> ## 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.surreal
## SurrealStorage
```python theme={"system"}
class SurrealStorage(BaseVectorStorage):
```
An implementation of the `BaseVectorStorage` using SurrealDB,
a scalable, distributed database with WebSocket support, for
efficient vector storage and similarity search.
SurrealDB official site and documentation can be found at:
`SurrealDB `\_
**Parameters:**
* **url** (str): WebSocket URL for connecting to SurrealDB (default: "ws\://localhost:8000/rpc").
* **table** (str): Name of the table used for storing vectors (default: "vector\_store").
* **vector\_dim** (int): Dimensionality of the stored vectors.
* **distance** (VectorDistance): Distance metric used for similarity comparisons (default: VectorDistance.COSINE).
* **namespace** (str): SurrealDB namespace to use (default: "default"). (default: `"default"`)
* **database** (str): SurrealDB database name (default: "demo"). (default: `"demo"`)
* **user** (str): Username for authentication (default: "root"). (default: `"root"`)
* **password** (str): Password for authentication (default: "root"). (default: `"root"`)
**Note:**
* SurrealDB supports flexible schema and powerful querying capabilities
via SQL-like syntax over WebSocket.
* This implementation manages connection setup and ensures the target
table exists.
* Suitable for applications requiring distributed vector storage and
search with real-time updates.
### **init**
```python theme={"system"}
def __init__(self):
```
Initialize SurrealStorage with connection settings and ensure
the target table exists.
**Parameters:**
* **url** (str): WebSocket URL for connecting to SurrealDB. (default: :obj:`"ws://localhost:8000/rpc"`)
* **table** (str): Name of the table used for vector storage. (default: :obj:`"vector_store"`)
* **vector\_dim** (int): Dimensionality of the stored vectors. (default: :obj:`786`)
* **distance** (VectorDistance): Distance metric for similarity searches. (default: :obj:`VectorDistance.COSINE`)
* **namespace** (str): SurrealDB namespace to use. (default: :obj:`"default"`)
* **database** (str): SurrealDB database name. (default: :obj:`"demo"`)
* **user** (str): Username for authentication. (default: :obj:`"root"`)
* **password** (str): Password for authentication. (default: :obj:`"root"`)
### \_table\_exists
```python theme={"system"}
def _table_exists(self):
```
**Returns:**
bool: True if the table exists, False otherwise.
### \_get\_table\_info
```python theme={"system"}
def _get_table_info(self):
```
**Returns:**
Dict\[str, int]: A dictionary with 'dim' and 'count' keys.
### \_create\_table
```python theme={"system"}
def _create_table(self):
```
Define and create the vector storage table with HNSW index.
Documentation: [https://surrealdb.com/docs/surrealdb/reference-guide/](https://surrealdb.com/docs/surrealdb/reference-guide/)
vector-search#vector-search-cheat-sheet
### \_drop\_table
```python theme={"system"}
def _drop_table(self):
```
Drop the vector storage table if it exists.
### \_check\_and\_create\_table
```python theme={"system"}
def _check_and_create_table(self):
```
Check if the table exists and matches the expected vector
dimension. If not, create a new table.
### \_validate\_and\_convert\_records
```python theme={"system"}
def _validate_and_convert_records(self, records: List[VectorRecord]):
```
Validate and convert VectorRecord instances into
SurrealDB-compatible dictionaries.
**Parameters:**
* **records** (List\[VectorRecord]): List of vector records to insert.
**Returns:**
List\[Dict]: Transformed list of dicts ready for insertion.
### query
```python theme={"system"}
def query(self, query: VectorDBQuery, **kwargs: Any):
```
Perform a top-k similarity search using the configured distance
metric.
**Parameters:**
* **query** (VectorDBQuery): Query containing the query vector and top\_k value.
**Returns:**
List\[VectorDBQueryResult]: Ranked list of matching records
with similarity scores.
### add
```python theme={"system"}
def add(self, records: List[VectorRecord], **kwargs):
```
Insert validated vector records into the SurrealDB table.
**Parameters:**
* **records** (List\[VectorRecord]): List of vector records to add.
### delete
```python theme={"system"}
def delete(
self,
ids: Optional[List[str]] = None,
if_all: bool = False,
**kwargs
):
```
Delete specific records by ID or clear the entire table.
**Parameters:**
* **ids** (Optional\[List\[str]]): List of record IDs to delete.
* **if\_all** (bool): Whether to delete all records in the table.
### status
```python theme={"system"}
def status(self):
```
**Returns:**
VectorDBStatus: Object containing vector table metadata.
### clear
```python theme={"system"}
def clear(self):
```
Reset the vector table by dropping and recreating it.
### 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 SurrealDB client.