Protokoll #23910

{
    "event": "PreToolUse",
    "tool_name": "Read",
    "tool_input": {
        "file_path": "\/var\/www\/scripts\/pipeline\/embed.py",
        "limit": 50
    }
}

{
    "tool_response": {
        "type": "text",
        "file": {
            "filePath": "\/var\/www\/scripts\/pipeline\/embed.py",
            "content": "\"\"\"\nEmbedding generation for KI-System Pipeline\nUses Ollama (mxbai-embed-large) for vector embeddings.\n\"\"\"\n\nimport json\nimport re\nimport uuid\n\nimport requests\n\nfrom config import EMBEDDING_DIMENSION, EMBEDDING_MODEL, OLLAMA_HOST, QDRANT_HOST, QDRANT_PORT\nfrom db import db\n\n# Max chars for mxbai-embed model (512 token context, varies by content)\n# Conservative limit to handle German compound words and special chars\nMAX_EMBED_CHARS = 800\n\n\ndef get_embedding(text):\n    \"\"\"Get embedding vector from Ollama.\"\"\"\n    # Skip empty content\n    if not text or not text.strip():\n        return None\n\n    # Collapse consecutive dots\/periods (table of contents, etc.)\n    text = re.sub(r\"\\.{3,}\", \"...\", text)\n\n    # Truncate if too long for model context\n    if len(text) > MAX_EMBED_CHARS:\n        text = text[:MAX_EMBED_CHARS]\n\n    try:\n        response = requests.post(\n            f\"{OLLAMA_HOST}\/api\/embeddings\",\n            json={\"model\": EMBEDDING_MODEL, \"prompt\": text},\n            timeout=60,\n        )\n        response.raise_for_status()\n        data = response.json()\n        return data.get(\"embedding\")\n    except Exception as e:\n        db.log(\"ERROR\", f\"Embedding generation failed: {e}\")\n        return None\n\n\ndef store_in_qdrant(collection, point_id, vector, payload):\n    \"\"\"Store embedding in Qdrant.\"\"\"\n    try:\n        response = requests.put(",
            "numLines": 50,
            "startLine": 1,
            "totalLines": 205
        }
    }
}