New creates a new Service.

NewService creates a new Service.

AttributionSourceId: Identifier for the source contributing to this attribution.

BatchCreateChunksRequest: Request to batch create `Chunk`s.

BatchCreateChunksResponse: Response from `BatchCreateChunks` containing a list of created `Chunk`s.

BatchDeleteChunksRequest: Request to batch delete `Chunk`s.

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 `Chunk`s.

BatchUpdateChunksResponse: Response from `BatchUpdateChunks` containing a list of updated `Chunk`s.

Blob: Raw media bytes.

CachedContent: Content that has been preprocessed and can be used in subsequent request to GenerativeService.

CachedContentUsageMetadata: Metadata on the usage of the cached content.

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.

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 `Document`s.

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`.

CreateChunkRequest: Request to create a `Chunk`.

CreateFileRequest: Request for `CreateFile`.

CreateFileResponse: Response for `CreateFile`.

CustomMetadata: User provided metadata stored as key-value pairs.

Dataset: Dataset for training or validation.

DeleteChunkRequest: Request to delete a `Chunk`.

Document: A `Document` is a collection of `Chunk`s.

EmbedContentRequest: Request containing the `Content` for the model to embed.

EmbedContentResponse: The response to an `EmbedContentRequest`.

Embedding: A list of floats representing the embedding.

EmbedTextRequest: Request to get a text embedding from the model.

EmbedTextResponse: The response to a EmbedTextRequest.

Empty: A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs.

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 (https://spec.openapis.org/oas/v3.0.3).

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.

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 candidates.

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.

GroundingAttribution: Attribution for a source that contributed to an answer.

GroundingPassage: Passage included inline with a grounding configuration.

GroundingPassageId: Identifier for a part within a `GroundingPassage`.

GroundingPassages: A repeated list of passages.

Hyperparameters: Hyperparameters controlling the tuning process.

InputFeedback: Feedback related to the input data used to answer the question, as opposed to model-generated response to the question.

ListCachedContentsResponse: Response with CachedContents list.

ListChunksResponse: Response from `ListChunks` containing a paginated list of `Chunk`s.

ListCorporaResponse: Response from `ListCorpora` containing a paginated list of `Corpora`.

ListDocumentsResponse: Response from `ListDocuments` containing a paginated list of `Document`s.

ListFilesResponse: Response for `ListFiles`.

ListModelsResponse: Response from `ListModel` containing a paginated list of Models.

ListPermissionsResponse: Response from `ListPermissions` containing a paginated list of permissions.

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.

Model: Information about a Generative Language Model.

Operation: This resource represents a long-running operation that is the result of a network API call.

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.

PromptFeedback: A set of the feedback metadata the prompt specified in `GenerateContentRequest.content`.

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.

SemanticRetrieverChunk: Identifier for a `Chunk` retrieved via Semantic Retriever specified in the `GenerateAnswerRequest` using `SemanticRetrieverConfig`.

SemanticRetrieverConfig: Configuration for retrieving grounding content from a `Corpus` or `Document` created using the Semantic Retriever API.

Status: The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs.

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.

TuningSnapshot: Record for a single tuning step.

TuningTask: Tuning tasks that create tuned models.

UpdateChunkRequest: Request to update a `Chunk`.

UsageMetadata: Metadata on the generation request's token usage.

VideoMetadata: Metadata for a video `File`.