{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": "grep -rn \"mistral\\|gemma\" --include=\"*.py\" | grep -v \"__pycache__\" | grep -v \"\\.pyc\" | grep -v \"HARDCODED_MODELS\" | grep -v \"# \" | head -30",
"description": "Check for hardcoded model names"
}
}
{
"tool_response": {
"stdout": "knowledge\/utils.py:27: return ModelConfig(provider=\"ollama\", model_name=model_name or \"gemma3:27b-it-qat\")\nknowledge\/models.py:36: \"ollama\": ModelConfig(\"ollama\", \"gemma3:27b-it-qat\"),\ngenerate_entity_descriptions.py:30:DEFAULT_MODEL = \"mistral:latest\"\ngenerate_semantics.py:14:MODEL = \"gemma3:27b-it-qat\"\npipeline.py:242: analyzed_chunks = text_semantic_analyze.execute(chunk_data, {\"model\": \"mistral\"})\npipeline.py:286: analyzed_entities = knowledge_semantic_analyze.execute(entity_list, {\"model\": \"mistral\"})\npipeline.py:557: model = \"mistral:latest\"\nanalyzers\/entity_extractor.py:182:def extract_entities_ollama(text: str, model: str = \"gemma3:27b-it-qat\") -> list[dict]:\nmodel_registry.py:128: return \"ollama:mistral:latest\"\nmodel_registry.py:140: Example: 'ollama:gemma3:27b-it-qat' -> 'gemma3:27b-it-qat'\nstep_entity_enrich.py:20:DEFAULT_MODEL = \"mistral:latest\"\nknowledge.py:69: print(\" model: gemma3:27b-it-qat (ollama) oder claude-3-haiku-20240307 (anthropic)\")\nchat.py:104:def ask_ollama(question, context, model=\"gemma3:27b-it-qat\"):\nconfig.py:136: return \"ollama:gemma3:27b-it-qat\"\nconfig.py:142: Example: 'ollama:gemma3:27b-it-qat' -> 'gemma3:27b-it-qat'\nquality_test.py:30: \"gemma3\": \"gemma3:27b-it-qat\",\nsemantic_chunk\/ollama_service.py:16:ANALYSIS_MODEL = \"gemma3:27b-it-qat\"\nstep_semantic_extended.py:117: config: Step config with 'model' (default: mistral)\nstep_semantic_extended.py:126: model = config.get(\"model\", \"mistral\")\nstep_semantic_extended.py:277: config: Step config with 'model' (default: mistral)\nstep_semantic_extended.py:286: model = config.get(\"model\", \"mistral\")",
"stderr": "",
"interrupted": false,
"isImage": false
}
}