Skip to main content
Valkey 是一个开源的高性能键值数据存储，支持缓存、消息队列等工作负载，并可作为主数据库使用。Valkey 既可以作为独立守护进程运行，也可以在集群中运行，并提供复制和高可用性选项。
本页面介绍如何使用 Amazon ElastiCache for Valkey 或 Amazon MemoryDB for Valkey 配合 Valkey 向量存储。

​
设置

安装所需的依赖项： 
pip install "langchain-aws[valkey]"
Valkey 集成需要 langchain-aws>=1.5.0。如果您使用的是更早的版本，请直接安装该依赖项：
pip install langchain-aws valkey-glide-sync

​
基本用法

​
使用 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)

​
使用 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)

​
连接 URL

ValkeyVectorStore 支持多种连接 URL 格式： 
# 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"}]
)

​
元数据过滤

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)
)

​
自定义向量模式

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 参考

有关详细的 API 文档，请参阅 ValkeyVectorStore。
Edit this page on GitHub or file an issue.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.