"""Retrieval module for querying vector storage and returning relevant documents with metadata."""

import os
from typing import List, Optional

from dotenv import load_dotenv
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from loguru import logger

from vector_storage import initialize_vector_store

# Load environment variables
load_dotenv()


class VectorStoreRetriever(BaseRetriever):
    """
    A custom retriever that uses the Qdrant vector store to retrieve relevant documents.
    """

    vector_store: object  # Qdrant vector store instance
    top_k: int = 5  # Number of documents to retrieve

    def _get_relevant_documents(
        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
    ) -> List[Document]:
        """
        Retrieve relevant documents based on the query.

        Args:
            query: The query string to search for
            run_manager: Callback manager for the run

        Returns:
            List of relevant documents with metadata
        """
        logger.info(f"Searching for documents related to query: {query[:50]}...")

        try:
            # Perform similarity search on the vector store
            results = self.vector_store.similarity_search(query, k=self.top_k)

            logger.info(f"Found {len(results)} relevant documents")

            return results
        except Exception as e:
            logger.error(f"Error during similarity search: {str(e)}")
            return []


def create_retriever(collection_name: str = "documents_langchain", top_k: int = 5):
    """
    Create and return a retriever instance connected to the vector store.

    Args:
        collection_name: Name of the Qdrant collection to use
        top_k: Number of documents to retrieve

    Returns:
        VectorStoreRetriever instance
    """
    logger.info(
        f"Initializing vector store for retrieval from collection: {collection_name}"
    )

    # Initialize the vector store
    vector_store = initialize_vector_store(collection_name=collection_name)

    # Create and return the retriever
    retriever = VectorStoreRetriever(vector_store=vector_store, top_k=top_k)

    return retriever


def search_documents_with_metadata(
    query: str, collection_name: str = "documents_langchain", top_k: int = 5
) -> List[dict]:
    """
    Search for documents and return them with detailed metadata.

    Args:
        query: The query string to search for
        collection_name: Name of the Qdrant collection to use
        top_k: Number of documents to retrieve

    Returns:
        List of dictionaries containing document content and metadata
    """
    logger.info(f"Starting document search with metadata for query: {query}")

    # Initialize the vector store
    vector_store = initialize_vector_store(collection_name=collection_name)

    try:
        # Standard similarity search
        documents = vector_store.similarity_search(query, k=top_k)

        # Format results to include content and metadata
        formatted_results = []
        for doc in documents:
            formatted_result = {
                "content": doc.page_content,
                "metadata": doc.metadata,
                "source": doc.metadata.get("source", "Unknown"),
                "filename": doc.metadata.get("filename", "Unknown"),
                "page_number": doc.metadata.get(
                    "page_number", doc.metadata.get("page", "N/A")
                ),
                "file_extension": doc.metadata.get("file_extension", "N/A"),
                "file_size": doc.metadata.get("file_size", "N/A"),
            }
            formatted_results.append(formatted_result)

        logger.info(
            f"Metadata search completed, returned {len(formatted_results)} documents"
        )

        return formatted_results

    except Exception as e:
        logger.error(f"Error during document search with metadata: {str(e)}")
        return []