保存和检索见解

在为 AgentCore 内存配置了至少一种长期记忆策略并且该策略处于活动状态后，该服务将自动开始处理对话数据以提取和存储见解。此过程包括两个不同的步骤：保存原始对话，然后在处理结构化见解后对其进行检索。

第 1 步：保存对话事件以触发提取

当你使用该create_event操作将对话数据保存到短期记忆时，就会触发整个长期记忆过程。每次记录事件时，您都是在为主动记忆策略提供新的原始材料以供分析。

以下示例说明如何将多回合对话保存到内存资源。

示例：将对话另存为一系列事件

#'memory_id' is the ID of your memory resource with an active summary strategy.

from bedrock_agentcore.memory.session import MemorySessionManager
from bedrock_agentcore.memory.constants import ConversationalMessage, MessageRole
import time

actor_id = "User84"
session_id = "OrderSupportSession1"

# Create session manager
session_manager = MemorySessionManager(
    memory_id=memory_id,
    region_name="us-west-2"
)

# Create a session
session = session_manager.create_memory_session(
    actor_id=actor_id,
    session_id=session_id
)

print("Capturing conversational events...")

# Add all conversation turns
session.add_turns(
    messages=[
        ConversationalMessage("Hi, I'm having trouble with my order #12345", MessageRole.USER),
        ConversationalMessage("I am sorry to hear that. Let me look up your order.", MessageRole.ASSISTANT),
        ConversationalMessage("lookup_order(order_id='12345')", MessageRole.TOOL),
        ConversationalMessage("I see your order was shipped 3 days ago. What specific issue are you experiencing?", MessageRole.ASSISTANT),
        ConversationalMessage("The package arrived damaged", MessageRole.USER),
    ]
)

print("Conversation turns added successfully!")

第 2 步：检索提取的见解

长期记忆的提取和整合是一个在后台运行的异步过程。可能需要一分钟或更长时间才能检索来自新对话的见解。您的应用程序逻辑应考虑这种延迟。

要检索结构化见解，您可以使用retrieve_memory_records操作。此操作对长期内存存储执行强大的语义搜索。您必须提供您在策略中定义的正确namespace信息以及描述您要查找的信息的正确信息。searchQuery

以下示例演示如何等待处理，然后检索上一步中保存的对话摘要。

示例：等待并检索会话摘要

# 'session' is an existing session object that you created when adding the coversation turns
# session should be created on a memory resource with an active summary strategy.

# --- Example 1: Retrieve the user's shipping issues under a specific namespace ---
memories = session.search_long_term_memories(
    namespace=f"/summaries/{actor_id}/{session_id}/",
    query="What problem did the user report with their order?",
    top_k=5
)

# --- Example 2: Retrieve the user's shipping issues under a particular namespace hierarchy (e.g.: shipping issues across multiple sessions) ---
memories = session.search_long_term_memories(
    namespace_path=f"/summaries/{actor_id}/",
    query="What problem did the user report with their order?",
    top_k=5
)

print(f"Found {len(memories)} memories:")
for memory_record in memories:
    print(f"Retrieved Issue Detail: {memory_record}")
    print("--------------------------------------------------------------------")

# Example Output:
# Retrieved Issue Detail: The user reported that their package for order #12345 arrived damaged.