"""
Vector storage configuration for the RAG solution using LlamaIndex and Qdrant.

This module provides initialization and configuration for:
- Qdrant vector storage connection
- Embedding model based on configured strategy
- Automatic collection creation
"""

import os
from typing import Optional

from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.qdrant import QdrantVectorStore
from loguru import logger
from qdrant_client import QdrantClient

# Import the new configuration module
from config import get_embedding_model


def initialize_vector_storage(
    collection_name: str = "documents_llamaindex",
    host: str = "localhost",
    port: int = 6333,
    grpc_port: int = 6334,
) -> tuple[QdrantVectorStore, VectorStoreIndex]:
    """
    Initialize Qdrant vector storage with embedding model based on configured strategy.

    Args:
        collection_name: Name of the Qdrant collection
        host: Qdrant host address
        port: Qdrant REST API port
        grpc_port: Qdrant gRPC API port

    Returns:
        Tuple of (QdrantVectorStore, VectorStoreIndex)
    """
    logger.info(f"Initializing vector storage with collection: {collection_name}")

    try:
        # Initialize Qdrant client
        client = QdrantClient(host=host, port=port)

        # Get the embedding model based on the configured strategy
        embed_model = get_embedding_model()

        # Get a test embedding to determine the correct dimensions
        test_embedding = embed_model.get_text_embedding("test")
        embedding_dimension = len(test_embedding)
        logger.info(f"Detected embedding dimension: {embedding_dimension}")

        # Check if collection exists, create if it doesn't
        collections = client.get_collections().collections
        collection_names = [coll.name for coll in collections]

        if collection_name not in collection_names:
            logger.info(f"Collection '{collection_name}' does not exist, creating...")
            client.create_collection(
                collection_name=collection_name,
                vectors_config={
                    "size": embedding_dimension,  # Use the actual embedding size
                    "distance": "Cosine",  # Cosine distance is commonly used
                },
            )
            logger.info(
                f"Collection '{collection_name}' created successfully with dimension {embedding_dimension}"
            )
        else:
            logger.info(f"Collection '{collection_name}' already exists")
            # Get the actual collection config to determine the vector size
            collection_info = client.get_collection(collection_name)
            # Access the vector configuration properly - handle different possible structures
            if (
                hasattr(collection_info.config.params, "vectors")
                and collection_info.config.params.vectors is not None
            ):
                existing_dimension = collection_info.config.params.vectors.size
                if existing_dimension != embedding_dimension:
                    logger.warning(
                        f"Existing collection dimension ({existing_dimension}) doesn't match embedding dimension ({embedding_dimension}), recreating..."
                    )
                    # Delete and recreate the collection with the correct dimensions
                    client.delete_collection(collection_name)
                    client.create_collection(
                        collection_name=collection_name,
                        vectors_config={
                            "size": embedding_dimension,  # Use the detected size
                            "distance": "Cosine",
                        },
                    )
                    logger.info(
                        f"Collection '{collection_name}' recreated with dimension {embedding_dimension}"
                    )
                else:
                    logger.info(
                        f"Using existing collection with matching dimension: {embedding_dimension}"
                    )
            else:
                # Last resort: recreate the collection with the correct dimensions
                logger.warning(
                    f"Could not determine vector dimension for existing collection, recreating..."
                )
                # Delete and recreate the collection with the correct dimensions
                client.delete_collection(collection_name)
                client.create_collection(
                    collection_name=collection_name,
                    vectors_config={
                        "size": embedding_dimension,  # Use the detected size
                        "distance": "Cosine",
                    },
                )
                logger.info(
                    f"Collection '{collection_name}' recreated with dimension {embedding_dimension}"
                )

        # Initialize the Qdrant vector store
        vector_store = QdrantVectorStore(client=client, collection_name=collection_name)

        # Create index from vector store with the embedding model we already created
        index = VectorStoreIndex.from_vector_store(
            vector_store=vector_store, embed_model=embed_model
        )

        logger.info("Vector storage initialized successfully")
        return vector_store, index

    except Exception as e:
        logger.error(f"Failed to initialize vector storage: {str(e)}")
        raise


def get_vector_store_and_index() -> tuple[QdrantVectorStore, VectorStoreIndex]:
    """
    Convenience function to get the initialized vector store and index.

    Returns:
        Tuple of (QdrantVectorStore, VectorStoreIndex)
    """
    return initialize_vector_storage()


if __name__ == "__main__":
    # Example usage
    logger.info("Testing vector storage initialization...")
    try:
        vector_store, index = get_vector_store_and_index()
        logger.info("Vector storage test successful!")
    except Exception as e:
        logger.error(f"Vector storage test failed: {e}")