from abc import ABC, abstractmethod, abstractproperty
from typing import Dict, Any, List, Optional, Tuple

from flask import current_app
from sqlalchemy import func, or_, desc
from sqlalchemy.exc import SQLAlchemyError

from common.extensions import db
from common.models.document import Document, DocumentVersion, Catalog, Retriever
from common.utils.model_utils import get_embedding_model_and_class
from eveai_chat_workers.retrievers.retriever_typing import RetrieverResult, RetrieverArguments, RetrieverMetadata
from config.logging_config import TuningLogger


class BaseRetriever(ABC):
    """Base class for all retrievers"""

    def __init__(self, tenant_id: int, retriever_id: int):
        self.tenant_id = tenant_id
        self.retriever_id = retriever_id
        self.retriever = Retriever.query.get_or_404(retriever_id)
        self.tuning = False
        self.tuning_logger = None
        self._setup_tuning_logger()

    @property
    @abstractmethod
    def type(self) -> str:
        """The type of the retriever"""
        raise NotImplementedError

    def _setup_tuning_logger(self):
        try:
            self.tuning_logger = TuningLogger(
                'tuning',
                tenant_id=self.tenant_id,
                retriever_id=self.retriever_id,
            )
            # Verify logger is working with a test message
            if self.tuning:
                self.tuning_logger.log_tuning('retriever', "Tuning logger initialized")
        except Exception as e:
            current_app.logger.error(f"Failed to setup tuning logger: {str(e)}")
            raise

    def log_tuning(self, message: str, data: Dict[str, Any] = None) -> None:
        if self.tuning and self.tuning_logger:
            try:
                self.tuning_logger.log_tuning('retriever', message, data)
            except Exception as e:
                current_app.logger.error(f"Processor: Error in tuning logging: {e}")

    def setup_standard_retrieval_params(self) -> Tuple[Any, Any, Any, float, int]:
        """
        Set up common parameters needed for standard retrieval functionality

        Returns:
            Tuple containing:
            - embedding_model: The model to use for embeddings
            - embedding_model_class: The class for storing embeddings
            - catalog_id: ID of the catalog
            - similarity_threshold: Threshold for similarity matching
            - k: Maximum number of results to return
        """
        catalog_id = self.retriever.catalog_id
        catalog = Catalog.query.get_or_404(catalog_id)
        embedding_model = "mistral.mistral-embed"

        embedding_model, embedding_model_class = get_embedding_model_and_class(
            self.tenant_id, catalog_id, embedding_model
        )

        similarity_threshold = self.retriever.configuration.get('es_similarity_threshold', 0.3)
        k = self.retriever.configuration.get('es_k', 8)

        return embedding_model, embedding_model_class, catalog_id, similarity_threshold, k

    @abstractmethod
    def retrieve(self, arguments: RetrieverArguments) -> List[RetrieverResult]:
        """
        Retrieve relevant documents based on provided arguments

        Args:
            arguments: Dictionary of arguments for the retrieval operation

        Returns:
            List[Dict[str, Any]]: List of retrieved documents/content
        """
        raise NotImplementedError