from langchain_core.retrievers import BaseRetriever
from sqlalchemy import func, and_, or_
from sqlalchemy.exc import SQLAlchemyError
from pydantic import BaseModel, Field
from typing import Any, Dict
from flask import current_app

from common.extensions import db
from common.models.document import Document, DocumentVersion
from common.utils.datetime_utils import get_date_in_timezone


class EveAIRetriever(BaseRetriever):
    model_variables: Dict[str, Any] = Field(...)
    tenant_info: Dict[str, Any] = Field(...)

    def __init__(self, model_variables: Dict[str, Any], tenant_info: Dict[str, Any]):
        super().__init__()
        self.model_variables = model_variables
        self.tenant_info = tenant_info

    def _get_relevant_documents(self, query: str):
        current_app.logger.debug(f'Retrieving relevant documents for query: {query}')
        query_embedding = self._get_query_embedding(query)
        db_class = self.model_variables['embedding_db_model']
        similarity_threshold = self.model_variables['similarity_threshold']
        k = self.model_variables['k']

        try:
            current_date = get_date_in_timezone(self.tenant_info['timezone'])
            # Subquery to find the latest version of each document
            subquery = (
                db.session.query(
                    DocumentVersion.doc_id,
                    func.max(DocumentVersion.id).label('latest_version_id')
                )
                .group_by(DocumentVersion.doc_id)
                .subquery()
            )
            # Main query to filter embeddings
            query_obj = (
                db.session.query(db_class,
                                 db_class.embedding.cosine_distance(query_embedding).label('distance'))
                .join(DocumentVersion, db_class.doc_vers_id == DocumentVersion.id)
                .join(Document, DocumentVersion.doc_id == Document.id)
                .join(subquery, DocumentVersion.id == subquery.c.latest_version_id)
                .filter(
                    or_(Document.valid_from.is_(None), Document.valid_from <= current_date),
                    or_(Document.valid_to.is_(None), Document.valid_to >= current_date),
                    db_class.embedding.cosine_distance(query_embedding) < similarity_threshold
                )
                .order_by('distance')
                .limit(k)
            )

            res = query_obj.all()

            if self.tenant_info['rag_tuning']:
                current_app.rag_tuning_logger.debug(f'Retrieved {len(res)} relevant documents')
                current_app.rag_tuning_logger.debug(f'---------------------------------------')

            result = []
            for doc in res:
                if self.tenant_info['rag_tuning']:
                    current_app.rag_tuning_logger.debug(f'Document ID: {doc[0].id} - Distance: {doc[1]}\n')
                    current_app.rag_tuning_logger.debug(f'Chunk: \n {doc[0].chunk}\n\n')
                result.append(f'SOURCE: {doc[0].id}\n\n{doc[0].chunk}\n\n')

        except SQLAlchemyError as e:
            current_app.logger.error(f'Error retrieving relevant documents: {e}')
            db.session.rollback()
            return []
        return result

    def _get_query_embedding(self, query: str):
        embedding_model = self.model_variables['embedding_model']
        query_embedding = embedding_model.embed_query(query)
        return query_embedding