Package-level declarations
Types
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Contains details about the Lambda function containing the business logic that is carried out upon invoking the action or the custom control method for handling the information elicited from the user.
Contains details about an action group.
Contains details about an action group.
Contains details about an alias of an agent.
Contains details about the history of the alias.
Contains details about the routing configuration of the alias.
Contains details about an alias of an agent.
An agent collaborator.
An agent collaborator summary.
An agent descriptor.
Defines an agent node in your flow. You specify the agent to invoke at this point in the flow. For more information, see Node types in a flow in the Amazon Bedrock User Guide.
Contains details about a knowledge base that is associated with an agent.
Contains details about a knowledge base associated with an agent.
Contains details about an agent.
Contains details about a version of an agent.
Contains details about a version of an agent.
Enum representing the invocation state of an agent alias
Defines tools, at least one of which must be requested by the model. No text is generated but the results of tool use are sent back to the model to help generate a response. For more information, see Use a tool to complete an Amazon Bedrock model response.
Contains details about the OpenAPI schema for the action group. For more information, see Action group OpenAPI schemas. You can either include the schema directly in the payload field or you can upload it to an S3 bucket and specify the S3 bucket location in the s3 field.
Defines tools. The model automatically decides whether to call a tool or to generate text instead. For more information, see Use a tool to complete an Amazon Bedrock model response.
Base class for all service related exceptions thrown by the BedrockAgent client
Contains configurations for using Amazon Bedrock Data Automation as the parser for ingesting your data sources.
The vector configuration details for the Bedrock embeddings model.
Settings for a foundation model used to parse documents for a data source.
Context enrichment configuration is used to provide additional context to the RAG application using Amazon Bedrock foundation models.
Contains information about content defined inline in bytes.
Indicates where a cache checkpoint is located. All information before this checkpoint is cached to be accessed on subsequent requests.
Contains configurations to use a prompt in a conversational format. For more information, see Create a prompt using Prompt management.
Details about how to chunk the documents in the data source. A chunk refers to an excerpt from a data source that is returned when the knowledge base that it belongs to is queried.
Defines a collector node in your flow. This node takes an iteration of inputs and consolidates them into an array in the output. For more information, see Node types in a flow in the Amazon Bedrock User Guide.
Defines a condition node in your flow. You can specify conditions that determine which node comes next in the flow. For more information, see Node types in a flow in the Amazon Bedrock User Guide.
There was a conflict performing an operation.
The configuration of the Confluence content. For example, configuring specific types of Confluence content.
The configuration information to connect to Confluence as your data source.
The endpoint information to connect to your Confluence data source.
Contains the content for the message you pass to, or receive from a model. For more information, see Create a prompt using Prompt management.
Context enrichment configuration is used to provide additional context to the RAG application.
The configuration of filtering the data source content. For example, configuring regular expression patterns to include or exclude certain content.
Contains configurations for a query, each of which defines information about example queries to help the query engine generate appropriate SQL queries.
Contains information about the content to ingest into a knowledge base connected to a custom data source. Choose a sourceType and include the field that corresponds to it.
Contains information about the identifier of the document to ingest into a custom data source.
Details of custom orchestration.
Contains information about the Amazon S3 location of the file containing the content to ingest into a knowledge base connected to a custom data source.
Settings for customizing steps in the data source content ingestion pipeline.
Details about a cyclic connection detected in the flow.
Contains details about a data source.
The connection configuration for the data source.
Contains details about a data source.
Contains information about the content of a document. Choose a dataSourceType and include the field that corresponds to it.
Contains information that identifies the document.
Contains information about the metadata associate with the content to ingest into a knowledge base. Choose a type and include the field that corresponds to it.
Details about duplicate condition expressions found in a condition node.
Details about duplicate connections found between two nodes in the flow.
Bedrock models embedding data type. Can be either float32 or binary.
The configuration details for the embeddings model.
The strategy used for performing context enrichment.
Specifies a metadata field to include or exclude during the reranking process.
Configurations for when you choose fixed-size chunking. If you set the chunkingStrategy as NONE, exclude this field.
Determines how multiple nodes in a flow can run in parallel. Running nodes concurrently can improve your flow's performance.
Contains information about a version that the alias maps to.
Contains information about an alias of a flow.
Defines a condition in the condition node.
The configuration of a connection between a condition node and another node.
Contains information about a connection between two nodes in the flow.
The configuration of the connection.
The configuration of a connection originating from a node that isn't a Condition node.
The definition of the nodes and connections between nodes in the flow.
Contains configurations for a node in your flow. For more information, see Node types in a flow in the Amazon Bedrock User Guide.
Contains configurations for an input in an Amazon Bedrock Flows node.
Contains configurations for an output from a node.
Contains the definition of a flow.
Contains information about validation of the flow.
A union type containing various possible validation issues in the flow.
Contains information about a version of a flow.
Defines functions that each define parameters that the agent needs to invoke from the user. Each function represents an action in an action group.
Details about a guardrail associated with a resource.
Settings for hierarchical document chunking for a data source. Hierarchical chunking splits documents into layers of chunks where the first layer contains large chunks, and the second layer contains smaller chunks derived from the first layer.
Token settings for a layer in a hierarchical chunking configuration.
Details about incompatible data types in a connection between nodes.
Contains inference parameters to use when the agent invokes a foundation model in the part of the agent sequence defined by the promptType. For more information, see Inference parameters for foundation models.
Contains details about a data ingestion job. Data sources are ingested into a knowledge base so that Large Language Models (LLMs) can use your data.
The definition of a filter to filter the data.
The parameters of sorting the data.
Contains the statistics for the data ingestion job.
Contains details about a data ingestion job.
Contains configurations for an inline code node in your flow. Inline code nodes let you write and execute code directly within your flow, enabling data transformations, custom logic, and integrations without needing an external Lambda function.
Contains information about content defined inline to ingest into a data source. Choose a type and include the field that corresponds to it.
Contains configurations for the input flow node for a flow. This node takes the input from flow invocation and passes it to the next node in the data type that you specify.
A location for storing content from data sources temporarily as it is processed by custom components in the ingestion pipeline.
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Details about a flow that contains connections that violate loop boundary rules.
Contains configurations for an iterator node in a flow. Takes an input that is an array and iteratively sends each item of the array as an output to the following node. The size of the array is also returned in the output.
Settings for an Amazon Kendra knowledge base.
Contains information about a knowledge base.
Contains details about the vector embeddings configuration of the knowledge base.
Contains information about a document to ingest into a knowledge base and metadata to associate with it.
Contains the details for a document that was ingested or deleted.
Contains configurations for a knowledge base node in a flow. This node takes a query as the input and returns, as the output, the retrieved responses directly (as an array) or a response generated based on the retrieved responses. For more information, see Node types in a flow in the Amazon Bedrock User Guide.
Configures how the knowledge base orchestrates the retrieval and generation process, allowing for customization of prompts, inference parameters, and performance settings.
Defines a custom prompt template for orchestrating the retrieval and generation process.
Contains details about a knowledge base.
Contains configurations for a Lambda function node in the flow. You specify the Lambda function to invoke and the inputs into the function. The output is the response that is defined in the Lambda function. For more information, see Node types in a flow in the Amazon Bedrock User Guide.
Contains configurations for a Lex node in the flow. You specify a Amazon Lex bot to invoke. This node takes an utterance as the input and returns as the output the intent identified by the Amazon Lex bot. For more information, see Node types in a flow in the Amazon Bedrock User Guide.
Contains configurations for the controller node of a DoWhile loop in the flow.
Contains configurations for the nodes of a DoWhile loop in your flow.
Details about a flow that contains an incompatible node in a DoWhile loop.
Contains configurations for the input node of a DoWhile loop in the flow.
Details about a malformed condition expression in a node.
Details about a malformed input expression in a node.
Details of the memory configuration.
A message input or response from a model. For more information, see Create a prompt using Prompt management.
Contains information about a metadata attribute.
Contains the value of the metadata attribute. Choose a type and include the field that corresponds to it.
Specifies how metadata fields should be handled during the reranking process.
Details about mismatched input data types in a node.
Details about mismatched output data types in a node.
Details about a connection missing required configuration.
Details about a missing default condition in a conditional node.
Details about missing ending nodes (such as FlowOutputNode) in the flow.
Details about a flow that's missing a required LoopController node in a DoWhile loop.
Details about a flow that's missing a required LoopInput node in a DoWhile loop.
Details about a node missing a required configuration.
Details about a missing required input in a node.
Details about a missing required output in a node.
Details about missing starting nodes (such as FlowInputNode) in the flow.
Contains details about the storage configuration of the knowledge base in MongoDB Atlas.
Contains the names of the fields to which to map information about the vector store.
Details about a flow that contains multiple LoopController nodes in a DoWhile loop.
Details about a flow that contains multiple LoopInput nodes in a DoWhile loop.
Details about multiple connections to a single node input.
Contains details about the storage configuration of the knowledge base in Amazon Neptune Analytics. For more information, see Create a vector index in Amazon Neptune Analytics.
Contains the names of the fields to which to map information about the vector store.
Contains details about the Managed Cluster configuration of the knowledge base in Amazon OpenSearch Service. For more information, see Create a vector index in OpenSearch Managed Cluster.
Contains the names of the fields to which to map information about the vector store.
Contains details about the storage configuration of the knowledge base in Amazon OpenSearch Service. For more information, see Create a vector index in Amazon OpenSearch Service.
Contains the names of the fields to which to map information about the vector store.
Contains details about the Lambda function containing the orchestration logic carried out upon invoking the custom orchestration.
Contains configurations for an output flow node in the flow. You specify the data type expected for the input into the node in the type field and how to return the final output in the expression field.
Contains details about a parameter in a function for an action group.
Settings for parsing document contents. If you exclude this field, the default parser converts the contents of each document into text before splitting it into chunks. Specify the parsing strategy to use in the parsingStrategy field and include the relevant configuration, or omit it to use the Amazon Bedrock default parser. For more information, see Parsing options for your data source.
Instructions for interpreting the contents of a document.
The specific filters applied to your data source content. You can filter out or include certain content.
The configuration of filtering certain objects or content types of the data source.
The performance-related configuration options for the knowledge base retrieval and generation process.
Contains details about the storage configuration of the knowledge base in Pinecone. For more information, see Create a vector index in Pinecone.
Contains the names of the fields to which to map information about the vector store.
Contains specifications for an Amazon Bedrock agent with which to use the prompt. For more information, see Create a prompt using Prompt management and Automate tasks in your application using conversational agents.
Contains configurations to override a prompt template in one part of an agent sequence. For more information, see Advanced prompts.
Contains configurations for a prompt node in the flow. You can use a prompt from Prompt management or you can define one in this node. If the prompt contains variables, the inputs into this node will fill in the variables. The output from this node is the response generated by the model. For more information, see Node types in a flow in the Amazon Bedrock User Guide.
Contains configurations for a prompt defined inline in the node.
Contains configurations for a prompt from Prompt management to use in a node.
Contains configurations for a prompt and whether it is from Prompt management or defined inline.
Contains specifications for a generative AI resource with which to use the prompt. For more information, see Create a prompt using Prompt management.
Contains inference configurations for the prompt.
Contains information about a variable in the prompt.
Contains a key-value pair that defines a metadata tag and value to attach to a prompt variant. For more information, see Create a prompt using Prompt management.
Contains inference configurations related to model inference for a prompt. For more information, see Inference parameters.
Contains configurations to override prompts in different parts of an agent sequence. For more information, see Advanced prompts.
Contains information about a prompt in your Prompt management tool.
Contains the message for a prompt. For more information, see Construct and store reusable prompts with Prompt management in Amazon Bedrock.
Contains details about a variant of the prompt.
Contains information about a column in the current table for the query engine to consider.
Contains configurations for query generation. For more information, see Build a knowledge base by connecting to a structured data source in the Amazon Bedrock User Guide..
Contains information about a table for the query engine to consider.
Contains details about the storage configuration of the knowledge base in Amazon RDS. For more information, see Create a vector index in Amazon RDS.
Contains the names of the fields to which to map information about the vector store.
Contains details about the storage configuration of the knowledge base in Redis Enterprise Cloud. For more information, see Create a vector index in Redis Enterprise Cloud.
Contains the names of the fields to which to map information about the vector store.
Contains configurations for an Amazon Redshift database. For more information, see Build a knowledge base by connecting to a structured data source in the Amazon Bedrock User Guide.
Contains configurations for authentication to an Amazon Redshift provisioned data warehouse. Specify the type of authentication to use in the type field and include the corresponding field. If you specify IAM authentication, you don't need to include another field.
Contains configurations for a provisioned Amazon Redshift query engine.
Contains configurations for storage in Glue Data Catalog.
Contains configurations for an Amazon Redshift query engine. Specify the type of query engine in type and include the corresponding field. For more information, see Build a knowledge base by connecting to a structured data source in the Amazon Bedrock User Guide.
Contains configurations for storage in Amazon Redshift.
Contains configurations for Amazon Redshift data storage. Specify the data storage service to use in the type field and include the corresponding field. For more information, see Build a knowledge base by connecting to a structured data source in the Amazon Bedrock User Guide.
Specifies configurations for authentication to a Redshift Serverless. Specify the type of authentication to use in the type field and include the corresponding field. If you specify IAM authentication, you don't need to include another field.
Contains configurations for authentication to Amazon Redshift Serverless.
Whether the action requires user confirmation.
Configures the metadata fields to include or exclude during the reranking process when using selective mode.
The specified resource Amazon Resource Name (ARN) was not found. Check the Amazon Resource Name (ARN) and try your request again.
Contains configurations for a Retrieval node in a flow. This node retrieves data from the Amazon S3 location that you specify and returns it as the output.
Contains configurations for the Amazon S3 location from which to retrieve data to return as the output from the node.
Contains configurations for the service to use for retrieving data to return as the output from the node.
The configuration information to connect to Amazon S3 as your data source.
The identifier information for an Amazon S3 bucket.
An Amazon S3 location.
Contains the storage configuration of the knowledge base for S3 vectors.
The configuration of the Salesforce content. For example, configuring specific types of Salesforce content.
The configuration information to connect to Salesforce as your data source.
The endpoint information to connect to your Salesforce data source.
Settings for semantic document chunking for a data source. Semantic chunking splits a document into into smaller documents based on groups of similar content derived from the text with natural language processing.
Contains the configuration for server-side encryption.
The number of requests exceeds the service quota. Resubmit your request later.
Configuration for SESSION_SUMMARY memory type enabled for the agent.
The configuration of the SharePoint content. For example, configuring specific types of SharePoint content.
The configuration information to connect to SharePoint as your data source.
The endpoint information to connect to your SharePoint data source.
Defines a specific tool that the model must request. No text is generated but the results of tool use are sent back to the model to help generate a response. For more information, see Use a tool to complete an Amazon Bedrock model response.
Contains configurations for a knowledge base connected to an SQL database. Specify the SQL database type in the type field and include the corresponding field. For more information, see Build a knowledge base by connecting to a structured data source in the Amazon Bedrock User Guide.
Contains the storage configuration of the knowledge base.
Contains configurations for a Storage node in a flow. This node stores the input in an Amazon S3 location that you specify.
Contains configurations for the Amazon S3 location in which to store the input into the node.
Contains configurations for the service to use for storing the input into the node.
Specifies configurations for the storage location of the images extracted from multimodal documents in your data source. These images can be retrieved and returned to the end user.
Contains information about a storage location for images extracted from multimodal documents in your data source.
Contains a system prompt to provide context to the model or to describe how it should behave. For more information, see Create a prompt using Prompt management.
Contains information about content defined inline in text.
Contains configurations for a text prompt template. To include a variable, enclose a word in double curly braces as in {{variable}}.
The number of requests exceeds the limit. Resubmit your request later.
Contains configurations for a tool that a model can use when generating a response. For more information, see Use a tool to complete an Amazon Bedrock model response.
Defines which tools the model should request when invoked. For more information, see Use a tool to complete an Amazon Bedrock model response.
Configuration information for the tools that the model can use when generating a response. For more information, see Use a tool to complete an Amazon Bedrock model response.
The input schema for the tool. For more information, see Use a tool to complete an Amazon Bedrock model response.
Contains a specification for a tool. For more information, see Use a tool to complete an Amazon Bedrock model response.
A custom processing step for documents moving through a data source ingestion pipeline. To process documents after they have been converted into chunks, set the step to apply to POST_CHUNKING.
A Lambda function that processes documents.
A Lambda function that processes documents.
Details about an unfulfilled node input with no valid connections.
Details about an unknown condition for a connection.
Details about an unknown source node for a connection.
Details about an unknown source output for a connection.
Details about an unknown target node for a connection.
Details about an unknown target input for a connection.
Details about an unknown input for a node.
Details about an unknown output for a node.
Details about an unreachable node in the flow. A node is unreachable when there are no paths to it from any starting node.
Details about unsatisfied conditions for a connection. A condition is unsatisfied if it can never be true, for example two branches of condition node cannot be simultaneously true.
Details about an unspecified validation that doesn't fit other categories.
The configuration of web URLs that you want to crawl. You should be authorized to crawl the URLs.
Input validation failed. Check your request parameters and retry the request.
Stores information about a field passed inside a request that resulted in an validation error.
Contains details about how to ingest the documents in a data source.
Contains details about the model used to create vector embeddings for the knowledge base.
Configures the Amazon Bedrock reranker model to improve the relevance of retrieved results.
Configures the Amazon Bedrock model used for reranking retrieved results.
Specifies how retrieved results from a knowledge base are reranked to improve relevance.
The configuration of web URLs that you want to crawl. You should be authorized to crawl the URLs.
The rate limits for the URLs that you want to crawl. You should be authorized to crawl the URLs.
The configuration details for the web data source.
The configuration of the URL/URLs for the web content that you want to crawl. You should be authorized to crawl the URLs.