CfnKnowledgeBase
- class aws_cdk.aws_bedrock.CfnKnowledgeBase(scope, id, *, knowledge_base_configuration, name, role_arn, storage_configuration, description=None, tags=None)
Bases:
CfnResource
Specifies a knowledge base as a resource in a top-level template. Minimally, you must specify the following properties:.
Name – Specify a name for the knowledge base.
RoleArn – Specify the Amazon Resource Name (ARN) of the IAM role with permissions to invoke API operations on the knowledge base. For more information, see Create a service role for Knowledge base for Amazon Bedrock .
KnowledgeBaseConfiguration – Specify the embeddings configuration of the knowledge base. The following sub-properties are required:
Type – Specify the value
VECTOR
.StorageConfiguration – Specify information about the vector store in which the data source is stored. The following sub-properties are required:
Type – Specify the vector store service that you are using.
Redis Enterprise Cloud vector stores are currently unsupported in AWS CloudFormation .
For more information about using knowledge bases in Amazon Bedrock , see Knowledge base for Amazon Bedrock .
See the Properties section below for descriptions of both the required and optional properties.
- See:
- CloudformationResource:
AWS::Bedrock::KnowledgeBase
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock cfn_knowledge_base = bedrock.CfnKnowledgeBase(self, "MyCfnKnowledgeBase", knowledge_base_configuration=bedrock.CfnKnowledgeBase.KnowledgeBaseConfigurationProperty( type="type", vector_knowledge_base_configuration=bedrock.CfnKnowledgeBase.VectorKnowledgeBaseConfigurationProperty( embedding_model_arn="embeddingModelArn", # the properties below are optional embedding_model_configuration=bedrock.CfnKnowledgeBase.EmbeddingModelConfigurationProperty( bedrock_embedding_model_configuration=bedrock.CfnKnowledgeBase.BedrockEmbeddingModelConfigurationProperty( dimensions=123 ) ) ) ), name="name", role_arn="roleArn", storage_configuration=bedrock.CfnKnowledgeBase.StorageConfigurationProperty( type="type", # the properties below are optional opensearch_serverless_configuration=bedrock.CfnKnowledgeBase.OpenSearchServerlessConfigurationProperty( collection_arn="collectionArn", field_mapping=bedrock.CfnKnowledgeBase.OpenSearchServerlessFieldMappingProperty( metadata_field="metadataField", text_field="textField", vector_field="vectorField" ), vector_index_name="vectorIndexName" ), pinecone_configuration=bedrock.CfnKnowledgeBase.PineconeConfigurationProperty( connection_string="connectionString", credentials_secret_arn="credentialsSecretArn", field_mapping=bedrock.CfnKnowledgeBase.PineconeFieldMappingProperty( metadata_field="metadataField", text_field="textField" ), # the properties below are optional namespace="namespace" ), rds_configuration=bedrock.CfnKnowledgeBase.RdsConfigurationProperty( credentials_secret_arn="credentialsSecretArn", database_name="databaseName", field_mapping=bedrock.CfnKnowledgeBase.RdsFieldMappingProperty( metadata_field="metadataField", primary_key_field="primaryKeyField", text_field="textField", vector_field="vectorField" ), resource_arn="resourceArn", table_name="tableName" ) ), # the properties below are optional description="description", tags={ "tags_key": "tags" } )
- Parameters:
scope (
Construct
) – Scope in which this resource is defined.id (
str
) – Construct identifier for this resource (unique in its scope).knowledge_base_configuration (
Union
[IResolvable
,KnowledgeBaseConfigurationProperty
,Dict
[str
,Any
]]) – Contains details about the embeddings configuration of the knowledge base.name (
str
) – The name of the knowledge base.role_arn (
str
) – The Amazon Resource Name (ARN) of the IAM role with permissions to invoke API operations on the knowledge base.storage_configuration (
Union
[IResolvable
,StorageConfigurationProperty
,Dict
[str
,Any
]]) – Contains details about the storage configuration of the knowledge base.description (
Optional
[str
]) – The description of the knowledge base.tags (
Optional
[Mapping
[str
,str
]]) – Metadata that you can assign to a resource as key-value pairs. For more information, see the following resources:. - Tag naming limits and requirements - Tagging best practices
Methods
- add_deletion_override(path)
Syntactic sugar for
addOverride(path, undefined)
.- Parameters:
path (
str
) – The path of the value to delete.- Return type:
None
- add_dependency(target)
Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.
This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.
- Parameters:
target (
CfnResource
) –- Return type:
None
- add_depends_on(target)
(deprecated) Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.
- Parameters:
target (
CfnResource
) –- Deprecated:
use addDependency
- Stability:
deprecated
- Return type:
None
- add_metadata(key, value)
Add a value to the CloudFormation Resource Metadata.
- Parameters:
key (
str
) –value (
Any
) –
- See:
- Return type:
None
Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.
- add_override(path, value)
Adds an override to the synthesized CloudFormation resource.
To add a property override, either use
addPropertyOverride
or prefixpath
with “Properties.” (i.e.Properties.TopicName
).If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.
To include a literal
.
in the property name, prefix with a\
. In most programming languages you will need to write this as"\\."
because the\
itself will need to be escaped.For example:
cfn_resource.add_override("Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes", ["myattribute"]) cfn_resource.add_override("Properties.GlobalSecondaryIndexes.1.ProjectionType", "INCLUDE")
would add the overrides Example:
"Properties": { "GlobalSecondaryIndexes": [ { "Projection": { "NonKeyAttributes": [ "myattribute" ] ... } ... }, { "ProjectionType": "INCLUDE" ... }, ] ... }
The
value
argument toaddOverride
will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.- Parameters:
path (
str
) –The path of the property, you can use dot notation to override values in complex types. Any intermediate keys will be created as needed.
value (
Any
) –The value. Could be primitive or complex.
- Return type:
None
- add_property_deletion_override(property_path)
Adds an override that deletes the value of a property from the resource definition.
- Parameters:
property_path (
str
) – The path to the property.- Return type:
None
- add_property_override(property_path, value)
Adds an override to a resource property.
Syntactic sugar for
addOverride("Properties.<...>", value)
.- Parameters:
property_path (
str
) – The path of the property.value (
Any
) – The value.
- Return type:
None
- apply_removal_policy(policy=None, *, apply_to_update_replace_policy=None, default=None)
Sets the deletion policy of the resource based on the removal policy specified.
The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you’ve removed it from the CDK application or because you’ve made a change that requires the resource to be replaced.
The resource can be deleted (
RemovalPolicy.DESTROY
), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN
). In some cases, a snapshot can be taken of the resource prior to deletion (RemovalPolicy.SNAPSHOT
). A list of resources that support this policy can be found in the following link:- Parameters:
policy (
Optional
[RemovalPolicy
]) –apply_to_update_replace_policy (
Optional
[bool
]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: truedefault (
Optional
[RemovalPolicy
]) – The default policy to apply in case the removal policy is not defined. Default: - Default value is resource specific. To determine the default value for a resource, please consult that specific resource’s documentation.
- See:
- Return type:
None
- get_att(attribute_name, type_hint=None)
Returns a token for an runtime attribute of this resource.
Ideally, use generated attribute accessors (e.g.
resource.arn
), but this can be used for future compatibility in case there is no generated attribute.- Parameters:
attribute_name (
str
) – The name of the attribute.type_hint (
Optional
[ResolutionTypeHint
]) –
- Return type:
- get_metadata(key)
Retrieve a value value from the CloudFormation Resource Metadata.
- Parameters:
key (
str
) –- See:
- Return type:
Any
Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.
- inspect(inspector)
Examines the CloudFormation resource and discloses attributes.
- Parameters:
inspector (
TreeInspector
) – tree inspector to collect and process attributes.- Return type:
None
- obtain_dependencies()
Retrieves an array of resources this resource depends on.
This assembles dependencies on resources across stacks (including nested stacks) automatically.
- Return type:
List
[Union
[Stack
,CfnResource
]]
- obtain_resource_dependencies()
Get a shallow copy of dependencies between this resource and other resources in the same stack.
- Return type:
List
[CfnResource
]
- override_logical_id(new_logical_id)
Overrides the auto-generated logical ID with a specific ID.
- Parameters:
new_logical_id (
str
) – The new logical ID to use for this stack element.- Return type:
None
- remove_dependency(target)
Indicates that this resource no longer depends on another resource.
This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.
- Parameters:
target (
CfnResource
) –- Return type:
None
- replace_dependency(target, new_target)
Replaces one dependency with another.
- Parameters:
target (
CfnResource
) – The dependency to replace.new_target (
CfnResource
) – The new dependency to add.
- Return type:
None
- to_string()
Returns a string representation of this construct.
- Return type:
str
- Returns:
a string representation of this resource
Attributes
- CFN_RESOURCE_TYPE_NAME = 'AWS::Bedrock::KnowledgeBase'
- attr_created_at
The time at which the knowledge base was created.
- CloudformationAttribute:
CreatedAt
- attr_failure_reasons
A list of reasons that the API operation on the knowledge base failed.
- CloudformationAttribute:
FailureReasons
- attr_knowledge_base_arn
The Amazon Resource Name (ARN) of the knowledge base.
- CloudformationAttribute:
KnowledgeBaseArn
- attr_knowledge_base_id
The unique identifier of the knowledge base.
- CloudformationAttribute:
KnowledgeBaseId
- attr_status
The status of the knowledge base.
- CloudformationAttribute:
Status
- attr_updated_at
The time at which the knowledge base was last updated.
- CloudformationAttribute:
UpdatedAt
- cdk_tag_manager
Tag Manager which manages the tags for this resource.
- cfn_options
Options for this resource, such as condition, update policy etc.
- cfn_resource_type
AWS resource type.
- creation_stack
return:
the stack trace of the point where this Resource was created from, sourced from the +metadata+ entry typed +aws:cdk:logicalId+, and with the bottom-most node +internal+ entries filtered.
- description
The description of the knowledge base.
- knowledge_base_configuration
Contains details about the embeddings configuration of the knowledge base.
- logical_id
The logical ID for this CloudFormation stack element.
The logical ID of the element is calculated from the path of the resource node in the construct tree.
To override this value, use
overrideLogicalId(newLogicalId)
.- Returns:
the logical ID as a stringified token. This value will only get resolved during synthesis.
- name
The name of the knowledge base.
- node
The tree node.
- ref
Return a string that will be resolved to a CloudFormation
{ Ref }
for this element.If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through
Lazy.any({ produce: resource.ref })
.
- role_arn
The Amazon Resource Name (ARN) of the IAM role with permissions to invoke API operations on the knowledge base.
- stack
The stack in which this element is defined.
CfnElements must be defined within a stack scope (directly or indirectly).
- storage_configuration
Contains details about the storage configuration of the knowledge base.
- tags
Metadata that you can assign to a resource as key-value pairs.
For more information, see the following resources:.
Static Methods
- classmethod is_cfn_element(x)
Returns
true
if a construct is a stack element (i.e. part of the synthesized cloudformation template).Uses duck-typing instead of
instanceof
to allow stack elements from different versions of this library to be included in the same stack.- Parameters:
x (
Any
) –- Return type:
bool
- Returns:
The construct as a stack element or undefined if it is not a stack element.
- classmethod is_cfn_resource(x)
Check whether the given object is a CfnResource.
- Parameters:
x (
Any
) –- Return type:
bool
- classmethod is_construct(x)
Checks if
x
is a construct.Use this method instead of
instanceof
to properly detectConstruct
instances, even when the construct library is symlinked.Explanation: in JavaScript, multiple copies of the
constructs
library on disk are seen as independent, completely different libraries. As a consequence, the classConstruct
in each copy of theconstructs
library is seen as a different class, and an instance of one class will not test asinstanceof
the other class.npm install
will not create installations like this, but users may manually symlink construct libraries together or use a monorepo tool: in those cases, multiple copies of theconstructs
library can be accidentally installed, andinstanceof
will behave unpredictably. It is safest to avoid usinginstanceof
, and using this type-testing method instead.- Parameters:
x (
Any
) – Any object.- Return type:
bool
- Returns:
true if
x
is an object created from a class which extendsConstruct
.
BedrockEmbeddingModelConfigurationProperty
- class CfnKnowledgeBase.BedrockEmbeddingModelConfigurationProperty(*, dimensions=None)
Bases:
object
The vector configuration details for the Bedrock embeddings model.
- Parameters:
dimensions (
Union
[int
,float
,None
]) – The dimensions details for the vector configuration used on the Bedrock embeddings model.- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock bedrock_embedding_model_configuration_property = bedrock.CfnKnowledgeBase.BedrockEmbeddingModelConfigurationProperty( dimensions=123 )
Attributes
- dimensions
The dimensions details for the vector configuration used on the Bedrock embeddings model.
EmbeddingModelConfigurationProperty
- class CfnKnowledgeBase.EmbeddingModelConfigurationProperty(*, bedrock_embedding_model_configuration=None)
Bases:
object
The configuration details for the embeddings model.
- Parameters:
bedrock_embedding_model_configuration (
Union
[IResolvable
,BedrockEmbeddingModelConfigurationProperty
,Dict
[str
,Any
],None
]) – The vector configuration details on the Bedrock embeddings model.- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock embedding_model_configuration_property = bedrock.CfnKnowledgeBase.EmbeddingModelConfigurationProperty( bedrock_embedding_model_configuration=bedrock.CfnKnowledgeBase.BedrockEmbeddingModelConfigurationProperty( dimensions=123 ) )
Attributes
- bedrock_embedding_model_configuration
The vector configuration details on the Bedrock embeddings model.
KnowledgeBaseConfigurationProperty
- class CfnKnowledgeBase.KnowledgeBaseConfigurationProperty(*, type, vector_knowledge_base_configuration)
Bases:
object
Configurations to apply to a knowledge base attached to the agent during query.
For more information, see Knowledge base retrieval configurations .
- Parameters:
type (
str
) – The type of data that the data source is converted into for the knowledge base.vector_knowledge_base_configuration (
Union
[IResolvable
,VectorKnowledgeBaseConfigurationProperty
,Dict
[str
,Any
]]) – Contains details about the embeddings model that’sused to convert the data source.
- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock knowledge_base_configuration_property = bedrock.CfnKnowledgeBase.KnowledgeBaseConfigurationProperty( type="type", vector_knowledge_base_configuration=bedrock.CfnKnowledgeBase.VectorKnowledgeBaseConfigurationProperty( embedding_model_arn="embeddingModelArn", # the properties below are optional embedding_model_configuration=bedrock.CfnKnowledgeBase.EmbeddingModelConfigurationProperty( bedrock_embedding_model_configuration=bedrock.CfnKnowledgeBase.BedrockEmbeddingModelConfigurationProperty( dimensions=123 ) ) ) )
Attributes
- type
The type of data that the data source is converted into for the knowledge base.
- vector_knowledge_base_configuration
Contains details about the embeddings model that’sused to convert the data source.
OpenSearchServerlessConfigurationProperty
- class CfnKnowledgeBase.OpenSearchServerlessConfigurationProperty(*, collection_arn, field_mapping, vector_index_name)
Bases:
object
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 .
- Parameters:
collection_arn (
str
) – The Amazon Resource Name (ARN) of the OpenSearch Service vector store.field_mapping (
Union
[IResolvable
,OpenSearchServerlessFieldMappingProperty
,Dict
[str
,Any
]]) – Contains the names of the fields to which to map information about the vector store.vector_index_name (
str
) – The name of the vector store.
- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock open_search_serverless_configuration_property = bedrock.CfnKnowledgeBase.OpenSearchServerlessConfigurationProperty( collection_arn="collectionArn", field_mapping=bedrock.CfnKnowledgeBase.OpenSearchServerlessFieldMappingProperty( metadata_field="metadataField", text_field="textField", vector_field="vectorField" ), vector_index_name="vectorIndexName" )
Attributes
- collection_arn
The Amazon Resource Name (ARN) of the OpenSearch Service vector store.
- field_mapping
Contains the names of the fields to which to map information about the vector store.
OpenSearchServerlessFieldMappingProperty
- class CfnKnowledgeBase.OpenSearchServerlessFieldMappingProperty(*, metadata_field, text_field, vector_field)
Bases:
object
Contains the names of the fields to which to map information about the vector store.
- Parameters:
metadata_field (
str
) – The name of the field in which Amazon Bedrock stores metadata about the vector store.text_field (
str
) – The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.vector_field (
str
) – The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.
- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock open_search_serverless_field_mapping_property = bedrock.CfnKnowledgeBase.OpenSearchServerlessFieldMappingProperty( metadata_field="metadataField", text_field="textField", vector_field="vectorField" )
Attributes
- metadata_field
The name of the field in which Amazon Bedrock stores metadata about the vector store.
- text_field
The name of the field in which Amazon Bedrock stores the raw text from your data.
The text is split according to the chunking strategy you choose.
- vector_field
The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.
PineconeConfigurationProperty
- class CfnKnowledgeBase.PineconeConfigurationProperty(*, connection_string, credentials_secret_arn, field_mapping, namespace=None)
Bases:
object
Contains details about the storage configuration of the knowledge base in Pinecone.
For more information, see Create a vector index in Pinecone .
- Parameters:
connection_string (
str
) – The endpoint URL for your index management page.credentials_secret_arn (
str
) – The Amazon Resource Name (ARN) of the secret that you created in AWS Secrets Manager that is linked to your Pinecone API key.field_mapping (
Union
[IResolvable
,PineconeFieldMappingProperty
,Dict
[str
,Any
]]) – Contains the names of the fields to which to map information about the vector store.namespace (
Optional
[str
]) – The namespace to be used to write new data to your database.
- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock pinecone_configuration_property = bedrock.CfnKnowledgeBase.PineconeConfigurationProperty( connection_string="connectionString", credentials_secret_arn="credentialsSecretArn", field_mapping=bedrock.CfnKnowledgeBase.PineconeFieldMappingProperty( metadata_field="metadataField", text_field="textField" ), # the properties below are optional namespace="namespace" )
Attributes
- connection_string
The endpoint URL for your index management page.
- credentials_secret_arn
The Amazon Resource Name (ARN) of the secret that you created in AWS Secrets Manager that is linked to your Pinecone API key.
- field_mapping
Contains the names of the fields to which to map information about the vector store.
- namespace
The namespace to be used to write new data to your database.
PineconeFieldMappingProperty
- class CfnKnowledgeBase.PineconeFieldMappingProperty(*, metadata_field, text_field)
Bases:
object
Contains the names of the fields to which to map information about the vector store.
- Parameters:
metadata_field (
str
) – The name of the field in which Amazon Bedrock stores metadata about the vector store.text_field (
str
) – The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.
- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock pinecone_field_mapping_property = bedrock.CfnKnowledgeBase.PineconeFieldMappingProperty( metadata_field="metadataField", text_field="textField" )
Attributes
- metadata_field
The name of the field in which Amazon Bedrock stores metadata about the vector store.
- text_field
The name of the field in which Amazon Bedrock stores the raw text from your data.
The text is split according to the chunking strategy you choose.
RdsConfigurationProperty
- class CfnKnowledgeBase.RdsConfigurationProperty(*, credentials_secret_arn, database_name, field_mapping, resource_arn, table_name)
Bases:
object
Contains details about the storage configuration of the knowledge base in Amazon RDS.
For more information, see Create a vector index in Amazon RDS .
- Parameters:
credentials_secret_arn (
str
) – The Amazon Resource Name (ARN) of the secret that you created in AWS Secrets Manager that is linked to your Amazon RDS database.database_name (
str
) – The name of your Amazon RDS database.field_mapping (
Union
[IResolvable
,RdsFieldMappingProperty
,Dict
[str
,Any
]]) – Contains the names of the fields to which to map information about the vector store.resource_arn (
str
) – The Amazon Resource Name (ARN) of the vector store.table_name (
str
) – The name of the table in the database.
- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock rds_configuration_property = bedrock.CfnKnowledgeBase.RdsConfigurationProperty( credentials_secret_arn="credentialsSecretArn", database_name="databaseName", field_mapping=bedrock.CfnKnowledgeBase.RdsFieldMappingProperty( metadata_field="metadataField", primary_key_field="primaryKeyField", text_field="textField", vector_field="vectorField" ), resource_arn="resourceArn", table_name="tableName" )
Attributes
- credentials_secret_arn
The Amazon Resource Name (ARN) of the secret that you created in AWS Secrets Manager that is linked to your Amazon RDS database.
- database_name
The name of your Amazon RDS database.
- field_mapping
Contains the names of the fields to which to map information about the vector store.
- resource_arn
The Amazon Resource Name (ARN) of the vector store.
- table_name
The name of the table in the database.
RdsFieldMappingProperty
- class CfnKnowledgeBase.RdsFieldMappingProperty(*, metadata_field, primary_key_field, text_field, vector_field)
Bases:
object
Contains the names of the fields to which to map information about the vector store.
- Parameters:
metadata_field (
str
) – The name of the field in which Amazon Bedrock stores metadata about the vector store.primary_key_field (
str
) – The name of the field in which Amazon Bedrock stores the ID for each entry.text_field (
str
) – The name of the field in which Amazon Bedrock stores the raw text from your data. The text is split according to the chunking strategy you choose.vector_field (
str
) – The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.
- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock rds_field_mapping_property = bedrock.CfnKnowledgeBase.RdsFieldMappingProperty( metadata_field="metadataField", primary_key_field="primaryKeyField", text_field="textField", vector_field="vectorField" )
Attributes
- metadata_field
The name of the field in which Amazon Bedrock stores metadata about the vector store.
- primary_key_field
The name of the field in which Amazon Bedrock stores the ID for each entry.
- text_field
The name of the field in which Amazon Bedrock stores the raw text from your data.
The text is split according to the chunking strategy you choose.
- vector_field
The name of the field in which Amazon Bedrock stores the vector embeddings for your data sources.
StorageConfigurationProperty
- class CfnKnowledgeBase.StorageConfigurationProperty(*, type, opensearch_serverless_configuration=None, pinecone_configuration=None, rds_configuration=None)
Bases:
object
Contains the storage configuration of the knowledge base.
- Parameters:
type (
str
) – The vector store service in which the knowledge base is stored.opensearch_serverless_configuration (
Union
[IResolvable
,OpenSearchServerlessConfigurationProperty
,Dict
[str
,Any
],None
]) – Contains the storage configuration of the knowledge base in Amazon OpenSearch Service.pinecone_configuration (
Union
[IResolvable
,PineconeConfigurationProperty
,Dict
[str
,Any
],None
]) – Contains the storage configuration of the knowledge base in Pinecone.rds_configuration (
Union
[IResolvable
,RdsConfigurationProperty
,Dict
[str
,Any
],None
]) –Contains details about the storage configuration of the knowledge base in Amazon RDS. For more information, see Create a vector index in Amazon RDS .
- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock storage_configuration_property = bedrock.CfnKnowledgeBase.StorageConfigurationProperty( type="type", # the properties below are optional opensearch_serverless_configuration=bedrock.CfnKnowledgeBase.OpenSearchServerlessConfigurationProperty( collection_arn="collectionArn", field_mapping=bedrock.CfnKnowledgeBase.OpenSearchServerlessFieldMappingProperty( metadata_field="metadataField", text_field="textField", vector_field="vectorField" ), vector_index_name="vectorIndexName" ), pinecone_configuration=bedrock.CfnKnowledgeBase.PineconeConfigurationProperty( connection_string="connectionString", credentials_secret_arn="credentialsSecretArn", field_mapping=bedrock.CfnKnowledgeBase.PineconeFieldMappingProperty( metadata_field="metadataField", text_field="textField" ), # the properties below are optional namespace="namespace" ), rds_configuration=bedrock.CfnKnowledgeBase.RdsConfigurationProperty( credentials_secret_arn="credentialsSecretArn", database_name="databaseName", field_mapping=bedrock.CfnKnowledgeBase.RdsFieldMappingProperty( metadata_field="metadataField", primary_key_field="primaryKeyField", text_field="textField", vector_field="vectorField" ), resource_arn="resourceArn", table_name="tableName" ) )
Attributes
- opensearch_serverless_configuration
Contains the storage configuration of the knowledge base in Amazon OpenSearch Service.
- pinecone_configuration
Contains the storage configuration of the knowledge base in Pinecone.
- rds_configuration
Contains details about the storage configuration of the knowledge base in Amazon RDS.
For more information, see Create a vector index in Amazon RDS .
- type
The vector store service in which the knowledge base is stored.
VectorKnowledgeBaseConfigurationProperty
- class CfnKnowledgeBase.VectorKnowledgeBaseConfigurationProperty(*, embedding_model_arn, embedding_model_configuration=None)
Bases:
object
Contains details about the model used to create vector embeddings for the knowledge base.
- Parameters:
embedding_model_arn (
str
) – The Amazon Resource Name (ARN) of the model used to create vector embeddings for the knowledge base.embedding_model_configuration (
Union
[IResolvable
,EmbeddingModelConfigurationProperty
,Dict
[str
,Any
],None
]) – The embeddings model configuration details for the vector model used in Knowledge Base.
- See:
- ExampleMetadata:
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. from aws_cdk import aws_bedrock as bedrock vector_knowledge_base_configuration_property = bedrock.CfnKnowledgeBase.VectorKnowledgeBaseConfigurationProperty( embedding_model_arn="embeddingModelArn", # the properties below are optional embedding_model_configuration=bedrock.CfnKnowledgeBase.EmbeddingModelConfigurationProperty( bedrock_embedding_model_configuration=bedrock.CfnKnowledgeBase.BedrockEmbeddingModelConfigurationProperty( dimensions=123 ) ) )
Attributes
- embedding_model_arn
The Amazon Resource Name (ARN) of the model used to create vector embeddings for the knowledge base.
- embedding_model_configuration
The embeddings model configuration details for the vector model used in Knowledge Base.