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使用 ElastiCache for Valkey 实现语义缓存

以下演练展示了如何使用 ElastiCache 带有 Amazon Bedrock 的 Valkey 实现直读语义缓存。

步骤 1：创建 ElastiCache 适用于 Valkey 的集群

使用以下命令 ElastiCache 创建 8.2 或更高版本的 Valkey 集群： AWS CLI

aws elasticache create-replication-group \
  --replication-group-id "valkey-semantic-cache" \
  --cache-node-type cache.r7g.large \
  --engine valkey \
  --engine-version 8.2 \
  --num-node-groups 1 \
  --replicas-per-node-group 1

步骤 2：Connect 连接到集群并配置嵌入式组件

通过在 Amazon EC2 实例上运行的应用程序代码，连接到 ElastiCache 集群并设置嵌入模型：

from valkey.cluster import ValkeyCluster
from langchain_aws import BedrockEmbeddings

# Connect to ElastiCache for Valkey
valkey_client = ValkeyCluster(
    host="mycluster.xxxxxx.clustercfg.use1.cache.amazonaws.com",  # Your cluster endpoint
    port=6379,
    decode_responses=False
)

# Set up Amazon Bedrock Titan embeddings
embeddings = BedrockEmbeddings(
    model_id="amazon.titan-embed-text-v2:0",
    region_name="us-east-1"
)

将主机值替换为 ElastiCache 集群的配置终端节点。有关查找集群终端节点的说明，请参阅访问您的 ElastiCache 集群。

步骤 3：为语义缓存创建向量索引

配置使用带有余弦距离 ValkeyStore 的 HNSW 索引自动嵌入查询，以进行矢量搜索：

from langgraph_checkpoint_aws import ValkeyStore
from hashlib import md5

store = ValkeyStore(
    client=valkey_client,
    index={
        "collection_name": "semantic_cache",
        "embed": embeddings,
        "fields": ["query"],           # Fields to vectorize
        "index_type": "HNSW",          # Vector search algorithm
        "distance_metric": "COSINE",   # Similarity metric
        "dims": 1024                   # Titan V2 produces 1024-d vectors
    }
)
store.setup()

def cache_key_for_query(query: str):
    """Generate a deterministic cache key for a query."""
    return md5(query.encode("utf-8")).hexdigest()

第 4 步：实现缓存搜索和更新功能

创建函数以在缓存中搜索语义上相似的查询并存储新的查询-响应对：

def search_cache(user_message: str, k: int = 3, min_similarity: float = 0.8):
    """Look up a semantically similar cached response from ElastiCache."""
    hits = store.search(
        namespace="semantic-cache",
        query=user_message,
        limit=k
    )
    if not hits:
        return None

    # Sort by similarity score (highest first)
    hits = sorted(hits, key=lambda h: h["score"], reverse=True)
    top_hit = hits[0]
    score = top_hit["score"]

    if score < min_similarity:
        return None  # Below similarity threshold

    return top_hit["value"]["answer"]  # Return cached answer


def store_cache(user_message: str, result_message: str):
    """Store a new query-response pair in the semantic cache."""
    key = cache_key_for_query(user_message)
    store.put(
        namespace="semantic-cache",
        key=key,
        value={
            "query": user_message,
            "answer": result_message
        }
    )

第 5 步：实现直读缓存模式

将缓存集成到应用程序的请求处理中：

import time

def handle_query(user_message: str) -> dict:
    """Handle a user query with read-through semantic cache."""
    start = time.time()

    # Step 1: Search the semantic cache
    cached_response = search_cache(user_message, min_similarity=0.8)

    if cached_response:
        # Cache hit - return cached response
        elapsed = (time.time() - start) * 1000
        return {
            "response": cached_response,
            "source": "cache",
            "latency_ms": round(elapsed, 1),
        }

    # Step 2: Cache miss - invoke LLM
    llm_response = invoke_llm(user_message)  # Your LLM invocation function

    # Step 3: Store the response in cache for future reuse
    store_cache(user_message, llm_response)

    elapsed = (time.time() - start) * 1000
    return {
        "response": llm_response,
        "source": "llm",
        "latency_ms": round(elapsed, 1),
    }

底层 Valkey 命令

下表显示了用于实现语义缓存的 Valkey 命令：