{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": "grep -B5 -A40 \"def process_file\" \/opt\/scripts\/pipeline\/pipeline.py | head -60",
"description": "Check process_file function"
}
}
{
"tool_response": {
"stdout": "from detect import queue_files, scan_directory\nfrom embed import embed_chunks, embed_pending_chunks\nfrom extract import extract, get_full_text\n\n\ndef process_file(file_path):\n \"\"\"Process a single file through the pipeline.\"\"\"\n db.log(\"INFO\", f\"Processing: {file_path}\")\n\n # Extract text\n extraction = extract(file_path)\n if not extraction[\"success\"]:\n db.log(\"ERROR\", f\"Extraction failed: {extraction.get('error')}\")\n return False\n\n # Get document info\n file_name = Path(file_path).name\n file_stat = os.stat(file_path)\n\n import hashlib\n\n with open(file_path, \"rb\") as f:\n file_hash = hashlib.sha256(f.read()).hexdigest()\n\n # Insert document\n doc_id = db.insert_document(\n file_path=file_path,\n title=file_name,\n file_type=extraction[\"file_type\"],\n file_size=file_stat.st_size,\n file_hash=file_hash,\n )\n db.log(\"INFO\", f\"Created document: {doc_id}\")\n\n # Chunk content\n chunks = chunk_by_structure(extraction)\n db.log(\"INFO\", f\"Created {len(chunks)} chunks\")\n\n # Store chunks\n for i, chunk in enumerate(chunks):\n chunk_id = db.insert_chunk(\n doc_id=doc_id,\n chunk_index=i,\n content=chunk[\"content\"],\n heading_path=json.dumps(chunk.get(\"heading_path\", [])),\n position_start=chunk.get(\"position_start\", 0),",
"stderr": "",
"interrupted": false,
"isImage": false
}
}