Repository: mem0ai/mem0
File: embedchain/config/base_app_config.py (Python)

Code before:
import logging
from typing import Optional

from embedchain.config.base_config import BaseConfig
from embedchain.helpers.json_serializable import JSONSerializable
from embedchain.vectordb.base import BaseVectorDB


class BaseAppConfig(BaseConfig, JSONSerializable):
    """
    Parent config to initialize an instance of `App`.
    """

    def __init__(
        self,
        log_level: str = "WARNING",
        db: Optional[BaseVectorDB] = None,
        id: Optional[str] = None,
        collect_metrics: bool = True,
        collection_name: Optional[str] = None,
        cache: Optional[bool] = False,
    ):
        """
        Initializes a configuration class instance for an App.
        Most of the configuration is done in the `App` class itself.

        :param log_level: Debug level ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], defaults to "WARNING"
        :type log_level: str, optional
        :param db: A database class. It is recommended to set this directly in the `App` class, not this config,
        defaults to None
        :type db: Optional[BaseVectorDB], optional
        :param id: ID of the app. Document metadata will have this id., defaults to None
        :type id: Optional[str], optional
        :param collect_metrics: Send anonymous telemetry to improve embedchain, defaults to True
        :type collect_metrics: Optional[bool], optional
        :param collection_name: Default collection name. It's recommended to use app.db.set_collection_name() instead,
        defaults to None
        :type collection_name: Optional[str], optional
        """
        self._setup_logging(log_level)
        self.id = id
        self.collect_metrics = True if (collect_metrics is True or collect_metrics is None) else False
        self.collection_name = collection_name
        self.cache = cache

        if db:

Review comment (performance):
May be change this to be a dict instead of a bool so that we can configure the cache settings.

```
"cache": {
    "similarity_threshold": 0.5,
    "max_distance": 1.0
}
```

Code after:
import logging
from typing import Optional

from embedchain.config.base_config import BaseConfig
from embedchain.helpers.json_serializable import JSONSerializable
from embedchain.vectordb.base import BaseVectorDB


class BaseAppConfig(BaseConfig, JSONSerializable):
    """
    Parent config to initialize an instance of `App`.
    """

    def __init__(
        self,
        log_level: str = "WARNING",
        db: Optional[BaseVectorDB] = None,
        id: Optional[str] = None,
        collect_metrics: bool = True,
        collection_name: Optional[str] = None,
    ):
        """
        Initializes a configuration class instance for an App.
        Most of the configuration is done in the `App` class itself.

        :param log_level: Debug level ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], defaults to "WARNING"
        :type log_level: str, optional
        :param db: A database class. It is recommended to set this directly in the `App` class, not this config,
        defaults to None
        :type db: Optional[BaseVectorDB], optional
        :param id: ID of the app. Document metadata will have this id., defaults to None
        :type id: Optional[str], optional
        :param collect_metrics: Send anonymous telemetry to improve embedchain, defaults to True
        :type collect_metrics: Optional[bool], optional
        :param collection_name: Default collection name. It's recommended to use app.db.set_collection_name() instead,
        defaults to None
        :type collection_name: Optional[str], optional
        """
        self._setup_logging(log_level)
        self.id = id
        self.collect_metrics = True if (collect_metrics is True or collect_metrics is None) else False
        self.collection_name = collection_name

        if db:
            self._db = db
            logging.warning(