Ollama Embeddings API

openapi: 3.2.0
info:
  title: Ollama Embeddings API
  version: 0.1.0
  contact:
    name: Ollama Team
    url: https://ollama.com
  license:
    name: MIT
    url: https://opensource.org/licenses/MIT
  description: 'Operations tagged Embeddings across 2 of this provider''s published API definitions: ollama-api-openapi.yml, ollama-openai-compatibility-api-openapi.yml. Each path carries the servers of the definition it was published in.'
servers:
- url: http://localhost:11434
  description: Local Ollama Server
- url: http://localhost:11434/v1
  description: Local Ollama Server (OpenAI-compatible)
tags:
- name: Embeddings
  description: Generate vector embeddings from text input.
paths:
  /api/embed:
    post:
      operationId: generateEmbeddings
      summary: Ollama Generate embeddings
      description: Generate vector embeddings from a model for a given text input or array of text inputs. Useful for semantic search, clustering, and retrieval augmented generation.
      tags:
      - Embeddings
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/EmbedRequest'
      responses:
        '200':
          description: Successful embeddings response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/EmbedResponse'
        '400':
          description: Bad Request
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
        '404':
          description: Model not found
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
    servers:
    - url: http://localhost:11434
      description: Local Ollama Server
  /embeddings:
    post:
      operationId: createEmbedding
      summary: Ollama Create embeddings
      description: Creates an embedding vector representing the input text. Compatible with the OpenAI Embeddings API format.
      tags:
      - Embeddings
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/EmbeddingRequest'
      responses:
        '200':
          description: Successful embedding response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/EmbeddingResponse'
        '400':
          description: Bad Request
        '404':
          description: Model not found
      security:
      - bearerAuth: []
    servers:
    - url: http://localhost:11434/v1
      description: Local Ollama Server (OpenAI-compatible)
components:
  schemas:
    EmbedRequest:
      type: object
      description: Request body for generating vector embeddings from text.
      required:
      - model
      - input
      properties:
        model:
          type: string
          description: The name of the model to use for embedding generation.
        input:
          description: The text to generate embeddings for. Can be a single string or an array of strings for batch processing.
          oneOf:
          - type: string
          - type: array
            items:
              type: string
        truncate:
          type: boolean
          description: If true, truncates inputs that exceed the model's context window. If false, returns an error for oversized inputs.
          default: true
        dimensions:
          type: integer
          description: The number of dimensions for the output embeddings. Only supported by models that allow dimension reduction.
        keep_alive:
          description: How long the model stays loaded in memory after the request.
          oneOf:
          - type: string
          - type: number
        options:
          $ref: '#/components/schemas/ModelOptions'
    EmbedResponse:
      type: object
      description: Response object from an embedding generation request containing the computed vectors.
      properties:
        model:
          type: string
          description: The model that produced the embeddings.
        embeddings:
          type: array
          description: The array of embedding vectors, one per input text.
          items:
            type: array
            items:
              type: number
        total_duration:
          type: integer
          description: Total time spent generating embeddings in nanoseconds.
        load_duration:
          type: integer
          description: Time spent loading the model in nanoseconds.
        prompt_eval_count:
          type: integer
          description: Number of input tokens processed to generate the embeddings.
    ErrorResponse:
      type: object
      description: An error response returned when a request fails.
      properties:
        error:
          type: string
          description: A human-readable error message describing the problem.
    ModelOptions:
      type: object
      description: Runtime options that control text generation behavior. These override the model's default parameter values.
      properties:
        seed:
          type: integer
          description: Random seed for reproducible generation. Set to a specific value for deterministic output.
        temperature:
          type: number
          description: Controls the creativity of responses. Higher values produce more varied output. Range 0.0 to 2.0.
          minimum: 0.0
          maximum: 2.0
        top_k:
          type: integer
          description: Limits token selection to the top K most probable tokens. Lower values produce more focused output.
          minimum: 0
        top_p:
          type: number
          description: Nucleus sampling probability cutoff. Limits selection to the smallest set of tokens whose cumulative probability exceeds this value.
          minimum: 0.0
          maximum: 1.0
        min_p:
          type: number
          description: Minimum probability threshold relative to the most likely token. Tokens below this threshold are filtered out.
          minimum: 0.0
          maximum: 1.0
        stop:
          type: array
          description: A list of stop sequences. Generation halts when any of these strings are produced.
          items:
            type: string
        num_ctx:
          type: integer
          description: The maximum context window size in tokens.
        num_predict:
          type: integer
          description: The maximum number of tokens to generate in the response.
        repeat_penalty:
          type: number
          description: Penalty applied to repeated tokens to reduce repetition.
        repeat_last_n:
          type: integer
          description: Number of recent tokens to consider for repeat penalty.
        tfs_z:
          type: number
          description: Tail-free sampling parameter. Higher values reduce the impact of less probable tokens.
        mirostat:
          type: integer
          description: Enable Mirostat sampling for perplexity control. 0 is disabled, 1 uses Mirostat, 2 uses Mirostat 2.0.
          enum:
          - 0
          - 1
          - 2
        mirostat_tau:
          type: number
          description: Target entropy for Mirostat sampling.
        mirostat_eta:
          type: number
          description: Learning rate for Mirostat sampling.
    EmbeddingResponse:
      type: object
      description: Response object from an embedding request.
      properties:
        object:
          type: string
          description: The object type, always list.
          const: list
        data:
          type: array
          description: The list of embedding objects.
          items:
            type: object
            properties:
              object:
                type: string
                description: The object type, always embedding.
                const: embedding
              index:
                type: integer
                description: The index of the embedding in the list.
              embedding:
                type: array
                description: The embedding vector.
                items:
                  type: number
        model:
          type: string
          description: The model used to generate the embeddings.
        usage:
          $ref: '#/components/schemas/UsageStats'
    EmbeddingRequest:
      type: object
      description: Request body for creating embeddings in OpenAI-compatible format.
      required:
      - model
      - input
      properties:
        model:
          type: string
          description: The model to use for embedding generation.
        input:
          description: Input text to embed. Can be a string or array of strings.
          oneOf:
          - type: string
          - type: array
            items:
              type: string
        encoding_format:
          type: string
          description: The format of the returned embeddings.
          enum:
          - float
          - base64
        dimensions:
          type: integer
          description: The number of dimensions for the output embeddings.
    UsageStats:
      type: object
      description: Token usage statistics for the request.
      properties:
        prompt_tokens:
          type: integer
          description: Number of tokens in the prompt.
        completion_tokens:
          type: integer
          description: Number of tokens in the generated completion.
        total_tokens:
          type: integer
          description: Total number of tokens used in the request.
  securitySchemes:
    bearerAuth:
      type: http
      scheme: bearer
      description: API key authentication. The key is accepted but not validated by Ollama. Use any value such as ollama.
x-refined-from:
- ollama-api-openapi.yml
- ollama-openai-compatibility-api-openapi.yml