +317,9 @@ def call_gemini_with_retries(model_id, contents, config,
             if is_quota_error and model_id == MODEL_ID_pro and fallback_model_id:
                 tprint(f"\tGemini Pro quota hit ({e}). \n\n\tFalling back to Flash permanently...")
                 model_id = fallback_model_id
+                config = config.model_copy(update={"thinking_config": thinking_config(
+                    settings.THINKING_LEVEL_FLASH
+                )})
                 pro_quota_exhausted = True
                 continue # Retry immediately with Flash
 
@@ -506,7 +514,11 @@ def process_single_task(task_tuple, precomputed_response=None):
                     flash_count += 1
 
         try:
-            contents, config = prompting.generate_request(INPUT_DIR, file_path, label)
+            contents, config = prompting.generate_request(
+                INPUT_DIR, file_path, label,
+                thinking_level=(settings.THINKING_LEVEL_FLASH if use_flash
+                                else settings.THINKING_LEVEL_PRO),
+            )
             model_to_use = MODEL_ID_flash if use_flash else MODEL_ID_pro
 
             if precomputed_response:
@@ -891,13 +903,10 @@ def run_configured(args: argparse.Namespace) -> ExitCode:
                                 ],
                             }
                         ],
-                        "generation_config": {
-                            "temperature": 1.0,
-                            "topP": 0.95,
-                            "maxOutputTokens": 65535,
-                            "responseMimeType": "application/json",
-                            "responseSchema": prompting.UNROLLED_SCHEMA,
-                        },
+                        "generation_config": prompting.batch_generation_config(
+                            settings.THINKING_LEVEL_FLASH if use_flash
+                            else settings.THINKING_LEVEL_PRO
+                        ),
                     },
                 }
                 line = json.dumps(request)

copienator/commands/gemini_for_enonce.py

@@ -26,6 +26,7 @@ from copienator import (
     workspace_from_args,
 )
 from copienator import configuration as config
+from copienator.gemini_config import thinking_config
 from copienator.filesystem import staged_files
 from copienator.latex_json import normalize_literal_newlines, repair_latex_json
 from copienator.platform import validate_windows_labels
@@ -249,7 +250,7 @@ def _request_rubrics(client, group_context_text: str):
         config=types.GenerateContentConfig(
             automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=True),
             system_instruction="Rédige tous les barèmes et consignes de notation en français. Conserve les clés JSON, les labels et les formules mathématiques.",
-            temperature=0.2,
+            thinking_config=thinking_config(config.THINKING_LEVEL_LITE),
             response_mime_type="application/json",
             response_json_schema=GroupRubrics.model_json_schema(),
         ),
@@ -370,7 +371,7 @@ def refine_existing(workspace: EvaluationWorkspace, mode: str, *, api_client=Non
             ))])],
             config=types.GenerateContentConfig(
                 automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=True),
-                temperature=0.1, response_mime_type="application/json",
+                thinking_config=thinking_config(config.THINKING_LEVEL_LITE), response_mime_type="application/json",
                 response_json_schema=LabelGroups.model_json_schema(),
             ),
         )
@@ -488,7 +489,7 @@ def process_exam(
 
     config_1 = types.GenerateContentConfig(
         automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=True),
-        temperature=0.1,
+        thinking_config=thinking_config(config.THINKING_LEVEL_LITE),
         response_mime_type="application/json",
         response_json_schema=ExamQuestions.model_json_schema(),
     )
@@ -505,7 +506,7 @@ def process_exam(
 
     config_2 = types.GenerateContentConfig(
         automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=True),
-        temperature=0.1,
+        thinking_config=thinking_config(config.THINKING_LEVEL_LITE),
         response_mime_type="application/json",
         response_json_schema=ExamSolutions.model_json_schema(),
     )
@@ -540,7 +541,7 @@ def process_exam(
 
     config_3 = types.GenerateContentConfig(
         automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=True),
-        temperature=0.1,
+        thinking_config=thinking_config(config.THINKING_LEVEL_LITE),
         response_mime_type="application/json",
         response_json_schema=ExamContext.model_json_schema(),
     )

copienator/commands/gemini_for_labels.py

@@ -15,6 +15,7 @@ from google.genai import types
 from pydantic import BaseModel, Field
 
 from copienator import configuration as config
+from copienator.gemini_config import thinking_config
 from copienator import (
     CliError,
     EvaluationWorkspace,
@@ -277,8 +278,7 @@ def generate_request(
             retry_options=types.HttpRetryOptions(attempts=1),
         ),
         automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=True),
-        temperature=1.0,
-        top_p=0.95,
+        thinking_config=thinking_config(config.THINKING_LEVEL_FOR_LABEL),
         seed=seed,
         max_output_tokens=65535,
         response_mime_type= "application/json",

copienator/configuration.py

@@ -32,6 +32,10 @@ else:
     _configuration = _load_user_config(CONFIG_PATH)
 
 # Keep new optional settings compatible with older personal configuration files.
+THINKING_LEVEL_LITE = "low"
+THINKING_LEVEL_FOR_LABEL = "medium"
+THINKING_LEVEL_FLASH = "medium"
+THINKING_LEVEL_PRO = "high"
 ALWAYS_CROP = False
 SHOW_PERSONAL_STEPS = False
 RETURN_JPEG_ENABLED = True

copienator/gemini_config.py

@@ -0,0 +1,13 @@
+from google.genai import types
+
+
+def thinking_config(level: str | None) -> types.ThinkingConfig | None:
+    """Use a supported reasoning level, or omit it for the model default."""
+    if level is None:
+        return None
+    if level not in {"minimal", "low", "medium", "high"}:
+        raise ValueError(
+            f"Invalid Gemini thinking level {level!r}; "
+            'expected "minimal", "low", "medium", "high", or None'
+        )
+    return types.ThinkingConfig(thinking_level=level)

copienator/prompting.py

@@ -1,6 +1,8 @@
 from pathlib import Path
 import io
 from . import utils
+from . import configuration as settings
+from .gemini_config import thinking_config
 
 PERSPECTIVE_GUIDANCE = (
     "Ce barème est indicatif, si une réponse est entièrement correcte mais utilise "
@@ -173,8 +175,20 @@ UNROLLED_SCHEMA = {
 
 from google.genai import types
 
+def batch_generation_config(thinking_level):
+    """Build the REST generation config for a grading batch request."""
+    config = {
+        "maxOutputTokens": 65535,
+        "responseMimeType": "application/json",
+        "responseSchema": UNROLLED_SCHEMA,
+    }
+    thinking = thinking_config(thinking_level)
+    if thinking is not None:
+        config["thinkingConfig"] = thinking.model_dump(mode="json", by_alias=True, exclude_none=True)
+    return config
+
 # The root model for parsing is be: List[EvaluationEntry]
-def generate_request(input_dir, file, full_label):
+def generate_request(input_dir, file, full_label, *, thinking_level=settings.THINKING_LEVEL_PRO):
     """Generates request for Gemini."""
     prompt = make_prompt(input_dir, full_label)
     image_path = Path(file)
@@ -194,8 +208,7 @@ def generate_request(input_dir, file, full_label):
 
     generate_content_config = types.GenerateContentConfig(
         automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=True),
-        temperature=1.0,
-        top_p=0.95,
+        thinking_config=thinking_config(thinking_level),
         seed=0,
         max_output_tokens=65535,
         response_mime_type= "application/json",
@@ -272,7 +285,7 @@ Commentaires d'origine :
 
     config = types.GenerateContentConfig(
         automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=True),
-        temperature=1.0,
+        thinking_config=thinking_config(settings.THINKING_LEVEL_FLASH),
         response_mime_type="application/json",
         response_json_schema=TypeAdapter(List[FeedbackItem]).json_schema()
     )
@@ -303,7 +316,7 @@ Voici les labels possibles. Ta réponse doit être l'un d'entre eux :
             types.Part.from_text(text=prompt)])]
     config = types.GenerateContentConfig(
         automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=True),
-        temperature=1.0,
+        thinking_config=thinking_config(settings.THINKING_LEVEL_FLASH),
     )
     return contents, config
 
@@ -334,7 +347,7 @@ d'entre eux :
     ])]
     config = types.GenerateContentConfig(
         automatic_function_calling=types.AutomaticFunctionCallingConfig(disable=True),
-        temperature=1.0,
+        thinking_config=thinking_config(settings.THINKING_LEVEL_FLASH),
         response_mime_type="application/json",
     )
     return contents, config

default_config.py

@@ -27,20 +27,28 @@ FINAL_SCORE_OUTPUT_DIR = Path("Server") / "copies"
 FINAL_SCORE_HISTOGRAM_PATH = Path("histogramme.pdf")
 FINAL_SCORE_FONT_PATH = None
 
+# Niveaux de réflexion : "minimal", "low", "medium", "high", ou None
+# pour utiliser le défaut du modèle. Choisir un niveau supporté par le modèle
+# (Gemini 3.8 Flash ne supporte pas "minimal" ; Gemini 2.5 : utiliser None).
+
 # Modèle pour des choses très légères
 # Le modèle 3.5-flash-lite est un peu moins bon pour la duplication de contexte pour des questions, dans l'analyse de l'énoncé.
 # MODEL_LITE_ID = "gemini-3.5-flash-lite"
 MODEL_LITE_ID = "gemini-3.8-flash"
+THINKING_LEVEL_LITE = "low"
 
 # Modèle pour identifier visuellement les labels
 # MODEL_FOR_LABEL_ID = "gemini-3.5-flash-lite" # 3.5 flash lite marche plutôt mal, il insiste pour mettre les labels dans l'ordre, sans considérer ce qu'il y a écrit
 MODEL_FOR_LABEL_ID = "gemini-3.8-flash"
+THINKING_LEVEL_FOR_LABEL = "low"
 
 # Modèle pour des choses normales
 MODEL_FLASH_ID = "gemini-3.8-flash"
+THINKING_LEVEL_FLASH = "medium"
 
 # Modèle pour des choses dures
 MODEL_PRO_ID = "gemini-3.8-flash"
+THINKING_LEVEL_PRO = "medium"
 # MODEL_PRO_ID = "gemini-3.1-pro-preview"
 
 PAGE_SPLITTER_KB = {

tests/test_gemini_config.py

@@ -0,0 +1,83 @@
+import tempfile
+import unittest
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import Mock, patch
+
+from google.genai import types
+
+from copienator import configuration, prompting
+from copienator.commands import correction, gemini_for_enonce, gemini_for_labels
+from copienator.gemini_config import thinking_config
+
+
+class GeminiConfigTests(unittest.TestCase):
+    def assert_request_config(self, config, level):
+        data = config.model_dump(mode="json", by_alias=True, exclude_none=True)
+        for key in ("temperature", "topP", "topK", "thinkingBudget"):
+            self.assertNotIn(key, data)
+            self.assertNotIn(key, data.get("thinkingConfig", {}))
+        if level is None:
+            self.assertNotIn("thinkingConfig", data)
+        else:
+            self.assertEqual(data["thinkingConfig"], {"thinkingLevel": level.upper()})
+
+    def test_grading_live_and_batch_settings(self):
+        with tempfile.TemporaryDirectory() as directory:
+            image = Path(directory) / "answer.jpg"
+            image.write_bytes(b"image")
+            for level in ("minimal", "low", "medium", "high", None):
+                with self.subTest(level=level), patch.object(prompting, "make_prompt", return_value="Grade"):
+                    _, config = prompting.generate_request(directory, image, "Ex 1", thinking_level=level)
+                    self.assert_request_config(config, level)
+                    batch = prompting.batch_generation_config(level)
+                    self.assert_request_config(types.GenerateContentConfig.model_validate(batch), level)
+                    self.assertEqual(batch["responseSchema"], prompting.UNROLLED_SCHEMA)
+
+    def test_labels_and_statement_use_their_own_settings(self):
+        with tempfile.TemporaryDirectory() as directory:
+            image = Path(directory) / "answer.jpg"
+            image.write_bytes(b"image")
+            with patch.object(configuration, "THINKING_LEVEL_FOR_LABEL", "low"):
+                _, config = gemini_for_labels.generate_request(image, "Ex 1", "Student", [], [])
+                self.assert_request_config(config, "low")
+        client = Mock()
+        with patch.object(configuration, "THINKING_LEVEL_LITE", "high"):
+            gemini_for_enonce._request_rubrics(client, "Question")
+        self.assert_request_config(client.models.generate_content.call_args.kwargs["config"], "high")
+
+    def test_correction_helpers_use_flash_level(self):
+        with patch.object(configuration, "THINKING_LEVEL_FLASH", "low"), patch.object(
+            prompting, "get_single_image_bytes", return_value=b"image"
+        ):
+            requests = [
+                prompting.request_for_box_correction("answer.pdf", []),
+                prompting.request_for_wrong_label("answer.pdf", "Ex 1", "Question", "Ex 1"),
+                prompting.request_for_additional_answer("answer.pdf", "Ex 1", "Question", "Ex 1"),
+            ]
+        for _, config in requests:
+            self.assert_request_config(config, "low")
+
+    def test_quota_fallback_switches_thinking_level_without_mutating_request(self):
+        client = Mock()
+        client.models.generate_content_stream.side_effect = [
+            RuntimeError("429 daily quota exhausted"), [SimpleNamespace(text="result")],
+        ]
+        config = types.GenerateContentConfig(thinking_config=thinking_config("high"))
+        with patch.object(correction, "client", client), patch.object(
+            correction, "MODEL_ID_pro", "pro-model"
+        ), patch.object(correction, "pro_quota_exhausted", False), patch.object(
+            configuration, "THINKING_LEVEL_FLASH", "low"
+        ), patch.object(correction, "tprint"), patch.object(correction, "stop_requested") as stop:
+            stop.is_set.return_value = False
+            result = correction.call_gemini_with_retries("pro-model", [], config, "flash-model")
+        self.assertEqual(result, "result")
+        calls = client.models.generate_content_stream.call_args_list
+        self.assert_request_config(calls[0].kwargs["config"], "high")
+        self.assert_request_config(calls[1].kwargs["config"], "low")
+        self.assertEqual(calls[1].kwargs["model"], "flash-model")
+        self.assert_request_config(config, "high")
+
+    def test_invalid_level_fails_before_submission(self):
+        with self.assertRaisesRegex(ValueError, "Invalid Gemini thinking level"):
+            thinking_config("ultra")