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AWS Kit SDK
Utilisez le AWS SDK pour interagir directement avec le contrôle précis des opérations de la AgentCore mémoire. Les exemples suivants montrent comment accéder au AWS SDK avec le SDK pour Python (Boto3).
Installation des dépendances
pip install boto3
Ajouter de la mémoire à court terme
import boto3 from datetime import datetime # Initialize boto3 clients control_client = boto3.client('bedrock-agentcore-control', region_name='us-east-1') data_client = boto3.client('bedrock-agentcore', region_name='us-east-1') # Create short-term memory memory_response = control_client.create_memory( name="BasicMemory", description="Basic memory for short-term event storage", eventExpiryDuration=90 ) memory_id = memory_response['memory']['id'] actor_id = f"actor_{datetime.now().strftime('%Y%m%d%H%M%S')}" session_id = f"session_{datetime.now().strftime('%Y%m%d%H%M%S')}" # Create event with multiple conversation turns event = data_client.create_event( memoryId=memory_id, actorId=actor_id, sessionId=session_id, eventTimestamp=datetime.now(), payload=[ { 'conversational': { 'content': {'text': 'I like sushi with tuna'}, 'role': 'USER' } }, { 'conversational': { 'content': {'text': 'That sounds delicious! Tuna sushi is a great choice.'}, 'role': 'ASSISTANT' } }, { 'conversational': { 'content': {'text': 'I also like pizza'}, 'role': 'USER' } }, { 'conversational': { 'content': {'text': 'Pizza is another excellent choice! You have great taste in food.'}, 'role': 'ASSISTANT' } } ] )
Ajoutez de la mémoire à long terme grâce à des stratégies
import boto3 import time from datetime import datetime # Initialize boto3 clients control_client = boto3.client('bedrock-agentcore-control', region_name='us-east-1') data_client = boto3.client('bedrock-agentcore', region_name='us-east-1') # Create long-term memory memory_response = control_client.create_memory( name=f"ComprehensiveMemory", description="Memory with strategies for long-term memory extraction", eventExpiryDuration=90, memoryStrategies=[ { 'summaryMemoryStrategy': { 'name': 'SessionSummarizer', 'namespaceTemplates': ['/summaries/{actorId}/{sessionId}/'] } }, { 'userPreferenceMemoryStrategy': { 'name': 'PreferenceLearner', 'namespaceTemplates': ['/preferences/{actorId}/'] } }, { 'semanticMemoryStrategy': { 'name': 'FactExtractor', 'namespaceTemplates': ['/facts/{actorId}/'] } } ] ) memory_id = memory_response['memory']['id'] actor_id = f"actor_{datetime.now().strftime('%Y%m%d%H%M%S')}" session_id = f"session_{datetime.now().strftime('%Y%m%d%H%M%S')}" ########## Wait for long-term memory to become active ########## while True: mem_status_response = control_client.get_memory(memoryId=memory_id) status = mem_status_response.get('memory', {}).get('status') if status == 'ACTIVE': print("Memory resource is now ACTIVE.") break elif status == 'FAILED': raise Exception("Memory resource creation FAILED.") print("Waiting for memory to become active...") time.sleep(10) # Create single event with all conversation turns event = data_client.create_event( memoryId=memory_id, actorId=actor_id, sessionId=session_id, eventTimestamp=datetime.now(), payload=[ { 'conversational': { 'content': {'text': 'I like sushi with tuna'}, 'role': 'USER' } }, { 'conversational': { 'content': {'text': 'That sounds delicious! Tuna sushi is a great choice.'}, 'role': 'ASSISTANT' } }, { 'conversational': { 'content': {'text': 'I also like pizza'}, 'role': 'USER' } }, { 'conversational': { 'content': {'text': 'Pizza is another excellent choice! You have great taste in food.'}, 'role': 'ASSISTANT' } } ] )
La référence complète du AWS SDK Amazon Bedrock AgentCore Memory API est disponible à l'adresse suivante :