{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": ".\/venv\/bin\/python -c \"\nfrom embed import get_embedding, store_in_qdrant\nfrom db import db\nimport uuid\n\ndb.connect()\n\n# Get pending chunks\nchunks = db.get_chunks_for_embedding(5)\nprint(f'Found {len(chunks)} chunks')\n\nif chunks:\n chunk = chunks[0]\n print(f'Processing chunk {chunk[\\\"id\\\"]}...')\n \n # Generate embedding\n embedding = get_embedding(chunk['content'])\n if embedding:\n print(f' Embedding: {len(embedding)} dim')\n \n # Get doc info\n cursor = db.execute('SELECT filename, source_path FROM documents WHERE id = %s', (chunk['document_id'],))\n doc = cursor.fetchone()\n cursor.close()\n print(f' Document: {doc[\\\"filename\\\"] if doc else \\\"NOT FOUND\\\"}')\n \n point_id = str(uuid.uuid4())\n payload = {\n 'document_id': chunk['document_id'],\n 'document_title': doc['filename'] if doc else '',\n 'chunk_id': chunk['id'],\n 'content': chunk['content'][:500],\n 'source_path': doc['source_path'] if doc else '',\n }\n \n # Store in Qdrant\n success = store_in_qdrant('documents', point_id, embedding, payload)\n print(f' Qdrant store: {success}')\n \n if success:\n db.update_chunk_qdrant_id(chunk['id'], point_id)\n print(f' Updated chunk with qdrant_id: {point_id}')\n else:\n print(' Embedding failed!')\n\ndb.disconnect()\n\"",
"description": "Debug embed step by step"
}
}
{
"tool_response": {
"stdout": "Found 5 chunks\nProcessing chunk 8...\n Embedding failed!",
"stderr": "",
"interrupted": false,
"isImage": false
}
}