generativelanguage/v1/generative_service.proto

// Copyright 2023 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

syntax = "proto3";

package google.ai.generativelanguage.v1;

import "google/ai/generativelanguage/v1/citation.proto";
import "google/ai/generativelanguage/v1/content.proto";
import "google/ai/generativelanguage/v1/safety.proto";
import "google/api/annotations.proto";
import "google/api/client.proto";
import "google/api/field_behavior.proto";
import "google/api/resource.proto";

option go_package = "cloud.google.com/go/ai/generativelanguage/apiv1/generativelanguagepb;generativelanguagepb";
option java_multiple_files = true;
option java_outer_classname = "GenerativeServiceProto";
option java_package = "com.google.ai.generativelanguage.v1";

// API for using Large Models that generate multimodal content and have
// additional capabilities beyond text generation.
service GenerativeService {
  option (google.api.default_host) = "generativelanguage.googleapis.com";

  // Generates a response from the model given an input
  // `GenerateContentRequest`.
  rpc GenerateContent(GenerateContentRequest)
      returns (GenerateContentResponse) {
    option (google.api.http) = {
      post: "/v1/{model=models/*}:generateContent"
      body: "*"
      additional_bindings {
        post: "/v1/{model=tunedModels/*}:generateContent"
        body: "*"
      }
    };
    option (google.api.method_signature) = "model,contents";
  }

  // Generates a streamed response from the model given an input
  // `GenerateContentRequest`.
  rpc StreamGenerateContent(GenerateContentRequest)
      returns (stream GenerateContentResponse) {
    option (google.api.http) = {
      post: "/v1/{model=models/*}:streamGenerateContent"
      body: "*"
    };
    option (google.api.method_signature) = "model,contents";
  }

  // Generates an embedding from the model given an input `Content`.
  rpc EmbedContent(EmbedContentRequest) returns (EmbedContentResponse) {
    option (google.api.http) = {
      post: "/v1/{model=models/*}:embedContent"
      body: "*"
    };
    option (google.api.method_signature) = "model,content";
  }

  // Generates multiple embeddings from the model given input text in a
  // synchronous call.
  rpc BatchEmbedContents(BatchEmbedContentsRequest)
      returns (BatchEmbedContentsResponse) {
    option (google.api.http) = {
      post: "/v1/{model=models/*}:batchEmbedContents"
      body: "*"
    };
    option (google.api.method_signature) = "model,requests";
  }

  // Runs a model's tokenizer on input content and returns the token count.
  rpc CountTokens(CountTokensRequest) returns (CountTokensResponse) {
    option (google.api.http) = {
      post: "/v1/{model=models/*}:countTokens"
      body: "*"
    };
    option (google.api.method_signature) = "model,contents";
  }
}

// Type of task for which the embedding will be used.
enum TaskType {
  // Unset value, which will default to one of the other enum values.
  TASK_TYPE_UNSPECIFIED = 0;

  // Specifies the given text is a query in a search/retrieval setting.
  RETRIEVAL_QUERY = 1;

  // Specifies the given text is a document from the corpus being searched.
  RETRIEVAL_DOCUMENT = 2;

  // Specifies the given text will be used for STS.
  SEMANTIC_SIMILARITY = 3;

  // Specifies that the given text will be classified.
  CLASSIFICATION = 4;

  // Specifies that the embeddings will be used for clustering.
  CLUSTERING = 5;

  // Specifies that the given text will be used for question answering.
  QUESTION_ANSWERING = 6;

  // Specifies that the given text will be used for fact verification.
  FACT_VERIFICATION = 7;
}

// Request to generate a completion from the model.
message GenerateContentRequest {
  // Required. The name of the `Model` to use for generating the completion.
  //
  // Format: `name=models/{model}`.
  string model = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "generativelanguage.googleapis.com/Model"
    }
  ];

  // Required. The content of the current conversation with the model.
  //
  // For single-turn queries, this is a single instance. For multi-turn queries,
  // this is a repeated field that contains conversation history + latest
  // request.
  repeated Content contents = 2 [(google.api.field_behavior) = REQUIRED];

  // Optional. A list of unique `SafetySetting` instances for blocking unsafe
  // content.
  //
  // This will be enforced on the `GenerateContentRequest.contents` and
  // `GenerateContentResponse.candidates`. There should not be more than one
  // setting for each `SafetyCategory` type. The API will block any contents and
  // responses that fail to meet the thresholds set by these settings. This list
  // overrides the default settings for each `SafetyCategory` specified in the
  // safety_settings. If there is no `SafetySetting` for a given
  // `SafetyCategory` provided in the list, the API will use the default safety
  // setting for that category. Harm categories HARM_CATEGORY_HATE_SPEECH,
  // HARM_CATEGORY_SEXUALLY_EXPLICIT, HARM_CATEGORY_DANGEROUS_CONTENT,
  // HARM_CATEGORY_HARASSMENT are supported.
  repeated SafetySetting safety_settings = 3
      [(google.api.field_behavior) = OPTIONAL];

  // Optional. Configuration options for model generation and outputs.
  optional GenerationConfig generation_config = 4
      [(google.api.field_behavior) = OPTIONAL];
}

// Configuration options for model generation and outputs. Not all parameters
// may be configurable for every model.
message GenerationConfig {
  // Optional. Number of generated responses to return.
  //
  // Currently, this value can only be set to 1. If unset, this will default
  // to 1.
  optional int32 candidate_count = 1 [(google.api.field_behavior) = OPTIONAL];

  // Optional. The set of character sequences (up to 5) that will stop output
  // generation. If specified, the API will stop at the first appearance of a
  // stop sequence. The stop sequence will not be included as part of the
  // response.
  repeated string stop_sequences = 2 [(google.api.field_behavior) = OPTIONAL];

  // Optional. The maximum number of tokens to include in a candidate.
  //
  // Note: The default value varies by model, see the `Model.output_token_limit`
  // attribute of the `Model` returned from the `getModel` function.
  optional int32 max_output_tokens = 4 [(google.api.field_behavior) = OPTIONAL];

  // Optional. Controls the randomness of the output.
  //
  // Note: The default value varies by model, see the `Model.temperature`
  // attribute of the `Model` returned from the `getModel` function.
  //
  // Values can range from [0.0, 2.0].
  optional float temperature = 5 [(google.api.field_behavior) = OPTIONAL];

  // Optional. The maximum cumulative probability of tokens to consider when
  // sampling.
  //
  // The model uses combined Top-k and nucleus sampling.
  //
  // Tokens are sorted based on their assigned probabilities so that only the
  // most likely tokens are considered. Top-k sampling directly limits the
  // maximum number of tokens to consider, while Nucleus sampling limits number
  // of tokens based on the cumulative probability.
  //
  // Note: The default value varies by model, see the `Model.top_p`
  // attribute of the `Model` returned from the `getModel` function.
  optional float top_p = 6 [(google.api.field_behavior) = OPTIONAL];

  // Optional. The maximum number of tokens to consider when sampling.
  //
  // Models use nucleus sampling or combined Top-k and nucleus sampling.
  // Top-k sampling considers the set of `top_k` most probable tokens.
  // Models running with nucleus sampling don't allow top_k setting.
  //
  // Note: The default value varies by model, see the `Model.top_k`
  // attribute of the `Model` returned from the `getModel` function. Empty
  // `top_k` field in `Model` indicates the model doesn't apply top-k sampling
  // and doesn't allow setting `top_k` on requests.
  optional int32 top_k = 7 [(google.api.field_behavior) = OPTIONAL];
}

// Response from the model supporting multiple candidates.
//
// Note on safety ratings and content filtering. They are reported for both
// prompt in `GenerateContentResponse.prompt_feedback` and for each candidate
// in `finish_reason` and in `safety_ratings`. The API contract is that:
//  - either all requested candidates are returned or no candidates at all
//  - no candidates are returned only if there was something wrong with the
//    prompt (see `prompt_feedback`)
//  - feedback on each candidate is reported on `finish_reason` and
//    `safety_ratings`.
message GenerateContentResponse {
  // A set of the feedback metadata the prompt specified in
  // `GenerateContentRequest.content`.
  message PromptFeedback {
    // Specifies what was the reason why prompt was blocked.
    enum BlockReason {
      // Default value. This value is unused.
      BLOCK_REASON_UNSPECIFIED = 0;

      // Prompt was blocked due to safety reasons. You can inspect
      // `safety_ratings` to understand which safety category blocked it.
      SAFETY = 1;

      // Prompt was blocked due to unknown reaasons.
      OTHER = 2;
    }

    // Optional. If set, the prompt was blocked and no candidates are returned.
    // Rephrase your prompt.
    BlockReason block_reason = 1 [(google.api.field_behavior) = OPTIONAL];

    // Ratings for safety of the prompt.
    // There is at most one rating per category.
    repeated SafetyRating safety_ratings = 2;
  }

  // Candidate responses from the model.
  repeated Candidate candidates = 1;

  // Returns the prompt's feedback related to the content filters.
  PromptFeedback prompt_feedback = 2;
}

// A response candidate generated from the model.
message Candidate {
  // Defines the reason why the model stopped generating tokens.
  enum FinishReason {
    // Default value. This value is unused.
    FINISH_REASON_UNSPECIFIED = 0;

    // Natural stop point of the model or provided stop sequence.
    STOP = 1;

    // The maximum number of tokens as specified in the request was reached.
    MAX_TOKENS = 2;

    // The candidate content was flagged for safety reasons.
    SAFETY = 3;

    // The candidate content was flagged for recitation reasons.
    RECITATION = 4;

    // Unknown reason.
    OTHER = 5;
  }

  // Output only. Index of the candidate in the list of candidates.
  optional int32 index = 3 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Generated content returned from the model.
  Content content = 1 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Optional. Output only. The reason why the model stopped generating tokens.
  //
  // If empty, the model has not stopped generating the tokens.
  FinishReason finish_reason = 2 [
    (google.api.field_behavior) = OPTIONAL,
    (google.api.field_behavior) = OUTPUT_ONLY
  ];

  // List of ratings for the safety of a response candidate.
  //
  // There is at most one rating per category.
  repeated SafetyRating safety_ratings = 5;

  // Output only. Citation information for model-generated candidate.
  //
  // This field may be populated with recitation information for any text
  // included in the `content`. These are passages that are "recited" from
  // copyrighted material in the foundational LLM's training data.
  CitationMetadata citation_metadata = 6
      [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Token count for this candidate.
  int32 token_count = 7 [(google.api.field_behavior) = OUTPUT_ONLY];
}

// Request containing the `Content` for the model to embed.
message EmbedContentRequest {
  // Required. The model's resource name. This serves as an ID for the Model to
  // use.
  //
  // This name should match a model name returned by the `ListModels` method.
  //
  // Format: `models/{model}`
  string model = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "generativelanguage.googleapis.com/Model"
    }
  ];

  // Required. The content to embed. Only the `parts.text` fields will be
  // counted.
  Content content = 2 [(google.api.field_behavior) = REQUIRED];

  // Optional. Optional task type for which the embeddings will be used. Can
  // only be set for `models/embedding-001`.
  optional TaskType task_type = 3 [(google.api.field_behavior) = OPTIONAL];

  // Optional. An optional title for the text. Only applicable when TaskType is
  // `RETRIEVAL_DOCUMENT`.
  //
  // Note: Specifying a `title` for `RETRIEVAL_DOCUMENT` provides better quality
  // embeddings for retrieval.
  optional string title = 4 [(google.api.field_behavior) = OPTIONAL];

  // Optional. Optional reduced dimension for the output embedding. If set,
  // excessive values in the output embedding are truncated from the end.
  // Supported by `models/text-embedding-latest`.
  optional int32 output_dimensionality = 5
      [(google.api.field_behavior) = OPTIONAL];
}

// A list of floats representing an embedding.
message ContentEmbedding {
  // The embedding values.
  repeated float values = 1;
}

// The response to an `EmbedContentRequest`.
message EmbedContentResponse {
  // Output only. The embedding generated from the input content.
  ContentEmbedding embedding = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
}

// Batch request to get embeddings from the model for a list of prompts.
message BatchEmbedContentsRequest {
  // Required. The model's resource name. This serves as an ID for the Model to
  // use.
  //
  // This name should match a model name returned by the `ListModels` method.
  //
  // Format: `models/{model}`
  string model = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "generativelanguage.googleapis.com/Model"
    }
  ];

  // Required. Embed requests for the batch. The model in each of these requests
  // must match the model specified `BatchEmbedContentsRequest.model`.
  repeated EmbedContentRequest requests = 2
      [(google.api.field_behavior) = REQUIRED];
}

// The response to a `BatchEmbedContentsRequest`.
message BatchEmbedContentsResponse {
  // Output only. The embeddings for each request, in the same order as provided
  // in the batch request.
  repeated ContentEmbedding embeddings = 1
      [(google.api.field_behavior) = OUTPUT_ONLY];
}

// Counts the number of tokens in the `prompt` sent to a model.
//
// Models may tokenize text differently, so each model may return a different
// `token_count`.
message CountTokensRequest {
  // Required. The model's resource name. This serves as an ID for the Model to
  // use.
  //
  // This name should match a model name returned by the `ListModels` method.
  //
  // Format: `models/{model}`
  string model = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "generativelanguage.googleapis.com/Model"
    }
  ];

  // Required. The input given to the model as a prompt.
  repeated Content contents = 2 [(google.api.field_behavior) = REQUIRED];
}

// A response from `CountTokens`.
//
// It returns the model's `token_count` for the `prompt`.
message CountTokensResponse {
  // The number of tokens that the `model` tokenizes the `prompt` into.
  //
  // Always non-negative.
  int32 total_tokens = 1;
}