本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

多轉對話快取

對於具有多轉對話的應用程式，相同的使用者訊息可能意味著不同的內容，具體取決於內容。例如，在有關 Valkey 的對話中「告訴我更多」表示與有關 Python 的對話中「告訴我更多」不同的內容。

挑戰

單一提示快取適用於無狀態查詢。在多迴轉對話中，您必須快取完整的對話內容，而不只是最後一則訊息：

# "Tell me more" means nothing without context
# Conversation A: "What is Valkey?" -> "Tell me more"  (about Valkey)
# Conversation B: "What is Python?" -> "Tell me more"  (about Python)

策略：內容感知快取金鑰

內嵌完整對話內容的摘要，而不是僅內嵌最後一個使用者訊息。如此一來，類似對話流程中的類似後續問題可以重複使用快取的答案。

def build_context_string(messages: list) -> str:
    """Build a cacheable context string from conversation messages."""
    # Use last 3 turns (6 messages: user + assistant pairs)
    recent = messages[-6:]
    parts = []
    for msg in recent:
        role = msg["role"]
        content = msg["content"][:200]  # Truncate long messages
        parts.append(f"{role}: {content}")
    return " | ".join(parts)

每個使用者使用 TAG 篩選條件進行快取隔離

使用 TAG 欄位，依使用者、工作階段或其他維度隔離快取對話。這可防止針對另一個使用者傳回某個使用者的快取對話：

# Create index with TAG field for per-user isolation
valkey_client.execute_command(
    "FT.CREATE", "conv_cache_idx",
    "SCHEMA",
    "context_summary", "TEXT",
    "response", "TEXT",
    "user_id", "TAG",
    "turn_count", "NUMERIC",
    "embedding", "VECTOR", "HNSW", "6",
    "TYPE", "FLOAT32",
    "DIM", "1024",
    "DISTANCE_METRIC", "COSINE",
)

使用混合式篩選搜尋 (TAG + KNN)：

def lookup_conversation_cache(messages: list, user_id: str, threshold: float = 0.12):
    """Search cache for similar conversation contexts, scoped to a user.

    Note: FT.SEARCH with COSINE distance returns a distance score where
    0 = identical and 2 = opposite. A lower score means higher similarity.
    The threshold here is a maximum distance: only return results closer
    than this value.
    """
    context = build_context_string(messages)
    query_vec = get_embedding(context)

    # Hybrid search: filter by user_id TAG + KNN on context embedding
    results = valkey_client.execute_command(
        "FT.SEARCH", "conv_cache_idx",
        f"@user_id:{{{user_id}}}=>[KNN 1 @embedding $query_vec]",
        "PARAMS", "2", "query_vec", query_vec,
        "DIALECT", "2",
    )

    if results[0] > 0:
        fields = results[2]
        field_dict = {fields[j]: fields[j+1] for j in range(0, len(fields), 2)}
        distance = float(field_dict.get("__embedding_score", "999"))
        if distance < threshold:  # Lower distance = more similar
            return {"hit": True, "response": field_dict.get("response", ""), "distance": distance}

    return {"hit": False}

快取隔離策略