Class BedrockFoundationModel
- All Implemented Interfaces:
IBedrockInvokable
,software.amazon.jsii.JsiiSerializable
If you need to use a model name that doesn't exist as a static member, you
can instantiate a BedrockFoundationModel
object, e.g: new BedrockFoundationModel('my-model')
.
Example:
// Create a specialized agent Agent customerSupportAgent = Agent.Builder.create(this, "CustomerSupportAgent") .instruction("You specialize in answering customer support questions.") .foundationModel(BedrockFoundationModel.AMAZON_NOVA_LITE_V1) .build(); // Create an agent alias AgentAlias customerSupportAlias = AgentAlias.Builder.create(this, "CustomerSupportAlias") .agent(customerSupportAgent) .agentAliasName("production") .build(); // Create a main agent that collaborates with the specialized agent Agent mainAgent = Agent.Builder.create(this, "MainAgent") .instruction("You route specialized questions to other agents.") .foundationModel(BedrockFoundationModel.AMAZON_NOVA_LITE_V1) .agentCollaboration(Map.of( "type", AgentCollaboratorType.SUPERVISOR, "collaborators", List.of( AgentCollaborator.Builder.create() .agentAlias(customerSupportAlias) .collaborationInstruction("Route customer support questions to this agent.") .collaboratorName("CustomerSupport") .relayConversationHistory(true) .build()))) .build();
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Nested Class Summary
Nested ClassesModifier and TypeClassDescriptionstatic final class
(experimental) A fluent builder forBedrockFoundationModel
.Nested classes/interfaces inherited from class software.amazon.jsii.JsiiObject
software.amazon.jsii.JsiiObject.InitializationMode
Nested classes/interfaces inherited from interface software.amazon.awscdk.services.bedrock.alpha.IBedrockInvokable
IBedrockInvokable.Jsii$Default, IBedrockInvokable.Jsii$Proxy
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final BedrockFoundationModel
(experimental) AI21's Jamba 1.5 Large model optimized for text generation tasks.static final BedrockFoundationModel
(experimental) AI21's Jamba 1.5 Mini model, a lighter version optimized for faster processing.static final BedrockFoundationModel
(experimental) AI21's Jamba Instruct model, specifically designed for instruction-following tasks.static final BedrockFoundationModel
(experimental) Amazon's Nova Lite model, balancing performance with efficiency.static final BedrockFoundationModel
(experimental) Amazon's Nova Micro model, a lightweight model optimized for efficiency.static final BedrockFoundationModel
(experimental) Amazon's Nova Premier model, the most advanced in the Nova series.static final BedrockFoundationModel
(experimental) Amazon's Nova Pro model, offering advanced capabilities for complex tasks.static final BedrockFoundationModel
(experimental) Amazon's Titan Text Premier model, designed for high-quality text generation.static final BedrockFoundationModel
(experimental) Amazon's Titan Text Express model, optimized for fast text generation.static final BedrockFoundationModel
(experimental) Anthropic's Claude 3.5 Haiku model, optimized for quick responses.static final BedrockFoundationModel
(experimental) Anthropic's Claude 3.5 Sonnet V1 model, balanced performance model.static final BedrockFoundationModel
(experimental) Anthropic's Claude 3.5 Sonnet V2 model, optimized for agent interactions.static final BedrockFoundationModel
(experimental) Anthropic's Claude 3.7 Sonnet model, latest in the Claude 3 series.static final BedrockFoundationModel
(experimental) Anthropic's Claude Haiku model, optimized for efficiency.static final BedrockFoundationModel
(experimental) Anthropic's Claude Instant V1.2 model, legacy version.static final BedrockFoundationModel
(experimental) Anthropic's Claude Opus model, designed for advanced tasks.static final BedrockFoundationModel
(experimental) Anthropic's Claude Sonnet model, legacy version.static final BedrockFoundationModel
(experimental) Anthropic's Claude V2 model, legacy version.static final BedrockFoundationModel
(experimental) Anthropic's Claude V2.1 model, legacy version.static final BedrockFoundationModel
(experimental) Cohere's English embedding model, optimized for English text embeddings.static final BedrockFoundationModel
(experimental) Cohere's Multilingual embedding model, supporting multiple languages.static final BedrockFoundationModel
(experimental) Deepseek's R1 model, designed for general language understanding.static final BedrockFoundationModel
(experimental) Meta's Llama 3 70B Instruct model, large-scale instruction model.static final BedrockFoundationModel
(experimental) Meta's Llama 3 1.8B Instruct model, compact instruction-following model.static final BedrockFoundationModel
(experimental) Meta's Llama 3.2 1B Instruct model, ultra-lightweight model.static final BedrockFoundationModel
(experimental) Meta's Llama 3.2 11B Instruct model, mid-sized instruction model.static final BedrockFoundationModel
(experimental) Meta's Llama 3.2 3B Instruct model, compact efficient model.static final BedrockFoundationModel
(experimental) Meta's Llama 3.3 70B Instruct model, latest large-scale model.static final BedrockFoundationModel
(experimental) Meta's Llama 4 Maverick 17B Instruct model, innovative mid-sized model.static final BedrockFoundationModel
(experimental) Meta's Llama 4 Scout 17B Instruct model, analytical mid-sized model.static final BedrockFoundationModel
(experimental) Mistral's 7B Instruct model, efficient instruction-following model.static final BedrockFoundationModel
(experimental) Mistral's Large 2402 model, high-capacity language model.static final BedrockFoundationModel
(experimental) Mistral's Large 2407 model, updated large-scale model.static final BedrockFoundationModel
(experimental) Mistral's Mixtral 8x7B Instruct model, mixture-of-experts architecture.static final BedrockFoundationModel
(experimental) Mistral's Pixtral Large 2502 model, specialized large model.static final BedrockFoundationModel
(experimental) Mistral's Small 2402 model, compact efficient model.static final BedrockFoundationModel
(experimental) Amazon's Titan Embed Text V1 model for text embeddings.static final BedrockFoundationModel
(experimental) Amazon's Titan Embed Text V2 model with 1024-dimensional vectors.static final BedrockFoundationModel
(experimental) Amazon's Titan Embed Text V2 model with 256-dimensional vectors.static final BedrockFoundationModel
(experimental) Amazon's Titan Embed Text V2 model with 512-dimensional vectors. -
Constructor Summary
ConstructorsModifierConstructorDescriptionBedrockFoundationModel
(String value) BedrockFoundationModel
(String value, BedrockFoundationModelProps props) protected
BedrockFoundationModel
(software.amazon.jsii.JsiiObject.InitializationMode initializationMode) protected
BedrockFoundationModel
(software.amazon.jsii.JsiiObjectRef objRef) -
Method Summary
Modifier and TypeMethodDescriptionasArn()
(experimental) Returns the ARN of the foundation model in the following format:arn:${Partition}:bedrock:${Region}::foundation-model/${ResourceId}
.asIModel()
(experimental) Returns the IModel.static BedrockFoundationModel
fromCdkFoundationModel
(FoundationModel modelId) (experimental) Creates a BedrockFoundationModel from a FoundationModel.static BedrockFoundationModel
fromCdkFoundationModel
(FoundationModel modelId, BedrockFoundationModelProps props) (experimental) Creates a BedrockFoundationModel from a FoundationModel.static BedrockFoundationModel
(experimental) Creates a BedrockFoundationModel from a FoundationModelIdentifier.static BedrockFoundationModel
(experimental) Creates a BedrockFoundationModel from a FoundationModelIdentifier.(experimental) The ARN used for invoking the model.(experimental) The ARN of the foundation model.(experimental) The unique identifier of the foundation model.(experimental) The vector types supported by this model for embeddings.(experimental) Whether this model supports integration with Bedrock Agents.(experimental) Whether this model supports cross-region inference.(experimental) Whether this model supports integration with Bedrock Knowledge Base.(experimental) The dimensionality of the vector embeddings produced by this model.grantInvoke
(IGrantable grantee) (experimental) Gives the appropriate policies to invoke and use the Foundation Model in the stack region.grantInvokeAllRegions
(IGrantable grantee) (experimental) Gives the appropriate policies to invoke and use the Foundation Model in all regions.toString()
(experimental) Returns a string representation of an object.Methods inherited from class software.amazon.jsii.JsiiObject
jsiiAsyncCall, jsiiAsyncCall, jsiiCall, jsiiCall, jsiiGet, jsiiGet, jsiiSet, jsiiStaticCall, jsiiStaticCall, jsiiStaticGet, jsiiStaticGet, jsiiStaticSet, jsiiStaticSet
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
Methods inherited from interface software.amazon.jsii.JsiiSerializable
$jsii$toJson
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Field Details
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AI21_JAMBA_1_5_LARGE_V1
(experimental) AI21's Jamba 1.5 Large model optimized for text generation tasks. Suitable for complex language understanding and generation tasks.Features:
- Supports Bedrock Agents integration
- Optimized for natural language processing
- Best for: Content generation, summarization, and complex text analysis
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AI21_JAMBA_1_5_MINI_V1
(experimental) AI21's Jamba 1.5 Mini model, a lighter version optimized for faster processing. Balances performance with efficiency for general text tasks.Features:
- Supports Bedrock Agents integration
- Faster response times compared to larger models
- Best for: Quick text processing, basic content generation
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AI21_JAMBA_INSTRUCT_V1
(experimental) AI21's Jamba Instruct model, specifically designed for instruction-following tasks. Optimized for understanding and executing specific instructions.Features:
- Supports Bedrock Agents integration
- Enhanced instruction understanding
- Best for: Task-specific instructions, command processing
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AMAZON_NOVA_LITE_V1
(experimental) Amazon's Nova Lite model, balancing performance with efficiency.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Optimized for agents
- Best for: General-purpose language tasks, moderate complexity
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AMAZON_NOVA_MICRO_V1
(experimental) Amazon's Nova Micro model, a lightweight model optimized for efficiency.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Optimized for agents
- Best for: Quick processing tasks, basic language understanding
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AMAZON_NOVA_PREMIER_V1
(experimental) Amazon's Nova Premier model, the most advanced in the Nova series.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Optimized for agents
- Best for: High-end applications, complex analysis, premium performance
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AMAZON_NOVA_PRO_V1
(experimental) Amazon's Nova Pro model, offering advanced capabilities for complex tasks.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Optimized for agents
- Best for: Complex language tasks, professional applications
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AMAZON_TITAN_PREMIER_V1_0
(experimental) Amazon's Titan Text Premier model, designed for high-quality text generation. Offers enhanced capabilities for complex language tasks.Features:
- Supports Bedrock Agents integration
- Advanced language understanding
- Best for: Complex content generation, detailed analysis
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AMAZON_TITAN_TEXT_EXPRESS_V1
(experimental) Amazon's Titan Text Express model, optimized for fast text generation. Provides quick responses while maintaining good quality output.Features:
- Supports Bedrock Agents integration
- Fast response times
- Best for: Real-time applications, chatbots, quick content generation
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ANTHROPIC_CLAUDE_3_5_HAIKU_V1_0
(experimental) Anthropic's Claude 3.5 Haiku model, optimized for quick responses. Lightweight model focused on speed and efficiency.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Optimized for agents
- Best for: Fast responses, lightweight processing
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ANTHROPIC_CLAUDE_3_5_SONNET_V1_0
@Stability(Experimental) public static final BedrockFoundationModel ANTHROPIC_CLAUDE_3_5_SONNET_V1_0(experimental) Anthropic's Claude 3.5 Sonnet V1 model, balanced performance model. Offers good balance between performance and efficiency.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Optimized for agents
- Best for: General language tasks, balanced performance
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ANTHROPIC_CLAUDE_3_5_SONNET_V2_0
@Stability(Experimental) public static final BedrockFoundationModel ANTHROPIC_CLAUDE_3_5_SONNET_V2_0(experimental) Anthropic's Claude 3.5 Sonnet V2 model, optimized for agent interactions. Enhanced version with improved performance and capabilities.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Optimized for agents
- Best for: Agent-based applications, complex dialogue
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ANTHROPIC_CLAUDE_3_7_SONNET_V1_0
@Stability(Experimental) public static final BedrockFoundationModel ANTHROPIC_CLAUDE_3_7_SONNET_V1_0(experimental) Anthropic's Claude 3.7 Sonnet model, latest in the Claude 3 series. Advanced language model with enhanced capabilities.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: Complex reasoning, analysis, and content generation
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ANTHROPIC_CLAUDE_HAIKU_V1_0
(experimental) Anthropic's Claude Haiku model, optimized for efficiency. Fast and efficient model for lightweight tasks.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Optimized for agents
- Best for: Quick responses, simple tasks
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ANTHROPIC_CLAUDE_INSTANT_V1_2
(experimental) Anthropic's Claude Instant V1.2 model, legacy version. Fast and efficient model optimized for quick responses.Features:
- Supports Bedrock Agents integration
- Legacy model with EOL date
- Optimized for agents
- Best for: Quick responses, simple tasks, legacy applications
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ANTHROPIC_CLAUDE_OPUS_V1_0
(experimental) Anthropic's Claude Opus model, designed for advanced tasks. High-performance model with extensive capabilities.Features:
- Supports Bedrock Agents integration
- Optimized for agents
- Best for: Complex reasoning, research, and analysis
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ANTHROPIC_CLAUDE_SONNET_V1_0
(experimental) Anthropic's Claude Sonnet model, legacy version. Balanced model for general-purpose tasks.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Legacy model with EOL date
- Best for: General language tasks, standard applications
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ANTHROPIC_CLAUDE_V2
(experimental) Anthropic's Claude V2 model, legacy version. Original Claude V2 model with broad capabilities.Features:
- Supports Bedrock Agents integration
- Legacy model with EOL date
- Optimized for agents
- Best for: General language tasks, legacy applications
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ANTHROPIC_CLAUDE_V2_1
(experimental) Anthropic's Claude V2.1 model, legacy version. Improved version of Claude V2 with enhanced capabilities.Features:
- Supports Bedrock Agents integration
- Legacy model with EOL date
- Optimized for agents
- Best for: General language tasks, legacy applications
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COHERE_EMBED_ENGLISH_V3
(experimental) Cohere's English embedding model, optimized for English text embeddings. Specialized for semantic understanding of English content.Features:
- Supports Knowledge Base integration
- 1024-dimensional vectors
- Supports both floating-point and binary vectors
- Best for: English text embeddings, semantic search, content similarity
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COHERE_EMBED_MULTILINGUAL_V3
(experimental) Cohere's Multilingual embedding model, supporting multiple languages. Enables semantic understanding across different languages.Features:
- Supports Knowledge Base integration
- 1024-dimensional vectors
- Supports both floating-point and binary vectors
- Best for: Cross-lingual embeddings, multilingual semantic search
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DEEPSEEK_R1_V1
(experimental) Deepseek's R1 model, designed for general language understanding. Balanced model for various language tasks.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: General language tasks, content generation
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META_LLAMA_3_1_70_B_INSTRUCT_V1
(experimental) Meta's Llama 3 70B Instruct model, large-scale instruction model. High-capacity model for complex language understanding.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: Complex instructions, advanced language tasks
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META_LLAMA_3_1_8_B_INSTRUCT_V1
(experimental) Meta's Llama 3 1.8B Instruct model, compact instruction-following model. Efficient model optimized for instruction-based tasks.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: Lightweight instruction processing, quick responses
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META_LLAMA_3_2_11_B_INSTRUCT_V1
(experimental) Meta's Llama 3.2 11B Instruct model, mid-sized instruction model. Balanced model for general instruction processing.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: General instruction tasks, balanced performance
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META_LLAMA_3_2_1_B_INSTRUCT_V1
(experimental) Meta's Llama 3.2 1B Instruct model, ultra-lightweight model. Most compact model in the Llama 3.2 series.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: Simple instructions, fastest response times
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META_LLAMA_3_2_3_B_INSTRUCT_V1
(experimental) Meta's Llama 3.2 3B Instruct model, compact efficient model. Lightweight model for basic instruction processing.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: Basic instructions, efficient processing
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META_LLAMA_3_3_70_B_INSTRUCT_V1
(experimental) Meta's Llama 3.3 70B Instruct model, latest large-scale model. Advanced model with enhanced capabilities.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: Complex reasoning, advanced language tasks
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META_LLAMA_4_MAVERICK_17_B_INSTRUCT_V1
@Stability(Experimental) public static final BedrockFoundationModel META_LLAMA_4_MAVERICK_17_B_INSTRUCT_V1(experimental) Meta's Llama 4 Maverick 17B Instruct model, innovative mid-sized model. Specialized for creative and dynamic responses.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: Creative tasks, innovative solutions
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META_LLAMA_4_SCOUT_17_B_INSTRUCT_V1
@Stability(Experimental) public static final BedrockFoundationModel META_LLAMA_4_SCOUT_17_B_INSTRUCT_V1(experimental) Meta's Llama 4 Scout 17B Instruct model, analytical mid-sized model. Focused on precise and analytical responses.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: Analytical tasks, precise responses
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MISTRAL_7_B_INSTRUCT_V0
(experimental) Mistral's 7B Instruct model, efficient instruction-following model. Balanced performance for instruction processing.Features:
- Supports Bedrock Agents integration
- Optimized for instruction tasks
- Best for: General instruction processing, balanced performance
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MISTRAL_LARGE_2402_V1
(experimental) Mistral's Large 2402 model, high-capacity language model. Advanced model for complex language understanding.Features:
- Supports Bedrock Agents integration
- Enhanced language capabilities
- Best for: Complex reasoning, detailed analysis
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MISTRAL_LARGE_2407_V1
(experimental) Mistral's Large 2407 model, updated large-scale model. Enhanced version with improved capabilities.Features:
- Supports Bedrock Agents integration
- Advanced language processing
- Best for: Sophisticated language tasks, complex analysis
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MISTRAL_MIXTRAL_8_X7_B_INSTRUCT_V0
@Stability(Experimental) public static final BedrockFoundationModel MISTRAL_MIXTRAL_8_X7_B_INSTRUCT_V0(experimental) Mistral's Mixtral 8x7B Instruct model, mixture-of-experts architecture. Advanced model combining multiple expert networks.Features:
- Supports Bedrock Agents integration
- Specialized expert networks
- Best for: Complex tasks, diverse language understanding
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MISTRAL_PIXTRAL_LARGE_2502_V1
(experimental) Mistral's Pixtral Large 2502 model, specialized large model. Advanced model with cross-region support.Features:
- Supports Bedrock Agents integration
- Cross-region support
- Best for: Advanced language tasks, distributed applications
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MISTRAL_SMALL_2402_V1
(experimental) Mistral's Small 2402 model, compact efficient model. Optimized for quick responses and efficiency.Features:
- Supports Bedrock Agents integration
- Efficient processing
- Best for: Quick responses, basic language tasks
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TITAN_EMBED_TEXT_V1
(experimental) Amazon's Titan Embed Text V1 model for text embeddings.Features:
- Supports Knowledge Base integration
- 1536-dimensional vectors
- Floating-point vector type
- Best for: Text embeddings, semantic search, document similarity
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TITAN_EMBED_TEXT_V2_1024
(experimental) Amazon's Titan Embed Text V2 model with 1024-dimensional vectors.Features:
- Supports Knowledge Base integration
- 1024-dimensional vectors
- Supports both floating-point and binary vectors
- Best for: High-dimensional embeddings, advanced semantic search
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TITAN_EMBED_TEXT_V2_256
(experimental) Amazon's Titan Embed Text V2 model with 256-dimensional vectors.Features:
- Supports Knowledge Base integration
- 256-dimensional vectors
- Supports both floating-point and binary vectors
- Best for: Efficient embeddings with lower dimensionality
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TITAN_EMBED_TEXT_V2_512
(experimental) Amazon's Titan Embed Text V2 model with 512-dimensional vectors.Features:
- Supports Knowledge Base integration
- 512-dimensional vectors
- Supports both floating-point and binary vectors
- Best for: Balanced performance and dimensionality
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Constructor Details
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BedrockFoundationModel
protected BedrockFoundationModel(software.amazon.jsii.JsiiObjectRef objRef) -
BedrockFoundationModel
protected BedrockFoundationModel(software.amazon.jsii.JsiiObject.InitializationMode initializationMode) -
BedrockFoundationModel
@Stability(Experimental) public BedrockFoundationModel(@NotNull String value, @Nullable BedrockFoundationModelProps props) - Parameters:
value
- This parameter is required.props
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BedrockFoundationModel
- Parameters:
value
- This parameter is required.
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Method Details
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fromCdkFoundationModel
@Stability(Experimental) @NotNull public static BedrockFoundationModel fromCdkFoundationModel(@NotNull FoundationModel modelId, @Nullable BedrockFoundationModelProps props) (experimental) Creates a BedrockFoundationModel from a FoundationModel.Use this method when you have a foundation model from the CDK.
- Parameters:
modelId
-- The foundation model.
props
-- Optional properties for the model.
- Returns:
- A new BedrockFoundationModel instance
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fromCdkFoundationModel
@Stability(Experimental) @NotNull public static BedrockFoundationModel fromCdkFoundationModel(@NotNull FoundationModel modelId) (experimental) Creates a BedrockFoundationModel from a FoundationModel.Use this method when you have a foundation model from the CDK.
- Parameters:
modelId
-- The foundation model.
- Returns:
- A new BedrockFoundationModel instance
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fromCdkFoundationModelId
@Stability(Experimental) @NotNull public static BedrockFoundationModel fromCdkFoundationModelId(@NotNull FoundationModelIdentifier modelId, @Nullable BedrockFoundationModelProps props) (experimental) Creates a BedrockFoundationModel from a FoundationModelIdentifier.Use this method when you have a model identifier from the CDK.
- Parameters:
modelId
-- The foundation model identifier.
props
-- Optional properties for the model.
- Returns:
- A new BedrockFoundationModel instance
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fromCdkFoundationModelId
@Stability(Experimental) @NotNull public static BedrockFoundationModel fromCdkFoundationModelId(@NotNull FoundationModelIdentifier modelId) (experimental) Creates a BedrockFoundationModel from a FoundationModelIdentifier.Use this method when you have a model identifier from the CDK.
- Parameters:
modelId
-- The foundation model identifier.
- Returns:
- A new BedrockFoundationModel instance
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asArn
(experimental) Returns the ARN of the foundation model in the following format:arn:${Partition}:bedrock:${Region}::foundation-model/${ResourceId}
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asIModel
(experimental) Returns the IModel. -
grantInvoke
(experimental) Gives the appropriate policies to invoke and use the Foundation Model in the stack region.- Specified by:
grantInvoke
in interfaceIBedrockInvokable
- Parameters:
grantee
- This parameter is required.
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grantInvokeAllRegions
(experimental) Gives the appropriate policies to invoke and use the Foundation Model in all regions.- Parameters:
grantee
- This parameter is required.
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toString
(experimental) Returns a string representation of an object. -
getInvokableArn
(experimental) The ARN used for invoking the model.This is the same as modelArn for foundation models.
- Specified by:
getInvokableArn
in interfaceIBedrockInvokable
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getModelArn
(experimental) The ARN of the foundation model.Format: arn:${Partition}:bedrock:${Region}::foundation-model/${ResourceId}
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getModelId
(experimental) The unique identifier of the foundation model. -
getSupportsAgents
(experimental) Whether this model supports integration with Bedrock Agents.When true, the model can be used with Bedrock Agents for automated task execution.
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getSupportsCrossRegion
(experimental) Whether this model supports cross-region inference.When true, the model can be used with Cross-Region Inference Profiles.
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getSupportsKnowledgeBase
(experimental) Whether this model supports integration with Bedrock Knowledge Base.When true, the model can be used for knowledge base operations.
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getSupportedVectorType
(experimental) The vector types supported by this model for embeddings.Defines whether the model supports floating-point or binary vectors.
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getVectorDimensions
(experimental) The dimensionality of the vector embeddings produced by this model.Only applicable for embedding models.
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