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289
참고/ontocast-main/ontocast/agent/render_ontology.py
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289
참고/ontocast-main/ontocast/agent/render_ontology.py
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"""Ontology triple rendering agent for OntoCast.
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This module provides functionality for rendering RDF triples from ontologies into
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human-readable formats, making the ontological knowledge more accessible and
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understandable.
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The agent decides between generating bare Turtle for fresh ontologies and SPARQL operations for updates.
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"""
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import logging
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from langchain_core.output_parsers import PydanticOutputParser
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from langchain_core.prompts import PromptTemplate
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from ontocast.agent.common import call_llm_with_retry, render_suggestions_prompt
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from ontocast.onto.enum import FailureStage, Status, WorkflowNode
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from ontocast.onto.model import GraphUpdateRenderReport, OntologyRenderReport
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from ontocast.onto.rdfgraph import RDFGraph
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from ontocast.onto.unit_states import UnitOntologyState
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from ontocast.prompt.common import (
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ontology_template,
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output_instruction_sparql,
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output_instruction_ttl,
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text_template,
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)
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from ontocast.prompt.common import system_preamble_ontology as system_preamble
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from ontocast.prompt.render_ontology import (
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general_ontology_instruction,
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intro_instruction_fresh,
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intro_instruction_update,
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prefix_instruction,
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prefix_instruction_fresh,
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template_prompt,
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)
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from ontocast.tool.atomic import AtomicToolBox
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logger = logging.getLogger(__name__)
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def _extract_known_prefixes(state: UnitOntologyState) -> dict[str, str]:
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"""Extract ontology prefixes used to patch missing declarations in LLM TTL output."""
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current = state.current_ontology or state.ontology_snapshot
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known_prefixes: dict[str, str] = {}
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if current and current.graph:
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for prefix, namespace_uri in current.graph.namespaces():
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if prefix: # Skip empty prefixes
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known_prefixes[prefix] = str(namespace_uri)
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if current.prefix and current.namespace:
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known_prefixes[current.prefix] = current.namespace
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return known_prefixes
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async def render_ontology(
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state: UnitOntologyState, tools: AtomicToolBox
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) -> UnitOntologyState:
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"""Structured hybrid ontology renderer with Turtle/SPARQL decision logic.
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This function decides between generating bare Turtle for fresh ontologies
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and SPARQL operations for updates based on whether the ontology exists.
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Args:
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state: The current unit ontology state
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tools: The toolbox containing necessary tools
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Returns:
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UnitOntologyState: Updated state with rendered ontology
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"""
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progress_info = state.get_content_unit_progress_string()
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logger.info(
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f"Ontology Renderer for {progress_info}: visit {state.node_visits[WorkflowNode.TEXT_TO_ONTOLOGY]}/{state.max_visits_per_node}"
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)
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current = state.current_ontology or state.ontology_snapshot
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# Guardrail for map/reduce flow: if a non-null snapshot exists, stay in update mode.
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has_seed_ontology = not state.ontology_snapshot.is_null()
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has_no_seed_ontology = current.is_null() and not has_seed_ontology
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if has_no_seed_ontology:
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return await render_ontology_fresh(state, tools)
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else:
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return await render_ontology_update(state, tools)
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async def render_ontology_fresh(
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state: UnitOntologyState, tools: AtomicToolBox
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) -> UnitOntologyState:
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"""Render ontology triples into a human-readable format.
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This function takes the triples from the current ontology and renders them
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into a more accessible format, making the ontological knowledge easier to
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understand.
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Args:
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state: The current agent state containing the ontology to render.
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tools: The toolbox instance providing utility functions.
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Returns:
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AgentState: Updated state with rendered triples.
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"""
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parser = PydanticOutputParser(pydantic_object=OntologyRenderReport)
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logger.info("Rendering fresh ontology")
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intro_instruction = intro_instruction_fresh.format(
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current_domain=state.current_domain
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)
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output_instruction = output_instruction_ttl
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ontology_ttl = ""
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improvement_instruction_str = ""
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general_ontology_instruction_str = general_ontology_instruction.format(
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prefix_instruction=prefix_instruction_fresh
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)
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text_chapter = text_template.format(text=state.content_unit.text)
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external_evidence = state.external_evidence_text
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if external_evidence:
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state.mark_external_evidence_used(WorkflowNode.TEXT_TO_ONTOLOGY)
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prompt = PromptTemplate(
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template=template_prompt,
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input_variables=[
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"preamble",
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"intro_instruction",
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"ontology_instruction",
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"output_instruction",
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"user_instruction",
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"improvement_instruction",
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"ontology_ttl",
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"text",
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"external_evidence",
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"format_instructions",
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],
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)
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try:
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llm_tool = await tools.get_llm_tool(state.budget_tracker)
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render_report: OntologyRenderReport = await call_llm_with_retry(
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llm_tool=llm_tool,
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prompt=prompt,
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parser=parser,
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prompt_kwargs={
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"preamble": system_preamble,
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"intro_instruction": intro_instruction,
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"ontology_instruction": general_ontology_instruction_str,
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"output_instruction": output_instruction,
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"ontology_ttl": ontology_ttl,
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"user_instruction": state.ontology_user_instruction,
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"improvement_instruction": improvement_instruction_str,
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"text": text_chapter,
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"external_evidence": external_evidence,
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"format_instructions": parser.get_format_instructions(),
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},
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)
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state.set_external_evidence_request(
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WorkflowNode.TEXT_TO_ONTOLOGY, render_report.external_evidence_request
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)
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state.current_ontology = render_report.ontology
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state.current_ontology.graph.sanitize_prefixes_namespaces()
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num_triples = len(state.current_ontology.graph)
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logger.info(f"New ontology created with {num_triples} triple(s).")
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# Track triples in budget tracker (fresh ontology)
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state.budget_tracker.add_ontology_update(
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num_operations=1, num_triples=num_triples
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)
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state.clear_failure()
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state.set_node_status(WorkflowNode.TEXT_TO_ONTOLOGY, Status.SUCCESS)
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return state
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except Exception as e:
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logger.error(f"Failed to generate triples: {str(e)}")
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state.set_node_status(WorkflowNode.TEXT_TO_ONTOLOGY, Status.FAILED)
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state.set_failure(FailureStage.GENERATE_TTL_FOR_ONTOLOGY, str(e))
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return state
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async def render_ontology_update(
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state: UnitOntologyState, tools: AtomicToolBox
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) -> UnitOntologyState:
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"""Render ontology triples into a human-readable format.
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This function takes the triples from the current ontology and renders them
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into a more accessible format, making the ontological knowledge easier to
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understand.
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Args:
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state: The current unit ontology state containing the ontology to render.
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tools: The toolbox instance providing utility functions.
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Returns:
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UnitOntologyState: Updated state with rendered triples.
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"""
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parser = PydanticOutputParser(pydantic_object=GraphUpdateRenderReport)
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current = state.current_ontology or state.ontology_snapshot
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ontology_iri = current.iri
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ontology_desc = current.describe()
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intro_instruction = intro_instruction_update.format(
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ontology_iri=ontology_iri, ontology_desc=ontology_desc
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)
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ontology_chapter = ontology_template.format(
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ontology_ttl=current.graph.serialize(format="turtle")
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)
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output_instruction = output_instruction_sparql
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improvement_instruction_str = render_suggestions_prompt(
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state.suggestions, WorkflowNode.TEXT_TO_ONTOLOGY
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)
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general_ontology_instruction_str = general_ontology_instruction.format(
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prefix_instruction=prefix_instruction.format(ontology_prefix=current.prefix),
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ontology_prefix=current.prefix,
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)
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text_chapter = text_template.format(text=state.content_unit.text)
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external_evidence = state.external_evidence_text
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if external_evidence:
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state.mark_external_evidence_used(WorkflowNode.TEXT_TO_ONTOLOGY)
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prompt = PromptTemplate(
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template=template_prompt,
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input_variables=[
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"preamble",
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"intro_instruction",
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"ontology_instruction",
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"output_instruction",
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"user_instruction",
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"improvement_instruction",
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"ontology_ttl",
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"text",
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"external_evidence",
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"format_instructions",
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],
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)
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known_prefixes = _extract_known_prefixes(state)
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try:
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llm_tool = await tools.get_llm_tool(state.budget_tracker)
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# Set known prefixes in context before parsing
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RDFGraph.set_known_prefixes(known_prefixes if known_prefixes else None)
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render_report: GraphUpdateRenderReport = await call_llm_with_retry(
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llm_tool=llm_tool,
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prompt=prompt,
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parser=parser,
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prompt_kwargs={
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"preamble": system_preamble,
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"intro_instruction": intro_instruction,
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"ontology_instruction": general_ontology_instruction_str,
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"output_instruction": output_instruction,
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"improvement_instruction": improvement_instruction_str,
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"ontology_ttl": ontology_chapter,
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"user_instruction": state.ontology_user_instruction,
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"text": text_chapter,
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"external_evidence": external_evidence,
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"format_instructions": parser.get_format_instructions(),
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},
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)
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state.set_external_evidence_request(
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WorkflowNode.TEXT_TO_ONTOLOGY, render_report.external_evidence_request
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)
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graph_update = render_report.graph_update
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state.ontology_updates.append(graph_update)
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state.update_ontology()
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num_operations, num_triples = graph_update.count_total_triples()
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logger.info(
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f"Ontology update has {num_operations} operation(s) "
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f"with {num_triples} total triple(s)."
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)
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# Track triples in budget tracker
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state.budget_tracker.add_ontology_update(num_operations, num_triples)
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state.clear_failure()
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state.set_node_status(WorkflowNode.TEXT_TO_ONTOLOGY, Status.SUCCESS)
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return state
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except Exception as e:
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logger.error(f"Failed to generate ontology update: {str(e)}")
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state.set_node_status(WorkflowNode.TEXT_TO_ONTOLOGY, Status.FAILED)
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state.set_failure(FailureStage.GENERATE_SPARQL_UPDATE_FOR_ONTOLOGY, str(e))
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return state
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finally:
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# Clear the context after parsing
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RDFGraph.set_known_prefixes(None)
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