src/ctp_slack_bot/db/mongo_db.py

#from motor.motor_asyncio import AsyncIOMotorClient
from loguru import logger
from pydantic import BaseModel, model_validator
#from pymongo import IndexModel, ASCENDING
from typing import Optional, Self

from ctp_slack_bot.core.config import Settings

class MongoDB(BaseModel):
    """
    MongoDB connection and initialization class.
    Handles connection to MongoDB, database selection, and index creation.
    """

    settings: Settings

    @model_validator(mode='after')
    def post_init(self: Self) -> Self:
        logger.debug("Created {}", self.__class__.__name__)
        return self

    def __init__(self: Self, settings: Settings) -> Self:
        #self.client: Optional[AsyncIOMotorClient] = None
        #self.db = None
        #self.vector_collection = None
        #self.initialized = False
        pass # The above initialization needs to be done some other way.

    # async def connect(self):
    #     """
    #     Connect to MongoDB using connection string from settings.
    #     """
    #     if self.client is not None:
    #         return

    #     if not settings.MONGODB_URI:
    #         raise ValueError("MONGODB_URI is not set in environment variables")

    #     try:
    #         # Create MongoDB connection
    #         self.client = AsyncIOMotorClient(settings.MONGODB_URI.get_secret_value())
    #         self.db = self.client[settings.MONGODB_DB_NAME]
    #         self.vector_collection = self.db["vector_store"]
    #         logger.info(f"Connected to MongoDB: {settings.MONGODB_DB_NAME}")
    #     except Exception as e:
    #         logger.error(f"Error connecting to MongoDB: {str(e)}")
    #         raise

    # async def initialize(self):
    #     """
    #     Initialize MongoDB with required collections and indexes.
    #     """
    #     if self.initialized:
    #         return
        
    #     if not self.client:
    #         await self.connect()
        
    #     try:
    #         # Create vector index for similarity search
    #         await self.create_vector_index()
    #         self.initialized = True
    #         logger.info("MongoDB initialized successfully")
    #     except Exception as e:
    #         logger.error(f"Error initializing MongoDB: {str(e)}")
    #         raise

    # async def create_vector_index(self):
    #     """
    #     Create vector index for similarity search using MongoDB Atlas Vector Search.
    #     """
    #     try:
    #         # Check if index already exists
    #         existing_indexes = await self.vector_collection.list_indexes().to_list(length=None)
    #         index_names = [index.get('name') for index in existing_indexes]
            
    #         if "vector_index" not in index_names:
    #             # Create vector search index
    #             index_definition = {
    #                 "mappings": {
    #                     "dynamic": True,
    #                     "fields": {
    #                         "embedding": {
    #                             "dimensions": settings.VECTOR_DIMENSION,
    #                             "similarity": "cosine",
    #                             "type": "knnVector"
    #                         }
    #                     }
    #                 }
    #             }
                
    #             # Create the index
    #             await self.db.command({
    #                 "createIndexes": self.vector_collection.name,
    #                 "indexes": [
    #                     {
    #                         "name": "vector_index",
    #                         "key": {"embedding": "vector"},
    #                         "weights": {"embedding": 1},
    #                         "vectorSearchOptions": index_definition
    #                     }
    #                 ]
    #             })
                
    #             # Create additional metadata indexes for filtering
    #             await self.vector_collection.create_index([("metadata.source", ASCENDING)])
    #             await self.vector_collection.create_index([("metadata.timestamp", ASCENDING)])
                
    #             logger.info("Vector search index created")
    #         else:
    #             logger.info("Vector search index already exists")
                
    #     except Exception as e:
    #         logger.error(f"Error creating vector index: {str(e)}")
    #         raise

    # async def close(self):
    #     """
    #     Close MongoDB connection.
    #     """
    #     if self.client:
    #         self.client.close()
    #         self.client = None
    #         self.db = None
    #         self.vector_collection = None
    #         self.initialized = False
    #         logger.info("MongoDB connection closed")