{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": "cd \/var\/www\/scripts\/pipeline && grep -n \"^def \\|^class \\|^ def \" knowledge.py | head -40",
"description": "Find functions and classes in knowledge.py"
}
}
{
"tool_response": {
"stdout": "30:class KnowledgeLevel(Enum):\n38:class KnowledgeType(Enum):\n48:class ModelConfig:\n65:class KnowledgeExtractor:\n79: def __init__(self, model_config: ModelConfig | None = None):\n87: def _init_anthropic(self):\n98: def _call_llm(self, prompt: str, json_output: bool = True) -> str:\n151: def _parse_json(self, text: str) -> dict:\n170: def extract_entities(self, text: str, level: KnowledgeLevel, source_id: int) -> list[dict]:\n222: def _store_entity(self, entity: dict, level: KnowledgeLevel, source_id: int) -> dict | None:\n269: def extract_semantics(self, entities: list[dict], text: str, level: KnowledgeLevel, source_id: int) -> list[dict]:\n336: def _store_semantic(\n416: def extract_ontology(self, entities: list[dict], text: str, level: KnowledgeLevel, source_id: int) -> list[dict]:\n490: def _store_ontology(\n552: def extract_taxonomy(self, entities: list[dict], text: str, level: KnowledgeLevel, source_id: int) -> list[dict]:\n627: def _store_taxonomy_mapping(\n704: def _store_knowledge(self, level: KnowledgeLevel, source_id: int, knowledge_type: KnowledgeType, data: dict):\n736: def analyze_page(self, page_id: int, text: str) -> dict:\n764: def analyze_section(self, section_id: int, text: str) -> dict:\n781: def analyze_document(self, document_id: int, text: str) -> dict:\n804:def get_model_config(provider: str = \"ollama\", model_name: str | None = None) -> ModelConfig:\n821:def process_document_knowledge(document_id: int, provider: str = \"ollama\", model_name: str | None = None) -> dict:",
"stderr": "",
"interrupted": false,
"isImage": false
}
}