58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
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import logging
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from ontocast.onto.rdfgraph import RDFGraph
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from ontocast.onto.state import AgentState
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from ontocast.onto.unit_states import UnitOntologyState
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logger = logging.getLogger(__name__)
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def build_ontology_delta_graph(result: UnitOntologyState) -> RDFGraph:
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"""Build a delta graph from a unit ontology result.
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If update operations exist, only inserted triples are aggregated.
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Otherwise, the current ontology snapshot is used as the delta.
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"""
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if result.all_updates:
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delta_graph = RDFGraph()
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for graph_update in result.all_updates:
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insert_graph = graph_update.extract_insert_graph()
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for triple in insert_graph:
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delta_graph.add(triple)
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for prefix, namespace_uri in insert_graph.namespaces():
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if prefix:
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delta_graph.bind(prefix, namespace_uri)
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return delta_graph
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return result.current_ontology.graph.copy()
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def build_document_excerpt(state: AgentState) -> str:
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"""Create a representative excerpt from sampled source units."""
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excerpt_parts: list[str] = []
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if state.content_units:
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unit_count = len(state.content_units)
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if unit_count == 1:
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sample_indices = [0]
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elif unit_count == 2:
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sample_indices = [0, 1]
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else:
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sample_indices = [0, 1, unit_count // 2, unit_count - 1]
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visited_indices: set[int] = set()
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for index in sample_indices:
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if index in visited_indices or index < 0 or index >= unit_count:
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continue
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visited_indices.add(index)
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unit_text = state.content_units[index].text.strip()
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if not unit_text:
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continue
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excerpt_parts.append(unit_text)
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if excerpt_parts:
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return "\n\n[...]\n\n".join(excerpt_parts)
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if state.input_text:
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return state.input_text
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return ""
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