{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": ".\/venv\/bin\/python -c \"\nfrom db import db\nimport requests\nfrom config import OLLAMA_HOST, EMBEDDING_MODEL\n\ndb.connect()\n\n# Get chunk content\ncursor = db.execute('SELECT content FROM chunks WHERE id = 8')\nchunk = cursor.fetchone()\ncursor.close()\n\ncontent = chunk['content']\nprint(f'Content length: {len(content)}')\nprint(f'Content preview: {content[:200]}...')\nprint()\n\n# Try embedding directly\ntry:\n response = requests.post(\n f'{OLLAMA_HOST}\/api\/embeddings',\n json={'model': EMBEDDING_MODEL, 'prompt': content},\n timeout=60\n )\n print(f'Status: {response.status_code}')\n if response.status_code != 200:\n print(f'Error: {response.text}')\n else:\n data = response.json()\n if 'embedding' in data:\n print(f'Embedding size: {len(data[\\\"embedding\\\"])}')\n else:\n print(f'Response: {data}')\nexcept Exception as e:\n print(f'Exception: {e}')\n\ndb.disconnect()\n\"",
"description": "Debug specific chunk embedding"
}
}
{
"tool_response": {
"stdout": "Content length: 1787\nContent preview: HERZLICH WILLKOMMEN! An das Kapitel „Blick über den Tellerrand“ hast du dich mittlerweile be-\nstimmt schon gewöhnt. Und nun hältst du ein ganzes Booklet in der \nHand, das im Grunde genommen ein einzig...\n\nStatus: 500\nError: {\"error\":\"the input length exceeds the context length\"}",
"stderr": "",
"interrupted": false,
"isImage": false
}
}