# ValkeyVectorStore

> **Class** in `langchain_aws`

📖 [View in docs](https://reference.langchain.com/python/langchain-aws/vectorstores/valkey/base/ValkeyVectorStore)

Valkey vector database.

To use, you should have the `valkey-glide-sync` python package installed:

    ```bash
    pip install langchain-aws[valkey]
    ```

Connection URL schemas:
- valkey://<host>:<port> # simple connection
- valkey://<username>:<password>@<host>:<port> # connection with authentication
- valkeyss://<host>:<port> # connection with SSL
- valkeyss://<username>:<password>@<host>:<port> # connection with SSL and auth

Examples:

    Initialize and load documents:
    ```python
    from langchain_aws.vectorstores import ValkeyVectorStore
    from langchain_aws.embeddings import BedrockEmbeddings

    embeddings = BedrockEmbeddings()
    vds = ValkeyVectorStore.from_documents(
        documents,
        embeddings,
        valkey_url="valkey://cluster_endpoint:6379",
    )
    ```

    Initialize with texts and metadata:
    ```python
    vds = ValkeyVectorStore.from_texts(
        texts,
        metadata,
        embeddings,
        valkey_url="valkey://cluster_endpoint:6379",
    )
    ```

## Signature

```python
ValkeyVectorStore(
    self,
    valkey_url: str,
    index_name: str,
    embedding: Embeddings,
    vector_schema: Optional[Dict[str, Union[str, int]]] = None,
    relevance_score_fn: Optional[Callable[[float], float]] = None,
    key_prefix: Optional[str] = None,
    cluster_mode: Optional[bool] = None,
    **kwargs: Any = {},
)
```

## Parameters

| Name | Type | Required | Description |
|------|------|----------|-------------|
| `valkey_url` | `str` | Yes | Connection URL for Valkey server. |
| `index_name` | `str` | Yes | Name of the index. |
| `embedding` | `Embeddings` | Yes | Embeddings object. |
| `vector_schema` | `Optional[Dict[str, Union[str, int]]]` | No | Vector schema configuration. (default: `None`) |
| `relevance_score_fn` | `Optional[Callable[[float], float]]` | No | Function to compute relevance score. (default: `None`) |
| `key_prefix` | `Optional[str]` | No | Prefix for document keys. (default: `None`) |
| `cluster_mode` | `Optional[bool]` | No | If True, create cluster client. If False, create standalone client. If None (default), try cluster first and fall back to standalone. (default: `None`) |
| `**kwargs` | `Any` | No | Additional arguments to pass to GLIDE client. (default: `{}`) |

## Extends

- `VectorStore`

## Constructors

```python
__init__(
    self,
    valkey_url: str,
    index_name: str,
    embedding: Embeddings,
    vector_schema: Optional[Dict[str, Union[str, int]]] = None,
    relevance_score_fn: Optional[Callable[[float], float]] = None,
    key_prefix: Optional[str] = None,
    cluster_mode: Optional[bool] = None,
    **kwargs: Any = {},
)
```

| Name | Type |
|------|------|
| `valkey_url` | `str` |
| `index_name` | `str` |
| `embedding` | `Embeddings` |
| `vector_schema` | `Optional[Dict[str, Union[str, int]]]` |
| `relevance_score_fn` | `Optional[Callable[[float], float]]` |
| `key_prefix` | `Optional[str]` |
| `cluster_mode` | `Optional[bool]` |


## Properties

- `DEFAULT_VECTOR_SCHEMA`
- `model_config`
- `index_name`
- `client`
- `relevance_score_fn`
- `vector_schema`
- `key_prefix`
- `embeddings`

## Methods

- [`add_texts()`](https://reference.langchain.com/python/langchain-aws/vectorstores/valkey/base/ValkeyVectorStore/add_texts)
- [`similarity_search()`](https://reference.langchain.com/python/langchain-aws/vectorstores/valkey/base/ValkeyVectorStore/similarity_search)
- [`similarity_search_by_vector()`](https://reference.langchain.com/python/langchain-aws/vectorstores/valkey/base/ValkeyVectorStore/similarity_search_by_vector)
- [`similarity_search_with_score()`](https://reference.langchain.com/python/langchain-aws/vectorstores/valkey/base/ValkeyVectorStore/similarity_search_with_score)
- [`similarity_search_with_score_by_vector()`](https://reference.langchain.com/python/langchain-aws/vectorstores/valkey/base/ValkeyVectorStore/similarity_search_with_score_by_vector)
- [`from_texts()`](https://reference.langchain.com/python/langchain-aws/vectorstores/valkey/base/ValkeyVectorStore/from_texts)
- [`from_existing_index()`](https://reference.langchain.com/python/langchain-aws/vectorstores/valkey/base/ValkeyVectorStore/from_existing_index)
- [`delete()`](https://reference.langchain.com/python/langchain-aws/vectorstores/valkey/base/ValkeyVectorStore/delete)

---

[View source on GitHub](https://github.com/langchain-ai/langchain-aws/blob/11c5c131f922af35aee690326efd363b490da2e9/libs/aws/langchain_aws/vectorstores/valkey/base.py#L50)