aws-sdk-java-v2-bedrock

仓库: giuseppe-trisciuoglio/developer-kit
安装量: 314
排名: #2941

安装

npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill aws-sdk-java-v2-bedrock

AWS SDK for Java 2.x - Amazon Bedrock When to Use Use this skill when: Listing and inspecting foundation models on Amazon Bedrock Invoking foundation models for text generation (Claude, Llama, Titan) Generating images with AI models (Stable Diffusion) Creating text embeddings for RAG applications Implementing streaming responses for real-time generation Working with multiple AI providers through unified API Integrating generative AI into Spring Boot applications Building AI-powered chatbots and assistants Overview Amazon Bedrock provides access to foundation models from leading AI providers through a unified API. This skill covers patterns for working with various models including Claude, Llama, Titan, and Stability Diffusion using AWS SDK for Java 2.x. Instructions Follow these steps to work with Amazon Bedrock: Set Up AWS Credentials - Configure credentials with appropriate Bedrock permissions Enable Bedrock Access - Request access to specific foundation models in the AWS Console Add Dependencies - Include bedrock and bedrockruntime dependencies Create Clients - Instantiate BedrockClient for management and BedrockRuntimeClient for invocation List Models - Query available foundation models and their capabilities Invoke Models - Build proper payloads for each model provider's format Handle Streaming - Implement streaming response handlers for real-time generation Integrate with Spring - Configure beans and services for enterprise applications Quick Start Dependencies

< dependency

< groupId

software.amazon.awssdk </ groupId

< artifactId

bedrock </ artifactId

</ dependency

< dependency

< groupId

software.amazon.awssdk </ groupId

< artifactId

bedrockruntime </ artifactId

</ dependency

<
dependency
>
<
groupId
>
org.json
</
groupId
>
<
artifactId
>
json
</
artifactId
>
<
version
>
20231013
</
version
>
</
dependency
>
Basic Client Setup
import
software
.
amazon
.
awssdk
.
regions
.
Region
;
import
software
.
amazon
.
awssdk
.
services
.
bedrock
.
BedrockClient
;
import
software
.
amazon
.
awssdk
.
services
.
bedrockruntime
.
BedrockRuntimeClient
;
// Model management client
BedrockClient
bedrockClient
=
BedrockClient
.
builder
(
)
.
region
(
Region
.
US_EAST_1
)
.
build
(
)
;
// Model invocation client
BedrockRuntimeClient
bedrockRuntimeClient
=
BedrockRuntimeClient
.
builder
(
)
.
region
(
Region
.
US_EAST_1
)
.
build
(
)
;
Core Patterns
Model Discovery
import
software
.
amazon
.
awssdk
.
services
.
bedrock
.
model
.
*
;
import
java
.
util
.
List
;
public
List
<
FoundationModelSummary
>
listFoundationModels
(
BedrockClient
bedrockClient
)
{
return
bedrockClient
.
listFoundationModels
(
)
.
modelSummaries
(
)
;
}
Model Invocation
import
software
.
amazon
.
awssdk
.
core
.
SdkBytes
;
import
software
.
amazon
.
awssdk
.
services
.
bedrockruntime
.
model
.
*
;
import
org
.
json
.
JSONObject
;
public
String
invokeModel
(
BedrockRuntimeClient
client
,
String
modelId
,
String
prompt
)
{
JSONObject
payload
=
createPayload
(
modelId
,
prompt
)
;
InvokeModelResponse
response
=
client
.
invokeModel
(
request
->
request
.
modelId
(
modelId
)
.
body
(
SdkBytes
.
fromUtf8String
(
payload
.
toString
(
)
)
)
)
;
return
extractTextFromResponse
(
modelId
,
response
.
body
(
)
.
asUtf8String
(
)
)
;
}
private
JSONObject
createPayload
(
String
modelId
,
String
prompt
)
{
if
(
modelId
.
startsWith
(
"anthropic.claude"
)
)
{
return
new
JSONObject
(
)
.
put
(
"anthropic_version"
,
"bedrock-2023-05-31"
)
.
put
(
"max_tokens"
,
1000
)
.
put
(
"messages"
,
new
JSONObject
[
]
{
new
JSONObject
(
)
.
put
(
"role"
,
"user"
)
.
put
(
"content"
,
prompt
)
}
)
;
}
else
if
(
modelId
.
startsWith
(
"amazon.titan"
)
)
{
return
new
JSONObject
(
)
.
put
(
"inputText"
,
prompt
)
.
put
(
"textGenerationConfig"
,
new
JSONObject
(
)
.
put
(
"maxTokenCount"
,
512
)
.
put
(
"temperature"
,
0.7
)
)
;
}
else
if
(
modelId
.
startsWith
(
"meta.llama"
)
)
{
return
new
JSONObject
(
)
.
put
(
"prompt"
,
"[INST] "
+
prompt
+
" [/INST]"
)
.
put
(
"max_gen_len"
,
512
)
.
put
(
"temperature"
,
0.7
)
;
}
throw
new
IllegalArgumentException
(
"Unsupported model: "
+
modelId
)
;
}
Streaming Responses
public
void
streamResponse
(
BedrockRuntimeClient
client
,
String
modelId
,
String
prompt
)
{
JSONObject
payload
=
createPayload
(
modelId
,
prompt
)
;
InvokeModelWithResponseStreamRequest
streamRequest
=
InvokeModelWithResponseStreamRequest
.
builder
(
)
.
modelId
(
modelId
)
.
body
(
SdkBytes
.
fromUtf8String
(
payload
.
toString
(
)
)
)
.
build
(
)
;
client
.
invokeModelWithResponseStream
(
streamRequest
,
InvokeModelWithResponseStreamResponseHandler
.
builder
(
)
.
onEventStream
(
stream
->
{
stream
.
forEach
(
event
->
{
if
(
event
instanceof
PayloadPart
)
{
PayloadPart
payloadPart
=
(
PayloadPart
)
event
;
String
chunk
=
payloadPart
.
bytes
(
)
.
asUtf8String
(
)
;
processChunk
(
modelId
,
chunk
)
;
}
}
)
;
}
)
.
build
(
)
)
;
}
Text Embeddings
public
double
[
]
createEmbeddings
(
BedrockRuntimeClient
client
,
String
text
)
{
String
modelId
=
"amazon.titan-embed-text-v1"
;
JSONObject
payload
=
new
JSONObject
(
)
.
put
(
"inputText"
,
text
)
;
InvokeModelResponse
response
=
client
.
invokeModel
(
request
->
request
.
modelId
(
modelId
)
.
body
(
SdkBytes
.
fromUtf8String
(
payload
.
toString
(
)
)
)
)
;
JSONObject
responseBody
=
new
JSONObject
(
response
.
body
(
)
.
asUtf8String
(
)
)
;
JSONArray
embeddingArray
=
responseBody
.
getJSONArray
(
"embedding"
)
;
double
[
]
embeddings
=
new
double
[
embeddingArray
.
length
(
)
]
;
for
(
int
i
=
0
;
i
<
embeddingArray
.
length
(
)
;
i
++
)
{
embeddings
[
i
]
=
embeddingArray
.
getDouble
(
i
)
;
}
return
embeddings
;
}
Spring Boot Integration
@Configuration
public
class
BedrockConfiguration
{
@Bean
public
BedrockClient
bedrockClient
(
)
{
return
BedrockClient
.
builder
(
)
.
region
(
Region
.
US_EAST_1
)
.
build
(
)
;
}
@Bean
public
BedrockRuntimeClient
bedrockRuntimeClient
(
)
{
return
BedrockRuntimeClient
.
builder
(
)
.
region
(
Region
.
US_EAST_1
)
.
build
(
)
;
}
}
@Service
public
class
BedrockAIService
{
private
final
BedrockRuntimeClient
bedrockRuntimeClient
;
@Value
(
"${bedrock.default-model-id:anthropic.claude-sonnet-4-5-20250929-v1:0}"
)
private
String
defaultModelId
;
public
BedrockAIService
(
BedrockRuntimeClient
bedrockRuntimeClient
)
{
this
.
bedrockRuntimeClient
=
bedrockRuntimeClient
;
}
public
String
generateText
(
String
prompt
)
{
return
generateText
(
prompt
,
defaultModelId
)
;
}
public
String
generateText
(
String
prompt
,
String
modelId
)
{
Map
<
String
,
Object
>
payload
=
createPayload
(
modelId
,
prompt
)
;
String
payloadJson
=
new
ObjectMapper
(
)
.
writeValueAsString
(
payload
)
;
InvokeModelResponse
response
=
bedrockRuntimeClient
.
invokeModel
(
request
->
request
.
modelId
(
modelId
)
.
body
(
SdkBytes
.
fromUtf8String
(
payloadJson
)
)
)
;
return
extractTextFromResponse
(
modelId
,
response
.
body
(
)
.
asUtf8String
(
)
)
;
}
}
Basic Usage Example
BedrockRuntimeClient
client
=
BedrockRuntimeClient
.
builder
(
)
.
region
(
Region
.
US_EAST_1
)
.
build
(
)
;
String
prompt
=
"Explain quantum computing in simple terms"
;
String
response
=
invokeModel
(
client
,
"anthropic.claude-sonnet-4-5-20250929-v1:0"
,
prompt
)
;
System
.
out
.
println
(
response
)
;
Best Practices
Model Selection
Claude 4.5 Sonnet
Best for complex reasoning, analysis, and creative tasks
Claude 4.5 Haiku
Fast and affordable for real-time applications
Claude 3.7 Sonnet
Most advanced reasoning capabilities
Llama 3.1
Latest generation open-source alternative, good for general tasks
Titan
AWS native, cost-effective for simple text generation Performance Optimization Reuse client instances (don't create new clients for each request) Use async clients for I/O operations Implement streaming for long responses Cache foundation model lists Security Never log sensitive prompt data Use IAM roles for authentication (never access keys) Implement rate limiting for public applications Sanitize user inputs to prevent prompt injection Error Handling Implement retry logic for throttling (exponential backoff) Handle model-specific validation errors Validate responses before processing Use proper exception handling for different error types Cost Optimization Use appropriate max_tokens limits Choose cost-effective models for simple tasks Cache embeddings when possible Monitor usage and set budget alerts Common Model IDs // Claude Models public static final String CLAUDE_SONNET_4_5 = "anthropic.claude-sonnet-4-5-20250929-v1:0" ; public static final String CLAUDE_HAIKU_4_5 = "anthropic.claude-haiku-4-5-20251001-v1:0" ; public static final String CLAUDE_OPUS_4_1 = "anthropic.claude-opus-4-1-20250805-v1:0" ; public static final String CLAUDE_3_7_SONNET = "anthropic.claude-3-7-sonnet-20250219-v1:0" ; public static final String CLAUDE_OPUS_4 = "anthropic.claude-opus-4-20250514-v1:0" ; public static final String CLAUDE_SONNET_4 = "anthropic.claude-sonnet-4-20250514-v1:0" ; public static final String CLAUDE_3_5_SONNET_V2 = "anthropic.claude-3-5-sonnet-20241022-v2:0" ; public static final String CLAUDE_3_5_HAIKU = "anthropic.claude-3-5-haiku-20241022-v1:0" ; public static final String CLAUDE_3_OPUS = "anthropic.claude-3-opus-20240229-v1:0" ; // Llama Models public static final String LLAMA_3_3_70B = "meta.llama3-3-70b-instruct-v1:0" ; public static final String LLAMA_3_2_90B = "meta.llama3-2-90b-instruct-v1:0" ; public static final String LLAMA_3_2_11B = "meta.llama3-2-11b-instruct-v1:0" ; public static final String LLAMA_3_2_3B = "meta.llama3-2-3b-instruct-v1:0" ; public static final String LLAMA_3_2_1B = "meta.llama3-2-1b-instruct-v1:0" ; public static final String LLAMA_4_MAV_17B = "meta.llama4-maverick-17b-instruct-v1:0" ; public static final String LLAMA_4_SCOUT_17B = "meta.llama4-scout-17b-instruct-v1:0" ; public static final String LLAMA_3_1_405B = "meta.llama3-1-405b-instruct-v1:0" ; public static final String LLAMA_3_1_70B = "meta.llama3-1-70b-instruct-v1:0" ; public static final String LLAMA_3_1_8B = "meta.llama3-1-8b-instruct-v1:0" ; public static final String LLAMA_3_70B = "meta.llama3-70b-instruct-v1:0" ; public static final String LLAMA_3_8B = "meta.llama3-8b-instruct-v1:0" ; // Amazon Titan Models public static final String TITAN_TEXT_EXPRESS = "amazon.titan-text-express-v1" ; public static final String TITAN_TEXT_LITE = "amazon.titan-text-lite-v1" ; public static final String TITAN_EMBEDDINGS = "amazon.titan-embed-text-v1" ; public static final String TITAN_IMAGE_GENERATOR = "amazon.titan-image-generator-v1" ; // Stable Diffusion public static final String STABLE_DIFFUSION_XL = "stability.stable-diffusion-xl-v1" ; // Mistral AI Models public static final String MISTRAL_LARGE_2407 = "mistral.mistral-large-2407-v1:0" ; public static final String MISTRAL_LARGE_2402 = "mistral.mistral-large-2402-v1:0" ; public static final String MISTRAL_SMALL_2402 = "mistral.mistral-small-2402-v1:0" ; public static final String MISTRAL_PIXTRAL_2502 = "mistral.pixtral-large-2502-v1:0" ; public static final String MISTRAL_MIXTRAL_8X7B = "mistral.mixtral-8x7b-instruct-v0:1" ; public static final String MISTRAL_7B = "mistral.mistral-7b-instruct-v0:2" ; // Amazon Nova Models public static final String NOVA_PREMIER = "amazon.nova-premier-v1:0" ; public static final String NOVA_PRO = "amazon.nova-pro-v1:0" ; public static final String NOVA_LITE = "amazon.nova-lite-v1:0" ; public static final String NOVA_MICRO = "amazon.nova-micro-v1:0" ; public static final String NOVA_CANVAS = "amazon.nova-canvas-v1:0" ; public static final String NOVA_REEL = "amazon.nova-reel-v1:1" ; // Other Models public static final String COHERE_COMMAND = "cohere.command-text-v14" ; public static final String DEEPSEEK_R1 = "deepseek.r1-v1:0" ; public static final String DEEPSEEK_V3_1 = "deepseek.v3-v1:0" ; Examples Example 1: Simple Text Generation with Claude public String generateWithClaude ( BedrockRuntimeClient client , String prompt ) { JSONObject payload = new JSONObject ( ) . put ( "anthropic_version" , "bedrock-2023-05-31" ) . put ( "max_tokens" , 1000 ) . put ( "messages" , new JSONObject [ ] { new JSONObject ( ) . put ( "role" , "user" ) . put ( "content" , prompt ) } ) ; InvokeModelResponse response = client . invokeModel ( InvokeModelRequest . builder ( ) . modelId ( "anthropic.claude-sonnet-4-5-20250929-v1:0" ) . body ( SdkBytes . fromUtf8String ( payload . toString ( ) ) ) . build ( ) ) ; JSONObject responseBody = new JSONObject ( response . body ( ) . asUtf8String ( ) ) ; return responseBody . getJSONArray ( "content" ) . getJSONObject ( 0 ) . getString ( "text" ) ; } Example 2: Streaming Response public void streamResponse ( BedrockRuntimeClient client , String modelId , String prompt ) { JSONObject payload = new JSONObject ( ) . put ( "anthropic_version" , "bedrock-2023-05-31" ) . put ( "max_tokens" , 500 ) . put ( "messages" , new JSONObject [ ] { new JSONObject ( ) . put ( "role" , "user" ) . put ( "content" , prompt ) } ) ; InvokeModelWithResponseStreamRequest request = InvokeModelWithResponseStreamRequest . builder ( ) . modelId ( modelId ) . body ( SdkBytes . fromUtf8String ( payload . toString ( ) ) ) . build ( ) ; client . invokeModelWithResponseStream ( request , InvokeModelWithResponseStreamResponseHandler . builder ( ) . onEventStream ( stream -> stream . forEach ( event -> { if ( event instanceof PayloadPart ) { String chunk = ( ( PayloadPart ) event ) . bytes ( ) . asUtf8String ( ) ; System . out . print ( chunk ) ; } } ) ) . build ( ) ) ; } Example 3: Spring Boot Service @Service public class BedrockService { private final BedrockRuntimeClient client ; private final ObjectMapper mapper ; @Value ( "${bedrock.model:anthropic.claude-sonnet-4-5-20250929-v1:0}" ) private String modelId ; public String generate ( String prompt ) { try { Map < String , Object

payload

Map . of ( "anthropic_version" , "bedrock-2023-05-31" , "max_tokens" , 1000 , "messages" , List . of ( Map . of ( "role" , "user" , "content" , prompt ) ) ) ; InvokeModelResponse response = client . invokeModel ( InvokeModelRequest . builder ( ) . modelId ( modelId ) . body ( SdkBytes . fromUtf8String ( mapper . writeValueAsString ( payload ) ) ) . build ( ) ) ; return extractText ( response . body ( ) . asUtf8String ( ) ) ; } catch ( Exception e ) { throw new RuntimeException ( "Bedrock invocation failed" , e ) ; } } } See the examples directory for comprehensive usage patterns. Advanced Topics See the Advanced Topics for: Multi-model service patterns Advanced error handling with retries Batch processing strategies Performance optimization techniques Custom response parsing Model Reference See the Model Reference for: Detailed model specifications Payload/response formats for each provider Performance characteristics Model selection guidelines Configuration templates Testing Strategies See the Testing Strategies for: Unit testing with mocked clients Integration testing with LocalStack Performance testing Streaming response testing Test data management

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