> ## 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.milvus
## MilvusStorage
```python theme={"system"}
class MilvusStorage(BaseVectorStorage):
```
An implementation of the `BaseVectorStorage` for interacting with
Milvus, a cloud-native vector search engine.
The detailed information about Milvus is available at:
`Milvus `\_
**Parameters:**
* **vector\_dim** (int): The dimension of storing vectors.
* **url\_and\_api\_key** (Tuple\[str, str]): Tuple containing the URL and API key for connecting to a remote Milvus instance. URL maps to Milvus uri concept, typically "endpoint:port". API key maps to Milvus token concept, for self-hosted it's "username:pwd", for Zilliz Cloud (fully-managed Milvus) it's API Key.
* **collection\_name** (Optional\[str], optional): Name for the collection in the Milvus. If not provided, set it to the current time with iso format. (default: :obj:`None`) \*\*kwargs (Any): Additional keyword arguments for initializing `MilvusClient`.
### **init**
```python theme={"system"}
def __init__(
self,
vector_dim: int,
url_and_api_key: Tuple[str, str],
collection_name: Optional[str] = None,
**kwargs: Any
):
```
### \_create\_client
```python theme={"system"}
def _create_client(self, url_and_api_key: Tuple[str, str], **kwargs: Any):
```
Initializes the Milvus client with the provided connection details.
**Parameters:**
* **url\_and\_api\_key** (Tuple\[str, str]): The URL and API key for the Milvus server. \*\*kwargs: Additional keyword arguments passed to the Milvus client.
### \_check\_and\_create\_collection
```python theme={"system"}
def _check_and_create_collection(self):
```
Checks if the specified collection exists in Milvus and creates it
if it doesn't, ensuring it matches the specified vector dimensionality.
### \_create\_collection
```python theme={"system"}
def _create_collection(self, collection_name: str, **kwargs: Any):
```
Creates a new collection in the database.
**Parameters:**
* **collection\_name** (str): Name of the collection to be created. \*\*kwargs (Any): Additional keyword arguments pass to create collection.
### \_delete\_collection
```python theme={"system"}
def _delete_collection(self, collection_name: str):
```
Deletes an existing collection from the database.
**Parameters:**
* **collection** (str): Name of the collection to be deleted.
### \_collection\_exists
```python theme={"system"}
def _collection_exists(self, collection_name: str):
```
Checks whether a collection with the specified name exists in the
database.
**Parameters:**
* **collection\_name** (str): The name of the collection to check.
**Returns:**
bool: True if the collection exists, False otherwise.
### \_generate\_collection\_name
```python theme={"system"}
def _generate_collection_name(self):
```
**Returns:**
str: A unique, valid collection name.
### \_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.
### \_validate\_and\_convert\_vectors
```python theme={"system"}
def _validate_and_convert_vectors(self, records: List[VectorRecord]):
```
Validates and converts VectorRecord instances to the format
expected by Milvus.
**Parameters:**
* **records** (List\[VectorRecord]): List of vector records to validate and convert.
**Returns:**
List\[dict]: A list of dictionaries formatted for Milvus insertion.
### add
```python theme={"system"}
def add(self, records: List[VectorRecord], **kwargs):
```
Adds a list of vectors to the specified collection.
**Parameters:**
* **records** (List\[VectorRecord]): List of vectors to be added. \*\*kwargs (Any): Additional keyword arguments pass to insert.
### delete
```python theme={"system"}
def delete(self, ids: List[str], **kwargs: Any):
```
Deletes a list of vectors identified by their IDs from the
storage. If unsure of ids you can first query the collection to grab
the corresponding data.
**Parameters:**
* **ids** (List\[str]): List of unique identifiers for the vectors to be deleted. \*\*kwargs (Any): Additional keyword arguments passed to delete.
### status
```python theme={"system"}
def status(self):
```
**Returns:**
VectorDBStatus: An object containing information about the
collection's status.
### query
```python theme={"system"}
def query(self, query: VectorDBQuery, **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. \*\*kwargs (Any): Additional keyword arguments passed to search.
**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):
```
Removes all vectors from the Milvus collection. This method
deletes the existing collection and then recreates it with the same
schema to effectively remove all stored vectors.
### load
```python theme={"system"}
def load(self):
```
Load the collection hosted on cloud service.
### client
```python theme={"system"}
def client(self):
```
**Returns:**
Any: The Milvus client instance.