"""
Original Knowledge Extraction Method (3-Task Approach)

Copied from core/agent_monitoring.py and adapted for evaluation framework.
Uses the original 3-task CrewAI approach with separate agents for entity extraction,
relationship analysis, and knowledge graph building.
"""

# Import the LiteLLM fix FIRST, before any other imports that might use LiteLLM
import os
import sys

# Add the parent directory to the path to ensure imports work correctly
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
import json
import logging
from typing import Any, Dict

from crewai import Agent, Crew, Process, Task

from evaluation.knowledge_extraction.baselines.base_method import BaseKnowledgeExtractionMethod
from evaluation.knowledge_extraction.utils.models import Entity, KnowledgeGraph, Relation

# Import shared prompt templates
from evaluation.knowledge_extraction.utils.prompts import (
    ENTITY_EXTRACTION_INSTRUCTION_PROMPT,
    ENTITY_EXTRACTION_SYSTEM_PROMPT,
    GRAPH_BUILDER_INSTRUCTION_PROMPT,
    GRAPH_BUILDER_SYSTEM_PROMPT,
    RELATION_EXTRACTION_INSTRUCTION_PROMPT,
    RELATION_EXTRACTION_SYSTEM_PROMPT,
)
from utils.fix_litellm_stop_param import *  # This applies the patches  # noqa: F403

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Set higher log levels for noisy libraries
logging.getLogger("openai").setLevel(logging.WARNING)
logging.getLogger("httpx").setLevel(logging.WARNING)
logging.getLogger("litellm").setLevel(logging.WARNING)
logging.getLogger("chromadb").setLevel(logging.WARNING)

# Set default verbosity level
verbose_level = 0

# Set environment variables
os.environ["OPENAI_MODEL_NAME"] = "gpt-5-mini"

class OriginalKnowledgeExtractionMethod(BaseKnowledgeExtractionMethod):
    """Original 3-task knowledge extraction method using CrewAI."""
    
    def __init__(self, **kwargs):
        super().__init__("original_method", **kwargs)
        self._setup_agents_and_tasks()
    
    def _setup_agents_and_tasks(self):
        """Set up the CrewAI agents and tasks."""
        
        # Create agents
        self.entity_extractor_agent = Agent(
            role="Entity Extractor",
            goal="Identify and categorize entities from agent system data sources with clear descriptions",
            backstory=ENTITY_EXTRACTION_SYSTEM_PROMPT,
            verbose=bool(verbose_level),
            llm=os.environ["OPENAI_MODEL_NAME"]
        )

        self.relationship_analyzer_agent = Agent(
            role="Relationship Analyzer",
            goal="Discover standard relationships between entities in the system using only predefined relationship types",
            backstory=RELATION_EXTRACTION_SYSTEM_PROMPT,
            verbose=bool(verbose_level),
            llm=os.environ["OPENAI_MODEL_NAME"]
        )

        self.knowledge_graph_builder_agent = Agent(
            role="Knowledge Graph Builder",
            goal="Structure entities and relationships into a comprehensive knowledge graph with overall system assessment",
            backstory=GRAPH_BUILDER_SYSTEM_PROMPT,
            verbose=bool(verbose_level),
            llm=os.environ["OPENAI_MODEL_NAME"]
        )

        # Create tasks
        self.entity_extraction_task = Task(
            description=ENTITY_EXTRACTION_INSTRUCTION_PROMPT,
            agent=self.entity_extractor_agent,
            expected_output="A structured list of entities with their properties",
            output_pydantic=Entity,
        )

        self.relationship_analysis_task = Task(
            description=RELATION_EXTRACTION_INSTRUCTION_PROMPT,
            agent=self.relationship_analyzer_agent,
            expected_output="A structured list of relationships between entities",
            context=[self.entity_extraction_task],
            output_pydantic=Relation,
        )

        self.knowledge_graph_creation_task = Task(
            description=GRAPH_BUILDER_INSTRUCTION_PROMPT,
            agent=self.knowledge_graph_builder_agent,
            expected_output="A complete knowledge graph saved to JSON",
            context=[self.entity_extraction_task, self.relationship_analysis_task],
            output_pydantic=KnowledgeGraph,
        )

        # Create crew
        self.agent_monitoring_crew = Crew(
            agents=[self.entity_extractor_agent, self.relationship_analyzer_agent, self.knowledge_graph_builder_agent],
            tasks=[self.entity_extraction_task, self.relationship_analysis_task, self.knowledge_graph_creation_task],
            verbose=bool(verbose_level),
            memory=False,
            planning=False,
            process=Process.sequential,
        )
    
    def process_text(self, text: str) -> Dict[str, Any]:
        """
        Process input text using the original 3-task CrewAI approach.
        
        Args:
            text: Input text to process
            
        Returns:
            Dictionary with kg_data, metadata, success, and optional error
        """
        try:
            # Run the crew with proper input mechanism
            result = self.agent_monitoring_crew.kickoff(inputs={"input_data": text})
            
            # Extract the knowledge graph from the result
            if hasattr(result, 'pydantic') and result.pydantic:
                kg_data = result.pydantic.dict()
            elif hasattr(result, 'raw'):
                # Try to parse as JSON
                try:
                    kg_data = json.loads(result.raw)
                except:  # noqa: E722
                    kg_data = {"entities": [], "relations": [], "error": "Failed to parse result"}
            else:
                kg_data = {"entities": [], "relations": [], "error": "Unknown result format"}
            
            return {
                "success": True,
                "kg_data": kg_data,
                "metadata": {
                    "approach": "original_3_task",
                    "tasks_executed": 3,
                    "agents_used": 3,
                    "method": self.method_name
                }
            }
            
        except Exception as e:
            logger.error(f"Error in original knowledge extraction method: {e}")
            return {
                "success": False,
                "error": str(e),
                "kg_data": {"entities": [], "relations": []},
                "metadata": {
                    "approach": "original_3_task",
                    "tasks_executed": 0,
                    "agents_used": 0,
                    "method": self.method_name
                }
            }
    
    def extract_knowledge_graph(self, trace_data: str) -> Dict[str, Any]:
        """
        Extract knowledge graph from trace data.
        
        Args:
            trace_data: Agent trace data as JSON string
            
        Returns:
            Dictionary with entities and relations
        """
        # Pass the JSON string directly to process_text without re-encoding
        result = self.process_text(trace_data)
        
        # Return just the knowledge graph data
        if result.get("success", False):
            return result.get("kg_data", {"entities": [], "relations": []})
        else:
            # Return empty knowledge graph on failure
            return {"entities": [], "relations": []}