airbyte 0.7.0-beta2, Mar 7 25

airbyte 0.7.0-beta2 published on Friday, Mar 7, 2025 by airbytehq

airbyte.DestinationPinecone

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airbyte 0.7.0-beta2 published on Friday, Mar 7, 2025 by airbytehq

Example Usage

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package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.airbyte.DestinationPinecone;
import com.pulumi.airbyte.DestinationPineconeArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationEmbeddingArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationEmbeddingAzureOpenAiArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationEmbeddingCohereArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationEmbeddingFakeArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationEmbeddingOpenAiArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationEmbeddingOpenAiCompatibleArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationIndexingArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationProcessingArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationProcessingTextSplitterArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeaderArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguageArgs;
import com.pulumi.airbyte.inputs.DestinationPineconeConfigurationProcessingTextSplitterBySeparatorArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var myDestinationPinecone = new DestinationPinecone("myDestinationPinecone", DestinationPineconeArgs.builder()
            .configuration(DestinationPineconeConfigurationArgs.builder()
                .embedding(DestinationPineconeConfigurationEmbeddingArgs.builder()
                    .azureOpenAi(DestinationPineconeConfigurationEmbeddingAzureOpenAiArgs.builder()
                        .apiBase("https://your-resource-name.openai.azure.com")
                        .deployment("your-resource-name")
                        .openaiKey("...my_openai_key...")
                        .build())
                    .cohere(DestinationPineconeConfigurationEmbeddingCohereArgs.builder()
                        .cohereKey("...my_cohere_key...")
                        .build())
                    .fake()
                    .openAi(DestinationPineconeConfigurationEmbeddingOpenAiArgs.builder()
                        .openaiKey("...my_openai_key...")
                        .build())
                    .openAiCompatible(DestinationPineconeConfigurationEmbeddingOpenAiCompatibleArgs.builder()
                        .apiKey("...my_api_key...")
                        .baseUrl("https://your-service-name.com")
                        .dimensions(1536)
                        .modelName("text-embedding-ada-002")
                        .build())
                    .build())
                .indexing(DestinationPineconeConfigurationIndexingArgs.builder()
                    .index("...my_index...")
                    .pineconeEnvironment("us-west1-gcp")
                    .pineconeKey("...my_pinecone_key...")
                    .build())
                .omit_raw_text(true)
                .processing(DestinationPineconeConfigurationProcessingArgs.builder()
                    .chunkOverlap(4)
                    .chunkSize(7074)
                    .fieldNameMappings(DestinationPineconeConfigurationProcessingFieldNameMappingArgs.builder()
                        .fromField("...my_from_field...")
                        .toField("...my_to_field...")
                        .build())
                    .metadataFields("...")
                    .textFields("...")
                    .textSplitter(DestinationPineconeConfigurationProcessingTextSplitterArgs.builder()
                        .byMarkdownHeader(DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeaderArgs.builder()
                            .splitLevel(1)
                            .build())
                        .byProgrammingLanguage(DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguageArgs.builder()
                            .language("ruby")
                            .build())
                        .bySeparator(DestinationPineconeConfigurationProcessingTextSplitterBySeparatorArgs.builder()
                            .keepSeparator(true)
                            .separators("...")
                            .build())
                        .build())
                    .build())
                .build())
            .definitionId("ae1c7c9a-2b03-4eee-843a-7ed67978265e")
            .workspaceId("a8bd2b0a-cdce-4816-9962-7547715c4567")
            .build());

    }
}

resources:
  myDestinationPinecone:
    type: airbyte:DestinationPinecone
    properties:
      configuration:
        embedding:
          azureOpenAi:
            apiBase: https://your-resource-name.openai.azure.com
            deployment: your-resource-name
            openaiKey: '...my_openai_key...'
          cohere:
            cohereKey: '...my_cohere_key...'
          fake: {}
          openAi:
            openaiKey: '...my_openai_key...'
          openAiCompatible:
            apiKey: '...my_api_key...'
            baseUrl: https://your-service-name.com
            dimensions: 1536
            modelName: text-embedding-ada-002
        indexing:
          index: '...my_index...'
          pineconeEnvironment: us-west1-gcp
          pineconeKey: '...my_pinecone_key...'
        omit_raw_text: true
        processing:
          chunkOverlap: 4
          chunkSize: 7074
          fieldNameMappings:
            - fromField: '...my_from_field...'
              toField: '...my_to_field...'
          metadataFields:
            - '...'
          textFields:
            - '...'
          textSplitter:
            byMarkdownHeader:
              splitLevel: 1
            byProgrammingLanguage:
              language: ruby
            bySeparator:
              keepSeparator: true
              separators:
                - '...'
      definitionId: ae1c7c9a-2b03-4eee-843a-7ed67978265e
      workspaceId: a8bd2b0a-cdce-4816-9962-7547715c4567

Create DestinationPinecone Resource

Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

Constructor syntax

new DestinationPinecone(name: string, args: DestinationPineconeArgs, opts?: CustomResourceOptions);

@overload
def DestinationPinecone(resource_name: str,
                        args: DestinationPineconeArgs,
                        opts: Optional[ResourceOptions] = None)

@overload
def DestinationPinecone(resource_name: str,
                        opts: Optional[ResourceOptions] = None,
                        configuration: Optional[DestinationPineconeConfigurationArgs] = None,
                        workspace_id: Optional[str] = None,
                        definition_id: Optional[str] = None,
                        name: Optional[str] = None)

func NewDestinationPinecone(ctx *Context, name string, args DestinationPineconeArgs, opts ...ResourceOption) (*DestinationPinecone, error)

public DestinationPinecone(string name, DestinationPineconeArgs args, CustomResourceOptions? opts = null)

public DestinationPinecone(String name, DestinationPineconeArgs args)
public DestinationPinecone(String name, DestinationPineconeArgs args, CustomResourceOptions options)

type: airbyte:DestinationPinecone
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

Parameters

name string: The unique name of the resource.
args DestinationPineconeArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

resource_name str: The unique name of the resource.
args DestinationPineconeArgs: The arguments to resource properties.
opts ResourceOptions: Bag of options to control resource's behavior.

ctx Context: Context object for the current deployment.
name string: The unique name of the resource.
args DestinationPineconeArgs: The arguments to resource properties.
opts ResourceOption: Bag of options to control resource's behavior.

name string: The unique name of the resource.
args DestinationPineconeArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

name String: The unique name of the resource.
args DestinationPineconeArgs: The arguments to resource properties.
options CustomResourceOptions: Bag of options to control resource's behavior.

Constructor example

The following reference example uses placeholder values for all input properties.

var destinationPineconeResource = new Airbyte.DestinationPinecone("destinationPineconeResource", new()
{
    Configuration = new Airbyte.Inputs.DestinationPineconeConfigurationArgs
    {
        Embedding = new Airbyte.Inputs.DestinationPineconeConfigurationEmbeddingArgs
        {
            AzureOpenAi = new Airbyte.Inputs.DestinationPineconeConfigurationEmbeddingAzureOpenAiArgs
            {
                ApiBase = "string",
                Deployment = "string",
                OpenaiKey = "string",
            },
            Cohere = new Airbyte.Inputs.DestinationPineconeConfigurationEmbeddingCohereArgs
            {
                CohereKey = "string",
            },
            Fake = null,
            OpenAi = new Airbyte.Inputs.DestinationPineconeConfigurationEmbeddingOpenAiArgs
            {
                OpenaiKey = "string",
            },
            OpenAiCompatible = new Airbyte.Inputs.DestinationPineconeConfigurationEmbeddingOpenAiCompatibleArgs
            {
                BaseUrl = "string",
                Dimensions = 0,
                ApiKey = "string",
                ModelName = "string",
            },
        },
        Indexing = new Airbyte.Inputs.DestinationPineconeConfigurationIndexingArgs
        {
            Index = "string",
            PineconeEnvironment = "string",
            PineconeKey = "string",
        },
        Processing = new Airbyte.Inputs.DestinationPineconeConfigurationProcessingArgs
        {
            ChunkSize = 0,
            ChunkOverlap = 0,
            FieldNameMappings = new[]
            {
                new Airbyte.Inputs.DestinationPineconeConfigurationProcessingFieldNameMappingArgs
                {
                    FromField = "string",
                    ToField = "string",
                },
            },
            MetadataFields = new[]
            {
                "string",
            },
            TextFields = new[]
            {
                "string",
            },
            TextSplitter = new Airbyte.Inputs.DestinationPineconeConfigurationProcessingTextSplitterArgs
            {
                ByMarkdownHeader = new Airbyte.Inputs.DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeaderArgs
                {
                    SplitLevel = 0,
                },
                ByProgrammingLanguage = new Airbyte.Inputs.DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguageArgs
                {
                    Language = "string",
                },
                BySeparator = new Airbyte.Inputs.DestinationPineconeConfigurationProcessingTextSplitterBySeparatorArgs
                {
                    KeepSeparator = false,
                    Separators = new[]
                    {
                        "string",
                    },
                },
            },
        },
        OmitRawText = false,
    },
    WorkspaceId = "string",
    DefinitionId = "string",
    Name = "string",
});

example, err := airbyte.NewDestinationPinecone(ctx, "destinationPineconeResource", &airbyte.DestinationPineconeArgs{
Configuration: &.DestinationPineconeConfigurationArgs{
Embedding: &.DestinationPineconeConfigurationEmbeddingArgs{
AzureOpenAi: &.DestinationPineconeConfigurationEmbeddingAzureOpenAiArgs{
ApiBase: pulumi.String("string"),
Deployment: pulumi.String("string"),
OpenaiKey: pulumi.String("string"),
},
Cohere: &.DestinationPineconeConfigurationEmbeddingCohereArgs{
CohereKey: pulumi.String("string"),
},
Fake: &.DestinationPineconeConfigurationEmbeddingFakeArgs{
},
OpenAi: &.DestinationPineconeConfigurationEmbeddingOpenAiArgs{
OpenaiKey: pulumi.String("string"),
},
OpenAiCompatible: &.DestinationPineconeConfigurationEmbeddingOpenAiCompatibleArgs{
BaseUrl: pulumi.String("string"),
Dimensions: pulumi.Float64(0),
ApiKey: pulumi.String("string"),
ModelName: pulumi.String("string"),
},
},
Indexing: &.DestinationPineconeConfigurationIndexingArgs{
Index: pulumi.String("string"),
PineconeEnvironment: pulumi.String("string"),
PineconeKey: pulumi.String("string"),
},
Processing: &.DestinationPineconeConfigurationProcessingArgs{
ChunkSize: pulumi.Float64(0),
ChunkOverlap: pulumi.Float64(0),
FieldNameMappings: .DestinationPineconeConfigurationProcessingFieldNameMappingArray{
&.DestinationPineconeConfigurationProcessingFieldNameMappingArgs{
FromField: pulumi.String("string"),
ToField: pulumi.String("string"),
},
},
MetadataFields: pulumi.StringArray{
pulumi.String("string"),
},
TextFields: pulumi.StringArray{
pulumi.String("string"),
},
TextSplitter: &.DestinationPineconeConfigurationProcessingTextSplitterArgs{
ByMarkdownHeader: &.DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeaderArgs{
SplitLevel: pulumi.Float64(0),
},
ByProgrammingLanguage: &.DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguageArgs{
Language: pulumi.String("string"),
},
BySeparator: &.DestinationPineconeConfigurationProcessingTextSplitterBySeparatorArgs{
KeepSeparator: pulumi.Bool(false),
Separators: pulumi.StringArray{
pulumi.String("string"),
},
},
},
},
OmitRawText: pulumi.Bool(false),
},
WorkspaceId: pulumi.String("string"),
DefinitionId: pulumi.String("string"),
Name: pulumi.String("string"),
})

var destinationPineconeResource = new DestinationPinecone("destinationPineconeResource", DestinationPineconeArgs.builder()
    .configuration(DestinationPineconeConfigurationArgs.builder()
        .embedding(DestinationPineconeConfigurationEmbeddingArgs.builder()
            .azureOpenAi(DestinationPineconeConfigurationEmbeddingAzureOpenAiArgs.builder()
                .apiBase("string")
                .deployment("string")
                .openaiKey("string")
                .build())
            .cohere(DestinationPineconeConfigurationEmbeddingCohereArgs.builder()
                .cohereKey("string")
                .build())
            .fake()
            .openAi(DestinationPineconeConfigurationEmbeddingOpenAiArgs.builder()
                .openaiKey("string")
                .build())
            .openAiCompatible(DestinationPineconeConfigurationEmbeddingOpenAiCompatibleArgs.builder()
                .baseUrl("string")
                .dimensions(0)
                .apiKey("string")
                .modelName("string")
                .build())
            .build())
        .indexing(DestinationPineconeConfigurationIndexingArgs.builder()
            .index("string")
            .pineconeEnvironment("string")
            .pineconeKey("string")
            .build())
        .processing(DestinationPineconeConfigurationProcessingArgs.builder()
            .chunkSize(0)
            .chunkOverlap(0)
            .fieldNameMappings(DestinationPineconeConfigurationProcessingFieldNameMappingArgs.builder()
                .fromField("string")
                .toField("string")
                .build())
            .metadataFields("string")
            .textFields("string")
            .textSplitter(DestinationPineconeConfigurationProcessingTextSplitterArgs.builder()
                .byMarkdownHeader(DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeaderArgs.builder()
                    .splitLevel(0)
                    .build())
                .byProgrammingLanguage(DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguageArgs.builder()
                    .language("string")
                    .build())
                .bySeparator(DestinationPineconeConfigurationProcessingTextSplitterBySeparatorArgs.builder()
                    .keepSeparator(false)
                    .separators("string")
                    .build())
                .build())
            .build())
        .omitRawText(false)
        .build())
    .workspaceId("string")
    .definitionId("string")
    .name("string")
    .build());

destination_pinecone_resource = airbyte.DestinationPinecone("destinationPineconeResource",
    configuration={
        "embedding": {
            "azure_open_ai": {
                "api_base": "string",
                "deployment": "string",
                "openai_key": "string",
            },
            "cohere": {
                "cohere_key": "string",
            },
            "fake": {},
            "open_ai": {
                "openai_key": "string",
            },
            "open_ai_compatible": {
                "base_url": "string",
                "dimensions": 0,
                "api_key": "string",
                "model_name": "string",
            },
        },
        "indexing": {
            "index": "string",
            "pinecone_environment": "string",
            "pinecone_key": "string",
        },
        "processing": {
            "chunk_size": 0,
            "chunk_overlap": 0,
            "field_name_mappings": [{
                "from_field": "string",
                "to_field": "string",
            }],
            "metadata_fields": ["string"],
            "text_fields": ["string"],
            "text_splitter": {
                "by_markdown_header": {
                    "split_level": 0,
                },
                "by_programming_language": {
                    "language": "string",
                },
                "by_separator": {
                    "keep_separator": False,
                    "separators": ["string"],
                },
            },
        },
        "omit_raw_text": False,
    },
    workspace_id="string",
    definition_id="string",
    name="string")

const destinationPineconeResource = new airbyte.DestinationPinecone("destinationPineconeResource", {
    configuration: {
        embedding: {
            azureOpenAi: {
                apiBase: "string",
                deployment: "string",
                openaiKey: "string",
            },
            cohere: {
                cohereKey: "string",
            },
            fake: {},
            openAi: {
                openaiKey: "string",
            },
            openAiCompatible: {
                baseUrl: "string",
                dimensions: 0,
                apiKey: "string",
                modelName: "string",
            },
        },
        indexing: {
            index: "string",
            pineconeEnvironment: "string",
            pineconeKey: "string",
        },
        processing: {
            chunkSize: 0,
            chunkOverlap: 0,
            fieldNameMappings: [{
                fromField: "string",
                toField: "string",
            }],
            metadataFields: ["string"],
            textFields: ["string"],
            textSplitter: {
                byMarkdownHeader: {
                    splitLevel: 0,
                },
                byProgrammingLanguage: {
                    language: "string",
                },
                bySeparator: {
                    keepSeparator: false,
                    separators: ["string"],
                },
            },
        },
        omitRawText: false,
    },
    workspaceId: "string",
    definitionId: "string",
    name: "string",
});

type: airbyte:DestinationPinecone
properties:
    configuration:
        embedding:
            azureOpenAi:
                apiBase: string
                deployment: string
                openaiKey: string
            cohere:
                cohereKey: string
            fake: {}
            openAi:
                openaiKey: string
            openAiCompatible:
                apiKey: string
                baseUrl: string
                dimensions: 0
                modelName: string
        indexing:
            index: string
            pineconeEnvironment: string
            pineconeKey: string
        omitRawText: false
        processing:
            chunkOverlap: 0
            chunkSize: 0
            fieldNameMappings:
                - fromField: string
                  toField: string
            metadataFields:
                - string
            textFields:
                - string
            textSplitter:
                byMarkdownHeader:
                    splitLevel: 0
                byProgrammingLanguage:
                    language: string
                bySeparator:
                    keepSeparator: false
                    separators:
                        - string
    definitionId: string
    name: string
    workspaceId: string

DestinationPinecone Resource Properties

To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

Inputs

In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

The DestinationPinecone resource accepts the following input properties:

Configuration DestinationPineconeConfiguration: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
WorkspaceId string
DefinitionId string: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
Name string: Name of the destination e.g. dev-mysql-instance.

Configuration DestinationPineconeConfigurationArgs: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
WorkspaceId string
DefinitionId string: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
Name string: Name of the destination e.g. dev-mysql-instance.

configuration DestinationPineconeConfiguration: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
workspaceId String
definitionId String: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
name String: Name of the destination e.g. dev-mysql-instance.

configuration DestinationPineconeConfiguration: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
workspaceId string
definitionId string: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
name string: Name of the destination e.g. dev-mysql-instance.

configuration DestinationPineconeConfigurationArgs: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
workspace_id str
definition_id str: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
name str: Name of the destination e.g. dev-mysql-instance.

configuration Property Map: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
workspaceId String
definitionId String: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
name String: Name of the destination e.g. dev-mysql-instance.

Outputs

All input properties are implicitly available as output properties. Additionally, the DestinationPinecone resource produces the following output properties:

CreatedAt double
DestinationId string
DestinationType string
Id string: The provider-assigned unique ID for this managed resource.

CreatedAt float64
DestinationId string
DestinationType string
Id string: The provider-assigned unique ID for this managed resource.

createdAt Double
destinationId String
destinationType String
id String: The provider-assigned unique ID for this managed resource.

createdAt number
destinationId string
destinationType string
id string: The provider-assigned unique ID for this managed resource.

created_at float
destination_id str
destination_type str
id str: The provider-assigned unique ID for this managed resource.

createdAt Number
destinationId String
destinationType String
id String: The provider-assigned unique ID for this managed resource.

Look up Existing DestinationPinecone Resource

Get an existing DestinationPinecone resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

public static get(name: string, id: Input<ID>, state?: DestinationPineconeState, opts?: CustomResourceOptions): DestinationPinecone

@staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        configuration: Optional[DestinationPineconeConfigurationArgs] = None,
        created_at: Optional[float] = None,
        definition_id: Optional[str] = None,
        destination_id: Optional[str] = None,
        destination_type: Optional[str] = None,
        name: Optional[str] = None,
        workspace_id: Optional[str] = None) -> DestinationPinecone

func GetDestinationPinecone(ctx *Context, name string, id IDInput, state *DestinationPineconeState, opts ...ResourceOption) (*DestinationPinecone, error)

public static DestinationPinecone Get(string name, Input<string> id, DestinationPineconeState? state, CustomResourceOptions? opts = null)

public static DestinationPinecone get(String name, Output<String> id, DestinationPineconeState state, CustomResourceOptions options)

resources:  _:    type: airbyte:DestinationPinecone    get:      id: ${id}

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

resource_name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

The following state arguments are supported:

Configuration DestinationPineconeConfiguration: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
CreatedAt double
DefinitionId string: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
DestinationId string
DestinationType string
Name string: Name of the destination e.g. dev-mysql-instance.
WorkspaceId string

Configuration DestinationPineconeConfigurationArgs: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
CreatedAt float64
DefinitionId string: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
DestinationId string
DestinationType string
Name string: Name of the destination e.g. dev-mysql-instance.
WorkspaceId string

configuration DestinationPineconeConfiguration: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
createdAt Double
definitionId String: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
destinationId String
destinationType String
name String: Name of the destination e.g. dev-mysql-instance.
workspaceId String

configuration DestinationPineconeConfiguration: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
createdAt number
definitionId string: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
destinationId string
destinationType string
name string: Name of the destination e.g. dev-mysql-instance.
workspaceId string

configuration DestinationPineconeConfigurationArgs: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
created_at float
definition_id str: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
destination_id str
destination_type str
name str: Name of the destination e.g. dev-mysql-instance.
workspace_id str

configuration Property Map: The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration, as well as to provide type safety for the configuration passed to the destination.
createdAt Number
definitionId String: The UUID of the connector definition. One of configuration.destinationType or definitionId must be provided. Requires replacement if changed.
destinationId String
destinationType String
name String: Name of the destination e.g. dev-mysql-instance.
workspaceId String

Supporting Types

DestinationPineconeConfiguration, DestinationPineconeConfigurationArgs

Embedding DestinationPineconeConfigurationEmbedding: Embedding configuration
Indexing DestinationPineconeConfigurationIndexing: Pinecone is a popular vector store that can be used to store and retrieve embeddings.
Processing DestinationPineconeConfigurationProcessing
OmitRawText bool: Do not store the text that gets embedded along with the vector and the metadata in the destination. If set to true, only the vector and the metadata will be stored - in this case raw text for LLM use cases needs to be retrieved from another source. Default: false

Embedding DestinationPineconeConfigurationEmbedding: Embedding configuration
Indexing DestinationPineconeConfigurationIndexing: Pinecone is a popular vector store that can be used to store and retrieve embeddings.
Processing DestinationPineconeConfigurationProcessing
OmitRawText bool: Do not store the text that gets embedded along with the vector and the metadata in the destination. If set to true, only the vector and the metadata will be stored - in this case raw text for LLM use cases needs to be retrieved from another source. Default: false

embedding DestinationPineconeConfigurationEmbedding: Embedding configuration
indexing DestinationPineconeConfigurationIndexing: Pinecone is a popular vector store that can be used to store and retrieve embeddings.
processing DestinationPineconeConfigurationProcessing
omitRawText Boolean: Do not store the text that gets embedded along with the vector and the metadata in the destination. If set to true, only the vector and the metadata will be stored - in this case raw text for LLM use cases needs to be retrieved from another source. Default: false

embedding DestinationPineconeConfigurationEmbedding: Embedding configuration
indexing DestinationPineconeConfigurationIndexing: Pinecone is a popular vector store that can be used to store and retrieve embeddings.
processing DestinationPineconeConfigurationProcessing
omitRawText boolean: Do not store the text that gets embedded along with the vector and the metadata in the destination. If set to true, only the vector and the metadata will be stored - in this case raw text for LLM use cases needs to be retrieved from another source. Default: false

embedding DestinationPineconeConfigurationEmbedding: Embedding configuration
indexing DestinationPineconeConfigurationIndexing: Pinecone is a popular vector store that can be used to store and retrieve embeddings.
processing DestinationPineconeConfigurationProcessing
omit_raw_text bool: Do not store the text that gets embedded along with the vector and the metadata in the destination. If set to true, only the vector and the metadata will be stored - in this case raw text for LLM use cases needs to be retrieved from another source. Default: false

embedding Property Map: Embedding configuration
indexing Property Map: Pinecone is a popular vector store that can be used to store and retrieve embeddings.
processing Property Map
omitRawText Boolean: Do not store the text that gets embedded along with the vector and the metadata in the destination. If set to true, only the vector and the metadata will be stored - in this case raw text for LLM use cases needs to be retrieved from another source. Default: false

DestinationPineconeConfigurationEmbedding, DestinationPineconeConfigurationEmbeddingArgs

AzureOpenAi DestinationPineconeConfigurationEmbeddingAzureOpenAi: Use the Azure-hosted OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
Cohere DestinationPineconeConfigurationEmbeddingCohere: Use the Cohere API to embed text.
Fake DestinationPineconeConfigurationEmbeddingFake: Use a fake embedding made out of random vectors with 1536 embedding dimensions. This is useful for testing the data pipeline without incurring any costs.
OpenAi DestinationPineconeConfigurationEmbeddingOpenAi: Use the OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
OpenAiCompatible DestinationPineconeConfigurationEmbeddingOpenAiCompatible: Use a service that's compatible with the OpenAI API to embed text.

AzureOpenAi DestinationPineconeConfigurationEmbeddingAzureOpenAi: Use the Azure-hosted OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
Cohere DestinationPineconeConfigurationEmbeddingCohere: Use the Cohere API to embed text.
Fake DestinationPineconeConfigurationEmbeddingFake: Use a fake embedding made out of random vectors with 1536 embedding dimensions. This is useful for testing the data pipeline without incurring any costs.
OpenAi DestinationPineconeConfigurationEmbeddingOpenAi: Use the OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
OpenAiCompatible DestinationPineconeConfigurationEmbeddingOpenAiCompatible: Use a service that's compatible with the OpenAI API to embed text.

azureOpenAi DestinationPineconeConfigurationEmbeddingAzureOpenAi: Use the Azure-hosted OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
cohere DestinationPineconeConfigurationEmbeddingCohere: Use the Cohere API to embed text.
fake DestinationPineconeConfigurationEmbeddingFake: Use a fake embedding made out of random vectors with 1536 embedding dimensions. This is useful for testing the data pipeline without incurring any costs.
openAi DestinationPineconeConfigurationEmbeddingOpenAi: Use the OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
openAiCompatible DestinationPineconeConfigurationEmbeddingOpenAiCompatible: Use a service that's compatible with the OpenAI API to embed text.

azureOpenAi DestinationPineconeConfigurationEmbeddingAzureOpenAi: Use the Azure-hosted OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
cohere DestinationPineconeConfigurationEmbeddingCohere: Use the Cohere API to embed text.
fake DestinationPineconeConfigurationEmbeddingFake: Use a fake embedding made out of random vectors with 1536 embedding dimensions. This is useful for testing the data pipeline without incurring any costs.
openAi DestinationPineconeConfigurationEmbeddingOpenAi: Use the OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
openAiCompatible DestinationPineconeConfigurationEmbeddingOpenAiCompatible: Use a service that's compatible with the OpenAI API to embed text.

azure_open_ai DestinationPineconeConfigurationEmbeddingAzureOpenAi: Use the Azure-hosted OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
cohere DestinationPineconeConfigurationEmbeddingCohere: Use the Cohere API to embed text.
fake DestinationPineconeConfigurationEmbeddingFake: Use a fake embedding made out of random vectors with 1536 embedding dimensions. This is useful for testing the data pipeline without incurring any costs.
open_ai DestinationPineconeConfigurationEmbeddingOpenAi: Use the OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
open_ai_compatible DestinationPineconeConfigurationEmbeddingOpenAiCompatible: Use a service that's compatible with the OpenAI API to embed text.

azureOpenAi Property Map: Use the Azure-hosted OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
cohere Property Map: Use the Cohere API to embed text.
fake Property Map: Use a fake embedding made out of random vectors with 1536 embedding dimensions. This is useful for testing the data pipeline without incurring any costs.
openAi Property Map: Use the OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions.
openAiCompatible Property Map: Use a service that's compatible with the OpenAI API to embed text.

DestinationPineconeConfigurationEmbeddingAzureOpenAi, DestinationPineconeConfigurationEmbeddingAzureOpenAiArgs

ApiBase string: The base URL for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
Deployment string: The deployment for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
OpenaiKey string: The API key for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource

ApiBase string: The base URL for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
Deployment string: The deployment for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
OpenaiKey string: The API key for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource

apiBase String: The base URL for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
deployment String: The deployment for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
openaiKey String: The API key for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource

apiBase string: The base URL for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
deployment string: The deployment for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
openaiKey string: The API key for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource

api_base str: The base URL for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
deployment str: The deployment for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
openai_key str: The API key for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource

apiBase String: The base URL for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
deployment String: The deployment for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource
openaiKey String: The API key for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource

DestinationPineconeConfigurationEmbeddingCohere, DestinationPineconeConfigurationEmbeddingCohereArgs

CohereKey string

CohereKey string

cohereKey String

cohereKey string

cohere_key str

cohereKey String

DestinationPineconeConfigurationEmbeddingOpenAi, DestinationPineconeConfigurationEmbeddingOpenAiArgs

OpenaiKey string

OpenaiKey string

openaiKey String

openaiKey string

openai_key str

openaiKey String

DestinationPineconeConfigurationEmbeddingOpenAiCompatible, DestinationPineconeConfigurationEmbeddingOpenAiCompatibleArgs

BaseUrl string: The base URL for your OpenAI-compatible service
Dimensions double: The number of dimensions the embedding model is generating
ApiKey string: Default: ""
ModelName string: The name of the model to use for embedding. Default: "text-embedding-ada-002"

BaseUrl string: The base URL for your OpenAI-compatible service
Dimensions float64: The number of dimensions the embedding model is generating
ApiKey string: Default: ""
ModelName string: The name of the model to use for embedding. Default: "text-embedding-ada-002"

baseUrl String: The base URL for your OpenAI-compatible service
dimensions Double: The number of dimensions the embedding model is generating
apiKey String: Default: ""
modelName String: The name of the model to use for embedding. Default: "text-embedding-ada-002"

baseUrl string: The base URL for your OpenAI-compatible service
dimensions number: The number of dimensions the embedding model is generating
apiKey string: Default: ""
modelName string: The name of the model to use for embedding. Default: "text-embedding-ada-002"

base_url str: The base URL for your OpenAI-compatible service
dimensions float: The number of dimensions the embedding model is generating
api_key str: Default: ""
model_name str: The name of the model to use for embedding. Default: "text-embedding-ada-002"

baseUrl String: The base URL for your OpenAI-compatible service
dimensions Number: The number of dimensions the embedding model is generating
apiKey String: Default: ""
modelName String: The name of the model to use for embedding. Default: "text-embedding-ada-002"

DestinationPineconeConfigurationIndexing, DestinationPineconeConfigurationIndexingArgs

Index string: Pinecone index in your project to load data into
PineconeEnvironment string: Pinecone Cloud environment to use
PineconeKey string: The Pinecone API key to use matching the environment (copy from Pinecone console)

Index string: Pinecone index in your project to load data into
PineconeEnvironment string: Pinecone Cloud environment to use
PineconeKey string: The Pinecone API key to use matching the environment (copy from Pinecone console)

index String: Pinecone index in your project to load data into
pineconeEnvironment String: Pinecone Cloud environment to use
pineconeKey String: The Pinecone API key to use matching the environment (copy from Pinecone console)

index string: Pinecone index in your project to load data into
pineconeEnvironment string: Pinecone Cloud environment to use
pineconeKey string: The Pinecone API key to use matching the environment (copy from Pinecone console)

index str: Pinecone index in your project to load data into
pinecone_environment str: Pinecone Cloud environment to use
pinecone_key str: The Pinecone API key to use matching the environment (copy from Pinecone console)

index String: Pinecone index in your project to load data into
pineconeEnvironment String: Pinecone Cloud environment to use
pineconeKey String: The Pinecone API key to use matching the environment (copy from Pinecone console)

DestinationPineconeConfigurationProcessing, DestinationPineconeConfigurationProcessingArgs

ChunkSize double: Size of chunks in tokens to store in vector store (make sure it is not too big for the context if your LLM)
ChunkOverlap double: Size of overlap between chunks in tokens to store in vector store to better capture relevant context. Default: 0
FieldNameMappings List<DestinationPineconeConfigurationProcessingFieldNameMapping>: List of fields to rename. Not applicable for nested fields, but can be used to rename fields already flattened via dot notation.
MetadataFields List<string>: List of fields in the record that should be stored as metadata. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered metadata fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array. When specifying nested paths, all matching values are flattened into an array set to a field named by the path.
TextFields List<string>: List of fields in the record that should be used to calculate the embedding. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered text fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array.
TextSplitter DestinationPineconeConfigurationProcessingTextSplitter: Split text fields into chunks based on the specified method.

ChunkSize float64: Size of chunks in tokens to store in vector store (make sure it is not too big for the context if your LLM)
ChunkOverlap float64: Size of overlap between chunks in tokens to store in vector store to better capture relevant context. Default: 0
FieldNameMappings []DestinationPineconeConfigurationProcessingFieldNameMapping: List of fields to rename. Not applicable for nested fields, but can be used to rename fields already flattened via dot notation.
MetadataFields []string: List of fields in the record that should be stored as metadata. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered metadata fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array. When specifying nested paths, all matching values are flattened into an array set to a field named by the path.
TextFields []string: List of fields in the record that should be used to calculate the embedding. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered text fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array.
TextSplitter DestinationPineconeConfigurationProcessingTextSplitter: Split text fields into chunks based on the specified method.

chunkSize Double: Size of chunks in tokens to store in vector store (make sure it is not too big for the context if your LLM)
chunkOverlap Double: Size of overlap between chunks in tokens to store in vector store to better capture relevant context. Default: 0
fieldNameMappings List<DestinationPineconeConfigurationProcessingFieldNameMapping>: List of fields to rename. Not applicable for nested fields, but can be used to rename fields already flattened via dot notation.
metadataFields List<String>: List of fields in the record that should be stored as metadata. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered metadata fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array. When specifying nested paths, all matching values are flattened into an array set to a field named by the path.
textFields List<String>: List of fields in the record that should be used to calculate the embedding. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered text fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array.
textSplitter DestinationPineconeConfigurationProcessingTextSplitter: Split text fields into chunks based on the specified method.

chunkSize number: Size of chunks in tokens to store in vector store (make sure it is not too big for the context if your LLM)
chunkOverlap number: Size of overlap between chunks in tokens to store in vector store to better capture relevant context. Default: 0
fieldNameMappings DestinationPineconeConfigurationProcessingFieldNameMapping[]: List of fields to rename. Not applicable for nested fields, but can be used to rename fields already flattened via dot notation.
metadataFields string[]: List of fields in the record that should be stored as metadata. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered metadata fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array. When specifying nested paths, all matching values are flattened into an array set to a field named by the path.
textFields string[]: List of fields in the record that should be used to calculate the embedding. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered text fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array.
textSplitter DestinationPineconeConfigurationProcessingTextSplitter: Split text fields into chunks based on the specified method.

chunk_size float: Size of chunks in tokens to store in vector store (make sure it is not too big for the context if your LLM)
chunk_overlap float: Size of overlap between chunks in tokens to store in vector store to better capture relevant context. Default: 0
field_name_mappings Sequence[DestinationPineconeConfigurationProcessingFieldNameMapping]: List of fields to rename. Not applicable for nested fields, but can be used to rename fields already flattened via dot notation.
metadata_fields Sequence[str]: List of fields in the record that should be stored as metadata. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered metadata fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array. When specifying nested paths, all matching values are flattened into an array set to a field named by the path.
text_fields Sequence[str]: List of fields in the record that should be used to calculate the embedding. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered text fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array.
text_splitter DestinationPineconeConfigurationProcessingTextSplitter: Split text fields into chunks based on the specified method.

chunkSize Number: Size of chunks in tokens to store in vector store (make sure it is not too big for the context if your LLM)
chunkOverlap Number: Size of overlap between chunks in tokens to store in vector store to better capture relevant context. Default: 0
fieldNameMappings List<Property Map>: List of fields to rename. Not applicable for nested fields, but can be used to rename fields already flattened via dot notation.
metadataFields List<String>: List of fields in the record that should be stored as metadata. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered metadata fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array. When specifying nested paths, all matching values are flattened into an array set to a field named by the path.
textFields List<String>: List of fields in the record that should be used to calculate the embedding. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered text fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. user.name will access the name field in the user object. It's also possible to use wildcards to access all fields in an object, e.g. users.*.name will access all names fields in all entries of the users array.
textSplitter Property Map: Split text fields into chunks based on the specified method.

DestinationPineconeConfigurationProcessingFieldNameMapping, DestinationPineconeConfigurationProcessingFieldNameMappingArgs

FromField string: The field name in the source
ToField string: The field name to use in the destination

FromField string: The field name in the source
ToField string: The field name to use in the destination

fromField String: The field name in the source
toField String: The field name to use in the destination

fromField string: The field name in the source
toField string: The field name to use in the destination

from_field str: The field name in the source
to_field str: The field name to use in the destination

fromField String: The field name in the source
toField String: The field name to use in the destination

DestinationPineconeConfigurationProcessingTextSplitter, DestinationPineconeConfigurationProcessingTextSplitterArgs

ByMarkdownHeader DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeader: Split the text by Markdown headers down to the specified header level. If the chunk size fits multiple sections, they will be combined into a single chunk.
ByProgrammingLanguage DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguage: Split the text by suitable delimiters based on the programming language. This is useful for splitting code into chunks.
BySeparator DestinationPineconeConfigurationProcessingTextSplitterBySeparator: Split the text by the list of separators until the chunk size is reached, using the earlier mentioned separators where possible. This is useful for splitting text fields by paragraphs, sentences, words, etc.

ByMarkdownHeader DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeader: Split the text by Markdown headers down to the specified header level. If the chunk size fits multiple sections, they will be combined into a single chunk.
ByProgrammingLanguage DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguage: Split the text by suitable delimiters based on the programming language. This is useful for splitting code into chunks.
BySeparator DestinationPineconeConfigurationProcessingTextSplitterBySeparator: Split the text by the list of separators until the chunk size is reached, using the earlier mentioned separators where possible. This is useful for splitting text fields by paragraphs, sentences, words, etc.

byMarkdownHeader DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeader: Split the text by Markdown headers down to the specified header level. If the chunk size fits multiple sections, they will be combined into a single chunk.
byProgrammingLanguage DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguage: Split the text by suitable delimiters based on the programming language. This is useful for splitting code into chunks.
bySeparator DestinationPineconeConfigurationProcessingTextSplitterBySeparator: Split the text by the list of separators until the chunk size is reached, using the earlier mentioned separators where possible. This is useful for splitting text fields by paragraphs, sentences, words, etc.

byMarkdownHeader DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeader: Split the text by Markdown headers down to the specified header level. If the chunk size fits multiple sections, they will be combined into a single chunk.
byProgrammingLanguage DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguage: Split the text by suitable delimiters based on the programming language. This is useful for splitting code into chunks.
bySeparator DestinationPineconeConfigurationProcessingTextSplitterBySeparator: Split the text by the list of separators until the chunk size is reached, using the earlier mentioned separators where possible. This is useful for splitting text fields by paragraphs, sentences, words, etc.

by_markdown_header DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeader: Split the text by Markdown headers down to the specified header level. If the chunk size fits multiple sections, they will be combined into a single chunk.
by_programming_language DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguage: Split the text by suitable delimiters based on the programming language. This is useful for splitting code into chunks.
by_separator DestinationPineconeConfigurationProcessingTextSplitterBySeparator: Split the text by the list of separators until the chunk size is reached, using the earlier mentioned separators where possible. This is useful for splitting text fields by paragraphs, sentences, words, etc.

byMarkdownHeader Property Map: Split the text by Markdown headers down to the specified header level. If the chunk size fits multiple sections, they will be combined into a single chunk.
byProgrammingLanguage Property Map: Split the text by suitable delimiters based on the programming language. This is useful for splitting code into chunks.
bySeparator Property Map: Split the text by the list of separators until the chunk size is reached, using the earlier mentioned separators where possible. This is useful for splitting text fields by paragraphs, sentences, words, etc.

DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeader, DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeaderArgs

SplitLevel double: Level of markdown headers to split text fields by. Headings down to the specified level will be used as split points. Default: 1

SplitLevel float64: Level of markdown headers to split text fields by. Headings down to the specified level will be used as split points. Default: 1

splitLevel Double: Level of markdown headers to split text fields by. Headings down to the specified level will be used as split points. Default: 1

splitLevel number: Level of markdown headers to split text fields by. Headings down to the specified level will be used as split points. Default: 1

split_level float: Level of markdown headers to split text fields by. Headings down to the specified level will be used as split points. Default: 1

splitLevel Number: Level of markdown headers to split text fields by. Headings down to the specified level will be used as split points. Default: 1

DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguage, DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguageArgs

Language string: Split code in suitable places based on the programming language. must be one of ["cpp", "go", "java", "js", "php", "proto", "python", "rst", "ruby", "rust", "scala", "swift", "markdown", "latex", "html", "sol"]

Language string: Split code in suitable places based on the programming language. must be one of ["cpp", "go", "java", "js", "php", "proto", "python", "rst", "ruby", "rust", "scala", "swift", "markdown", "latex", "html", "sol"]

language String: Split code in suitable places based on the programming language. must be one of ["cpp", "go", "java", "js", "php", "proto", "python", "rst", "ruby", "rust", "scala", "swift", "markdown", "latex", "html", "sol"]

language string: Split code in suitable places based on the programming language. must be one of ["cpp", "go", "java", "js", "php", "proto", "python", "rst", "ruby", "rust", "scala", "swift", "markdown", "latex", "html", "sol"]

language str: Split code in suitable places based on the programming language. must be one of ["cpp", "go", "java", "js", "php", "proto", "python", "rst", "ruby", "rust", "scala", "swift", "markdown", "latex", "html", "sol"]

language String: Split code in suitable places based on the programming language. must be one of ["cpp", "go", "java", "js", "php", "proto", "python", "rst", "ruby", "rust", "scala", "swift", "markdown", "latex", "html", "sol"]

DestinationPineconeConfigurationProcessingTextSplitterBySeparator, DestinationPineconeConfigurationProcessingTextSplitterBySeparatorArgs

KeepSeparator bool: Whether to keep the separator in the resulting chunks. Default: false
Separators List<string>: List of separator strings to split text fields by. The separator itself needs to be wrapped in double quotes, e.g. to split by the dot character, use ".". To split by a newline, use "\n".

KeepSeparator bool: Whether to keep the separator in the resulting chunks. Default: false
Separators []string: List of separator strings to split text fields by. The separator itself needs to be wrapped in double quotes, e.g. to split by the dot character, use ".". To split by a newline, use "\n".

keepSeparator Boolean: Whether to keep the separator in the resulting chunks. Default: false
separators List<String>: List of separator strings to split text fields by. The separator itself needs to be wrapped in double quotes, e.g. to split by the dot character, use ".". To split by a newline, use "\n".

keepSeparator boolean: Whether to keep the separator in the resulting chunks. Default: false
separators string[]: List of separator strings to split text fields by. The separator itself needs to be wrapped in double quotes, e.g. to split by the dot character, use ".". To split by a newline, use "\n".

keep_separator bool: Whether to keep the separator in the resulting chunks. Default: false
separators Sequence[str]: List of separator strings to split text fields by. The separator itself needs to be wrapped in double quotes, e.g. to split by the dot character, use ".". To split by a newline, use "\n".

keepSeparator Boolean: Whether to keep the separator in the resulting chunks. Default: false
separators List<String>: List of separator strings to split text fields by. The separator itself needs to be wrapped in double quotes, e.g. to split by the dot character, use ".". To split by a newline, use "\n".

Import

$ pulumi import airbyte:index/destinationPinecone:DestinationPinecone my_airbyte_destination_pinecone ""

To learn more about importing existing cloud resources, see Importing resources.

Package Details

Repository: airbyte airbytehq/terraform-provider-airbyte
License
Notes: This Pulumi package is based on the airbyte Terraform Provider.

airbyte 0.7.0-beta2 published on Friday, Mar 7, 2025 by airbytehq

airbytehq/terraform-provider-airbyte

airbyte.DestinationPinecone

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Example Usage

Create DestinationPinecone Resource

Constructor syntax

Parameters

Constructor example

DestinationPinecone Resource Properties

Inputs

Outputs

Look up Existing DestinationPinecone Resource

Supporting Types

DestinationPineconeConfiguration, DestinationPineconeConfigurationArgs

DestinationPineconeConfigurationEmbedding, DestinationPineconeConfigurationEmbeddingArgs

DestinationPineconeConfigurationEmbeddingAzureOpenAi, DestinationPineconeConfigurationEmbeddingAzureOpenAiArgs

DestinationPineconeConfigurationEmbeddingCohere, DestinationPineconeConfigurationEmbeddingCohereArgs

DestinationPineconeConfigurationEmbeddingOpenAi, DestinationPineconeConfigurationEmbeddingOpenAiArgs

DestinationPineconeConfigurationEmbeddingOpenAiCompatible, DestinationPineconeConfigurationEmbeddingOpenAiCompatibleArgs

DestinationPineconeConfigurationIndexing, DestinationPineconeConfigurationIndexingArgs

DestinationPineconeConfigurationProcessing, DestinationPineconeConfigurationProcessingArgs

DestinationPineconeConfigurationProcessingFieldNameMapping, DestinationPineconeConfigurationProcessingFieldNameMappingArgs

DestinationPineconeConfigurationProcessingTextSplitter, DestinationPineconeConfigurationProcessingTextSplitterArgs

DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeader, DestinationPineconeConfigurationProcessingTextSplitterByMarkdownHeaderArgs

DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguage, DestinationPineconeConfigurationProcessingTextSplitterByProgrammingLanguageArgs

DestinationPineconeConfigurationProcessingTextSplitterBySeparator, DestinationPineconeConfigurationProcessingTextSplitterBySeparatorArgs

Import

Package Details

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