Files
t6_mem0/embedchain/config/vectordb/qdrant.py
2024-06-28 11:18:22 -07:00

49 lines
2.2 KiB
Python

from typing import Optional
from embedchain.config.vectordb.base import BaseVectorDbConfig
from embedchain.helpers.json_serializable import register_deserializable
@register_deserializable
class QdrantDBConfig(BaseVectorDbConfig):
"""
Config to initialize a qdrant client.
:param: url. qdrant url or list of nodes url to be used for connection
"""
def __init__(
self,
collection_name: Optional[str] = None,
dir: Optional[str] = None,
hnsw_config: Optional[dict[str, any]] = None,
quantization_config: Optional[dict[str, any]] = None,
on_disk: Optional[bool] = None,
batch_size: Optional[int] = 10,
**extra_params: dict[str, any],
):
"""
Initializes a configuration class instance for a qdrant client.
:param collection_name: Default name for the collection, defaults to None
:type collection_name: Optional[str], optional
:param dir: Path to the database directory, where the database is stored, defaults to None
:type dir: Optional[str], optional
:param hnsw_config: Params for HNSW index
:type hnsw_config: Optional[dict[str, any]], defaults to None
:param quantization_config: Params for quantization, if None - quantization will be disabled
:type quantization_config: Optional[dict[str, any]], defaults to None
:param on_disk: If true - point`s payload will not be stored in memory.
It will be read from the disk every time it is requested.
This setting saves RAM by (slightly) increasing the response time.
Note: those payload values that are involved in filtering and are indexed - remain in RAM.
:type on_disk: bool, optional, defaults to None
:param batch_size: Number of items to insert in one batch, defaults to 10
:type batch_size: Optional[int], optional
"""
self.hnsw_config = hnsw_config
self.quantization_config = quantization_config
self.on_disk = on_disk
self.batch_size = batch_size
self.extra_params = extra_params
super().__init__(collection_name=collection_name, dir=dir)