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

# Valkey

> [Valkey](https://valkey.io/) is an open source, high-performance key/value datastore that supports workloads such as caching, message queues, and can act as a primary database. Valkey can run as either a standalone daemon or in a cluster, with options for replication and high availability.

This page covers how to use the Valkey vector store with [Amazon ElastiCache for Valkey](https://aws.amazon.com/elasticache/valkey/) or [Amazon MemoryDB for Valkey](https://aws.amazon.com/memorydb/).

## Setup

Install the required dependencies:

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install "langchain-aws[valkey]"
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add langchain-aws --extra valkey
  ```
</CodeGroup>

<Note>
  The Valkey integration requires `langchain-aws>=1.5.0`. If you're using an earlier version, install the dependency directly:

  ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install langchain-aws valkey-glide-sync
  ```
</Note>

## Basic Usage

### With Bedrock Embeddings

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_aws import BedrockEmbeddings
from langchain_aws.vectorstores import ValkeyVectorStore

# Initialize embeddings
embeddings = BedrockEmbeddings(
    model_id="amazon.titan-embed-text-v1",
    region_name="us-east-1"
)

# Create vector store from texts
vectorstore = ValkeyVectorStore.from_texts(
    texts=["Valkey is fast", "Valkey supports vector search"],
    embedding=embeddings,
    valkey_url="valkey://localhost:6379",
    index_name="my_index"
)

# Perform similarity search
results = vectorstore.similarity_search("fast database", k=2)
for doc in results:
    print(doc.page_content)
```

### With Ollama Embeddings

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_ollama import OllamaEmbeddings
from langchain_aws.vectorstores import ValkeyVectorStore

# Initialize Ollama embeddings
embeddings = OllamaEmbeddings(
    model="nomic-embed-text",
    base_url="http://localhost:11434"
)

# Create vector store
vectorstore = ValkeyVectorStore(
    embedding=embeddings,
    valkey_url="valkey://localhost:6379",
    index_name="my_index",
    vector_schema={
        "name": "content_vector",
        "algorithm": "FLAT",
        "dims": 768,  # nomic-embed-text dimension
        "distance_metric": "COSINE",
        "datatype": "FLOAT32",
    }
)

# Add texts
vectorstore.add_texts(
    texts=["Document 1", "Document 2"],
    metadatas=[{"source": "doc1"}, {"source": "doc2"}]
)

# Search
results = vectorstore.similarity_search("query", k=2)
```

## Connection URLs

ValkeyVectorStore supports various connection URL formats:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# Standalone
valkey_url = "valkey://localhost:6379"

# With authentication
valkey_url = "valkey://username:password@host:6379"

# SSL/TLS
valkey_url = "valkeyss://host:6379"

# SSL with authentication
valkey_url = "valkeyss://username:password@host:6379"
```

## AWS ElastiCache for Valkey

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_aws import BedrockEmbeddings
from langchain_aws.vectorstores import ValkeyVectorStore

embeddings = BedrockEmbeddings()

# Connect to ElastiCache cluster
vectorstore = ValkeyVectorStore(
    embedding=embeddings,
    valkey_url="valkeyss://my-cluster.cache.amazonaws.com:6379",
    index_name="my_index"
)

# Add documents
vectorstore.add_texts(
    texts=["Document 1", "Document 2"],
    metadatas=[{"source": "doc1"}, {"source": "doc2"}]
)
```

## Metadata Filtering

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_aws.vectorstores.valkey.filters import ValkeyTag, ValkeyNum

# Add documents with metadata
vectorstore.add_texts(
    texts=["AI article from 2024", "ML paper from 2023"],
    metadatas=[
        {"category": "ai", "year": 2024},
        {"category": "ml", "year": 2023}
    ]
)

# Search with filters
filter_expr = (ValkeyTag("category") == "ai") & (ValkeyNum("year") >= 2024)
results = vectorstore.similarity_search(
    "artificial intelligence",
    k=5,
    filter=str(filter_expr)
)
```

## Custom Vector Schema

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_aws.vectorstores import ValkeyVectorStore

vectorstore = ValkeyVectorStore(
    embedding=embeddings,
    valkey_url="valkey://localhost:6379",
    index_name="my_index",
    vector_schema={
        "name": "content_vector",
        "algorithm": "HNSW",  # or "FLAT"
        "dims": 1536,
        "distance_metric": "COSINE",  # or "L2", "IP"
        "datatype": "FLOAT32",
    }
)
```

## API Reference

For detailed API documentation, see [`ValkeyVectorStore`](https://reference.langchain.com/python/langchain-aws/vectorstores/valkey/base/ValkeyVectorStore).

***

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