{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": "\/opt\/scripts\/python-check.sh 2>&1 | tail -100",
"description": "Check Python contract violations"
}
}
{
"tool_response": {
"stdout": " --> \/opt\/scripts\/pipeline\/generate_semantics.py:208:19\n |\n206 | success += 1\n207 | else:\n208 | print(f\" -> FAILED\")\n | ^^^^^^^^^^^^^^\n209 | failed += 1\n |\nhelp: Remove extraneous `f` prefix\n\nI001 [*] Import block is un-sorted or un-formatted\n --> \/opt\/scripts\/pipeline\/semantic_chunk_analyzer.py:23:1\n |\n21 | \"\"\"\n22 |\n23 | \/ import json\n24 | | import sys\n25 | | from dataclasses import dataclass\n26 | | from typing import Optional\n27 | |\n28 | | import requests\n29 | |\n30 | | from config import DB_CONFIG, OLLAMA_HOST\n31 | | from db import db\n | |_________________^\n |\nhelp: Organize imports\n\nF401 [*] `config.DB_CONFIG` imported but unused\n --> \/opt\/scripts\/pipeline\/semantic_chunk_analyzer.py:30:20\n |\n28 | import requests\n29 |\n30 | from config import DB_CONFIG, OLLAMA_HOST\n | ^^^^^^^^^\n31 | from db import db\n |\nhelp: Remove unused import: `config.DB_CONFIG`\n\nUP045 [*] Use `X | None` for type annotations\n --> \/opt\/scripts\/pipeline\/semantic_chunk_analyzer.py:56:18\n |\n54 | name: str\n55 | entity_type: str # PERSON, ORGANIZATION, CONCEPT, LOCATION, OTHER\n56 | description: Optional[str] = None\n | ^^^^^^^^^^^^^\n |\nhelp: Convert to `X | None`\n\nUP045 [*] Use `X | None` for type annotations\n --> \/opt\/scripts\/pipeline\/semantic_chunk_analyzer.py:76:66\n |\n74 | self.model = model\n75 |\n76 | def generate(self, prompt: str, json_format: bool = True) -> Optional[dict]:\n | ^^^^^^^^^^^^^^\n77 | \"\"\"Generiere Antwort von Ollama.\"\"\"\n78 | try:\n |\nhelp: Convert to `X | None`\n\nUP045 [*] Use `X | None` for type annotations\n --> \/opt\/scripts\/pipeline\/semantic_chunk_analyzer.py:103:41\n |\n101 | return None\n102 |\n103 | def _parse_json(self, text: str) -> Optional[dict]:\n | ^^^^^^^^^^^^^^\n104 | \"\"\"Parse JSON aus Antwort.\"\"\"\n105 | try:\n |\nhelp: Convert to `X | None`\n\nUP045 [*] Use `X | None` for type annotations\n --> \/opt\/scripts\/pipeline\/semantic_chunk_analyzer.py:140:52\n |\n138 | self.llm = llm\n139 |\n140 | def analyze(self, chunk_id: int, text: str) -> Optional[ChunkSemantics]:\n | ^^^^^^^^^^^^^^^^^^^^^^^^\n141 | \"\"\"Analysiere einen Chunk.\"\"\"\n142 | result = self.llm.generate(self.PROMPT.format(text=text[:2000]))\n |\nhelp: Convert to `X | None`\n\nFound 12 errors.\n[*] 11 fixable with the `--fix` option.\n\u001b[0;31m✗ Ruff: Linting errors found\u001b[0m\n\n\u001b[1;33m[2\/3] Ruff - Format Check\u001b[0m\nWould reformat: \/opt\/scripts\/pipeline\/generate_semantics.py\nWould reformat: \/opt\/scripts\/pipeline\/semantic_chunk_analyzer.py\n2 files would be reformatted, 13 files already formatted\n\u001b[0;31m✗ Ruff: Formatting issues found (run with --fix to auto-format)\u001b[0m\n\n\u001b[1;33m[3\/3] mypy - Type Checking\u001b[0m\n\u001b[0;32m✓ mypy: Type check complete\u001b[0m\n\n=== Summary ===\n\u001b[0;31m2 check(s) failed\u001b[0m",
"stderr": "",
"interrupted": false,
"isImage": false
}
}