Amazon Bedrock SDK AgentCore

Verwenden Sie das Amazon Bedrock AgentCore Python SDK für eine Abstraktion auf höherer Ebene, die Speicheroperationen vereinfacht und praktische Methoden für allgemeine Anwendungsfälle bietet.

Abhängigkeiten installieren

pip install bedrock-agentcore

Kurzzeitgedächtnis hinzufügen

from bedrock_agentcore.memory import MemoryClient

client = MemoryClient(region_name="us-east-1")

memory = client.create_memory(
    name="CustomerSupportAgentMemory",
    description="Memory for customer support conversations",
)

client.create_event(
    memory_id=memory.get("id"), # This is the id from create_memory or list_memories
    actor_id="User84",  # This is the identifier of the actor, could be an agent or end-user.
    session_id="OrderSupportSession1", #Unique id for a particular request/conversation.
    messages=[
        ("Hi, I'm having trouble with my order #12345", "USER"),
        ("I'm sorry to hear that. Let me look up your order.", "ASSISTANT"),
        ("lookup_order(order_id='12345')", "TOOL"),
        ("I see your order was shipped 3 days ago. What specific issue are you experiencing?", "ASSISTANT"),
        ("Actually, before that - I also want to change my email address", "USER"),
        (
            "Of course! I can help with both. Let's start with updating your email. What's your new email?",
            "ASSISTANT",
        ),
        ("newemail@example.com", "USER"),
        ("update_customer_email(old='old@example.com', new='newemail@example.com')", "TOOL"),
        ("Email updated successfully! Now, about your order issue?", "ASSISTANT"),
        ("The package arrived damaged", "USER"),
    ],
)

Fügen Sie das Langzeitgedächtnis mit Strategien hinzu

from bedrock_agentcore.memory import MemoryClient
import time

client = MemoryClient(region_name="us-east-1")

memory = client.create_memory_and_wait(
    name="MyAgentMemory",
    strategies=[{
        "summaryMemoryStrategy": {
            # Name of the extraction model/strategy
            "name": "SessionSummarizer",
            # Organize facts by session ID for easy retrieval
            # Example: "summaries/session123" contains summary of session123
            "namespaceTemplates": ["/summaries/{actorId}/{sessionId}/"]
        }
    }]
)

event = client.create_event(
    memory_id=memory.get("id"), # This is the id from create_memory or list_memories
    actor_id="User84",  # This is the identifier of the actor, could be an agent or end-user.
    session_id="OrderSupportSession1",
    messages=[
        ("Hi, I'm having trouble with my order #12345", "USER"),
        ("I'm sorry to hear that. Let me look up your order.", "ASSISTANT"),
        ("lookup_order(order_id='12345')", "TOOL"),
        ("I see your order was shipped 3 days ago. What specific issue are you experiencing?", "ASSISTANT"),
        ("Actually, before that - I also want to change my email address", "USER"),
        (
            "Of course! I can help with both. Let's start with updating your email. What's your new email?",
            "ASSISTANT",
        ),
        ("newemail@example.com", "USER"),
        ("update_customer_email(old='old@example.com', new='newemail@example.com')", "TOOL"),
        ("Email updated successfully! Now, about your order issue?", "ASSISTANT"),
        ("The package arrived damaged", "USER"),
    ],
)

# Wait for meaningful memories to be extracted from the conversation.
time.sleep(60)

# Query for the summary of the issue using the namespace set in summary strategy above
memories = client.retrieve_memories(
    memory_id=memory.get("id"),
    namespace=f"/summaries/User84/OrderSupportSession1/",
    query="can you summarize the support issue"
)