from .configuration import Configuration
from langchain_core.runnables import RunnableConfig
from .state import State
from typing import Any, Dict, List
from app.config import config as app_config
from .prompts import get_citation_system_prompt
from langchain_core.messages import HumanMessage, SystemMessage

async def rerank_documents(state: State, config: RunnableConfig) -> Dict[str, Any]:
    """
    Rerank the documents based on relevance to the sub-section title.
    
    This node takes the relevant documents provided in the configuration,
    reranks them using the reranker service based on the sub-section title,
    and updates the state with the reranked documents.
    
    Returns:
        Dict containing the reranked documents.
    """
    # Get configuration and relevant documents
    configuration = Configuration.from_runnable_config(config)
    documents = configuration.relevant_documents
    sub_section_questions = configuration.sub_section_questions

    # If no documents were provided, return empty list
    if not documents or len(documents) == 0:
        return {
            "reranked_documents": []
        }
    
    # Get reranker service from app config
    reranker_service = getattr(app_config, "reranker_service", None)
    
    # Use documents as is if no reranker service is available
    reranked_docs = documents
    
    if reranker_service:
        try:
            # Use the sub-section questions for reranking context
            rerank_query = "\n".join(sub_section_questions)
            
            # Convert documents to format expected by reranker if needed
            reranker_input_docs = [
                {
                    "chunk_id": doc.get("chunk_id", f"chunk_{i}"),
                    "content": doc.get("content", ""),
                    "score": doc.get("score", 0.0),
                    "document": {
                        "id": doc.get("document", {}).get("id", ""),
                        "title": doc.get("document", {}).get("title", ""),
                        "document_type": doc.get("document", {}).get("document_type", ""),
                        "metadata": doc.get("document", {}).get("metadata", {})
                    }
                } for i, doc in enumerate(documents)
            ]
            
            # Rerank documents using the section title
            reranked_docs = reranker_service.rerank_documents(rerank_query, reranker_input_docs)
            
            # Sort by score in descending order
            reranked_docs.sort(key=lambda x: x.get("score", 0), reverse=True)
            
            print(f"Reranked {len(reranked_docs)} documents for section: {configuration.sub_section_title}")
        except Exception as e:
            print(f"Error during reranking: {str(e)}")
            # Use original docs if reranking fails
    
    return {
        "reranked_documents": reranked_docs
    }

async def write_sub_section(state: State, config: RunnableConfig) -> Dict[str, Any]:
    """
    Write the sub-section using the provided documents.
    
    This node takes the relevant documents provided in the configuration and uses
    an LLM to generate a comprehensive answer to the sub-section title with
    proper citations. The citations follow IEEE format using source IDs from the
    documents.
    
    Returns:
        Dict containing the final answer in the "final_answer" key.
    """
    
    # Get configuration and relevant documents from configuration
    configuration = Configuration.from_runnable_config(config)
    documents = configuration.relevant_documents
    
    # Initialize LLM
    llm = app_config.fast_llm_instance
    
    # If no documents were provided, return a message indicating this
    if not documents or len(documents) == 0:
        return {
            "final_answer": "No relevant documents were provided to answer this question. Please provide documents or try a different approach."
        }
    
    # Prepare documents for citation formatting
    formatted_documents = []
    for i, doc in enumerate(documents):
        # Extract content and metadata
        content = doc.get("content", "")
        doc_info = doc.get("document", {})
        document_id = doc_info.get("id", f"{i+1}")  # Use document ID or index+1 as source_id
        
        # Format document according to the citation system prompt's expected format
        formatted_doc = f"""
        <document>
            <metadata>
                <source_id>{document_id}</source_id>
            </metadata>
            <content>
                {content}
            </content>
        </document>
        """
        formatted_documents.append(formatted_doc)
    
    # Create the query that uses the section title and questions
    section_title = configuration.sub_section_title
    sub_section_questions = configuration.sub_section_questions
    documents_text = "\n".join(formatted_documents)
    
    # Format the questions as bullet points for clarity
    questions_text = "\n".join([f"- {question}" for question in sub_section_questions])
    
    # Construct a clear, structured query for the LLM
    human_message_content = f"""
    Please write a comprehensive answer for the title: 
    
    <title>
        {section_title}
    </title>
    
    Focus on answering these specific questions related to the title:
    <questions>
        {questions_text}
    </questions>

    Use the provided documents as your source material and cite them properly using the IEEE citation format [X] where X is the source_id.
    <documents>
        {documents_text}
    </documents>
    """
    
    # Create messages for the LLM
    messages = [
        SystemMessage(content=get_citation_system_prompt()),
        HumanMessage(content=human_message_content)
    ]
    
    # Call the LLM and get the response
    response = await llm.ainvoke(messages)
    final_answer = response.content
    
    return {
        "final_answer": final_answer
    }