SDK for Rust を使用した Amazon Bedrock ランタイムの例 - AWS SDKコードの例

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SDK for Rust を使用した Amazon Bedrock ランタイムの例

次のコード例は、Amazon Bedrock Runtime で AWS SDK for Rust を使用してアクションを実行し、一般的なシナリオを実装する方法を示しています。

各例には、完全なソースコードへのリンクが含まれています。ここでは、コンテキストでコードを設定および実行する方法の手順を確認できます。

トピック

Anthropic Claude

次のコード例は、Bedrock の Converse API を使用して Anthropic Claude にテキストメッセージを送信する方法を示しています。

Rust のSDK
注記

GitHub には他にもあります。用例一覧を検索し、AWS コード例リポジトリでの設定と実行の方法を確認してください。

Bedrock の Converse API を使用して、Anthropic Claude にテキストメッセージを送信します。

#[tokio::main] async fn main() -> Result<(), BedrockConverseError> { tracing_subscriber::fmt::init(); let sdk_config = aws_config::defaults(BehaviorVersion::latest()) .region(CLAUDE_REGION) .load() .await; let client = Client::new(&sdk_config); let response = client .converse() .model_id(MODEL_ID) .messages( Message::builder() .role(ConversationRole::User) .content(ContentBlock::Text(USER_MESSAGE.to_string())) .build() .map_err(|_| "failed to build message")?, ) .send() .await; match response { Ok(output) => { let text = get_converse_output_text(output)?; println!("{}", text); Ok(()) } Err(e) => Err(e .as_service_error() .map(BedrockConverseError::from) .unwrap_or_else(|| BedrockConverseError("Unknown service error".into()))), } } fn get_converse_output_text(output: ConverseOutput) -> Result<String, BedrockConverseError> { let text = output .output() .ok_or("no output")? .as_message() .map_err(|_| "output not a message")? .content() .first() .ok_or("no content in message")? .as_text() .map_err(|_| "content is not text")? .to_string(); Ok(text) }

ステートメント、エラーユーティリティ、定数を使用します。

use aws_config::BehaviorVersion; use aws_sdk_bedrockruntime::{ operation::converse::{ConverseError, ConverseOutput}, types::{ContentBlock, ConversationRole, Message}, Client, }; // Set the model ID, e.g., Claude 3 Haiku. const MODEL_ID: &str = "anthropic.claude-3-haiku-20240307-v1:0"; const CLAUDE_REGION: &str = "us-east-1"; // Start a conversation with the user message. const USER_MESSAGE: &str = "Describe the purpose of a 'hello world' program in one line."; #[derive(Debug)] struct BedrockConverseError(String); impl std::fmt::Display for BedrockConverseError { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { write!(f, "Can't invoke '{}'. Reason: {}", MODEL_ID, self.0) } } impl std::error::Error for BedrockConverseError {} impl From<&str> for BedrockConverseError { fn from(value: &str) -> Self { BedrockConverseError(value.to_string()) } } impl From<&ConverseError> for BedrockConverseError { fn from(value: &ConverseError) -> Self { BedrockConverseError::from(match value { ConverseError::ModelTimeoutException(_) => "Model took too long", ConverseError::ModelNotReadyException(_) => "Model is not ready", _ => "Unknown", }) } }

次のコード例は、Bedrock の Converse API を使用して Anthropic Claude にテキストメッセージを送信し、レスポンスストリームをリアルタイムで処理する方法を示しています。

Rust のSDK
注記

GitHub には他にもあります。用例一覧を検索し、AWS コード例リポジトリでの設定と実行の方法を確認してください。

Bedrock の ConverseStream APIWord を使用して、Anthropic Claude にテキストメッセージを送信し、返信トークンをストリーミングします。

#[tokio::main] async fn main() -> Result<(), BedrockConverseStreamError> { tracing_subscriber::fmt::init(); let sdk_config = aws_config::defaults(BehaviorVersion::latest()) .region(CLAUDE_REGION) .load() .await; let client = Client::new(&sdk_config); let response = client .converse_stream() .model_id(MODEL_ID) .messages( Message::builder() .role(ConversationRole::User) .content(ContentBlock::Text(USER_MESSAGE.to_string())) .build() .map_err(|_| "failed to build message")?, ) .send() .await; let mut stream = match response { Ok(output) => Ok(output.stream), Err(e) => Err(BedrockConverseStreamError::from( e.as_service_error().unwrap(), )), }?; loop { let token = stream.recv().await; match token { Ok(Some(text)) => { let next = get_converse_output_text(text)?; print!("{}", next); Ok(()) } Ok(None) => break, Err(e) => Err(e .as_service_error() .map(BedrockConverseStreamError::from) .unwrap_or(BedrockConverseStreamError( "Unknown error receiving stream".into(), ))), }? } println!(); Ok(()) } fn get_converse_output_text( output: ConverseStreamOutputType, ) -> Result<String, BedrockConverseStreamError> { Ok(match output { ConverseStreamOutputType::ContentBlockDelta(event) => match event.delta() { Some(delta) => delta.as_text().cloned().unwrap_or_else(|_| "".into()), None => "".into(), }, _ => "".into(), }) }

ステートメント、エラーユーティリティ、定数を使用します。

use aws_config::BehaviorVersion; use aws_sdk_bedrockruntime::{ error::ProvideErrorMetadata, operation::converse_stream::ConverseStreamError, types::{ error::ConverseStreamOutputError, ContentBlock, ConversationRole, ConverseStreamOutput as ConverseStreamOutputType, Message, }, Client, }; // Set the model ID, e.g., Claude 3 Haiku. const MODEL_ID: &str = "anthropic.claude-3-haiku-20240307-v1:0"; const CLAUDE_REGION: &str = "us-east-1"; // Start a conversation with the user message. const USER_MESSAGE: &str = "Describe the purpose of a 'hello world' program in one line."; #[derive(Debug)] struct BedrockConverseStreamError(String); impl std::fmt::Display for BedrockConverseStreamError { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { write!(f, "Can't invoke '{}'. Reason: {}", MODEL_ID, self.0) } } impl std::error::Error for BedrockConverseStreamError {} impl From<&str> for BedrockConverseStreamError { fn from(value: &str) -> Self { BedrockConverseStreamError(value.into()) } } impl From<&ConverseStreamError> for BedrockConverseStreamError { fn from(value: &ConverseStreamError) -> Self { BedrockConverseStreamError( match value { ConverseStreamError::ModelTimeoutException(_) => "Model took too long", ConverseStreamError::ModelNotReadyException(_) => "Model is not ready", _ => "Unknown", } .into(), ) } } impl From<&ConverseStreamOutputError> for BedrockConverseStreamError { fn from(value: &ConverseStreamOutputError) -> Self { match value { ConverseStreamOutputError::ValidationException(ve) => BedrockConverseStreamError( ve.message().unwrap_or("Unknown ValidationException").into(), ), ConverseStreamOutputError::ThrottlingException(te) => BedrockConverseStreamError( te.message().unwrap_or("Unknown ThrottlingException").into(), ), value => BedrockConverseStreamError( value .message() .unwrap_or("Unknown StreamOutput exception") .into(), ), } } }
  • API の詳細については、ConverseStream AWS SDK for Rust API リファレンス」を参照してください。

次のコード例は、アプリケーション、生成 AI モデル、接続されたツールまたは APIs 間の一般的なインタラクションを構築し、AI と外部世界のインタラクションを仲介する方法を示しています。これは、外部気象APIを AI モデルに接続する例を使用して、ユーザー入力に基づいてリアルタイムの気象情報を提供できるようにします。

Rust のSDK
注記

GitHub には他にもあります。用例一覧を検索し、AWS コード例リポジトリでの設定と実行の方法を確認してください。

デモのプライマリシナリオとロジック。これにより、ユーザー、Amazon Bedrock Converse API、気象ツール間の会話が調整されます。

#[derive(Debug)] #[allow(dead_code)] struct InvokeToolResult(String, ToolResultBlock); struct ToolUseScenario { client: Client, conversation: Vec<Message>, system_prompt: SystemContentBlock, tool_config: ToolConfiguration, } impl ToolUseScenario { fn new(client: Client) -> Self { let system_prompt = SystemContentBlock::Text(SYSTEM_PROMPT.into()); let tool_config = ToolConfiguration::builder() .tools(Tool::ToolSpec( ToolSpecification::builder() .name(TOOL_NAME) .description(TOOL_DESCRIPTION) .input_schema(ToolInputSchema::Json(make_tool_schema())) .build() .unwrap(), )) .build() .unwrap(); ToolUseScenario { client, conversation: vec![], system_prompt, tool_config, } } async fn run(&mut self) -> Result<(), ToolUseScenarioError> { loop { let input = get_input().await?; if input.is_none() { break; } let message = Message::builder() .role(User) .content(ContentBlock::Text(input.unwrap())) .build() .map_err(ToolUseScenarioError::from)?; self.conversation.push(message); let response = self.send_to_bedrock().await?; self.process_model_response(response).await?; } Ok(()) } async fn send_to_bedrock(&mut self) -> Result<ConverseOutput, ToolUseScenarioError> { debug!("Sending conversation to bedrock"); self.client .converse() .model_id(MODEL_ID) .set_messages(Some(self.conversation.clone())) .system(self.system_prompt.clone()) .tool_config(self.tool_config.clone()) .send() .await .map_err(ToolUseScenarioError::from) } async fn process_model_response( &mut self, mut response: ConverseOutput, ) -> Result<(), ToolUseScenarioError> { let mut iteration = 0; while iteration < MAX_RECURSIONS { iteration += 1; let message = if let Some(ref output) = response.output { if output.is_message() { Ok(output.as_message().unwrap().clone()) } else { Err(ToolUseScenarioError( "Converse Output is not a message".into(), )) } } else { Err(ToolUseScenarioError("Missing Converse Output".into())) }?; self.conversation.push(message.clone()); match response.stop_reason { StopReason::ToolUse => { response = self.handle_tool_use(&message).await?; } StopReason::EndTurn => { print_model_response(&message.content[0])?; return Ok(()); } _ => (), } } Err(ToolUseScenarioError( "Exceeded MAX_ITERATIONS when calling tools".into(), )) } async fn handle_tool_use( &mut self, message: &Message, ) -> Result<ConverseOutput, ToolUseScenarioError> { let mut tool_results: Vec<ContentBlock> = vec![]; for block in &message.content { match block { ContentBlock::Text(_) => print_model_response(block)?, ContentBlock::ToolUse(tool) => { let tool_response = self.invoke_tool(tool).await?; tool_results.push(ContentBlock::ToolResult(tool_response.1)); } _ => (), }; } let message = Message::builder() .role(User) .set_content(Some(tool_results)) .build()?; self.conversation.push(message); self.send_to_bedrock().await } async fn invoke_tool( &mut self, tool: &ToolUseBlock, ) -> Result<InvokeToolResult, ToolUseScenarioError> { match tool.name() { TOOL_NAME => { println!( "\x1b[0;90mExecuting tool: {TOOL_NAME} with input: {:?}...\x1b[0m", tool.input() ); let content = fetch_weather_data(tool).await?; println!( "\x1b[0;90mTool responded with {:?}\x1b[0m", content.content() ); Ok(InvokeToolResult(tool.tool_use_id.clone(), content)) } _ => Err(ToolUseScenarioError(format!( "The requested tool with name {} does not exist", tool.name() ))), } } } #[tokio::main] async fn main() { tracing_subscriber::fmt::init(); let sdk_config = aws_config::defaults(BehaviorVersion::latest()) .region(CLAUDE_REGION) .load() .await; let client = Client::new(&sdk_config); let mut scenario = ToolUseScenario::new(client); header(); if let Err(err) = scenario.run().await { println!("There was an error running the scenario! {}", err.0) } footer(); }

デモで使用される気象ツール。このスクリプトは、ツール仕様を定義し、Open-Meteo API から を使用して気象データを取得するロジックを実装します。

const ENDPOINT: &str = "https://api.open-meteo.com/v1/forecast"; async fn fetch_weather_data( tool_use: &ToolUseBlock, ) -> Result<ToolResultBlock, ToolUseScenarioError> { let input = tool_use.input(); let latitude = input .as_object() .unwrap() .get("latitude") .unwrap() .as_string() .unwrap(); let longitude = input .as_object() .unwrap() .get("longitude") .unwrap() .as_string() .unwrap(); let params = [ ("latitude", latitude), ("longitude", longitude), ("current_weather", "true"), ]; debug!("Calling {ENDPOINT} with {params:?}"); let response = reqwest::Client::new() .get(ENDPOINT) .query(&params) .send() .await .map_err(|e| ToolUseScenarioError(format!("Error requesting weather: {e:?}")))? .error_for_status() .map_err(|e| ToolUseScenarioError(format!("Failed to request weather: {e:?}")))?; debug!("Response: {response:?}"); let bytes = response .bytes() .await .map_err(|e| ToolUseScenarioError(format!("Error reading response: {e:?}")))?; let result = String::from_utf8(bytes.to_vec()) .map_err(|_| ToolUseScenarioError("Response was not utf8".into()))?; Ok(ToolResultBlock::builder() .tool_use_id(tool_use.tool_use_id()) .content(ToolResultContentBlock::Text(result)) .build()?) }

メッセージコンテンツブロックを印刷するユーティリティ

fn print_model_response(block: &ContentBlock) -> Result<(), ToolUseScenarioError> { if block.is_text() { let text = block.as_text().unwrap(); println!("\x1b[0;90mThe model's response:\x1b[0m\n{text}"); Ok(()) } else { Err(ToolUseScenarioError(format!( "Content block is not text ({block:?})" ))) } }

ステートメント、エラーユーティリティ、定数を使用します。

use std::{collections::HashMap, io::stdin}; use aws_config::BehaviorVersion; use aws_sdk_bedrockruntime::{ error::{BuildError, SdkError}, operation::converse::{ConverseError, ConverseOutput}, types::{ ContentBlock, ConversationRole::User, Message, StopReason, SystemContentBlock, Tool, ToolConfiguration, ToolInputSchema, ToolResultBlock, ToolResultContentBlock, ToolSpecification, ToolUseBlock, }, Client, }; use aws_smithy_runtime_api::http::Response; use aws_smithy_types::Document; use tracing::debug; /// This demo illustrates a tool use scenario using Amazon Bedrock's Converse API and a weather tool. /// The script interacts with a foundation model on Amazon Bedrock to provide weather information based on user /// input. It uses the Open-Meteo API (https://open-meteo.com) to retrieve current weather data for a given location. // Set the model ID, e.g., Claude 3 Haiku. const MODEL_ID: &str = "anthropic.claude-3-haiku-20240307-v1:0"; const CLAUDE_REGION: &str = "us-east-1"; const SYSTEM_PROMPT: &str = "You are a weather assistant that provides current weather data for user-specified locations using only the Weather_Tool, which expects latitude and longitude. Infer the coordinates from the location yourself. If the user provides coordinates, infer the approximate location and refer to it in your response. To use the tool, you strictly apply the provided tool specification. - Explain your step-by-step process, and give brief updates before each step. - Only use the Weather_Tool for data. Never guess or make up information. - Repeat the tool use for subsequent requests if necessary. - If the tool errors, apologize, explain weather is unavailable, and suggest other options. - Report temperatures in °C (°F) and wind in km/h (mph). Keep weather reports concise. Sparingly use emojis where appropriate. - Only respond to weather queries. Remind off-topic users of your purpose. - Never claim to search online, access external data, or use tools besides Weather_Tool. - Complete the entire process until you have all required data before sending the complete response. "; // The maximum number of recursive calls allowed in the tool_use_demo function. // This helps prevent infinite loops and potential performance issues. const MAX_RECURSIONS: i8 = 5; const TOOL_NAME: &str = "Weather_Tool"; const TOOL_DESCRIPTION: &str = "Get the current weather for a given location, based on its WGS84 coordinates."; fn make_tool_schema() -> Document { Document::Object(HashMap::<String, Document>::from([ ("type".into(), Document::String("object".into())), ( "properties".into(), Document::Object(HashMap::from([ ( "latitude".into(), Document::Object(HashMap::from([ ("type".into(), Document::String("string".into())), ( "description".into(), Document::String("Geographical WGS84 latitude of the location.".into()), ), ])), ), ( "longitude".into(), Document::Object(HashMap::from([ ("type".into(), Document::String("string".into())), ( "description".into(), Document::String( "Geographical WGS84 longitude of the location.".into(), ), ), ])), ), ])), ), ( "required".into(), Document::Array(vec![ Document::String("latitude".into()), Document::String("longitude".into()), ]), ), ])) } #[derive(Debug)] struct ToolUseScenarioError(String); impl std::fmt::Display for ToolUseScenarioError { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { write!(f, "Tool use error with '{}'. Reason: {}", MODEL_ID, self.0) } } impl From<&str> for ToolUseScenarioError { fn from(value: &str) -> Self { ToolUseScenarioError(value.into()) } } impl From<BuildError> for ToolUseScenarioError { fn from(value: BuildError) -> Self { ToolUseScenarioError(value.to_string().clone()) } } impl From<SdkError<ConverseError, Response>> for ToolUseScenarioError { fn from(value: SdkError<ConverseError, Response>) -> Self { ToolUseScenarioError(match value.as_service_error() { Some(value) => value.meta().message().unwrap_or("Unknown").into(), None => "Unknown".into(), }) } }