D'autres exemples de AWS SDK sont disponibles dans le référentiel [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub .
Les traductions sont fournies par des outils de traduction automatique. En cas de conflit entre le contenu d'une traduction et celui de la version originale en anglais, la version anglaise prévaudra.
# Exemples d’agents Amazon Bedrock avec le kit SDK pour Python (Boto3)
Les exemples de code suivants vous montrent comment effectuer des actions et implémenter des scénarios courants en utilisant AWS SDK pour Python (Boto3) les agents Amazon Bedrock.
Les *actions* sont des extraits de code de programmes plus larges et doivent être exécutées dans leur contexte. Alors que les actions vous indiquent comment appeler des fonctions de service individuelles, vous pouvez les voir en contexte dans leurs scénarios associés.
Les *scénarios* sont des exemples de code qui vous montrent comment accomplir des tâches spécifiques en appelant plusieurs fonctions au sein d’un même service ou combinés à d’autres Services AWS.
Chaque exemple inclut un lien vers le code source complet, où vous trouverez des instructions sur la configuration et l’exécution du code en contexte.
**Topics**
+ [Actions](#actions)
+ [Scénarios](#scenarios)
## Actions
### `CreateAgent`
L'exemple de code suivant montre comment utiliser`CreateAgent`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Créez un agent.
```
def create_agent(self, agent_name, foundation_model, role_arn, instruction):
"""
Creates an agent that orchestrates interactions between foundation models,
data sources, software applications, user conversations, and APIs to carry
out tasks to help customers.
:param agent_name: A name for the agent.
:param foundation_model: The foundation model to be used for orchestration by the agent.
:param role_arn: The ARN of the IAM role with permissions needed by the agent.
:param instruction: Instructions that tell the agent what it should do and how it should
interact with users.
:return: The response from Amazon Bedrock Agents if successful, otherwise raises an exception.
"""
try:
response = self.client.create_agent(
agentName=agent_name,
foundationModel=foundation_model,
agentResourceRoleArn=role_arn,
instruction=instruction,
)
except ClientError as e:
logger.error(f"Error: Couldn't create agent. Here's why: {e}")
raise
else:
return response["agent"]
```
+ Pour plus de détails sur l'API, consultez [CreateAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgent)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `CreateAgentActionGroup`
L'exemple de code suivant montre comment utiliser`CreateAgentActionGroup`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Création d’un groupe d’actions d’agent.
```
def create_agent_action_group(
self, name, description, agent_id, agent_version, function_arn, api_schema
):
"""
Creates an action group for an agent. An action group defines a set of actions that an
agent should carry out for the customer.
:param name: The name to give the action group.
:param description: The description of the action group.
:param agent_id: The unique identifier of the agent for which to create the action group.
:param agent_version: The version of the agent for which to create the action group.
:param function_arn: The ARN of the Lambda function containing the business logic that is
carried out upon invoking the action.
:param api_schema: Contains the OpenAPI schema for the action group.
:return: Details about the action group that was created.
"""
try:
response = self.client.create_agent_action_group(
actionGroupName=name,
description=description,
agentId=agent_id,
agentVersion=agent_version,
actionGroupExecutor={"lambda": function_arn},
apiSchema={"payload": api_schema},
)
agent_action_group = response["agentActionGroup"]
except ClientError as e:
logger.error(f"Error: Couldn't create agent action group. Here's why: {e}")
raise
else:
return agent_action_group
```
+ Pour plus de détails sur l'API, consultez [CreateAgentActionGroup](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgentActionGroup)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `CreateAgentAlias`
L'exemple de code suivant montre comment utiliser`CreateAgentAlias`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Créez un alias d’agent.
```
def create_agent_alias(self, name, agent_id):
"""
Creates an alias of an agent that can be used to deploy the agent.
:param name: The name of the alias.
:param agent_id: The unique identifier of the agent.
:return: Details about the alias that was created.
"""
try:
response = self.client.create_agent_alias(
agentAliasName=name, agentId=agent_id
)
agent_alias = response["agentAlias"]
except ClientError as e:
logger.error(f"Couldn't create agent alias. {e}")
raise
else:
return agent_alias
```
+ Pour plus de détails sur l'API, consultez [CreateAgentAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgentAlias)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `CreateFlow`
L'exemple de code suivant montre comment utiliser`CreateFlow`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Créez un flux Amazon Bedrock.
```
def create_flow(client, flow_name, flow_description, role_arn, flow_def):
"""
Creates an Amazon Bedrock flow.
Args:
client: Amazon Bedrock agent boto3 client.
flow_name (str): The name for the new flow.
role_arn (str): The ARN for the IAM role that use flow uses.
flow_def (json): The JSON definition of the flow that you want to create.
Returns:
dict: The response from CreateFlow.
"""
try:
logger.info("Creating flow: %s.", flow_name)
response = client.create_flow(
name=flow_name,
description=flow_description,
executionRoleArn=role_arn,
definition=flow_def
)
logger.info("Successfully created flow: %s. ID: %s",
flow_name,
{response['id']})
return response
except ClientError as e:
logger.exception("Client error creating flow: %s", {str(e)})
raise
except Exception as e:
logger.exception("Unexepcted error creating flow: %s", {str(e)})
raise
```
+ Pour plus de détails sur l'API, consultez [CreateFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlow)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `CreateFlowAlias`
L'exemple de code suivant montre comment utiliser`CreateFlowAlias`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Créez un alias pour un flux Amazon Bedrock.
```
def create_flow_alias(client, flow_id, flow_version, name, description):
"""
Creates an alias for an Amazon Bedrock flow.
Args:
client: bedrock agent boto3 client.
flow_id (str): The identifier of the flow.
Returns:
str: The ID for the flow alias.
"""
try:
logger.info("Creating flow alias for flow: %s.", flow_id)
response = client.create_flow_alias(
flowIdentifier=flow_id,
name=name,
description=description,
routingConfiguration=[
{
"flowVersion": flow_version
}
]
)
logger.info("Successfully created flow alias for %s.", flow_id)
return response['id']
except ClientError as e:
logging.exception("Client error creating alias for flow: %s - %s",
flow_id, str(e))
raise
except Exception as e:
logging.exception("Unexpected error creating alias for flow : %s - %s",
flow_id, str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [CreateFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlowAlias)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `CreateFlowVersion`
L'exemple de code suivant montre comment utiliser`CreateFlowVersion`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Créez une version d’un flux Amazon Bedrock.
```
def create_flow_version(client, flow_id, description):
"""
Creates a version of an Amazon Bedrock flow.
Args:
client: Amazon Bedrock agent boto3 client.
flow_id (str): The identifier of the flow.
description (str) : A description for the flow.
Returns:
str: The version for the flow.
"""
try:
logger.info("Creating flow version for flow: %s.", flow_id)
# Call CreateFlowVersion operation
response = client.create_flow_version(
flowIdentifier=flow_id,
description=description
)
logging.info("Successfully created flow version %s for flow %s.",
response['version'], flow_id)
return response['version']
except ClientError as e:
logging.exception("Client error creating flow: %s", str(e))
raise
except Exception as e:
logging.exception("Unexpected error creating flow : %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [CreateFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlowVersion)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `CreateKnowledgeBase`
L'exemple de code suivant montre comment utiliser`CreateKnowledgeBase`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Créez une base de connaissances Amazon Bedrock.
```
def create_knowledge_base(bedrock_agent_client, name, role_arn, description=None):
"""
Creates a new knowledge base.
Args:
bedrock_agent_client: The Boto3 Bedrock Agent client.
name (str): The name of the knowledge base.
role_arn (str): The ARN of the IAM role that the knowledge base assumes to access resources.
description (str, optional): A description of the knowledge base.
Returns:
dict: The details of the created knowledge base.
"""
try:
kwargs = {
"name": name,
"roleArn": role_arn,
"knowledgeBaseConfiguration": {
"type": "VECTOR",
"vectorKnowledgeBaseConfiguration": {
"embeddingModelArn": "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v1"
}
},
"storageConfiguration": {
"type": "OPENSEARCH_SERVERLESS",
# Note: You will need to create an OpenSearch Serverless collection first and replace this ARN
# with your actual collection ARN from the OpenSearch console. If you use the console instead,
# you can use the quick-create flow to have Knowledge Bases create the collection for you.
"opensearchServerlessConfiguration": {
"collectionArn": "arn:aws:aoss:us-east-1::123456789012:collection/abcdefgh12345678defgh",
"fieldMapping": {
"metadataField": "metadata",
"textField": "text",
"vectorField": "vector"
},
"vectorIndexName": "test-uuid"
},
},
"clientToken": "test-client-token-" + str(uuid.uuid4())
}
if description:
kwargs["description"] = description
response = bedrock_agent_client.create_knowledge_base(**kwargs)
logger.info("Created knowledge base with ID: %s", response["knowledgeBase"]["knowledgeBaseId"])
return response["knowledgeBase"]
except ClientError as err:
logger.error(
"Couldn't create knowledge base. Here's why: %s: %s",
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
```
+ Pour plus de détails sur l'API, consultez [CreateKnowledgeBase](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateKnowledgeBase)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `CreatePrompt`
L'exemple de code suivant montre comment utiliser`CreatePrompt`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Créez une invite gérée par Amazon Bedrock.
```
def create_prompt(client, prompt_name, prompt_description, prompt_template, model_id=None):
"""
Creates an Amazon Bedrock managed prompt.
Args:
client: Amazon Bedrock Agent boto3 client.
prompt_name (str): The name for the new prompt.
prompt_description (str): The description for the new prompt.
prompt_template (str): The template for the prompt.
model_id (str, optional): The model ID to associate with the prompt.
Returns:
dict: The response from CreatePrompt.
"""
try:
logger.info("Creating prompt: %s.", prompt_name)
# Create a variant with the template
variant = {
"name": "default",
"templateType": "TEXT",
"templateConfiguration": {
"text": {
"text": prompt_template,
"inputVariables": []
}
}
}
# Extract input variables from the template
# Look for patterns like {{variable_name}}
variables = re.findall(r'{{(.*?)}}', prompt_template)
for var in variables:
variant["templateConfiguration"]["text"]["inputVariables"].append({"name": var.strip()})
# Add model ID if provided
if model_id:
variant["modelId"] = model_id
# Create the prompt with the variant
create_params = {
'name': prompt_name,
'description': prompt_description,
'variants': [variant]
}
response = client.create_prompt(**create_params)
logger.info("Successfully created prompt: %s. ID: %s",
prompt_name,
response['id'])
return response
except ClientError as e:
logger.exception("Client error creating prompt: %s", str(e))
raise
except Exception as e:
logger.exception("Unexpected error creating prompt: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [CreatePrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreatePrompt)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `CreatePromptVersion`
L'exemple de code suivant montre comment utiliser`CreatePromptVersion`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Créez une version d’une invite gérée par Amazon Bedrock.
```
def create_prompt_version(client, prompt_id, description=None):
"""
Creates a version of an Amazon Bedrock managed prompt.
Args:
client: Amazon Bedrock Agent boto3 client.
prompt_id (str): The identifier of the prompt to create a version for.
description (str, optional): A description for the version.
Returns:
dict: The response from CreatePromptVersion.
"""
try:
logger.info("Creating version for prompt ID: %s.", prompt_id)
create_params = {
'promptIdentifier': prompt_id
}
if description:
create_params['description'] = description
response = client.create_prompt_version(**create_params)
logger.info("Successfully created prompt version: %s", response['version'])
logger.info("Prompt version ARN: %s", response['arn'])
return response
except ClientError as e:
logger.exception("Client error creating prompt version: %s", str(e))
raise
except Exception as e:
logger.exception("Unexpected error creating prompt version: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [CreatePromptVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreatePromptVersion)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DeleteAgent`
L'exemple de code suivant montre comment utiliser`DeleteAgent`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Supprimez un agent.
```
def delete_agent(self, agent_id):
"""
Deletes an Amazon Bedrock agent.
:param agent_id: The unique identifier of the agent to delete.
:return: The response from Amazon Bedrock Agents if successful, otherwise raises an exception.
"""
try:
response = self.client.delete_agent(
agentId=agent_id, skipResourceInUseCheck=False
)
except ClientError as e:
logger.error(f"Couldn't delete agent. {e}")
raise
else:
return response
```
+ Pour plus de détails sur l'API, consultez [DeleteAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteAgent)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DeleteAgentAlias`
L'exemple de code suivant montre comment utiliser`DeleteAgentAlias`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Supprimez un alias d’agent.
```
def delete_agent_alias(self, agent_id, agent_alias_id):
"""
Deletes an alias of an Amazon Bedrock agent.
:param agent_id: The unique identifier of the agent that the alias belongs to.
:param agent_alias_id: The unique identifier of the alias to delete.
:return: The response from Amazon Bedrock Agents if successful, otherwise raises an exception.
"""
try:
response = self.client.delete_agent_alias(
agentId=agent_id, agentAliasId=agent_alias_id
)
except ClientError as e:
logger.error(f"Couldn't delete agent alias. {e}")
raise
else:
return response
```
+ Pour plus de détails sur l'API, consultez [DeleteAgentAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteAgentAlias)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DeleteFlow`
L'exemple de code suivant montre comment utiliser`DeleteFlow`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Supprimez un flux Amazon Bedrock.
```
def delete_flow(client, flow_id):
"""
Deletes an Amazon Bedrock flow.
Args:
client: Amazon Bedrock agent boto3 client.
flow_id (str): The identifier of the flow that you want to delete.
Returns:
dict: The response from the DeleteFLow operation.
"""
try:
logger.info("Deleting flow ID: %s.",
flow_id)
# Call DeleteFlow operation
response = client.delete_flow(
flowIdentifier=flow_id,
skipResourceInUseCheck=True
)
logger.info("Finished deleting flow ID: %s", flow_id)
return response
except ClientError as e:
logger.exception("Client error deleting flow: %s", {str(e)})
raise
except Exception as e:
logger.exception("Unexepcted error deleting flow: %s", {str(e)})
raise
```
+ Pour plus de détails sur l'API, consultez [DeleteFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlow)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DeleteFlowAlias`
L'exemple de code suivant montre comment utiliser`DeleteFlowAlias`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Supprimez un alias pour un flux Amazon Bedrock.
```
def delete_flow_alias(client, flow_id, flow_alias_id):
"""
Deletes an Amazon Bedrock flow alias.
Args:
client: bedrock agent boto3 client.
flow_id (str): The identifier of the flow.
Returns:
dict: The response from the call to DetectFLowAlias
"""
try:
logger.info("Deleting flow alias %s for flow: %s.", flow_alias_id, flow_id)
# Delete the flow alias.
response = client.delete_flow_alias(
aliasIdentifier=flow_alias_id,
flowIdentifier=flow_id
)
logging.info("Successfully deleted flow version for %s.", flow_id)
return response
except ClientError as e:
logging.exception("Client error deleting flow version: %s", str(e))
raise
except Exception as e:
logging.exception("Unexpected deleting flow version: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [DeleteFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlowAlias)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DeleteFlowVersion`
L'exemple de code suivant montre comment utiliser`DeleteFlowVersion`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Supprimez une version d’un flux Amazon Bedrock.
```
def delete_flow_version(client, flow_id, flow_version):
"""
Deletes a version of an Amazon Bedrock flow.
Args:
client: Amazon Bedrock agent boto3 client.
flow_id (str): The identifier of the flow.
Returns:
dict: The response from DeleteFlowVersion.
"""
try:
logger.info("Deleting flow version %s for flow: %s.",flow_version, flow_id)
# Call DeleteFlowVersion operation
response = client.delete_flow_version(
flowIdentifier=flow_id,
flowVersion=flow_version
)
logging.info("Successfully deleted flow version %s for %s.",
flow_version,
flow_id)
return response
except ClientError as e:
logging.exception("Client error deleting flow version: %s ", str(e))
raise
except Exception as e:
logging.exception("Unexpected deleting flow version: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [DeleteFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlowVersion)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DeleteKnowledgeBase`
L'exemple de code suivant montre comment utiliser`DeleteKnowledgeBase`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Supprimez une base de connaissances Amazon Bedrock.
```
def delete_knowledge_base(bedrock_agent_client, knowledge_base_id):
"""
Deletes a knowledge base.
Args:
bedrock_agent_client: The Boto3 Bedrock Agent client.
knowledge_base_id (str): The ID of the knowledge base to delete.
Returns:
bool: True if the deletion was successful.
"""
try:
bedrock_agent_client.delete_knowledge_base(
knowledgeBaseId=knowledge_base_id
)
logger.info("Deleted knowledge base: %s", knowledge_base_id)
return True
except ClientError as err:
logger.error(
"Couldn't delete knowledge base %s. Here's why: %s: %s",
knowledge_base_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
```
+ Pour plus de détails sur l'API, consultez [DeleteKnowledgeBase](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteKnowledgeBase)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `DeletePrompt`
L'exemple de code suivant montre comment utiliser`DeletePrompt`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Supprimez une invite gérée par Amazon Bedrock.
```
def delete_prompt(client, prompt_id):
"""
Deletes an Amazon Bedrock managed prompt.
Args:
client: Amazon Bedrock Agent boto3 client.
prompt_id (str): The identifier of the prompt that you want to delete.
Returns:
dict: The response from the DeletePrompt operation.
"""
try:
logger.info("Deleting prompt ID: %s.", prompt_id)
response = client.delete_prompt(
promptIdentifier=prompt_id
)
logger.info("Finished deleting prompt ID: %s", prompt_id)
return response
except ClientError as e:
logger.exception("Client error deleting prompt: %s", str(e))
raise
except Exception as e:
logger.exception("Unexpected error deleting prompt: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [DeletePrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeletePrompt)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `GetAgent`
L'exemple de code suivant montre comment utiliser`GetAgent`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Obtenez un agent.
```
def get_agent(self, agent_id, log_error=True):
"""
Gets information about an agent.
:param agent_id: The unique identifier of the agent.
:param log_error: Whether to log any errors that occur when getting the agent.
If True, errors will be logged to the logger. If False, errors
will still be raised, but not logged.
:return: The information about the requested agent.
"""
try:
response = self.client.get_agent(agentId=agent_id)
agent = response["agent"]
except ClientError as e:
if log_error:
logger.error(f"Couldn't get agent {agent_id}. {e}")
raise
else:
return agent
```
+ Pour plus de détails sur l'API, consultez [GetAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetAgent)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `GetFlow`
L'exemple de code suivant montre comment utiliser`GetFlow`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Obtenez un flux Amazon Bedrock.
```
def get_flow(client, flow_id):
"""
Gets an Amazon Bedrock flow.
Args:
client: bedrock agent boto3 client.
flow_id (str): The identifier of the flow that you want to get.
Returns:
dict: The response from the GetFlow operation.
"""
try:
logger.info("Getting flow ID: %s.",
flow_id)
# Call GetFlow operation.
response = client.get_flow(
flowIdentifier=flow_id
)
logger.info("Retrieved flow ID: %s. Name: %s", flow_id,
response['name'])
return response
except ClientError as e:
logger.exception("Client error getting flow: %s", {str(e)})
raise
except Exception as e:
logger.exception("Unexepcted error getting flow: %s", {str(e)})
raise
```
+ Pour plus de détails sur l'API, consultez [GetFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlow)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `GetFlowVersion`
L'exemple de code suivant montre comment utiliser`GetFlowVersion`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Obtenez une version d’un flux Amazon Bedrock.
```
def get_flow_version(client, flow_id, flow_version):
"""
Gets information about a version of an Amazon Bedrock flow.
Args:
client: Amazon Bedrock agent boto3 client.
flow_id (str): The identifier of the flow.
flow_version (str): The flow version of the flow.
Returns:
dict: The response from the call to GetFlowVersion.
"""
try:
logger.info("Deleting flow version for flow: %s.", flow_id)
# Call GetFlowVersion operation
response = client.get_flow_version(
flowIdentifier=flow_id,
flowVersion=flow_version
)
logging.info("Successfully got flow version %s information for flow %s.",
flow_version,
flow_id)
return response
except ClientError as e:
logging.exception("Client error getting flow version: %s", str(e))
raise
except Exception as e:
logging.exception("Unexpected error getting flow version: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [GetFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlowVersion)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `GetKnowledgeBase`
L'exemple de code suivant montre comment utiliser`GetKnowledgeBase`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Obtenez une base de connaissances Amazon Bedrock.
```
def get_knowledge_base(bedrock_agent_client, knowledge_base_id):
"""
Gets details about a specific knowledge base.
Args:
bedrock_agent_client: The Boto3 Bedrock Agent client.
knowledge_base_id (str): The ID of the knowledge base.
Returns:
dict: The details of the knowledge base.
"""
try:
response = bedrock_agent_client.get_knowledge_base(
knowledgeBaseId=knowledge_base_id
)
logger.info("Retrieved knowledge base: %s", knowledge_base_id)
return response["knowledgeBase"]
except ClientError as err:
logger.error(
"Couldn't get knowledge base %s. Here's why: %s: %s",
knowledge_base_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
```
+ Pour plus de détails sur l'API, consultez [GetKnowledgeBase](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetKnowledgeBase)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `GetPrompt`
L'exemple de code suivant montre comment utiliser`GetPrompt`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Obtenez une invite gérée par Amazon Bedrock.
```
def get_prompt(client, prompt_id):
"""
Gets an Amazon Bedrock managed prompt.
Args:
client: Amazon Bedrock Agent boto3 client.
prompt_id (str): The identifier of the prompt that you want to get.
Returns:
dict: The response from the GetPrompt operation.
"""
try:
logger.info("Getting prompt ID: %s.", prompt_id)
response = client.get_prompt(
promptIdentifier=prompt_id
)
logger.info("Retrieved prompt ID: %s. Name: %s",
prompt_id,
response['name'])
return response
except ClientError as e:
logger.exception("Client error getting prompt: %s", str(e))
raise
except Exception as e:
logger.exception("Unexpected error getting prompt: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [GetPrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetPrompt)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListAgentActionGroups`
L'exemple de code suivant montre comment utiliser`ListAgentActionGroups`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Répertoriez les groupes d’actions d’un agent.
```
def list_agent_action_groups(self, agent_id, agent_version):
"""
List the action groups for a version of an Amazon Bedrock Agent.
:param agent_id: The unique identifier of the agent.
:param agent_version: The version of the agent.
:return: The list of action group summaries for the version of the agent.
"""
try:
action_groups = []
paginator = self.client.get_paginator("list_agent_action_groups")
for page in paginator.paginate(
agentId=agent_id,
agentVersion=agent_version,
PaginationConfig={"PageSize": 10},
):
action_groups.extend(page["actionGroupSummaries"])
except ClientError as e:
logger.error(f"Couldn't list action groups. {e}")
raise
else:
return action_groups
```
+ Pour plus de détails sur l'API, consultez [ListAgentActionGroups](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgentActionGroups)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListAgentKnowledgeBases`
L'exemple de code suivant montre comment utiliser`ListAgentKnowledgeBases`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Répertoriez les bases de connaissances associées à un agent.
```
def list_agent_knowledge_bases(self, agent_id, agent_version):
"""
List the knowledge bases associated with a version of an Amazon Bedrock Agent.
:param agent_id: The unique identifier of the agent.
:param agent_version: The version of the agent.
:return: The list of knowledge base summaries for the version of the agent.
"""
try:
knowledge_bases = []
paginator = self.client.get_paginator("list_agent_knowledge_bases")
for page in paginator.paginate(
agentId=agent_id,
agentVersion=agent_version,
PaginationConfig={"PageSize": 10},
):
knowledge_bases.extend(page["agentKnowledgeBaseSummaries"])
except ClientError as e:
logger.error(f"Couldn't list knowledge bases. {e}")
raise
else:
return knowledge_bases
```
+ Pour plus de détails sur l'API, consultez [ListAgentKnowledgeBases](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgentKnowledgeBases)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListAgents`
L'exemple de code suivant montre comment utiliser`ListAgents`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Répertoriez les agents associés à un compte.
```
def list_agents(self):
"""
List the available Amazon Bedrock Agents.
:return: The list of available bedrock agents.
"""
try:
all_agents = []
paginator = self.client.get_paginator("list_agents")
for page in paginator.paginate(PaginationConfig={"PageSize": 10}):
all_agents.extend(page["agentSummaries"])
except ClientError as e:
logger.error(f"Couldn't list agents. {e}")
raise
else:
return all_agents
```
+ Pour plus de détails sur l'API, consultez [ListAgents](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgents)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListFlowAliases`
L'exemple de code suivant montre comment utiliser`ListFlowAliases`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Répertoriez les alias d’un flux Amazon Bedrock.
```
def list_flow_aliases(client, flow_id):
"""
Lists the aliases of an Amazon Bedrock flow.
Args:
client: bedrock agent boto3 client.
flow_id (str): The identifier of the flow.
Returns:
dict: The response from ListFlowAliases.
"""
try:
finished = False
logger.info("Listing flow aliases for flow: %s.", flow_id)
print(f"Aliases for flow: {flow_id}")
response = client.list_flow_aliases(
flowIdentifier=flow_id,
maxResults=10)
while finished is False:
for alias in response['flowAliasSummaries']:
print(f"Alias Name: {alias['name']}")
print(f"ID: {alias['id']}")
print(f"Description: {alias.get('description', 'No description')}\n")
if 'nextToken' in response:
next_token = response['nextToken']
response = client.list_flow_aliases(maxResults=10,
nextToken=next_token)
else:
finished = True
logging.info("Successfully listed flow aliases for flow %s.",
flow_id)
return response
except ClientError as e:
logging.exception("Client error listing flow aliases: %s", str(e))
raise
except Exception as e:
logging.exception("Unexpected error listing flow aliases: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [ListFlowAliases](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListFlowAliases)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListFlowVersions`
L'exemple de code suivant montre comment utiliser`ListFlowVersions`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Répertoriez les versions d’un flux Amazon Bedrock.
```
def list_flow_versions(client, flow_id):
"""
Lists the versions of an Amazon Bedrock flow.
Args:
client: Amazon bedrock agent boto3 client.
flow_id (str): The identifier of the flow.
Returns:
dict: The response from ListFlowVersions.
"""
try:
finished = False
logger.info("Listing flow versions for flow: %s.", flow_id)
response = client.list_flow_versions(
flowIdentifier=flow_id,
maxResults=10)
while finished is False:
print(f"Versions for flow:{flow_id}")
for version in response['flowVersionSummaries']:
print(f"Version: {version['version']}")
print(f"Status: {version['status']}\n")
if 'nextToken' in response:
next_token = response['nextToken']
response = client.list_flow_versions(maxResults=10,
nextToken=next_token)
else:
finished = True
logging.info("Successfully listed flow versions for flow %s.",
flow_id)
return response
except ClientError as e:
logging.exception("Client error listing flow versions: %s", str(e))
raise
except Exception as e:
logging.exception("Unexpected error listing flow versions: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [ListFlowVersions](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListFlowVersions)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListFlows`
L'exemple de code suivant montre comment utiliser`ListFlows`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Répertoriez les flux Amazon Bedrock.
```
def list_flows(client):
"""
Lists versions of an Amazon Bedrock flow.
Args:
client: Amazon Bedrock agent boto3 client.
flow_id (str): The identifier of the flow.
Returns:
Nothing.
"""
try:
finished = False
logger.info("Listing flows:")
response = client.list_flows(maxResults=10)
while finished is False:
for flow in response['flowSummaries']:
print(f"ID: {flow['id']}")
print(f"Name: {flow['name']}")
print(
f"Description: {flow.get('description', 'No description')}")
print(f"Latest version: {flow['version']}")
print(f"Status: {flow['status']}\n")
if 'nextToken' in response:
next_token = response['nextToken']
response = client.list_flows(maxResults=10,
nextToken=next_token)
else:
finished = True
logging.info("Successfully listed flows.")
except ClientError as e:
logging.exception("Client error listing flow versions: %s", str(e))
raise
except Exception as e:
logging.exception("Unexpected error listing flow versions: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [ListFlows](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListFlows)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListKnowledgeBases`
L'exemple de code suivant montre comment utiliser`ListKnowledgeBases`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Répertoriez les bases de connaissances Amazon Bedrock.
```
def list_knowledge_bases(bedrock_agent_client, max_results=None):
"""
Lists the knowledge bases in your AWS account.
Args:
bedrock_agent_client: The Boto3 Bedrock Agent client.
max_results (int, optional): The maximum number of knowledge bases to return.
Returns:
list: A list of knowledge base details.
"""
try:
kwargs = {}
if max_results is not None:
kwargs["maxResults"] = max_results
# Initialize an empty list to store all knowledge bases
all_knowledge_bases = []
# Use paginator to handle pagination automatically
paginator = bedrock_agent_client.get_paginator('list_knowledge_bases')
page_iterator = paginator.paginate(**kwargs)
# Iterate through each page of results
for page in page_iterator:
all_knowledge_bases.extend(page.get('knowledgeBaseSummaries', []))
logger.info("Found %s knowledge bases.", len(all_knowledge_bases))
return all_knowledge_bases
except ClientError as err:
logger.error(
"Couldn't list knowledge bases. Here's why: %s: %s",
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
```
+ Pour plus de détails sur l'API, consultez [ListKnowledgeBases](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListKnowledgeBases)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `ListPrompts`
L'exemple de code suivant montre comment utiliser`ListPrompts`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Répertoriez les invites gérées par Amazon Bedrock.
```
def list_prompts(client, max_results=10):
"""
Lists Amazon Bedrock managed prompts.
Args:
client: Amazon Bedrock Agent boto3 client.
max_results (int): Maximum number of results to return per page.
Returns:
list: A list of prompt summaries.
"""
try:
logger.info("Listing prompts:")
# Create a paginator for the list_prompts operation
paginator = client.get_paginator('list_prompts')
# Create the pagination parameters
pagination_config = {
'maxResults': max_results
}
# Initialize an empty list to store all prompts
all_prompts = []
# Iterate through all pages
for page in paginator.paginate(**pagination_config):
all_prompts.extend(page.get('promptSummaries', []))
logger.info("Successfully listed %s prompts.", len(all_prompts))
return all_prompts
except ClientError as e:
logger.exception("Client error listing prompts: %s", str(e))
raise
except Exception as e:
logger.exception("Unexpected error listing prompts: %s", str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [ListPrompts](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListPrompts)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `PrepareAgent`
L'exemple de code suivant montre comment utiliser`PrepareAgent`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Préparez un agent pour des tests internes.
```
def prepare_agent(self, agent_id):
"""
Creates a DRAFT version of the agent that can be used for internal testing.
:param agent_id: The unique identifier of the agent to prepare.
:return: The response from Amazon Bedrock Agents if successful, otherwise raises an exception.
"""
try:
prepared_agent_details = self.client.prepare_agent(agentId=agent_id)
except ClientError as e:
logger.error(f"Couldn't prepare agent. {e}")
raise
else:
return prepared_agent_details
```
+ Pour plus de détails sur l'API, consultez [PrepareAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/PrepareAgent)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `PrepareFlow`
L'exemple de code suivant montre comment utiliser`PrepareFlow`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Préparez un flux Amazon Bedrock.
```
def prepare_flow(client, flow_id):
"""
Prepares an Amazon Bedrock Flow.
Args:
client: Amazon Bedrock agent boto3 client.
flow_id (str): The identifier of the flow that you want to prepare.
Returns:
str: The status of the flow preparation
"""
try:
# Prepare the flow.
logger.info("Preparing flow ID: %s",
flow_id)
response = client.prepare_flow(
flowIdentifier=flow_id
)
status = response.get('status')
while status == "Preparing":
logger.info("Preparing flow ID: %s. Status %s",
flow_id, status)
sleep(5)
response = client.get_flow(
flowIdentifier=flow_id
)
status = response.get('status')
print(f"Flow Status: {status}")
if status == "Prepared":
logger.info("Finished preparing flow ID: %s. Status %s",
flow_id, status)
else:
logger.warning("flow ID: %s not prepared. Status %s",
flow_id, status)
return status
except ClientError as e:
logger.exception("Client error preparing flow: %s", {str(e)})
raise
except Exception as e:
logger.exception("Unexepcted error preparing flow: %s", {str(e)})
raise
```
+ Pour plus de détails sur l'API, consultez [PrepareFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/PrepareFlow)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `UpdateFlow`
L'exemple de code suivant montre comment utiliser`UpdateFlow`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Mise à jour d’un flux Amazon Bedrock.
```
def update_flow(client, flow_id, flow_name, flow_description, role_arn, flow_def):
"""
Updates an Amazon Bedrock flow.
Args:
client: bedrock agent boto3 client.
flow_id (str): The ID for the flow that you want to update.
flow_name (str): The name for the flow.
role_arn (str): The ARN for the IAM role that use flow uses.
flow_def (json): The JSON definition of the flow that you want to create.
Returns:
dict: Flow information if successful.
"""
try:
logger.info("Updating flow: %s.", flow_id)
response = client.update_flow(
flowIdentifier=flow_id,
name=flow_name,
description=flow_description,
executionRoleArn=role_arn,
definition=flow_def
)
logger.info("Successfully updated flow: %s. ID: %s",
flow_name,
{response['id']})
return response
except ClientError as e:
logger.exception("Client error updating flow: %s", {str(e)})
raise
except Exception as e:
logger.exception("Unexepcted error updating flow: %s", {str(e)})
raise
```
+ Pour plus de détails sur l'API, consultez [UpdateFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/UpdateFlow)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `UpdateFlowAlias`
L'exemple de code suivant montre comment utiliser`UpdateFlowAlias`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Mettez à jour un alias pour un flux Amazon Bedrock.
```
def update_flow_alias(client, flow_id, alias_id, flow_version, name, description):
"""
Updates an alias for an Amazon Bedrock flow.
Args:
client: bedrock agent boto3 client.
flow_id (str): The identifier of the flow.
Returns:
str: The response from UpdateFlowAlias.
"""
try:
logger.info("Updating flow alias %s for flow: %s.", alias_id, flow_id)
response = client.update_flow_alias(
aliasIdentifier=alias_id,
flowIdentifier=flow_id,
name=name,
description=description,
routingConfiguration=[
{
"flowVersion": flow_version
}
]
)
logger.info("Successfully updated flow alias %s for %s.", alias_id, flow_id)
return response
except ClientError as e:
logging.exception("Client error updating alias %s for flow: %s - %s",
alias_id, flow_id, str(e))
raise
except Exception as e:
logging.exception("Unexpected error updating alias %s for flow : %s - %s",
alias_id, flow_id, str(e))
raise
```
+ Pour plus de détails sur l'API, consultez [UpdateFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/UpdateFlowAlias)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
### `UpdateKnowledgeBase`
L'exemple de code suivant montre comment utiliser`UpdateKnowledgeBase`.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Mettez à jour une base de connaissances Amazon Bedrock.
```
def update_knowledge_base(bedrock_agent_client, knowledge_base_id, name=None, description=None, role_arn=None):
"""
Updates an existing knowledge base.
Args:
bedrock_agent_client: The Boto3 Bedrock Agent client.
knowledge_base_id (str): The ID of the knowledge base to update.
name (str, optional): The new name for the knowledge base.
description (str, optional): The new description for the knowledge base.
role_arn (str, optional): The new IAM role ARN for the knowledge base.
Returns:
dict: The details of the updated knowledge base.
"""
try:
kwargs = {
"knowledgeBaseId": knowledge_base_id,
"knowledgeBaseConfiguration": {
"type": "VECTOR",
"vectorKnowledgeBaseConfiguration": {
"embeddingModelArn": "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v1"
}
}
}
if name:
kwargs["name"] = name
if description:
kwargs["description"] = description
if role_arn:
kwargs["roleArn"] = role_arn
response = bedrock_agent_client.update_knowledge_base(**kwargs)
logger.info("Updated knowledge base: %s", knowledge_base_id)
return response["knowledgeBase"]
except ClientError as err:
logger.error(
"Couldn't update knowledge base %s. Here's why: %s: %s",
knowledge_base_id,
err.response["Error"]["Code"],
err.response["Error"]["Message"],
)
raise
```
+ Pour plus de détails sur l'API, consultez [UpdateKnowledgeBase](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/UpdateKnowledgeBase)le *AWS manuel de référence de l'API SDK for Python (Boto3*).
## Scénarios
### Création et invocation d’un flux
L’exemple de code suivant illustre comment :
+ créer un rôle d’exécution pour le flux ;
+ créer le flux ;
+ déployer le flux entièrement configuré ;
+ invoquer le flux à l’aide des invites fournies par l’utilisateur ;
+ Supprimez toutes les ressources créées.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Génère une liste de lecture musicale en fonction du genre et du nombre de chansons spécifiés par l’utilisateur.
```
from datetime import datetime
import logging
import boto3
from botocore.exceptions import ClientError
from roles import create_flow_role, delete_flow_role, update_role_policy
from flow import create_flow, prepare_flow, delete_flow
from run_flow import run_playlist_flow
from flow_version import create_flow_version, delete_flow_version
from flow_alias import create_flow_alias, delete_flow_alias
logging.basicConfig(
level=logging.INFO
)
logger = logging.getLogger(__name__)
def create_input_node(name):
"""
Creates an input node configuration for an Amazon Bedrock flow.
The input node serves as the entry point for the flow and defines
the initial document structure that will be passed to subsequent nodes.
Args:
name (str): The name of the input node.
Returns:
dict: The input node configuration.
"""
return {
"type": "Input",
"name": name,
"outputs": [
{
"name": "document",
"type": "Object"
}
]
}
def create_prompt_node(name, model_id):
"""
Creates a prompt node configuration for a Bedrock flow that generates music playlists.
The prompt node defines an inline prompt template that creates a music playlist based on
a specified genre and number of songs. The prompt uses two variables that are mapped from
the input JSON object:
- {{genre}}: The genre of music to create a playlist for
- {{number}}: The number of songs to include in the playlist
Args:
name (str): The name of the prompt node.
model_id (str): The identifier of the foundation model to use for the prompt.
Returns:
dict: The prompt node.
"""
return {
"type": "Prompt",
"name": name,
"configuration": {
"prompt": {
"sourceConfiguration": {
"inline": {
"modelId": model_id,
"templateType": "TEXT",
"inferenceConfiguration": {
"text": {
"temperature": 0.8
}
},
"templateConfiguration": {
"text": {
"text": "Make me a {{genre}} playlist consisting of the following number of songs: {{number}}."
}
}
}
}
}
},
"inputs": [
{
"name": "genre",
"type": "String",
"expression": "$.data.genre"
},
{
"name": "number",
"type": "Number",
"expression": "$.data.number"
}
],
"outputs": [
{
"name": "modelCompletion",
"type": "String"
}
]
}
def create_output_node(name):
"""
Creates an output node configuration for a Bedrock flow.
The output node validates that the output from the last node is a string
and returns it unmodified. The input name must be "document".
Args:
name (str): The name of the output node.
Returns:
dict: The output node configuration containing the output node:
"""
return {
"type": "Output",
"name": name,
"inputs": [
{
"name": "document",
"type": "String",
"expression": "$.data"
}
]
}
def create_playlist_flow(client, flow_name, flow_description, role_arn, prompt_model_id):
"""
Creates the playlist generator flow.
Args:
client: bedrock agent boto3 client.
role_arn (str): Name for the new IAM role.
prompt_model_id (str): The id of the model to use in the prompt node.
Returns:
dict: The response from the create_flow operation.
"""
input_node = create_input_node("FlowInput")
prompt_node = create_prompt_node("MakePlaylist", prompt_model_id)
output_node = create_output_node("FlowOutput")
# Create connections between the nodes
connections = []
# First, create connections between the output of the flow
# input node and each input of the prompt node.
for prompt_node_input in prompt_node["inputs"]:
connections.append(
{
"name": "_".join([input_node["name"], prompt_node["name"],
prompt_node_input["name"]]),
"source": input_node["name"],
"target": prompt_node["name"],
"type": "Data",
"configuration": {
"data": {
"sourceOutput": input_node["outputs"][0]["name"],
"targetInput": prompt_node_input["name"]
}
}
}
)
# Then, create a connection between the output of the prompt node and the input of the flow output node
connections.append(
{
"name": "_".join([prompt_node["name"], output_node["name"]]),
"source": prompt_node["name"],
"target": output_node["name"],
"type": "Data",
"configuration": {
"data": {
"sourceOutput": prompt_node["outputs"][0]["name"],
"targetInput": output_node["inputs"][0]["name"]
}
}
}
)
flow_def = {
"nodes": [input_node, prompt_node, output_node],
"connections": connections
}
# Create the flow.
response = create_flow(
client, flow_name, flow_description, role_arn, flow_def)
return response
def get_model_arn(client, model_id):
"""
Gets the Amazon Resource Name (ARN) for a model.
Args:
client (str): Amazon Bedrock boto3 client.
model_id (str): The id of the model.
Returns:
str: The ARN of the model.
"""
try:
# Call GetFoundationModelDetails operation
response = client.get_foundation_model(modelIdentifier=model_id)
# Extract model ARN from the response
model_arn = response['modelDetails']['modelArn']
return model_arn
except ClientError as e:
logger.exception("Client error getting model ARN: %s", {str(e)})
raise
except Exception as e:
logger.exception("Unexpected error getting model ARN: %s", {str(e)})
raise
def prepare_flow_version_and_alias(bedrock_agent_client,
flow_id):
"""
Prepares the flow and then creates a flow version and flow alias.
Args:
bedrock_agent_client: Amazon Bedrock Agent boto3 client.
flowd_id (str): The ID of the flow that you want to prepare.
Returns: The flow_version and flow_alias.
"""
status = prepare_flow(bedrock_agent_client, flow_id)
flow_version = None
flow_alias = None
if status == 'Prepared':
# Create the flow version and alias.
flow_version = create_flow_version(bedrock_agent_client,
flow_id,
f"flow version for flow {flow_id}.")
flow_alias = create_flow_alias(bedrock_agent_client,
flow_id,
flow_version,
"latest",
f"Alias for flow {flow_id}, version {flow_version}")
return flow_version, flow_alias
def delete_role_resources(bedrock_agent_client,
iam_client,
role_name,
flow_id,
flow_version,
flow_alias):
"""
Deletes the flow, flow alias, flow version, and IAM roles.
Args:
bedrock_agent_client: Amazon Bedrock Agent boto3 client.
iam_client: Amazon IAM boto3 client.
role_name (str): The name of the IAM role.
flow_id (str): The id of the flow.
flow_version (str): The version of the flow.
flow_alias (str): The alias of the flow.
"""
if flow_id is not None:
if flow_alias is not None:
delete_flow_alias(bedrock_agent_client, flow_id, flow_alias)
if flow_version is not None:
delete_flow_version(bedrock_agent_client,
flow_id, flow_version)
delete_flow(bedrock_agent_client, flow_id)
if role_name is not None:
delete_flow_role(iam_client, role_name)
def main():
"""
Creates, runs, and optionally deletes a Bedrock flow for generating music playlists.
Note:
Requires valid AWS credentials in the default profile
"""
delete_choice = "y"
try:
# Get various boto3 clients.
session = boto3.Session(profile_name='default')
bedrock_agent_runtime_client = session.client('bedrock-agent-runtime')
bedrock_agent_client = session.client('bedrock-agent')
bedrock_client = session.client('bedrock')
iam_client = session.client('iam')
role_name = None
flow_id = None
flow_version = None
flow_alias = None
#Change the model as needed.
prompt_model_id = "amazon.nova-pro-v1:0"
# Base the flow name on the current date and time
current_time = datetime.now()
timestamp = current_time.strftime("%Y-%m-%d-%H-%M-%S")
flow_name = f"FlowPlayList_{timestamp}"
flow_description = "A flow to generate a music playlist."
# Create a role for the flow.
role_name = f"BedrockFlowRole-{flow_name}"
role = create_flow_role(iam_client, role_name)
role_arn = role['Arn']
# Create the flow.
response = create_playlist_flow(
bedrock_agent_client, flow_name, flow_description, role_arn, prompt_model_id)
flow_id = response.get('id')
if flow_id:
# Update accessible resources in the role.
model_arn = get_model_arn(bedrock_client, prompt_model_id)
update_role_policy(iam_client, role_name, [
response.get('arn'), model_arn])
# Prepare the flow and flow version.
flow_version, flow_alias = prepare_flow_version_and_alias(
bedrock_agent_client, flow_id)
# Run the flow.
if flow_version and flow_alias:
run_playlist_flow(bedrock_agent_runtime_client,
flow_id, flow_alias)
delete_choice = input("Delete flow? y or n : ").lower()
else:
print("Couldn't run. Deleting flow and role.")
delete_flow(bedrock_agent_client, flow_id)
delete_flow_role(iam_client, role_name)
else:
print("Couldn't create flow.")
except Exception as e:
print(f"Fatal error: {str(e)}")
finally:
if delete_choice == 'y':
delete_role_resources(bedrock_agent_client,
iam_client,
role_name,
flow_id,
flow_version,
flow_alias)
else:
print("Flow not deleted. ")
print(f"\tFlow ID: {flow_id}")
print(f"\tFlow version: {flow_version}")
print(f"\tFlow alias: {flow_alias}")
print(f"\tRole ARN: {role_arn}")
print("Done!")
if __name__ == "__main__":
main()
def invoke_flow(client, flow_id, flow_alias_id, input_data):
"""
Invoke an Amazon Bedrock flow and handle the response stream.
Args:
client: Boto3 client for Amazon Bedrock agent runtime.
flow_id: The ID of the flow to invoke.
flow_alias_id: The alias ID of the flow.
input_data: Input data for the flow.
Returns:
Dict containing flow status and flow output.
"""
response = None
request_params = None
request_params = {
"flowIdentifier": flow_id,
"flowAliasIdentifier": flow_alias_id,
"inputs": [input_data],
"enableTrace": True
}
response = client.invoke_flow(**request_params)
flow_status = ""
output= ""
# Process the streaming response
for event in response['responseStream']:
# Check if flow is complete.
if 'flowCompletionEvent' in event:
flow_status = event['flowCompletionEvent']['completionReason']
# Save the model output.
elif 'flowOutputEvent' in event:
output = event['flowOutputEvent']['content']['document']
logger.info("Output : %s", output)
# Log trace events.
elif 'flowTraceEvent' in event:
logger.info("Flow trace: %s", event['flowTraceEvent'])
return {
"flow_status": flow_status,
"output": output
}
def run_playlist_flow(bedrock_agent_client, flow_id, flow_alias_id):
"""
Runs the playlist generator flow.
Args:
bedrock_agent_client: Boto3 client for Amazon Bedrock agent runtime.
flow_id: The ID of the flow to run.
flow_alias_id: The alias ID of the flow.
"""
print ("Welcome to the playlist generator flow.")
# Get the initial prompt from the user.
genre = input("Enter genre: ")
number_of_songs = int(input("Enter number of songs: "))
# Use prompt to create input data for the input node.
flow_input_data = {
"content": {
"document": {
"genre" : genre,
"number" : number_of_songs
}
},
"nodeName": "FlowInput",
"nodeOutputName": "document"
}
try:
result = invoke_flow(
bedrock_agent_client, flow_id, flow_alias_id, flow_input_data)
status = result['flow_status']
if status == "SUCCESS":
# The flow completed successfully.
logger.info("The flow %s successfully completed.", flow_id)
print(result['output'])
else:
logger.warning("Flow status: %s",status)
except ClientError as e:
print(f"Client error: {str(e)}")
logger.error("Client error: %s", {str(e)})
raise
except Exception as e:
logger.error("An error occurred: %s", {str(e)})
logger.error("Error type: %s", {type(e)})
raise
def create_flow_role(client, role_name):
"""
Creates an IAM role for Amazon Bedrock with permissions to run a flow.
Args:
role_name (str): Name for the new IAM role.
Returns:
str: The role Amazon Resource Name.
"""
# Trust relationship policy - allows Amazon Bedrock service to assume this role.
trust_policy = {
"Version":"2012-10-17",
"Statement": [{
"Effect": "Allow",
"Principal": {
"Service": "bedrock.amazonaws.com"
},
"Action": "sts:AssumeRole"
}]
}
# Basic inline policy for for running a flow.
resources = "*"
bedrock_policy = {
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"bedrock:InvokeModel",
"bedrock:Retrieve",
"bedrock:RetrieveAndGenerate"
],
# Using * as placeholder - Later you update with specific ARNs.
"Resource": resources
}
]
}
try:
# Create the IAM role with trust policy
logging.info("Creating role: %s",role_name)
role = client.create_role(
RoleName=role_name,
AssumeRolePolicyDocument=json.dumps(trust_policy),
Description="Role for Amazon Bedrock operations"
)
# Attach inline policy to the role
print("Attaching inline policy")
client.put_role_policy(
RoleName=role_name,
PolicyName=f"{role_name}-policy",
PolicyDocument=json.dumps(bedrock_policy)
)
logging.info("Create Role ARN: %s", role['Role']['Arn'])
return role['Role']
except ClientError as e:
logging.warning("Error creating role: %s", str(e))
raise
except Exception as e:
logging.warning("Unexpected error: %s", str(e))
raise
def update_role_policy(client, role_name, resource_arns):
"""
Updates an IAM role's inline policy with specific resource ARNs.
Args:
role_name (str): Name of the existing role.
resource_arns (list): List of resource ARNs to allow access to.
"""
updated_policy = {
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"bedrock:GetFlow",
"bedrock:InvokeModel",
"bedrock:Retrieve",
"bedrock:RetrieveAndGenerate"
],
"Resource": resource_arns
}
]
}
try:
client.put_role_policy(
RoleName=role_name,
PolicyName=f"{role_name}-policy",
PolicyDocument=json.dumps(updated_policy)
)
logging.info("Updated policy for role: %s",role_name)
except ClientError as e:
logging.warning("Error updating role policy: %s", str(e))
raise
def delete_flow_role(client, role_name):
"""
Deletes an IAM role.
Args:
role_name (str): Name of the role to delete.
"""
try:
# Detach and delete inline policies
policies = client.list_role_policies(RoleName=role_name)['PolicyNames']
for policy_name in policies:
client.delete_role_policy(RoleName=role_name, PolicyName=policy_name)
# Delete the role
client.delete_role(RoleName=role_name)
logging.info("Deleted role: %s", role_name)
except ClientError as e:
logging.info("Error Deleting role: %s", str(e))
raise
```
+ Pour plus de détails sur l’API, consultez les rubriques suivantes dans la *Référence des API du kit AWS SDK for Python (Boto3)*.
+ [CreateFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlow)
+ [CreateFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlowAlias)
+ [CreateFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlowVersion)
+ [DeleteFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlow)
+ [DeleteFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlowAlias)
+ [DeleteFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlowVersion)
+ [GetFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlow)
+ [GetFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlowAlias)
+ [GetFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlowVersion)
+ [InvokeFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-runtime-2023-12-12/InvokeFlow)
+ [PrepareFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/PrepareFlow)
### Création et invocation d’une invite gérée
L’exemple de code suivant illustre comment :
+ Créez une invite gérée.
+ Créez une version de l’invite.
+ Invoquez l’invite à l’aide de la version.
+ Nettoyez vos ressources (facultatif).
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Créez et invoquez une invite gérée.
```
import argparse
import boto3
import logging
import time
# Now import the modules
from prompt import create_prompt, create_prompt_version, delete_prompt
from run_prompt import invoke_prompt
logging.basicConfig(
level=logging.INFO,
format='%(levelname)s: %(message)s'
)
logger = logging.getLogger(__name__)
def run_scenario(bedrock_client, bedrock_runtime_client, model_id, cleanup=True):
"""
Runs the Amazon Bedrock managed prompt scenario.
Args:
bedrock_client: The Amazon Bedrock Agent client.
bedrock_runtime_client: The Amazon Bedrock Runtime client.
model_id (str): The model ID to use for the prompt.
cleanup (bool): Whether to clean up resources at the end of the scenario.
Returns:
dict: A dictionary containing the created resources.
"""
prompt_id = None
try:
# Step 1: Create a prompt
print("\n=== Step 1: Creating a prompt ===")
prompt_name = f"PlaylistGenerator-{int(time.time())}"
prompt_description = "Playlist generator"
prompt_template = """
Make me a {{genre}} playlist consisting of the following number of songs: {{number}}."""
create_response = create_prompt(
bedrock_client,
prompt_name,
prompt_description,
prompt_template,
model_id
)
prompt_id = create_response['id']
print(f"Created prompt: {prompt_name} with ID: {prompt_id}")
# Create a version of the prompt
print("\n=== Creating a version of the prompt ===")
version_response = create_prompt_version(
bedrock_client,
prompt_id,
description="Initial version of the product description generator"
)
prompt_version_arn = version_response['arn']
prompt_version = version_response['version']
print(f"Created prompt version: {prompt_version}")
print(f"Prompt version ARN: {prompt_version_arn}")
# Step 2: Invoke the prompt directly
print("\n=== Step 2: Invoking the prompt ===")
input_variables = {
"genre": "pop",
"number": "2",
}
# Use the ARN from the create_prompt_version response
result = invoke_prompt(
bedrock_runtime_client,
prompt_version_arn,
input_variables
)
# Display the playlist
print(f"\n{result}")
# Step 3: Clean up resources (optional)
if cleanup:
print("\n=== Step 3: Cleaning up resources ===")
# Delete the prompt
print(f"Deleting prompt {prompt_id}...")
delete_prompt(bedrock_client, prompt_id)
print("Cleanup complete")
else:
print("\n=== Resources were not cleaned up ===")
print(f"Prompt ID: {prompt_id}")
except Exception as e:
logger.exception("Error in scenario: %s", str(e))
# Attempt to clean up if an error occurred and cleanup was requested
if cleanup and prompt_id:
try:
print("\nCleaning up resources after error...")
# Delete the prompt
try:
delete_prompt(bedrock_client, prompt_id)
print("Cleanup after error complete")
except Exception as cleanup_error:
logger.error("Error during cleanup: %s", str(cleanup_error))
except Exception as final_error:
logger.error("Final error during cleanup: %s", str(final_error))
# Re-raise the original exception
raise
def main():
"""
Entry point for the Amazon Bedrock managed prompt scenario.
"""
parser = argparse.ArgumentParser(
description="Run the Amazon Bedrock managed prompt scenario."
)
parser.add_argument(
'--region',
default='us-east-1',
help="The AWS Region to use."
)
parser.add_argument(
'--model-id',
default='anthropic.claude-v2',
help="The model ID to use for the prompt."
)
parser.add_argument(
'--cleanup',
action='store_true',
default=True,
help="Clean up resources at the end of the scenario."
)
parser.add_argument(
'--no-cleanup',
action='store_false',
dest='cleanup',
help="Don't clean up resources at the end of the scenario."
)
args = parser.parse_args()
bedrock_client = boto3.client('bedrock-agent', region_name=args.region)
bedrock_runtime_client = boto3.client('bedrock-runtime', region_name=args.region)
print("=== Amazon Bedrock Managed Prompt Scenario ===")
print(f"Region: {args.region}")
print(f"Model ID: {args.model_id}")
print(f"Cleanup resources: {args.cleanup}")
try:
run_scenario(
bedrock_client,
bedrock_runtime_client,
args.model_id,
args.cleanup
)
except Exception as e:
logger.exception("Error running scenario: %s", str(e))
if __name__ == "__main__":
main()
```
+ Pour plus de détails sur l’API, consultez les rubriques suivantes dans la *Référence des API du kit AWS SDK for Python (Boto3)*.
+ [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse)
+ [CreatePrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreatePrompt)
+ [CreatePromptVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreatePromptVersion)
+ [DeletePrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeletePrompt)
### Création et invocation d’un agent
L’exemple de code suivant illustre comment :
+ Créez un rôle d’exécution pour l’agent.
+ Créer l’agent et déployer une version DRAFT.
+ Créer une fonction Lambda qui implémente les fonctionnalités de l’agent.
+ Créer un groupe d’actions qui connecte l’agent à la fonction Lambda.
+ Déployer l’agent entièrement configuré.
+ Invoquer l’agent à l’aide des invites fournies par l’utilisateur.
+ Supprimez toutes les ressources créées.
**Kit SDK for Python (Boto3)**
Il y en a plus à ce sujet GitHub. Trouvez l’exemple complet et découvrez comment le configurer et l’exécuter dans le [référentiel d’exemples de code AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Créez et invoquez un agent.
```
REGION = "us-east-1"
ROLE_POLICY_NAME = "agent_permissions"
class BedrockAgentScenarioWrapper:
"""Runs a scenario that shows how to get started using Amazon Bedrock Agents."""
def __init__(
self, bedrock_agent_client, runtime_client, lambda_client, iam_resource, postfix
):
self.iam_resource = iam_resource
self.lambda_client = lambda_client
self.bedrock_agent_runtime_client = runtime_client
self.postfix = postfix
self.bedrock_wrapper = BedrockAgentWrapper(bedrock_agent_client)
self.agent = None
self.agent_alias = None
self.agent_role = None
self.prepared_agent_details = None
self.lambda_role = None
self.lambda_function = None
def run_scenario(self):
print("=" * 88)
print("Welcome to the Amazon Bedrock Agents demo.")
print("=" * 88)
# Query input from user
print("Let's start with creating an agent:")
print("-" * 40)
name, foundation_model = self._request_name_and_model_from_user()
print("-" * 40)
# Create an execution role for the agent
self.agent_role = self._create_agent_role(foundation_model)
# Create the agent
self.agent = self._create_agent(name, foundation_model)
# Prepare a DRAFT version of the agent
self.prepared_agent_details = self._prepare_agent()
# Create the agent's Lambda function
self.lambda_function = self._create_lambda_function()
# Configure permissions for the agent to invoke the Lambda function
self._allow_agent_to_invoke_function()
self._let_function_accept_invocations_from_agent()
# Create an action group to connect the agent with the Lambda function
self._create_agent_action_group()
# If the agent has been modified or any components have been added, prepare the agent again
components = [self._get_agent()]
components += self._get_agent_action_groups()
components += self._get_agent_knowledge_bases()
latest_update = max(component["updatedAt"] for component in components)
if latest_update > self.prepared_agent_details["preparedAt"]:
self.prepared_agent_details = self._prepare_agent()
# Create an agent alias
self.agent_alias = self._create_agent_alias()
# Test the agent
self._chat_with_agent(self.agent_alias)
print("=" * 88)
print("Thanks for running the demo!\n")
if q.ask("Do you want to delete the created resources? [y/N] ", q.is_yesno):
self._delete_resources()
print("=" * 88)
print(
"All demo resources have been deleted. Thanks again for running the demo!"
)
else:
self._list_resources()
print("=" * 88)
print("Thanks again for running the demo!")
def _request_name_and_model_from_user(self):
existing_agent_names = [
agent["agentName"] for agent in self.bedrock_wrapper.list_agents()
]
while True:
name = q.ask("Enter an agent name: ", self.is_valid_agent_name)
if name.lower() not in [n.lower() for n in existing_agent_names]:
break
print(
f"Agent {name} conflicts with an existing agent. Please use a different name."
)
models = ["anthropic.claude-instant-v1", "anthropic.claude-v2"]
model_id = models[
q.choose("Which foundation model would you like to use? ", models)
]
return name, model_id
def _create_agent_role(self, model_id):
role_name = f"AmazonBedrockExecutionRoleForAgents_{self.postfix}"
model_arn = f"arn:aws:bedrock:{REGION}::foundation-model/{model_id}*"
print("Creating an an execution role for the agent...")
try:
role = self.iam_resource.create_role(
RoleName=role_name,
AssumeRolePolicyDocument=json.dumps(
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {"Service": "bedrock.amazonaws.com"},
"Action": "sts:AssumeRole",
}
],
}
),
)
role.Policy(ROLE_POLICY_NAME).put(
PolicyDocument=json.dumps(
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": "bedrock:InvokeModel",
"Resource": model_arn,
}
],
}
)
)
except ClientError as e:
logger.error(f"Couldn't create role {role_name}. Here's why: {e}")
raise
return role
def _create_agent(self, name, model_id):
print("Creating the agent...")
instruction = """
You are a friendly chat bot. You have access to a function called that returns
information about the current date and time. When responding with date or time,
please make sure to add the timezone UTC.
"""
agent = self.bedrock_wrapper.create_agent(
agent_name=name,
foundation_model=model_id,
instruction=instruction,
role_arn=self.agent_role.arn,
)
self._wait_for_agent_status(agent["agentId"], "NOT_PREPARED")
return agent
def _prepare_agent(self):
print("Preparing the agent...")
agent_id = self.agent["agentId"]
prepared_agent_details = self.bedrock_wrapper.prepare_agent(agent_id)
self._wait_for_agent_status(agent_id, "PREPARED")
return prepared_agent_details
def _create_lambda_function(self):
print("Creating the Lambda function...")
function_name = f"AmazonBedrockExampleFunction_{self.postfix}"
self.lambda_role = self._create_lambda_role()
try:
deployment_package = self._create_deployment_package(function_name)
lambda_function = self.lambda_client.create_function(
FunctionName=function_name,
Description="Lambda function for Amazon Bedrock example",
Runtime="python3.11",
Role=self.lambda_role.arn,
Handler=f"{function_name}.lambda_handler",
Code={"ZipFile": deployment_package},
Publish=True,
)
waiter = self.lambda_client.get_waiter("function_active_v2")
waiter.wait(FunctionName=function_name)
except ClientError as e:
logger.error(
f"Couldn't create Lambda function {function_name}. Here's why: {e}"
)
raise
return lambda_function
def _create_lambda_role(self):
print("Creating an execution role for the Lambda function...")
role_name = f"AmazonBedrockExecutionRoleForLambda_{self.postfix}"
try:
role = self.iam_resource.create_role(
RoleName=role_name,
AssumeRolePolicyDocument=json.dumps(
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {"Service": "lambda.amazonaws.com"},
"Action": "sts:AssumeRole",
}
],
}
),
)
role.attach_policy(
PolicyArn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole"
)
print(f"Created role {role_name}")
except ClientError as e:
logger.error(f"Couldn't create role {role_name}. Here's why: {e}")
raise
print("Waiting for the execution role to be fully propagated...")
wait(10)
return role
def _allow_agent_to_invoke_function(self):
policy = self.iam_resource.RolePolicy(
self.agent_role.role_name, ROLE_POLICY_NAME
)
doc = policy.policy_document
doc["Statement"].append(
{
"Effect": "Allow",
"Action": "lambda:InvokeFunction",
"Resource": self.lambda_function["FunctionArn"],
}
)
self.agent_role.Policy(ROLE_POLICY_NAME).put(PolicyDocument=json.dumps(doc))
def _let_function_accept_invocations_from_agent(self):
try:
self.lambda_client.add_permission(
FunctionName=self.lambda_function["FunctionName"],
SourceArn=self.agent["agentArn"],
StatementId="BedrockAccess",
Action="lambda:InvokeFunction",
Principal="bedrock.amazonaws.com",
)
except ClientError as e:
logger.error(
f"Couldn't grant Bedrock permission to invoke the Lambda function. Here's why: {e}"
)
raise
def _create_agent_action_group(self):
print("Creating an action group for the agent...")
try:
with open("./scenario_resources/api_schema.yaml") as file:
self.bedrock_wrapper.create_agent_action_group(
name="current_date_and_time",
description="Gets the current date and time.",
agent_id=self.agent["agentId"],
agent_version=self.prepared_agent_details["agentVersion"],
function_arn=self.lambda_function["FunctionArn"],
api_schema=json.dumps(yaml.safe_load(file)),
)
except ClientError as e:
logger.error(f"Couldn't create agent action group. Here's why: {e}")
raise
def _get_agent(self):
return self.bedrock_wrapper.get_agent(self.agent["agentId"])
def _get_agent_action_groups(self):
return self.bedrock_wrapper.list_agent_action_groups(
self.agent["agentId"], self.prepared_agent_details["agentVersion"]
)
def _get_agent_knowledge_bases(self):
return self.bedrock_wrapper.list_agent_knowledge_bases(
self.agent["agentId"], self.prepared_agent_details["agentVersion"]
)
def _create_agent_alias(self):
print("Creating an agent alias...")
agent_alias_name = "test_agent_alias"
agent_alias = self.bedrock_wrapper.create_agent_alias(
agent_alias_name, self.agent["agentId"]
)
self._wait_for_agent_status(self.agent["agentId"], "PREPARED")
return agent_alias
def _wait_for_agent_status(self, agent_id, status):
while self.bedrock_wrapper.get_agent(agent_id)["agentStatus"] != status:
wait(2)
def _chat_with_agent(self, agent_alias):
print("-" * 88)
print("The agent is ready to chat.")
print("Try asking for the date or time. Type 'exit' to quit.")
# Create a unique session ID for the conversation
session_id = uuid.uuid4().hex
while True:
prompt = q.ask("Prompt: ", q.non_empty)
if prompt == "exit":
break
response = asyncio.run(self._invoke_agent(agent_alias, prompt, session_id))
print(f"Agent: {response}")
async def _invoke_agent(self, agent_alias, prompt, session_id):
response = self.bedrock_agent_runtime_client.invoke_agent(
agentId=self.agent["agentId"],
agentAliasId=agent_alias["agentAliasId"],
sessionId=session_id,
inputText=prompt,
)
completion = ""
for event in response.get("completion"):
chunk = event["chunk"]
completion += chunk["bytes"].decode()
return completion
def _delete_resources(self):
if self.agent:
agent_id = self.agent["agentId"]
if self.agent_alias:
agent_alias_id = self.agent_alias["agentAliasId"]
print("Deleting agent alias...")
self.bedrock_wrapper.delete_agent_alias(agent_id, agent_alias_id)
print("Deleting agent...")
agent_status = self.bedrock_wrapper.delete_agent(agent_id)["agentStatus"]
while agent_status == "DELETING":
wait(5)
try:
agent_status = self.bedrock_wrapper.get_agent(
agent_id, log_error=False
)["agentStatus"]
except ClientError as err:
if err.response["Error"]["Code"] == "ResourceNotFoundException":
agent_status = "DELETED"
if self.lambda_function:
name = self.lambda_function["FunctionName"]
print(f"Deleting function '{name}'...")
self.lambda_client.delete_function(FunctionName=name)
if self.agent_role:
print(f"Deleting role '{self.agent_role.role_name}'...")
self.agent_role.Policy(ROLE_POLICY_NAME).delete()
self.agent_role.delete()
if self.lambda_role:
print(f"Deleting role '{self.lambda_role.role_name}'...")
for policy in self.lambda_role.attached_policies.all():
policy.detach_role(RoleName=self.lambda_role.role_name)
self.lambda_role.delete()
def _list_resources(self):
print("-" * 40)
print(f"Here is the list of created resources in '{REGION}'.")
print("Make sure you delete them once you're done to avoid unnecessary costs.")
if self.agent:
print(f"Bedrock Agent: {self.agent['agentName']}")
if self.lambda_function:
print(f"Lambda function: {self.lambda_function['FunctionName']}")
if self.agent_role:
print(f"IAM role: {self.agent_role.role_name}")
if self.lambda_role:
print(f"IAM role: {self.lambda_role.role_name}")
@staticmethod
def is_valid_agent_name(answer):
valid_regex = r"^[a-zA-Z0-9_-]{1,100}$"
return (
answer
if answer and len(answer) <= 100 and re.match(valid_regex, answer)
else None,
"I need a name for the agent, please. Valid characters are a-z, A-Z, 0-9, _ (underscore) and - (hyphen).",
)
@staticmethod
def _create_deployment_package(function_name):
buffer = io.BytesIO()
with zipfile.ZipFile(buffer, "w") as zipped:
zipped.write(
"./scenario_resources/lambda_function.py", f"{function_name}.py"
)
buffer.seek(0)
return buffer.read()
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
postfix = "".join(
random.choice(string.ascii_lowercase + "0123456789") for _ in range(8)
)
scenario = BedrockAgentScenarioWrapper(
bedrock_agent_client=boto3.client(
service_name="bedrock-agent", region_name=REGION
),
runtime_client=boto3.client(
service_name="bedrock-agent-runtime", region_name=REGION
),
lambda_client=boto3.client(service_name="lambda", region_name=REGION),
iam_resource=boto3.resource("iam"),
postfix=postfix,
)
try:
scenario.run_scenario()
except Exception as e:
logging.exception(f"Something went wrong with the demo. Here's what: {e}")
```
+ Pour plus de détails sur l’API, consultez les rubriques suivantes dans la *Référence des API du kit AWS SDK for Python (Boto3)*.
+ [CreateAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgent)
+ [CreateAgentActionGroup](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgentActionGroup)
+ [CreateAgentAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgentAlias)
+ [DeleteAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteAgent)
+ [DeleteAgentAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteAgentAlias)
+ [GetAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetAgent)
+ [ListAgentActionGroups](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgentActionGroups)
+ [ListAgentKnowledgeBases](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgentKnowledgeBases)
+ [ListAgents](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgents)
+ [PrepareAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/PrepareAgent)
### Orchestration d’applications d’IA générative avec Step Functions
L’exemple de code suivant montre comment créer et orchestrer des applications d’IA générative avec Amazon Bedrock et Step Functions.
**Kit SDK for Python (Boto3)**
Le scénario d’enchaînement des invites Amazon Bedrock sans serveur montre comment [AWS Step Functions](https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html), [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) et [https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html) peuvent être utilisés pour créer et orchestrer des applications d’IA générative complexes, sans serveur et hautement évolutives. Il contient les exemples pratiques suivants :
+ Rédigez une analyse d’un roman donné pour un blog littéraire. Cet exemple illustre une chaîne d’invites simple et séquentielle.
+ Générez une courte histoire sur un sujet donné. Cet exemple illustre comment l’IA peut traiter de manière itérative une liste d’éléments qu’elle a précédemment générée.
+ Créez un itinéraire pour un week-end de vacances vers une destination donnée. Cet exemple illustre comment paralléliser plusieurs invites distinctes.
+ Présentez des idées de films à un utilisateur humain agissant en tant que producteur de films. Cet exemple illustre comment paralléliser la même invite avec différents paramètres d’inférence, comment revenir à une étape précédente de la chaîne et comment inclure une entrée humaine dans le flux de travail.
+ Planifiez un repas en fonction des ingrédients que l’utilisateur a à portée de main. Cet exemple illustre comment les enchaînements des invites peuvent intégrer deux conversations distinctes basées sur l’IA, avec deux personnages d’IA engageant un débat entre eux pour améliorer le résultat final.
+ Trouvez et résumez le GitHub référentiel le plus populaire du moment. Cet exemple illustre le chaînage de plusieurs agents d'IA qui interagissent avec des agents externes APIs.
Pour le code source complet et les instructions de configuration et d'exécution, consultez le projet complet sur [GitHub](https://github.com/aws-samples/amazon-bedrock-serverless-prompt-chaining).
**Les services utilisés dans cet exemple**
+ Amazon Bedrock
+ Exécution d’Amazon Bedrock
+ Agents Amazon Bedrock
+ Exécution des agents Amazon Bedrock
+ Step Functions