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Fix str.format KeyError by using %-formatting for ontology JSON

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_Yusaki 2026-03-13 19:22:36 +07:00
parent b4c7f67b00
commit e14080129e
1 changed files with 17 additions and 32 deletions
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49
backend/app/services/llm_graph_builder.py
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@ -16,37 +16,22 @@ from .text_processor import TextProcessor
logger = logging.getLogger('mirofish.llm_graph_builder') logger = logging.getLogger('mirofish.llm_graph_builder')
EXTRACT_SYSTEM_PROMPT = """You are a knowledge graph extraction engine. Given a text chunk and an ontology schema, extract all entities and relationships. EXTRACT_SYSTEM_PROMPT_TEMPLATE = (
"You are a knowledge graph extraction engine. Given a text chunk and an ontology schema, "
ONTOLOGY SCHEMA: "extract all entities and relationships.\n\n"
{ontology_json} "ONTOLOGY SCHEMA:\n%s\n\n"
"RULES:\n"
RULES: "1. Extract entities that match the entity_types defined in the schema. Each entity needs: "
1. Extract entities that match the entity_types defined in the schema. Each entity needs: name, type (matching an entity_type name), summary (1-2 sentences), and any attributes defined for that type. "name, type (matching an entity_type name), summary (1-2 sentences), and any attributes defined for that type.\n"
2. Extract relationships between entities that match the edge_types defined in the schema. Each relationship needs: name (the edge type name), source (entity name), target (entity name), and a fact (short description of the relationship). "2. Extract relationships between entities that match the edge_types defined in the schema. "
3. Only extract entities and relationships that are explicitly mentioned or strongly implied in the text. "Each relationship needs: name (the edge type name), source (entity name), target (entity name), "
4. Use consistent entity names across extractions (e.g., always "Mira" not sometimes "Mira" and sometimes "Mira the Socializer"). "and a fact (short description of the relationship).\n"
5. If no entities or relationships are found, return empty arrays. "3. Only extract entities and relationships that are explicitly mentioned or strongly implied in the text.\n"
"4. Use consistent entity names across extractions.\n"
Return JSON in this exact format: "5. If no entities or relationships are found, return empty arrays.\n\n"
{ 'Return JSON with keys "entities" (array of objects with name, type, summary, attributes) '
"entities": [ 'and "relationships" (array of objects with name, source, target, fact).'
{ )
"name": "EntityName",
"type": "EntityTypeName",
"summary": "Brief description",
"attributes": {"attr_name": "attr_value"}
}
],
"relationships": [
{
"name": "EDGE_TYPE_NAME",
"source": "SourceEntityName",
"target": "TargetEntityName",
"fact": "Description of this relationship"
}
]
}"""
class LLMGraphBuilderService: class LLMGraphBuilderService:
@ -104,7 +89,7 @@ class LLMGraphBuilderService:
messages=[ messages=[
{ {
"role": "system", "role": "system",
"content": EXTRACT_SYSTEM_PROMPT.format(ontology_json=ontology_json) "content": EXTRACT_SYSTEM_PROMPT_TEMPLATE % ontology_json
}, },
{ {
"role": "user", "role": "user",