"""
LangGraph workflow implementation for the RAG Q&A Agent.
Defines the agent graph with plan, retrieve, answer, and reflect nodes.
"""

from typing import TypedDict, Annotated, Dict, Any, List
from langgraph.graph import StateGraph, END
from rag_pipeline import RAGPipeline
from llm_utils import LLMHandler
from reflection import ReflectionEvaluator
import operator


# Define the agent state
class AgentState(TypedDict):
    """State passed between nodes in the agent workflow."""
    query: str
    plan: str
    needs_retrieval: bool
    retrieved_context: str
    retrieved_chunks: List[Dict[str, Any]]
    answer: str
    reflection: Dict[str, Any]
    final_response: str
    iteration: int


class RAGAgent:
    """LangGraph-based RAG Q&A Agent with reflection."""
    
    def __init__(
        self,
        rag_pipeline: RAGPipeline,
        llm_handler: LLMHandler,
        reflection_evaluator: ReflectionEvaluator,
        max_iterations: int = 2
    ):
        """
        Initialize the RAG agent.
        
        Args:
            rag_pipeline: RAG pipeline for retrieval
            llm_handler: LLM handler for generation
            reflection_evaluator: Reflection evaluator
            max_iterations: Maximum reflection iterations
        """
        self.rag_pipeline = rag_pipeline
        self.llm_handler = llm_handler
        self.reflection_evaluator = reflection_evaluator
        self.max_iterations = max_iterations
        
        # Build the graph
        self.graph = self._build_graph()
        print("✓ RAG Agent workflow initialized")
    
    def _build_graph(self):
        """Build the LangGraph workflow."""
        # Create state graph
        workflow = StateGraph(AgentState)
        
        # Add nodes
        workflow.add_node("plan", self.plan_node)
        workflow.add_node("retrieve", self.retrieve_node)
        workflow.add_node("answer", self.answer_node)
        workflow.add_node("reflect", self.reflect_node)
        
        # Define edges
        workflow.set_entry_point("plan")
        
        # Plan -> Retrieve or Answer
        workflow.add_conditional_edges(
            "plan",
            self.should_retrieve,
            {
                True: "retrieve",
                False: "answer"
            }
        )
        
        # Retrieve -> Answer
        workflow.add_edge("retrieve", "answer")
        
        # Answer -> Reflect
        workflow.add_edge("answer", "reflect")
        
        # Reflect -> End or Answer (for regeneration)
        workflow.add_conditional_edges(
            "reflect",
            self.should_regenerate,
            {
                "accept": END,
                "regenerate": "answer",
                "end": END
            }
        )
        
        return workflow.compile()
    
    def plan_node(self, state: AgentState) -> AgentState:
        """
        Planning node: Analyze query and decide if retrieval is needed.
        
        Args:
            state: Current agent state
            
        Returns:
            Updated state with plan
        """
        print("\n" + "="*60)
        print("📋 NODE: PLAN")
        print("="*60 + "\n")
        
        query = state["query"]
        print(f"Query: {query}\n")
        
        # Use LLM to analyze query and create a plan
        planning_prompt = f"""Analyze the following user query and determine if it requires retrieving information from a knowledge base.

User Query: "{query}"

Consider:
1. Is this a factual question that would benefit from specific documentation or knowledge?
2. Is this a general question that can be answered without specific context?
3. Does this query ask about specific concepts, technologies, or topics?

Respond in the following format:
NEEDS_RETRIEVAL: [YES/NO]
REASONING: [Brief explanation]
PLAN: [How you will approach answering this query]"""
        
        system_message = "You are a query planning agent. Analyze queries and determine the best approach to answer them."
        
        plan_response = self.llm_handler.generate(
            planning_prompt,
            system_message
        )
        
        # Parse response
        needs_retrieval = "YES" in plan_response.upper().split("NEEDS_RETRIEVAL:")[1].split("\n")[0] if "NEEDS_RETRIEVAL:" in plan_response.upper() else True
        
        print(f"Plan Response:\n{plan_response}\n")
        print(f"Needs Retrieval: {needs_retrieval}")
        
        state["plan"] = plan_response
        state["needs_retrieval"] = needs_retrieval
        state["iteration"] = 0
        
        print("\n" + "="*60 + "\n")
        
        return state
    
    def should_retrieve(self, state: AgentState) -> bool:
        """Conditional edge: Determine if retrieval is needed."""
        return state["needs_retrieval"]
    
    def retrieve_node(self, state: AgentState) -> AgentState:
        """
        Retrieval node: Retrieve relevant context from vector store.
        
        Args:
            state: Current agent state
            
        Returns:
            Updated state with retrieved context
        """
        print("\n" + "="*60)
        print("🔍 NODE: RETRIEVE")
        print("="*60 + "\n")
        
        query = state["query"]
        
        # Retrieve context
        context, chunks = self.rag_pipeline.retrieve_context(query, top_k=3)
        
        print(f"Retrieved {len(chunks)} relevant chunks\n")
        
        # Display retrieved content preview
        for i, chunk in enumerate(chunks):
            preview = chunk['content'][:150] + "..." if len(chunk['content']) > 150 else chunk['content']
            print(f"Chunk {i+1} Preview: {preview}\n")
        
        state["retrieved_context"] = context
        state["retrieved_chunks"] = chunks
        
        print("="*60 + "\n")
        
        return state
    
    def answer_node(self, state: AgentState) -> AgentState:
        """
        Answer generation node: Generate answer using LLM.
        
        Args:
            state: Current agent state
            
        Returns:
            Updated state with generated answer
        """
        print("\n" + "="*60)
        print("💬 NODE: ANSWER")
        print("="*60 + "\n")
        
        query = state["query"]
        iteration = state.get("iteration", 0)
        
        if iteration > 0:
            print(f"[Regeneration attempt {iteration}]\n")
        
        # Check if we have retrieved context
        if state.get("retrieved_context"):
            # Generate answer with context
            context = state["retrieved_context"]
            
            # Check if this is a regeneration with feedback
            if "reflection" in state and iteration > 0:
                feedback = state["reflection"]["reasoning"]
                answer = self._generate_answer_with_feedback(query, context, feedback)
            else:
                answer = self.llm_handler.generate_with_context(
                    query,
                    context,
                    system_message="You are a helpful AI assistant. Answer questions accurately based on the provided context."
                )
        else:
            # Generate answer without context
            answer = self.llm_handler.generate(
                query,
                system_message="You are a helpful AI assistant. Answer questions concisely and accurately."
            )
        
        print(f"Generated Answer:\n{answer}\n")
        
        state["answer"] = answer
        
        print("="*60 + "\n")
        
        return state
    
    def _generate_answer_with_feedback(
        self,
        query: str,
        context: str,
        feedback: str
    ) -> str:
        """
        Generate answer incorporating feedback from reflection.
        
        Args:
            query: User query
            context: Retrieved context
            feedback: Feedback from reflection
            
        Returns:
            Regenerated answer
        """
        prompt = f"""The previous answer was not satisfactory. Here's the feedback:

{feedback}

Now, please generate a better answer to the following question using the context provided.

Context:
{context}

Question: {query}

Provide a comprehensive, accurate, and relevant answer that addresses the feedback."""
        
        system_message = "You are a helpful AI assistant. Learn from feedback and provide improved answers."
        
        return self.llm_handler.generate(prompt, system_message)
    
    def reflect_node(self, state: AgentState) -> AgentState:
        """
        Reflection node: Evaluate answer quality.
        
        Args:
            state: Current agent state
            
        Returns:
            Updated state with reflection results
        """
        query = state["query"]
        answer = state["answer"]
        context = state.get("retrieved_context", "")
        chunks = state.get("retrieved_chunks", [])
        
        # Evaluate answer
        reflection_result = self.reflection_evaluator.evaluate(
            query,
            answer,
            context,
            chunks
        )
        
        state["reflection"] = reflection_result
        
        return state
    
    def should_regenerate(self, state: AgentState) -> str:
        """
        Conditional edge: Determine if answer should be regenerated.
        
        Args:
            state: Current agent state
            
        Returns:
            Next node or END
        """
        reflection = state["reflection"]
        iteration = state.get("iteration", 0)
        
        recommendation = reflection.get("recommendation", "ACCEPT")
        
        # Accept answer if it's good enough or we've hit max iterations
        if recommendation == "ACCEPT" or iteration >= self.max_iterations:
            state["final_response"] = state["answer"]
            return "accept"
        
        # Regenerate if rejected and we haven't hit max iterations
        if recommendation == "REJECT" and iteration < self.max_iterations:
            state["iteration"] = iteration + 1
            print(f"\n⚠️  Answer rejected. Regenerating (iteration {state['iteration']})...\n")
            return "regenerate"
        
        # Otherwise, accept with partial relevance
        state["final_response"] = state["answer"]
        return "end"
    
    def query(self, question: str) -> Dict[str, Any]:
        """
        Process a query through the agent workflow.
        
        Args:
            question: User question
            
        Returns:
            Complete agent response with all state information
        """
        print("\n" + "="*70)
        print(" "*20 + "🤖 RAG Q&A AGENT 🤖")
        print("="*70 + "\n")
        print(f"User Query: {question}")
        print("="*70)
        
        # Initialize state
        initial_state = AgentState(
            query=question,
            plan="",
            needs_retrieval=True,
            retrieved_context="",
            retrieved_chunks=[],
            answer="",
            reflection={},
            final_response="",
            iteration=0
        )
        
        # Run the graph
        final_state = self.graph.invoke(initial_state)
        
        # Print final result
        print("\n" + "="*70)
        print("✅ FINAL RESPONSE")
        print("="*70 + "\n")
        print(final_state["final_response"])
        print("\n" + "="*70 + "\n")
        
        return final_state


def create_rag_agent(
    rag_pipeline: RAGPipeline,
    llm_handler: LLMHandler,
    reflection_evaluator: ReflectionEvaluator,
    max_iterations: int = 2
) -> RAGAgent:
    """
    Create and return a RAG agent instance.
    
    Args:
        rag_pipeline: RAG pipeline for retrieval
        llm_handler: LLM handler for generation
        reflection_evaluator: Reflection evaluator
        max_iterations: Maximum reflection iterations
        
    Returns:
        RAGAgent instance
    """
    return RAGAgent(rag_pipeline, llm_handler, reflection_evaluator, max_iterations)