{
"event": "PostToolUse",
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"filePath": "\/var\/www\/scripts\/pipeline\/step_semantic_extended.py",
"content": "\n def execute(self, chunks: list, config: dict) -> list:\n \"\"\"\n Analyze text semantics for each chunk.\n\n Args:\n chunks: List of chunk dicts with 'id' and 'content'\n config: Step config with 'model' (default: mistral)\n\n Returns:\n list: Chunks with added 'text_semantics' field\n \"\"\"\n if self.progress:\n self.progress.update_step(\"text_semantic_analyze\")\n self.progress.add_log(f\"Textsemantik-Analyse für {len(chunks)} Chunks...\")\n\n model = config.get(\"model\", \"mistral\")\n analyzed = 0\n errors = 0\n\n for chunk in chunks:\n try:\n prompt = self.PROMPT_TEMPLATE.format(content=chunk[\"content\"][:2000])\n\n response = ollama.generate(\n model=model,\n prompt=prompt,\n options={\"num_predict\": 200},\n )\n",
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