project_6_89d52222/upstream_ref/xllm/xllm/proto/embedding.proto

syntax = "proto3";

option go_package = "jd.com/jd-infer/xllm;xllm";
package xllm.proto;

import "common.proto";
import "multimodal.proto";
import "tensor.proto";
import "embedding_data.proto";

message EmbeddingRequest {
  // ID of the model to use. You can use the ListModels endpoint to list available models.
  string model = 1;

  // Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays.
  // The input must not exceed the max input tokens for the model (8192 tokens for text-embedding-ada-002),
  // cannot be an empty string, and any array must be 2048 dimensions or less. Example Python code for counting tokens.
  // Some models may also impose a limit on total number of tokens summed across inputs.
  string input = 2;
  //oneof input {
  //  // string, The string that will be turned into an embedding.
  //  string input_str = 2;
  //
  //  // array, The array of strings that will be turned into an embedding.
  //  repeated string input_arr_str = 3;
  //
  //  // array, The array of integers that will be turned into an embedding.
  //  repeated int32 input_arr_int = 4;
  //
  //  // array, The array of arrays containing integers that will be turned into an embedding.
  //  repeated repeated int32 input_arr_arr_int = 5;
  //}

  // The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
  optional int32 dimensions = 6;

  // The format to return the embeddings in. Can be either float or base64.
  // [default = "float"]
  optional string encoding_format = 7;

  // A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
  optional string user = 8;

  optional string service_request_id = 9;

  optional bool add_special_tokens = 10;
}

message EmbeddingResponseData {
  // The index of the embedding in the input array.
  int32 index = 1;

  // The object type of the embedding.
  // [default = "embedding"]
  string object = 2;

  // The embedding vector.
  repeated float embedding = 3;
  repeated Embedding mm_embeddings = 4;
  //oneof embedding {
  //  // float, The embedding vector as an array of floats.
  //  repeated float float_values = 3;
  //
  //  // string, The embedding vector as a base64 encoded string.
  //  string base64_data = 4;
  //}
}

message EmbeddingResponse {
  // The ID of the embedding response.
  string id = 1;

  // The object type of the embedding response.
  // [default = "list"]
  string object = 2;

  // The Unix timestamp of when the embedding response was created.
  int64 created = 3;

  // The model used to generate the embedding response.
  string model = 4;

  // The list of embedding response data.
  repeated EmbeddingResponseData data = 5;

  // Usage information for the embedding response.
  Usage usage = 6;
}

message MMEmbeddingRequest {
  // ID of the model to use. You can use the ListModels endpoint to list available models.
  string model = 1;

  repeated MMChatMessage messages = 3;

  // The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
  optional int32 dimensions = 6;

  // The format to return the embeddings in. Can be either float or base64.
  // [default = "float"]
  optional string encoding_format = 7;

  // A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
  optional string user = 8;

  optional string service_request_id = 9;
}