Valkey

Valkey 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 or Amazon MemoryDB for Valkey.

设置

Install the required dependencies:

pip

pip install "langchain-aws[valkey]"

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

pip install langchain-aws valkey-glide-sync

基本用法

With Bedrock Embeddings

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

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:

# 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

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

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

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.

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