> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cyborg.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Types

## DBConfig

The `DBConfig` class specifies the storage location for the index, with options for in-memory storage, databases, or file-based storage.

### Parameters

| Parameter           | Type     | Default | Description                                  |
| ------------------- | -------- | ------- | -------------------------------------------- |
| `location`          | `string` | -       | DB location (`redis`, `postgres`, `memory`)  |
| `table_name`        | `string` | None    | *(Optional)* Table name (`postgres`-only)    |
| `connection_string` | `string` | None    | *(Optional)* Connection string to access DB. |

The supported `location` options are:

* `"redis"`: Use for high-speed, in-memory storage (recommended for `index_location`).
* `"postgres"`: Use for reliable, SQL-based storage (recommended for `config_location`).
* `"memory"` Use for temporary in-memory storage (for benchmarking and evaluation purposes).

### Example Usage

```python theme={null}
import cyborg_vector_search_py as cvs

index_location = cvs.DBConfig(location="redis",
                          connection_string="redis://localhost")

config_location = cvs.DBConfig(location="postgres",
                           table_name="config_table", connection_string="host=localhost dbname=postgres")
```

For more info, you can read about supported backing stores [here](../../guides/get-started/about#backing-stores).

***

## DistanceMetric

`DistanceMetric` is a string representing the distance metric used for the index. Options include:

* `"cosine"`: Cosine similarity.
* `"euclidean"`: Euclidean distance.
* `"squared_euclidean"`: Squared Euclidean distance.

***

## IndexConfig

The `IndexConfig` class defines the parameters for the type of index to be created. Each index type (e.g., `ivf`, `ivfflat`, `ivfpq`) has unique configuration options:

### IndexIVF

Ideal for large-scale datasets where fast retrieval is prioritized over high recall:

|  Speed  | Recall | Index Size |
| :-----: | :----: | :--------: |
| Fastest | Lowest |  Smallest  |

#### Parameters

| Parameter   | Type  | Description                                                                       |
| ----------- | ----- | --------------------------------------------------------------------------------- |
| `dimension` | `int` | Dimensionality of vector embeddings.                                              |
| `n_lists`   | `int` | Number of inverted index lists to create in the index (recommended base-2 value). |
| `metric`    | `str` | *(Optional)* Distance metric to use for index build and queries.                  |

#### Example Usage

```python theme={null}
import cyborg_vector_search_py as cvs

index_config = cvs.IndexIVF(dimension=128, n_lists=1024, metric="euclidean")
```

### IndexIVFFlat

Suitable for applications requiring high recall with less concern for memory usage:

| Speed |  Recall | Index Size |
| :---: | :-----: | :--------: |
|  Fast | Highest |   Biggest  |

#### Parameters

| Parameter   | Type  | Description                                                                       |
| ----------- | ----- | --------------------------------------------------------------------------------- |
| `dimension` | `int` | Dimensionality of vector embeddings.                                              |
| `n_lists`   | `int` | Number of inverted index lists to create in the index (recommended base-2 value). |
| `metric`    | `str` | *(Optional)* Distance metric to use for index build and queries.                  |

#### Example Usage

```python theme={null}
import cyborg_vector_search_py as cvs

index_config = cvs.IndexIVFFlat(dimension=128, n_lists=1024, metric="euclidean")
```

### IndexIVFPQ

Product Quantization compresses embeddings, making it suitable for balancing memory use and recall:

| Speed | Recall | Index Size |
| :---: | :----: | :--------: |
|  Fast |  High  |   Medium   |

#### Parameters

| Parameter   | Type  | Description                                                                          |
| ----------- | ----- | ------------------------------------------------------------------------------------ |
| `dimension` | `int` | Dimensionality of vector embeddings.                                                 |
| `n_lists`   | `int` | Number of inverted index lists to create in the index (recommended base-2 value).    |
| `pq_dim`    | `int` | Dimensionality of embeddings after quantization (less than or equal to `dimension`). |
| `pq_bits`   | `int` | Number of bits per dimension for PQ embeddings (between 1 and 16).                   |
| `metric`    | `str` | *(Optional)* Distance metric to use for index build and queries.                     |

#### Example Usage

```python theme={null}
import cyborg_vector_search_py as cvs

index_config = cvs.IndexIVFPQ(dimension=128, n_lists=1024, pq_dim=64, pq_bits=8, metric="euclidean")
```