Há mais exemplos de AWS SDK disponíveis no repositório [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub .
As traduções são geradas por tradução automática. Em caso de conflito entre o conteúdo da tradução e da versão original em inglês, a versão em inglês prevalecerá.
# Exemplos de código para Amazon Bedrock Agents Runtime usando AWS SDKs
Os exemplos de código a seguir mostram como usar o Amazon Bedrock Agents Runtime com um kit de desenvolvimento de AWS software (SDK).
As *noções básicas* são exemplos de código que mostram como realizar as operações essenciais em um serviço.
*Ações* são trechos de código de programas maiores e devem ser executadas em contexto. Embora as ações mostrem como chamar perfis de serviço individuais, você pode ver as ações no contexto em seus cenários relacionados.
*Cenários* são exemplos de código que mostram como realizar tarefas específicas chamando várias funções dentro de um serviço ou combinadas com outros Serviços da AWS.
**Mais atributos**
+ **[Guia do usuário do Amazon Bedrock Agents Runtime](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html)**: mais informações sobre o Amazon Bedrock Agents Runtime.
+ **[Referência de API do Amazon Bedrock Agents Runtime](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_Operations_Agents_for_Amazon_Bedrock_Runtime.html)**: detalhes sobre todas as ações disponíveis do Amazon Bedrock Agents Runtime.
+ **[AWS Centro do desenvolvedor](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23bedrock-agents)** — exemplos de código que você pode filtrar por categoria ou pesquisa de texto completo.
+ **[AWS Exemplos de SDK](https://github.com/awsdocs/aws-doc-sdk-examples)** — GitHub repositório com código completo nos idiomas preferidos. Inclui instruções para configurar e executar o código.
**Contents**
+ [Conceitos básicos](bedrock-agent-runtime_code_examples_basics.md)
+ [Conheça os conceitos básicos](bedrock-agent-runtime_example_bedrock-agent-runtime_Scenario_ConverseWithFlow_section.md)
+ [Ações](bedrock-agent-runtime_code_examples_actions.md)
+ [`InvokeAgent`](bedrock-agent-runtime_example_bedrock-agent-runtime_InvokeAgent_section.md)
+ [`InvokeFlow`](bedrock-agent-runtime_example_bedrock-agent-runtime_InvokeFlow_section.md)
+ [Cenários](bedrock-agent-runtime_code_examples_scenarios.md)
+ [Criar e invocar um fluxo](bedrock-agent-runtime_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md)
+ [Orquestrar aplicações de IA generativa com o Step Functions](bedrock-agent-runtime_example_cross_ServerlessPromptChaining_section.md)
# Exemplos básicos do Amazon Bedrock Agents Runtime usando AWS SDKs
Os exemplos de código a seguir mostram como usar os conceitos básicos do Amazon Bedrock Agents Runtime com. AWS SDKs
**Contents**
+ [Conheça os conceitos básicos](bedrock-agent-runtime_example_bedrock-agent-runtime_Scenario_ConverseWithFlow_section.md)
+ [Ações](bedrock-agent-runtime_code_examples_actions.md)
+ [`InvokeAgent`](bedrock-agent-runtime_example_bedrock-agent-runtime_InvokeAgent_section.md)
+ [`InvokeFlow`](bedrock-agent-runtime_example_bedrock-agent-runtime_InvokeFlow_section.md)
# Converse com um fluxo do Amazon Bedrock
O exemplo de código a seguir mostra como usar InvokeFlow para conversar com um fluxo do Amazon Bedrock que inclui um nó de agente.
Consulte mais informações em [Converse with an Amazon Bedrock flow](https://docs.aws.amazon.com/bedrock/latest/userguide/flows-multi-turn-invocation.html).
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent-runtime#code-examples).
```
"""
Shows how to run an Amazon Bedrock flow with InvokeFlow and handle muli-turn interaction
for a single conversation.
For more information, see https://docs.aws.amazon.com/bedrock/latest/userguide/flows-multi-turn-invocation.html.
"""
import logging
import boto3
import botocore
import botocore.exceptions
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def invoke_flow(client, flow_id, flow_alias_id, input_data, execution_id):
"""
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.
execution_id: Execution ID for continuing a flow. Use the value None on first run.
Returns:
Dict containing flow_complete status, input_required info, and execution_id
"""
response = None
request_params = None
if execution_id is None:
# Don't pass execution ID for first run.
request_params = {
"flowIdentifier": flow_id,
"flowAliasIdentifier": flow_alias_id,
"inputs": [input_data],
"enableTrace": True
}
else:
request_params = {
"flowIdentifier": flow_id,
"flowAliasIdentifier": flow_alias_id,
"executionId": execution_id,
"inputs": [input_data],
"enableTrace": True
}
response = client.invoke_flow(**request_params)
if "executionId" not in request_params:
execution_id = response['executionId']
input_required = None
flow_status = ""
# Process the streaming response
for event in response['responseStream']:
# Check if flow is complete.
if 'flowCompletionEvent' in event:
flow_status = event['flowCompletionEvent']['completionReason']
# Check if more input us needed from user.
elif 'flowMultiTurnInputRequestEvent' in event:
input_required = event
# Print the model output.
elif 'flowOutputEvent' in event:
print(event['flowOutputEvent']['content']['document'])
# Log trace events.
elif 'flowTraceEvent' in event:
logger.info("Flow trace: %s", event['flowTraceEvent'])
return {
"flow_status": flow_status,
"input_required": input_required,
"execution_id": execution_id
}
def converse_with_flow(bedrock_agent_client, flow_id, flow_alias_id):
"""
Run a conversation with the supplied 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.
"""
flow_execution_id = None
finished = False
# Get the intial prompt from the user.
user_input = input("Enter input: ")
# Use prompt to create input data.
flow_input_data = {
"content": {
"document": user_input
},
"nodeName": "FlowInputNode",
"nodeOutputName": "document"
}
try:
while not finished:
# Invoke the flow until successfully finished.
result = invoke_flow(
bedrock_agent_client, flow_id, flow_alias_id, flow_input_data, flow_execution_id)
status = result['flow_status']
flow_execution_id = result['execution_id']
more_input = result['input_required']
if status == "INPUT_REQUIRED":
# The flow needs more information from the user.
logger.info("The flow %s requires more input", flow_id)
user_input = input(
more_input['flowMultiTurnInputRequestEvent']['content']['document'] + ": ")
flow_input_data = {
"content": {
"document": user_input
},
"nodeName": more_input['flowMultiTurnInputRequestEvent']['nodeName'],
"nodeInputName": "agentInputText"
}
elif status == "SUCCESS":
# The flow completed successfully.
finished = True
logger.info("The flow %s successfully completed.", flow_id)
except botocore.exceptions.ClientError as e:
print(f"Client error: {str(e)}")
logger.error("Client error: %s", {str(e)})
except Exception as e:
print(f"An error occurred: {str(e)}")
logger.error("An error occurred: %s", {str(e)})
logger.error("Error type: %s", {type(e)})
def main():
"""
Main entry point for the script.
"""
# Replace these with your actual flow ID and flow alias ID.
FLOW_ID = 'YOUR_FLOW_ID'
FLOW_ALIAS_ID = 'YOUR_FLOW_ALIAS_ID'
logger.info("Starting conversation with FLOW: %s ID: %s",
FLOW_ID, FLOW_ALIAS_ID)
# Get the Bedrock agent runtime client.
session = boto3.Session(profile_name='default')
bedrock_agent_client = session.client('bedrock-agent-runtime')
# Start the conversation.
converse_with_flow(bedrock_agent_client, FLOW_ID, FLOW_ALIAS_ID)
logger.info("Conversation with FLOW: %s ID: %s finished",
FLOW_ID, FLOW_ALIAS_ID)
if __name__ == "__main__":
main()
```
+ Para obter detalhes da API, consulte a [InvokeFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-runtime-2023-12-12/InvokeFlow)Referência da API *AWS SDK for Python (Boto3*).
------
# Ações para o Amazon Bedrock Agents Runtime usando AWS SDKs
Os exemplos de código a seguir demonstram como realizar ações individuais do Amazon Bedrock Agents Runtime com AWS SDKs. Cada exemplo inclui um link para GitHub, onde você pode encontrar instruções para configurar e executar o código.
Esses trechos chamam a API do Amazon Bedrock Agents Runtime e são trechos de programas maiores que devem ser executados em contexto. É possível ver as ações em contexto em [Cenários para o Amazon Bedrock Agents Runtime usando AWS SDKs](bedrock-agent-runtime_code_examples_scenarios.md).
Os exemplos a seguir incluem apenas as ações mais utilizadas. Para obter uma lista completa, consulte a [Amazon Bedrock Agents Runtime API Reference](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_Operations_Agents_for_Amazon_Bedrock_Runtime.html).
**Topics**
+ [`InvokeAgent`](bedrock-agent-runtime_example_bedrock-agent-runtime_InvokeAgent_section.md)
+ [`InvokeFlow`](bedrock-agent-runtime_example_bedrock-agent-runtime_InvokeFlow_section.md)
# Use `InvokeAgent` com um AWS SDK
Os exemplos de código a seguir mostram como usar o `InvokeAgent`.
------
#### [ JavaScript ]
**SDK para JavaScript (v3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/bedrock-agent-runtime#code-examples).
```
import {
BedrockAgentRuntimeClient,
InvokeAgentCommand,
} from "@aws-sdk/client-bedrock-agent-runtime";
/**
* @typedef {Object} ResponseBody
* @property {string} completion
*/
/**
* Invokes a Bedrock agent to run an inference using the input
* provided in the request body.
*
* @param {string} prompt - The prompt that you want the Agent to complete.
* @param {string} sessionId - An arbitrary identifier for the session.
*/
export const invokeBedrockAgent = async (prompt, sessionId) => {
const client = new BedrockAgentRuntimeClient({ region: "us-east-1" });
// const client = new BedrockAgentRuntimeClient({
// region: "us-east-1",
// credentials: {
// accessKeyId: "accessKeyId", // permission to invoke agent
// secretAccessKey: "accessKeySecret",
// },
// });
const agentId = "AJBHXXILZN";
const agentAliasId = "AVKP1ITZAA";
const command = new InvokeAgentCommand({
agentId,
agentAliasId,
sessionId,
inputText: prompt,
});
try {
let completion = "";
const response = await client.send(command);
if (response.completion === undefined) {
throw new Error("Completion is undefined");
}
for await (const chunkEvent of response.completion) {
const chunk = chunkEvent.chunk;
console.log(chunk);
const decodedResponse = new TextDecoder("utf-8").decode(chunk.bytes);
completion += decodedResponse;
}
return { sessionId: sessionId, completion };
} catch (err) {
console.error(err);
}
};
// Call function if run directly
import { fileURLToPath } from "node:url";
if (process.argv[1] === fileURLToPath(import.meta.url)) {
const result = await invokeBedrockAgent("I need help.", "123");
console.log(result);
}
```
+ Para obter detalhes da API, consulte [InvokeAgent](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/bedrock-agent-runtime/command/InvokeAgentCommand)a *Referência AWS SDK para JavaScript da API*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent-runtime#code-examples).
Invoque um agente.
```
def invoke_agent(self, agent_id, agent_alias_id, session_id, prompt):
"""
Sends a prompt for the agent to process and respond to.
:param agent_id: The unique identifier of the agent to use.
:param agent_alias_id: The alias of the agent to use.
:param session_id: The unique identifier of the session. Use the same value across requests
to continue the same conversation.
:param prompt: The prompt that you want Claude to complete.
:return: Inference response from the model.
"""
try:
# Note: The execution time depends on the foundation model, complexity of the agent,
# and the length of the prompt. In some cases, it can take up to a minute or more to
# generate a response.
response = self.agents_runtime_client.invoke_agent(
agentId=agent_id,
agentAliasId=agent_alias_id,
sessionId=session_id,
inputText=prompt,
)
completion = ""
for event in response.get("completion"):
chunk = event["chunk"]
completion = completion + chunk["bytes"].decode()
except ClientError as e:
logger.error(f"Couldn't invoke agent. {e}")
raise
return completion
```
+ Para obter detalhes da API, consulte a [InvokeAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-runtime-2023-12-12/InvokeAgent)Referência da API *AWS SDK for Python (Boto3*).
------
#### [ Rust ]
**SDK para Rust**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/rustv1/examples/bedrock-agent-runtime#code-examples).
```
use aws_config::{BehaviorVersion, SdkConfig};
use aws_sdk_bedrockagentruntime::{
self as bedrockagentruntime,
types::{error::ResponseStreamError, ResponseStream},
};
#[allow(unused_imports)]
use mockall::automock;
const BEDROCK_AGENT_ID: &str = "AJBHXXILZN";
const BEDROCK_AGENT_ALIAS_ID: &str = "AVKP1ITZAA";
const BEDROCK_AGENT_REGION: &str = "us-east-1";
#[cfg(not(test))]
pub use EventReceiverImpl as EventReceiver;
#[cfg(test)]
pub use MockEventReceiverImpl as EventReceiver;
pub struct EventReceiverImpl {
inner: aws_sdk_bedrockagentruntime::primitives::event_stream::EventReceiver<
ResponseStream,
ResponseStreamError,
>,
}
#[cfg_attr(test, automock)]
impl EventReceiverImpl {
#[allow(dead_code)]
pub fn new(
inner: aws_sdk_bedrockagentruntime::primitives::event_stream::EventReceiver<
ResponseStream,
ResponseStreamError,
>,
) -> Self {
Self { inner }
}
pub async fn recv(
&mut self,
) -> Result<
Option,
aws_sdk_bedrockagentruntime::error::SdkError<
ResponseStreamError,
aws_smithy_types::event_stream::RawMessage,
>,
> {
self.inner.recv().await
}
}
#[tokio::main]
async fn main() -> Result<(), Box> {
let result = invoke_bedrock_agent("I need help.".to_string(), "123".to_string()).await?;
println!("{}", result);
Ok(())
}
async fn invoke_bedrock_agent(
prompt: String,
session_id: String,
) -> Result {
let sdk_config: SdkConfig = aws_config::defaults(BehaviorVersion::latest())
.region(BEDROCK_AGENT_REGION)
.load()
.await;
let bedrock_client = bedrockagentruntime::Client::new(&sdk_config);
let command_builder = bedrock_client
.invoke_agent()
.agent_id(BEDROCK_AGENT_ID)
.agent_alias_id(BEDROCK_AGENT_ALIAS_ID)
.session_id(session_id)
.input_text(prompt);
let response = command_builder.send().await?;
let response_stream = response.completion;
let event_receiver = EventReceiver::new(response_stream);
process_agent_response_stream(event_receiver).await
}
async fn process_agent_response_stream(
mut event_receiver: EventReceiver,
) -> Result {
let mut full_agent_text_response = String::new();
while let Some(event_result) = event_receiver.recv().await? {
match event_result {
ResponseStream::Chunk(chunk) => {
if let Some(bytes) = chunk.bytes {
match String::from_utf8(bytes.into_inner()) {
Ok(text_chunk) => {
full_agent_text_response.push_str(&text_chunk);
}
Err(e) => {
eprintln!("UTF-8 decoding error for chunk: {}", e);
}
}
}
}
_ => {
panic!("received an unhandled event type from Bedrock stream",);
}
}
}
Ok(full_agent_text_response)
}
#[cfg(test)]
mod test {
use super::*;
#[tokio::test]
async fn test_process_agent_response_stream() {
let mut mock = MockEventReceiverImpl::default();
mock.expect_recv().times(1).returning(|| {
Ok(Some(
aws_sdk_bedrockagentruntime::types::ResponseStream::Chunk(
aws_sdk_bedrockagentruntime::types::PayloadPart::builder()
.set_bytes(Some(aws_smithy_types::Blob::new(vec![
116, 101, 115, 116, 32, 99, 111, 109, 112, 108, 101, 116, 105, 111, 110,
])))
.build(),
),
))
});
// end the stream
mock.expect_recv().times(1).returning(|| Ok(None));
let response = process_agent_response_stream(mock).await.unwrap();
assert_eq!("test completion", response);
}
#[tokio::test]
#[should_panic(expected = "received an unhandled event type from Bedrock stream")]
async fn test_process_agent_response_stream_error() {
let mut mock = MockEventReceiverImpl::default();
mock.expect_recv().times(1).returning(|| {
Ok(Some(
aws_sdk_bedrockagentruntime::types::ResponseStream::Trace(
aws_sdk_bedrockagentruntime::types::TracePart::builder().build(),
),
))
});
let _ = process_agent_response_stream(mock).await.unwrap();
}
}
```
+ Para obter detalhes da API, consulte a [InvokeAgent](https://docs.rs/aws-sdk-bedrockagentruntime/latest/aws_sdk_bedrockagentruntime/client/struct.Client.html#method.invoke_agent)referência da *API AWS SDK for Rust*.
------
#### [ SAP ABAP ]
**SDK para SAP ABAP**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/bdz#code-examples).
```
DATA(lo_result) = lo_bdz->invokeagent(
iv_agentid = iv_agentid
iv_agentaliasid = iv_agentaliasid
iv_enabletrace = abap_true
iv_sessionid = CONV #( cl_system_uuid=>create_uuid_c26_static( ) )
iv_inputtext = |Let's play "rock, paper, scissors". I choose rock.| ).
DATA(lo_stream) = lo_result->get_completion( ).
TRY.
" loop while there are still events in the stream
WHILE lo_stream->/aws1/if_rt_stream_reader~data_available( ) = abap_true.
DATA(lo_evt) = lo_stream->read( ).
" each /AWS1/CL_BDZRESPONSESTREAM_EV event contains exactly one member
" all others are INITIAL. For each event, process the non-initial
" member if desired
IF lo_evt->get_chunk( ) IS NOT INITIAL.
" Process a Chunk event
DATA(lv_xstr) = lo_evt->get_chunk( )->get_bytes( ).
DATA(lv_answer) = /aws1/cl_rt_util=>xstring_to_string( lv_xstr ).
" the answer says something like "I chose paper, so you lost"
ELSEIF lo_evt->get_files( ) IS NOT INITIAL.
" process a Files event if desired
ELSEIF lo_evt->get_returncontrol( ) IS NOT INITIAL.
" process a ReturnControl event if desired
ELSEIF lo_evt->get_trace( ) IS NOT INITIAL.
" process a Trace event if desired
ENDIF.
ENDWHILE.
" the stream of events can possibly contain an exception
" which will be raised to break the loop
" catch /AWS1/CX_BDZACCESSDENIEDEX.
" catch /AWS1/CX_BDZINTERNALSERVEREX.
" catch /AWS1/CX_BDZMODELNOTREADYEX.
" catch /AWS1/CX_BDZVALIDATIONEX.
" catch /AWS1/CX_BDZTHROTTLINGEX.
" catch /AWS1/CX_BDZDEPENDENCYFAILEDEX.
" catch /AWS1/CX_BDZBADGATEWAYEX.
" catch /AWS1/CX_BDZRESOURCENOTFOUNDEX.
" catch /AWS1/CX_BDZSERVICEQUOTAEXCDEX.
" catch /AWS1/CX_BDZCONFLICTEXCEPTION.
ENDTRY.
```
+ Para obter detalhes da API, consulte a [InvokeAgent](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)referência da *API AWS SDK for SAP ABAP*.
------
# Use `InvokeFlow` com um AWS SDK
Os exemplos de código a seguir mostram como usar o `InvokeFlow`.
Exemplos de ações são trechos de código de programas maiores e devem ser executados em contexto. É possível ver essa ação em contexto nos seguintes exemplos de código:
+ [Conheça os conceitos básicos](bedrock-agent-runtime_example_bedrock-agent-runtime_Scenario_ConverseWithFlow_section.md)
+ [Criar e invocar um fluxo](bedrock-agent-runtime_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md)
------
#### [ JavaScript ]
**SDK para JavaScript (v3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/bedrock-agent-runtime#code-examples).
```
import { fileURLToPath } from "node:url";
import {
BedrockAgentRuntimeClient,
InvokeFlowCommand,
} from "@aws-sdk/client-bedrock-agent-runtime";
/**
* Invokes an alias of a flow to run the inputs that you specify and return
* the output of each node as a stream.
*
* @param {{
* flowIdentifier: string,
* flowAliasIdentifier: string,
* prompt?: string,
* region?: string
* }} options
* @returns {Promise} An object containing information about the output from flow invocation.
*/
export const invokeBedrockFlow = async ({
flowIdentifier,
flowAliasIdentifier,
prompt = "Hi, how are you?",
region = "us-east-1",
}) => {
const client = new BedrockAgentRuntimeClient({ region });
const command = new InvokeFlowCommand({
flowIdentifier,
flowAliasIdentifier,
inputs: [
{
content: {
document: prompt,
},
nodeName: "FlowInputNode",
nodeOutputName: "document",
},
],
});
let flowResponse = {};
const response = await client.send(command);
for await (const chunkEvent of response.responseStream) {
const { flowOutputEvent, flowCompletionEvent } = chunkEvent;
if (flowOutputEvent) {
flowResponse = { ...flowResponse, ...flowOutputEvent };
console.log("Flow output event:", flowOutputEvent);
} else if (flowCompletionEvent) {
flowResponse = { ...flowResponse, ...flowCompletionEvent };
console.log("Flow completion event:", flowCompletionEvent);
}
}
return flowResponse;
};
// Call function if run directly
import { parseArgs } from "node:util";
import {
isMain,
validateArgs,
} from "@aws-doc-sdk-examples/lib/utils/util-node.js";
const loadArgs = () => {
const options = {
flowIdentifier: {
type: "string",
required: true,
},
flowAliasIdentifier: {
type: "string",
required: true,
},
prompt: {
type: "string",
},
region: {
type: "string",
},
};
const results = parseArgs({ options });
const { errors } = validateArgs({ options }, results);
return { errors, results };
};
if (isMain(import.meta.url)) {
const { errors, results } = loadArgs();
if (!errors) {
invokeBedrockFlow(results.values);
} else {
console.error(errors.join("\n"));
}
}
```
+ Para obter detalhes da API, consulte [InvokeFlow](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/bedrock-agent-runtime/command/InvokeFlowCommand)a *Referência AWS SDK para JavaScript da API*.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent-runtime#code-examples).
Invoque um fluxo.
```
def invoke_flow(self, flow_id, flow_alias_id, input_data, execution_id):
"""
Invoke an Amazon Bedrock flow and handle the response stream.
Args:
param flow_id: The ID of the flow to invoke.
param flow_alias_id: The alias ID of the flow.
param input_data: Input data for the flow.
param execution_id: Execution ID for continuing a flow. Use the value None on first run.
Return: Response from the flow.
"""
try:
request_params = None
if execution_id is None:
# Don't pass execution ID for first run.
request_params = {
"flowIdentifier": flow_id,
"flowAliasIdentifier": flow_alias_id,
"inputs": input_data,
"enableTrace": True
}
else:
request_params = {
"flowIdentifier": flow_id,
"flowAliasIdentifier": flow_alias_id,
"executionId": execution_id,
"inputs": input_data,
"enableTrace": True
}
response = self.agents_runtime_client.invoke_flow(**request_params)
if "executionId" not in request_params:
execution_id = response['executionId']
result = ""
# Get the streaming response
for event in response['responseStream']:
result = result + str(event) + '\n'
print(result)
except ClientError as e:
logger.error("Couldn't invoke flow %s.", {e})
raise
return result
```
+ Para obter detalhes da API, consulte a [InvokeFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-runtime-2023-12-12/InvokeFlow)Referência da API *AWS SDK for Python (Boto3*).
------
# Cenários para o Amazon Bedrock Agents Runtime usando AWS SDKs
Os exemplos de código a seguir mostram como implementar cenários comuns no Amazon Bedrock Agents Runtime com AWS SDKs. Esses cenários mostram como executar tarefas específicas chamando vários perfis no Amazon Bedrock Agents Runtime ou em conjunto com outros Serviços da AWS. Cada cenário inclui um link para o código-fonte completo, onde podem ser encontradas instruções sobre como configurar e executar o código.
Os cenários têm como alvo um nível intermediário de experiência para ajudar você a compreender ações de serviço em contexto.
**Topics**
+ [Criar e invocar um fluxo](bedrock-agent-runtime_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md)
+ [Orquestrar aplicações de IA generativa com o Step Functions](bedrock-agent-runtime_example_cross_ServerlessPromptChaining_section.md)
# Um end-to-end exemplo mostrando como criar e invocar um fluxo do Amazon Bedrock usando um SDK AWS
O exemplo de código a seguir mostra como:
+ Criar um perfil de execução para o fluxo.
+ Criar o fluxo.
+ Implantar o fluxo totalmente configurado.
+ Invocar o fluxo com prompts fornecidos pelo usuário.
+ Exclua todos os recursos criados.
------
#### [ Python ]
**SDK para Python (Boto3)**
Tem mais sobre GitHub. Encontre o exemplo completo e saiba como configurar e executar no [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples).
Gera uma playlist de música com base no gênero e no número de músicas especificados pelo usuário.
```
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
```
+ Para ver detalhes da API, consulte os tópicos a seguir na *Referência da API do SDK da AWS para 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)
------
# Construir e orquestrar aplicações de IA generativa com o Amazon Bedrock e o Step Functions
O exemplo de código a seguir mostra como criar e orquestrar aplicações de IA generativa com o Amazon Bedrock e o Step Functions.
------
#### [ Python ]
**SDK para Python (Boto3)**
O cenário de encadeamento de prompts do Amazon Bedrock Sem Servidor demonstra como o [AWS Step Functions](https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html), o [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) e a documentação [https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html) podem ser usados para criar e orquestrar aplicações de IA generativa complexas, sem servidor e altamente escaláveis. Ele contém os seguintes exemplos de trabalho:
+ Escrever uma análise de um determinado romance para um blog de literatura. Este exemplo ilustra uma cadeia de prompts simples e sequencial.
+ Gerar uma história curta sobre um determinado tópico. Este exemplo ilustra como a IA pode processar uma lista de itens gerada anteriormente de forma iterativa.
+ Criar um itinerário para férias de fim de semana em um determinado destino. Este exemplo ilustra como paralelizar vários prompts distintos.
+ Lançar ideias de filmes para um usuário humano que atua como produtor de filmes. Este exemplo ilustra como paralelizar o mesmo prompt com diferentes parâmetros de inferência, como voltar a uma etapa anterior na cadeia e como incluir a entrada humana como parte do fluxo de trabalho.
+ Planejar uma refeição com base nos ingredientes que o usuário tem em mãos. Este exemplo ilustra como as cadeias de prompts podem incorporar duas conversas distintas de IA, com duas personas de IA participando de um debate entre si para melhorar o resultado final.
+ Encontre e resuma o repositório mais popular GitHub da atualidade. Este exemplo ilustra o encadeamento de vários agentes de IA que interagem com agentes externos. APIs
Para obter o código-fonte completo e as instruções de configuração e execução, consulte o projeto completo em [GitHub](https://github.com/aws-samples/amazon-bedrock-serverless-prompt-chaining).
**Serviços usados neste exemplo**
+ Amazon Bedrock
+ Amazon Bedrock Runtime
+ Amazon Bedrock Agents
+ Amazon Bedrock Agents Runtime
+ Step Functions
------