"""LangGraph nodes for RAG workflow"""

from src.state.rag_state import RAGState

class RAGNodes:
    """Contains node functions for RAG workflow"""
    
    def __init__(self, retriever, llm):
        """
        Initialize RAG nodes
        
        Args:
            retriever: Document retriever instance
            llm: Language model instance
        """
        self.retriever = retriever
        self.llm = llm
    
    def retrieve_docs(self, state: RAGState) -> RAGState:
        """
        Retrieve relevant documents node
        
        Args:
            state: Current RAG state
            
        Returns:
            Updated RAG state with retrieved documents
        """
        docs = self.retriever.invoke(state.question)
        return RAGState(
            question=state.question,
            retrieved_docs=docs
        )
    
    def generate_answer(self, state: RAGState) -> RAGState:
        """
        Generate answer from retrieved documents node
        
        Args:
            state: Current RAG state with retrieved documents
            
        Returns:
            Updated RAG state with generated answer
        """
        # Combine retrieved documents into context
        context = "\n\n".join([doc.page_content for doc in state.retrieved_docs])
        
        # Create prompt
        prompt = f"""You are a professional Project Analyst.
        Answer strictly using the context.
        If unknown, say you don't know.

        Context:
        {context}
        
        Question: {state.question}"""
        
        # Generate response
        response = self.llm.invoke(prompt)
        
        return RAGState(
            question=state.question,
            retrieved_docs=state.retrieved_docs,
            answer=response.content
        )