> ## Documentation Index
> Fetch the complete documentation index at: https://docs.camel-ai.org/llms.txt
> Use this file to discover all available pages before exploring further.
# Camel.storages.vectordb storages.qdrant
## QdrantStorage
```python theme={"system"}
class QdrantStorage(BaseVectorStorage):
```
An implementation of the `BaseVectorStorage` for interacting with
Qdrant, a vector search engine.
The detailed information about Qdrant is available at:
`Qdrant `\_
**Parameters:**
* **vector\_dim** (int): The dimension of storing vectors.
* **collection\_name** (Optional\[str], optional): Name for the collection in the Qdrant. If not provided, set it to the current time with iso format. (default: :obj:`None`)
* **url\_and\_api\_key** (Optional\[Tuple\[str, str]], optional): Tuple containing the URL and API key for connecting to a remote Qdrant instance. (default: :obj:`None`)
* **path** (Optional\[str], optional): Path to a directory for initializing a local Qdrant client. (default: :obj:`None`)
* **distance** (VectorDistance, optional): The distance metric for vector comparison (default: :obj:`VectorDistance.COSINE`)
* **delete\_collection\_on\_del** (bool, optional): Flag to determine if the collection should be deleted upon object destruction. (default: :obj:`False`) \*\*kwargs (Any): Additional keyword arguments for initializing `QdrantClient`.
**Note:**
* If `url_and_api_key` is provided, it takes priority and the client
will attempt to connect to the remote Qdrant instance using the URL
endpoint.
* If `url_and_api_key` is not provided and `path` is given, the client
will use the local path to initialize Qdrant.
* If neither `url_and_api_key` nor `path` is provided, the client will
be initialized with an in-memory storage (`":memory:"`).
### **init**
```python theme={"system"}
def __init__(
self,
vector_dim: int,
collection_name: Optional[str] = None,
url_and_api_key: Optional[Tuple[str, str]] = None,
path: Optional[str] = None,
distance: VectorDistance = VectorDistance.COSINE,
delete_collection_on_del: bool = False,
**kwargs: Any
):
```
### **del**
```python theme={"system"}
def __del__(self):
```
Deletes the collection if :obj:`del_collection` is set to
:obj:`True`.
### \_create\_client
```python theme={"system"}
def _create_client(
self,
url_and_api_key: Optional[Tuple[str, str]],
path: Optional[str],
**kwargs: Any
):
```
### \_check\_and\_create\_collection
```python theme={"system"}
def _check_and_create_collection(self):
```
### \_create\_collection
```python theme={"system"}
def _create_collection(
self,
collection_name: str,
size: int,
distance: VectorDistance = VectorDistance.COSINE,
**kwargs: Any
):
```
Creates a new collection in the database.
**Parameters:**
* **collection\_name** (str): Name of the collection to be created.
* **size** (int): Dimensionality of vectors to be stored in this collection.
* **distance** (VectorDistance, optional): The distance metric to be used for vector similarity. (default: :obj:`VectorDistance.COSINE`) \*\*kwargs (Any): Additional keyword arguments.
### \_delete\_collection
```python theme={"system"}
def _delete_collection(self, collection_name: str, **kwargs: Any):
```
Deletes an existing collection from the database.
**Parameters:**
* **collection** (str): Name of the collection to be deleted. \*\*kwargs (Any): Additional keyword arguments.
### \_collection\_exists
```python theme={"system"}
def _collection_exists(self, collection_name: str):
```
Returns whether the collection exists in the database
### \_generate\_collection\_name
```python theme={"system"}
def _generate_collection_name(self):
```
Generates a collection name if user doesn't provide
### \_get\_collection\_info
```python theme={"system"}
def _get_collection_info(self, collection_name: str):
```
Retrieves details of an existing collection.
**Parameters:**
* **collection\_name** (str): Name of the collection to be checked.
**Returns:**
Dict\[str, Any]: A dictionary containing details about the
collection.
### close\_client
```python theme={"system"}
def close_client(self, **kwargs):
```
Closes the client connection to the Qdrant storage.
### add
```python theme={"system"}
def add(self, records: List[VectorRecord], **kwargs):
```
Adds a list of vectors to the specified collection.
**Parameters:**
* **vectors** (List\[VectorRecord]): List of vectors to be added. \*\*kwargs (Any): Additional keyword arguments.
### update\_payload
```python theme={"system"}
def update_payload(
self,
ids: List[str],
payload: Dict[str, Any],
**kwargs: Any
):
```
Updates the payload of the vectors identified by their IDs.
**Parameters:**
* **ids** (List\[str]): List of unique identifiers for the vectors to be updated.
* **payload** (Dict\[str, Any]): List of payloads to be updated. \*\*kwargs (Any): Additional keyword arguments.
### delete\_collection
```python theme={"system"}
def delete_collection(self):
```
Deletes the entire collection in the Qdrant storage.
### delete
```python theme={"system"}
def delete(
self,
ids: Optional[List[str]] = None,
payload_filter: Optional[Dict[str, Any]] = None,
**kwargs: Any
):
```
Deletes points from the collection based on either IDs or payload
filters.
**Parameters:**
* **ids** (Optional\[List\[str]], optional): List of unique identifiers for the vectors to be deleted.
* **payload\_filter** (Optional\[Dict\[str, Any]], optional): A filter for the payload to delete points matching specific conditions. If `ids` is provided, `payload_filter` will be ignored unless both are combined explicitly. \*\*kwargs (Any): Additional keyword arguments pass to `QdrantClient. delete`.
**Note:**
* If `ids` is provided, the points with these IDs will be deleted
directly, and the `payload_filter` will be ignored.
* If `ids` is not provided but `payload_filter` is, then points
matching the `payload_filter` will be deleted.
### status
```python theme={"system"}
def status(self):
```
### query
```python theme={"system"}
def query(
self,
query: VectorDBQuery,
filter_conditions: Optional[Dict[str, Any]] = None,
**kwargs: Any
):
```
Searches for similar vectors in the storage based on the provided
query.
**Parameters:**
* **query** (VectorDBQuery): The query object containing the search vector and the number of top similar vectors to retrieve.
* **filter\_conditions** (Optional\[Dict\[str, Any]], optional): A dictionary specifying conditions to filter the query results. \*\*kwargs (Any): Additional keyword arguments.
**Returns:**
List\[VectorDBQueryResult]: A list of vectors retrieved from the
storage based on similarity to the query vector.
### clear
```python theme={"system"}
def clear(self):
```
Remove all vectors from the storage.
### load
```python theme={"system"}
def load(self):
```
Load the collection hosted on cloud service.
### client
```python theme={"system"}
def client(self):
```
Provides access to the underlying vector database client.