<a id="camel.storages.vectordb_storages.surreal"></a>

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage"></a>

## SurrealStorage

```python
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 <https://surrealdb.com>`_

**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.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage.__init__"></a>

### __init__

```python
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"`)

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage._table_exists"></a>

### _table_exists

```python
def _table_exists(self):
```

**Returns:**

  bool: True if the table exists, False otherwise.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage._get_table_info"></a>

### _get_table_info

```python
def _get_table_info(self):
```

**Returns:**

  Dict[str, int]: A dictionary with 'dim' and 'count' keys.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage._create_table"></a>

### _create_table

```python
def _create_table(self):
```

Define and create the vector storage table with HNSW index.

Documentation: https://surrealdb.com/docs/surrealdb/reference-guide/
vector-search#vector-search-cheat-sheet

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage._drop_table"></a>

### _drop_table

```python
def _drop_table(self):
```

Drop the vector storage table if it exists.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage._check_and_create_table"></a>

### _check_and_create_table

```python
def _check_and_create_table(self):
```

Check if the table exists and matches the expected vector
dimension. If not, create a new table.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage._validate_and_convert_records"></a>

### _validate_and_convert_records

```python
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.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage.query"></a>

### query

```python
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.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage.add"></a>

### add

```python
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.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage.delete"></a>

### delete

```python
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.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage.status"></a>

### status

```python
def status(self):
```

**Returns:**

  VectorDBStatus: Object containing vector table metadata.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage.clear"></a>

### clear

```python
def clear(self):
```

Reset the vector table by dropping and recreating it.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage.load"></a>

### load

```python
def load(self):
```

Load the collection hosted on cloud service.

<a id="camel.storages.vectordb_storages.surreal.SurrealStorage.client"></a>

### client

```python
def client(self):
```

Provides access to the underlying SurrealDB client.
