> ## 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.tidb
## EnumEncoder
```python theme={"system"}
class EnumEncoder(JSONEncoder):
```
### default
```python theme={"system"}
def default(self, obj):
```
## TiDBStorage
```python theme={"system"}
class TiDBStorage(BaseVectorStorage):
```
An implementation of the `BaseVectorStorage` for interacting with TiDB.
The detailed information about TiDB is available at:
`TiDB Vector Search `\_
**Parameters:**
* **vector\_dim** (int): The dimension of storing vectors.
* **url\_and\_api\_key** (Optional\[Union\[Tuple\[str, str], str]]): A tuple containing the database url and API key for connecting to a TiDB cluster. The URL should be in the format: "mysql+pymysql://``:``@``:``/``". TiDB will not use the API Key, but retains the definition for interface compatible.
* **collection\_name** (Optional\[str]): Name of the collection. The collection name will be used as the table name in TiDB. If not provided, set it to the current time with iso format. \*\*kwargs (Any): Additional keyword arguments for initializing TiDB connection.
### **init**
```python theme={"system"}
def __init__(
self,
vector_dim: int,
collection_name: Optional[str] = None,
url_and_api_key: Optional[Union[Tuple[str, str], str]] = None,
**kwargs: Any
):
```
### \_create\_client
```python theme={"system"}
def _create_client(self, database_url: Optional[str] = None, **kwargs: Any):
```
Initializes the TiDB client with the provided connection details.
**Parameters:**
* **database\_url** (Optional\[str]): The database connection string for the TiDB server. \*\*kwargs: Additional keyword arguments passed to the TiDB client.
### \_get\_table\_model
```python theme={"system"}
def _get_table_model(self, collection_name: str):
```
### \_open\_and\_create\_table
```python theme={"system"}
def _open_and_create_table(self):
```
Opens an existing table or creates a new table in TiDB.
### \_check\_table
```python theme={"system"}
def _check_table(self):
```
Ensuring the specified table matches the specified vector
dimensionality.
### \_generate\_table\_name
```python theme={"system"}
def _generate_table_name(self):
```
**Returns:**
str: A unique, valid table name.
### \_get\_table\_info
```python theme={"system"}
def _get_table_info(self):
```
**Returns:**
Dict\[str, Any]: A dictionary containing details about the
table.
### \_validate\_and\_convert\_vectors
```python theme={"system"}
def _validate_and_convert_vectors(self, records: List[VectorRecord]):
```
Validates and converts VectorRecord instances to VectorDBRecord
instances.
**Parameters:**
* **records** (List\[VectorRecord]): List of vector records to validate and convert.
**Returns:**
List\[VectorDBRecord]: A list of VectorDBRecord instances.
### add
```python theme={"system"}
def add(self, records: List[VectorRecord], **kwargs):
```
Adds a list of vectors to the specified table.
**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.
**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
table'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 TiDB table. This method
deletes the existing table 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 TiDB client instance.