Module google_api_proto::google::ai::generativelanguage::v1beta

Modules

• attribution_source_id
  Nested message and enum types in AttributionSourceId.
• cache_service_client
  Generated client implementations.
• cached_content
  Nested message and enum types in CachedContent.
• candidate
  Nested message and enum types in Candidate.
• chunk
  Nested message and enum types in Chunk.
• chunk_data
  Nested message and enum types in ChunkData.
• code_execution_result
  Nested message and enum types in CodeExecutionResult.
• condition
  Nested message and enum types in Condition.
• content_filter
  Nested message and enum types in ContentFilter.
• custom_metadata
  Nested message and enum types in CustomMetadata.
• dataset
  Nested message and enum types in Dataset.
• discuss_service_client
  Generated client implementations.
• executable_code
  Nested message and enum types in ExecutableCode.
• file
  Nested message and enum types in File.
• file_service_client
  Generated client implementations.
• function_calling_config
  Nested message and enum types in FunctionCallingConfig.
• generate_answer_request
  Nested message and enum types in GenerateAnswerRequest.
• generate_answer_response
  Nested message and enum types in GenerateAnswerResponse.
• generate_content_response
  Nested message and enum types in GenerateContentResponse.
• generative_service_client
  Generated client implementations.
• hyperparameters
  Nested message and enum types in Hyperparameters.
• model_service_client
  Generated client implementations.
• part
  Nested message and enum types in Part.
• permission
  Nested message and enum types in Permission.
• permission_service_client
  Generated client implementations.
• retriever_service_client
  Generated client implementations.
• safety_rating
  Nested message and enum types in SafetyRating.
• safety_setting
  Nested message and enum types in SafetySetting.
• text_service_client
  Generated client implementations.
• tuned_model
  Nested message and enum types in TunedModel.
• tuning_example
  Nested message and enum types in TuningExample.

Structs

• AttributionSourceId
  Identifier for the source contributing to this attribution.
• BatchCreateChunksRequest
  Request to batch create Chunks.
• BatchCreateChunksResponse
  Response from BatchCreateChunks containing a list of created Chunks.
• BatchDeleteChunksRequest
  Request to batch delete Chunks.
• BatchEmbedContentsRequest
  Batch request to get embeddings from the model for a list of prompts.
• BatchEmbedContentsResponse
  The response to a BatchEmbedContentsRequest.
• BatchEmbedTextRequest
  Batch request to get a text embedding from the model.
• BatchEmbedTextResponse
  The response to a EmbedTextRequest.
• BatchUpdateChunksRequest
  Request to batch update Chunks.
• BatchUpdateChunksResponse
  Response from BatchUpdateChunks containing a list of updated Chunks.
• Blob
  Raw media bytes.
• CachedContent
  Content that has been preprocessed and can be used in subsequent request to GenerativeService.
• Candidate
  A response candidate generated from the model.
• Chunk
  A Chunk is a subpart of a Document that is treated as an independent unit for the purposes of vector representation and storage. A Corpus can have a maximum of 1 million Chunks.
• ChunkData
  Extracted data that represents the Chunk content.
• CitationMetadata
  A collection of source attributions for a piece of content.
• CitationSource
  A citation to a source for a portion of a specific response.
• CodeExecution
  Tool that executes code generated by the model, and automatically returns the result to the model.
• CodeExecutionResult
  Result of executing the ExecutableCode.
• Condition
  Filter condition applicable to a single key.
• Content
  The base structured datatype containing multi-part content of a message.
• ContentEmbedding
  A list of floats representing an embedding.
• ContentFilter
  Content filtering metadata associated with processing a single request.
• Corpus
  A Corpus is a collection of Documents. A project can create up to 5 corpora.
• CountMessageTokensRequest
  Counts the number of tokens in the prompt sent to a model.
• CountMessageTokensResponse
  A response from CountMessageTokens.
• CountTextTokensRequest
  Counts the number of tokens in the prompt sent to a model.
• CountTextTokensResponse
  A response from CountTextTokens.
• CountTokensRequest
  Counts the number of tokens in the prompt sent to a model.
• CountTokensResponse
  A response from CountTokens.
• CreateCachedContentRequest
  Request to create CachedContent.
• CreateChunkRequest
  Request to create a Chunk.
• CreateCorpusRequest
  Request to create a Corpus.
• CreateDocumentRequest
  Request to create a Document.
• CreateFileRequest
  Request for CreateFile.
• CreateFileResponse
  Response for CreateFile.
• CreatePermissionRequest
  Request to create a Permission.
• CreateTunedModelMetadata
  Metadata about the state and progress of creating a tuned model returned from the long-running operation
• CreateTunedModelRequest
  Request to create a TunedModel.
• CustomMetadata
  User provided metadata stored as key-value pairs.
• Dataset
  Dataset for training or validation.
• DeleteCachedContentRequest
  Request to delete CachedContent.
• DeleteChunkRequest
  Request to delete a Chunk.
• DeleteCorpusRequest
  Request to delete a Corpus.
• DeleteDocumentRequest
  Request to delete a Document.
• DeleteFileRequest
  Request for DeleteFile.
• DeletePermissionRequest
  Request to delete the Permission.
• DeleteTunedModelRequest
  Request to delete a TunedModel.
• Document
  A Document is a collection of Chunks. A Corpus can have a maximum of 10,000 Documents.
• EmbedContentRequest
  Request containing the Content for the model to embed.
• EmbedContentResponse
  The response to an EmbedContentRequest.
• EmbedTextRequest
  Request to get a text embedding from the model.
• EmbedTextResponse
  The response to a EmbedTextRequest.
• Embedding
  A list of floats representing the embedding.
• Example
  An input/output example used to instruct the Model.
• ExecutableCode
  Code generated by the model that is meant to be executed, and the result returned to the model.
• File
  A file uploaded to the API.
• FileData
  URI based data.
• FunctionCall
  A predicted FunctionCall returned from the model that contains a string representing the FunctionDeclaration.name with the arguments and their values.
• FunctionCallingConfig
  Configuration for specifying function calling behavior.
• FunctionDeclaration
  Structured representation of a function declaration as defined by the OpenAPI 3.03 specification. Included in this declaration are the function name and parameters. This FunctionDeclaration is a representation of a block of code that can be used as a Tool by the model and executed by the client.
• FunctionResponse
  The result output from a FunctionCall that contains a string representing the FunctionDeclaration.name and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of aFunctionCall made based on model prediction.
• GenerateAnswerRequest
  Request to generate a grounded answer from the Model.
• GenerateAnswerResponse
  Response from the model for a grounded answer.
• GenerateContentRequest
  Request to generate a completion from the model.
• GenerateContentResponse
  Response from the model supporting multiple candidate responses.
• GenerateMessageRequest
  Request to generate a message response from the model.
• GenerateMessageResponse
  The response from the model.
• GenerateTextRequest
  Request to generate a text completion response from the model.
• GenerateTextResponse
  The response from the model, including candidate completions.
• GenerationConfig
  Configuration options for model generation and outputs. Not all parameters are configurable for every model.
• GetCachedContentRequest
  Request to read CachedContent.
• GetChunkRequest
  Request for getting information about a specific Chunk.
• GetCorpusRequest
  Request for getting information about a specific Corpus.
• GetDocumentRequest
  Request for getting information about a specific Document.
• GetFileRequest
  Request for GetFile.
• GetModelRequest
  Request for getting information about a specific Model.
• GetPermissionRequest
  Request for getting information about a specific Permission.
• GetTunedModelRequest
  Request for getting information about a specific Model.
• GroundingAttribution
  Attribution for a source that contributed to an answer.
• GroundingPassage
  Passage included inline with a grounding configuration.
• GroundingPassages
  A repeated list of passages.
• Hyperparameters
  Hyperparameters controlling the tuning process. Read more at https://ai.google.dev/docs/model_tuning_guidance
• ListCachedContentsRequest
  Request to list CachedContents.
• ListCachedContentsResponse
  Response with CachedContents list.
• ListChunksRequest
  Request for listing Chunks.
• ListChunksResponse
  Response from ListChunks containing a paginated list of Chunks. The Chunks are sorted by ascending chunk.create_time.
• ListCorporaRequest
  Request for listing Corpora.
• ListCorporaResponse
  Response from ListCorpora containing a paginated list of Corpora. The results are sorted by ascending corpus.create_time.
• ListDocumentsRequest
  Request for listing Documents.
• ListDocumentsResponse
  Response from ListDocuments containing a paginated list of Documents. The Documents are sorted by ascending document.create_time.
• ListFilesRequest
  Request for ListFiles.
• ListFilesResponse
  Response for ListFiles.
• ListModelsRequest
  Request for listing all Models.
• ListModelsResponse
  Response from ListModel containing a paginated list of Models.
• ListPermissionsRequest
  Request for listing permissions.
• ListPermissionsResponse
  Response from ListPermissions containing a paginated list of permissions.
• ListTunedModelsRequest
  Request for listing TunedModels.
• ListTunedModelsResponse
  Response from ListTunedModels containing a paginated list of Models.
• Message
  The base unit of structured text.
• MessagePrompt
  All of the structured input text passed to the model as a prompt.
• MetadataFilter
  User provided filter to limit retrieval based on Chunk or Document level metadata values. Example (genre = drama OR genre = action): key = “document.custom_metadata.genre” conditions = [{string_value = “drama”, operation = EQUAL}, {string_value = “action”, operation = EQUAL}]
• Model
  Information about a Generative Language Model.
• Part
  A datatype containing media that is part of a multi-part Content message.
• Permission
  Permission resource grants user, group or the rest of the world access to the PaLM API resource (e.g. a tuned model, corpus).
• QueryCorpusRequest
  Request for querying a Corpus.
• QueryCorpusResponse
  Response from QueryCorpus containing a list of relevant chunks.
• QueryDocumentRequest
  Request for querying a Document.
• QueryDocumentResponse
  Response from QueryDocument containing a list of relevant chunks.
• RelevantChunk
  The information for a chunk relevant to a query.
• SafetyFeedback
  Safety feedback for an entire request.
• SafetyRating
  Safety rating for a piece of content.
• SafetySetting
  Safety setting, affecting the safety-blocking behavior.
• Schema
  The Schema object allows the definition of input and output data types. These types can be objects, but also primitives and arrays. Represents a select subset of an OpenAPI 3.0 schema object.
• SemanticRetrieverConfig
  Configuration for retrieving grounding content from a Corpus or Document created using the Semantic Retriever API.
• StringList
  User provided string values assigned to a single metadata key.
• TextCompletion
  Output text returned from a model.
• TextPrompt
  Text given to the model as a prompt.
• Tool
  Tool details that the model may use to generate response.
• ToolConfig
  The Tool configuration containing parameters for specifying Tool use in the request.
• TransferOwnershipRequest
  Request to transfer the ownership of the tuned model.
• TransferOwnershipResponse
  Response from TransferOwnership.
• TunedModel
  A fine-tuned model created using ModelService.CreateTunedModel.
• TunedModelSource
  Tuned model as a source for training a new model.
• TuningExample
  A single example for tuning.
• TuningExamples
  A set of tuning examples. Can be training or validation data.
• TuningSnapshot
  Record for a single tuning step.
• TuningTask
  Tuning tasks that create tuned models.
• UpdateCachedContentRequest
  Request to update CachedContent.
• UpdateChunkRequest
  Request to update a Chunk.
• UpdateCorpusRequest
  Request to update a Corpus.
• UpdateDocumentRequest
  Request to update a Document.
• UpdatePermissionRequest
  Request to update the Permission.
• UpdateTunedModelRequest
  Request to update a TunedModel.
• VideoMetadata
  Metadata for a video File.

Enums

• HarmCategory
  The category of a rating.
• TaskType
  Type of task for which the embedding will be used.
• Type
  Type contains the list of OpenAPI data types as defined by https://spec.openapis.org/oas/v3.0.3#data-types