135 lines
4.3 KiB
Python
135 lines
4.3 KiB
Python
"""Enhanced fact criticism agent with memory and SPARQL operations.
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This module provides enhanced functionality for analyzing and validating facts
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with SPARQL operation support.
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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
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from ontocast.onto.enum import FailureStage, Status, WorkflowNode
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from ontocast.onto.model import FactsCritiqueReport, Suggestions
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from ontocast.onto.unit_states import UnitFactsState
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from ontocast.prompt.common import (
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facts_template,
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ontology_template,
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text_template,
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user_template,
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)
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from ontocast.prompt.criticise_facts import (
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evaluation_instruction,
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preamble,
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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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async def criticise_facts(
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state: UnitFactsState, tools: AtomicToolBox
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) -> UnitFactsState:
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"""Enhanced criticize facts with SPARQL operations.
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This function performs a critical analysis of the facts in the current content unit,
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with SPARQL operation support.
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Args:
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state: The current unit facts state containing the chunk to analyze.
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tools: The toolbox instance providing utility functions.
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Returns:
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UnitFactsState: Updated state with analysis results.
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"""
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if not state.content_unit:
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logger.warning("No current content unit to analyze")
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return state
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progress_info = state.get_content_unit_progress_string()
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logger.info(
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f"Facts critic for {progress_info}: visit {state.node_visits[WorkflowNode.CRITICISE_FACTS]}/{state.max_visits_per_node}"
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)
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llm_tool = await tools.get_llm_tool(state.budget_tracker)
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parser = PydanticOutputParser(pydantic_object=FactsCritiqueReport)
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ontology_ttl = state.ontology_snapshot.graph.serialize(format="turtle")
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ontology_chapter = ontology_template.format(
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ontology_ttl=ontology_ttl,
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)
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facts_ttl = state.content_unit.graph.serialize(format="turtle")
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facts_chapter = facts_template.format(
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facts_ttl=facts_ttl,
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)
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text_chapter = text_template.format(text=state.content_unit.text)
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user_instruction = (
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user_template.format(user_instruction=state.facts_user_instruction)
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if state.facts_user_instruction
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else ""
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)
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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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"evaluation_instruction",
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"user_instruction",
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"ontology_chapter",
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"facts_chapter",
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"text_chapter",
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"format_instructions",
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],
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)
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prompt_data = {
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"preamble": preamble,
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"evaluation_instruction": evaluation_instruction,
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"user_instruction": user_instruction,
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"ontology_chapter": ontology_chapter,
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"facts_chapter": facts_chapter,
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"text_chapter": text_chapter,
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"format_instructions": parser.get_format_instructions(),
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}
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try:
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critique: FactsCritiqueReport = 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=prompt_data,
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)
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state.set_external_evidence_request(
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WorkflowNode.CRITICISE_FACTS, critique.external_evidence_request
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)
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logger.debug(
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f"Parsed critique report - success: {critique.success}, "
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f"score: {critique.score}"
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)
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if critique.success or critique.score > 90:
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state.status = Status.SUCCESS
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state.set_node_status(WorkflowNode.CRITICISE_FACTS, Status.SUCCESS)
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logger.info("Facts critique passed")
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else:
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state.status = Status.FAILED
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state.set_node_status(WorkflowNode.CRITICISE_FACTS, Status.FAILED)
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state.failure_stage = FailureStage.FACTS_CRITIQUE
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state.suggestions = Suggestions.from_critique_report(critique)
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state.failure_reason = "Facts Critic suggests improvements"
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return state
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except Exception as e:
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logger.error(f"Failed to criticize facts: {str(e)}")
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state.set_failure(FailureStage.FACTS_CRITIQUE, str(e))
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state.set_node_status(WorkflowNode.CRITICISE_FACTS, Status.FAILED)
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return state
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