Google Gemini Embeddings API

openapi: 3.1.0
info:
  title: Google Gemini Content Generation Embeddings API
  description: Google's Gemini API provides access to generative AI models for text generation, multimodal understanding, and embedding creation. The API supports text, image, audio, and video inputs with configurable safety settings, generation parameters, and tool use capabilities.
  version: v1beta
  termsOfService: https://ai.google.dev/terms
  contact:
    name: Google AI Developer Support
    url: https://ai.google.dev/docs/support
  license:
    name: Apache 2.0
    url: https://www.apache.org/licenses/LICENSE-2.0
servers:
- url: https://generativelanguage.googleapis.com/v1beta
  description: Google Generative Language API v1beta
security:
- ApiKeyAuth: []
tags:
- name: Embeddings
  description: Generate text embedding vectors for semantic search, classification, clustering, and retrieval tasks using Gemini embedding models.
paths:
  /models/{model}:embedContent:
    post:
      operationId: embedContent
      tags:
      - Embeddings
      summary: Google Gemini Generates a text embedding vector from the input Content using the specified Gemini Embedding model.
      description: Generates an embedding from the model given input content. The embedding is a vector representation of the input text that captures semantic meaning. Embeddings can be used for semantic search, text classification, clustering, and retrieval tasks. Only the text parts of the input content are used for embedding generation.
      parameters:
      - name: model
        in: path
        required: true
        description: 'The model name to use for embedding. Format: models/{model}. Example: models/gemini-embedding-001.'
        schema:
          type: string
          example: gemini-embedding-001
      - name: key
        in: query
        required: true
        description: API key for authentication.
        schema:
          type: string
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/EmbedContentRequest'
      responses:
        '200':
          description: Successful response with the generated embedding vector.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/EmbedContentResponse'
        '400':
          description: Bad request.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
        '401':
          description: Unauthorized. Invalid or missing API key.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
        '404':
          description: Not found. The specified model does not exist.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
        '429':
          description: Too many requests. Rate limit exceeded.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
components:
  schemas:
    EmbedContentRequest:
      type: object
      description: Request containing the Content for the model to embed.
      required:
      - content
      properties:
        content:
          $ref: '#/components/schemas/Content'
        taskType:
          type: string
          description: The type of task for which the embedding will be used. Not supported by older embedding models.
          enum:
          - TASK_TYPE_UNSPECIFIED
          - RETRIEVAL_QUERY
          - RETRIEVAL_DOCUMENT
          - SEMANTIC_SIMILARITY
          - CLASSIFICATION
          - CLUSTERING
          - QUESTION_ANSWERING
          - FACT_VERIFICATION
          - CODE_RETRIEVAL_QUERY
        title:
          type: string
          description: '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.'
        outputDimensionality:
          type: integer
          format: int32
          description: Reduced dimension for the output embedding. If set, excessive values in the output embedding are truncated from the end. Supported by newer models since 2024 only.
    FunctionCall:
      type: object
      description: A predicted FunctionCall returned from the model that contains a string representing the FunctionDeclaration.name with the arguments and their values.
      properties:
        name:
          type: string
          description: Required. The name of the function to call. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 63.
        args:
          type: object
          description: The function parameters and values in JSON object format.
          additionalProperties: true
    Blob:
      type: object
      description: Raw media bytes with MIME type information.
      properties:
        mimeType:
          type: string
          description: 'The IANA standard MIME type of the source data. Examples: image/png, image/jpeg, audio/mp3, video/mp4.'
        data:
          type: string
          format: byte
          description: Raw bytes for media formats encoded as base64.
    FunctionResponse:
      type: object
      description: 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 call.
      properties:
        name:
          type: string
          description: Required. The name of the function to call. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 63.
        response:
          type: object
          description: Required. The function response in JSON object format.
          additionalProperties: true
    EmbedContentResponse:
      type: object
      description: The response to an EmbedContentRequest.
      properties:
        embedding:
          $ref: '#/components/schemas/ContentEmbedding'
    ContentEmbedding:
      type: object
      description: A list of floats representing an embedding.
      properties:
        values:
          type: array
          description: The embedding values.
          items:
            type: number
            format: float
    FileData:
      type: object
      description: URI based data for media uploaded via the Files API.
      properties:
        mimeType:
          type: string
          description: The IANA standard MIME type of the source data.
        fileUri:
          type: string
          description: URI of the file.
    Content:
      type: object
      description: The base structured datatype containing multi-part content of a message. A Content includes a role field designating the producer of the Content and a parts field containing multi-part data that contains the content of the message turn.
      properties:
        parts:
          type: array
          description: Ordered Parts that constitute a single message. Parts may have different MIME types.
          items:
            $ref: '#/components/schemas/Part'
        role:
          type: string
          description: The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
          enum:
          - user
          - model
    Part:
      type: object
      description: A datatype containing media that is part of a multi-part Content message. A Part consists of data which has an associated datatype. A Part can only contain one of the accepted types in its corresponding field.
      properties:
        text:
          type: string
          description: Inline text.
        inlineData:
          $ref: '#/components/schemas/Blob'
        fileData:
          $ref: '#/components/schemas/FileData'
        functionCall:
          $ref: '#/components/schemas/FunctionCall'
        functionResponse:
          $ref: '#/components/schemas/FunctionResponse'
    ErrorResponse:
      type: object
      description: Error response from the API.
      properties:
        error:
          type: object
          properties:
            code:
              type: integer
              description: HTTP error code.
            message:
              type: string
              description: Human-readable error message.
            status:
              type: string
              description: Error status string.
  securitySchemes:
    ApiKeyAuth:
      type: apiKey
      in: query
      name: key
      description: API key for authenticating requests. Obtain from Google AI Studio at https://aistudio.google.com/app/apikey.