> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lighton.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Create embeddings

> This endpoint can be used to convert text chunks into embeddings.

It is a simple proxy forwarding your requests to the desired model.

**Input Format:**
- `input`: Text string to convert to embeddings
- `encoding_format`: Format for returned embeddings (float or base64)
- `dimensions`: Optional dimension specification for output embeddings



## OpenAPI

````yaml /api-reference/openapi-v3.yaml post /api/v3/embeddings
openapi: 3.0.3
info:
  title: Paradigm API
  version: xenial-xerus (v3)
  description: >-
    A versatile and adaptable tool designed to integrate Generative AI into your
    applications
servers:
  - url: https://paradigm.lighton.ai
security: []
tags:
  - name: Agents
    description: Operations about agents
  - name: Threads
    description: Operations about agents conversation threads
  - name: Tools
    description: Operations about native tools
  - name: Models
    description: Operations about AI models
  - name: MCP
    description: Operations about MCP servers
  - name: Sources
    description: Operations about sources used by agents conversation threads
  - name: Artifacts
    description: Operations about artifacts generated by agents conversation threads
  - name: Agent
    description: >-
      Operations about agents (deprecated). Please use the 'Agents' API
      component instead.
  - name: Files
    description: Operations about files
  - name: Files Processing
    description: Operations about files processing
  - name: Tags
    description: Operations about tags
  - name: Workspaces
    description: Operations about workspaces
  - name: Users
    description: Operations about users
  - name: User Groups
    description: Operations about user groups
  - name: Companies
    description: Operations about companies
  - name: SCIM
    description: Operations about SCIM
paths:
  /api/v3/embeddings:
    post:
      tags:
        - Models
      summary: Create embeddings
      description: |-
        This endpoint can be used to convert text chunks into embeddings.

        It is a simple proxy forwarding your requests to the desired model.

        **Input Format:**
        - `input`: Text string to convert to embeddings
        - `encoding_format`: Format for returned embeddings (float or base64)
        - `dimensions`: Optional dimension specification for output embeddings
      operationId: api_v3_embeddings_create
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/EmbeddingsRequest'
            examples:
              BasicEmbeddingExample:
                value:
                  model: multilingual-e5-large
                  input: Hello, world!
                summary: Basic embedding example
          application/x-www-form-urlencoded:
            schema:
              $ref: '#/components/schemas/EmbeddingsRequest'
          multipart/form-data:
            schema:
              $ref: '#/components/schemas/EmbeddingsRequest'
        required: true
      responses:
        '200':
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/EmbeddingsResponse'
              examples:
                BasicEmbeddingExample:
                  value:
                    model: multilingual-e5-large
                    input: Hello, world!
                  summary: Basic embedding example
          description: ''
      security:
        - bearerAuth: []
components:
  schemas:
    EmbeddingsRequest:
      type: object
      description: Request serializer for embeddings endpoint (OpenAI-compatible).
      properties:
        model:
          type: string
          description: >-
            Model to use for generating embeddings, must exist and be configured
            from the admin
        input:
          type: string
          description: Input text to embed, encoded as a string or array of tokens
        encoding_format:
          allOf:
            - $ref: '#/components/schemas/EncodingFormatEnum'
          default: float
          description: |-
            The format to return the embeddings in

            * `float` - float
            * `base64` - base64
        dimensions:
          type: integer
          description: The number of dimensions the resulting output embeddings should have
        user:
          type: string
          description: A unique identifier representing your end-user
      required:
        - input
        - model
    EmbeddingsResponse:
      type: object
      description: Response serializer for embeddings endpoint results.
      properties:
        object:
          type: string
          default: list
          description: The object type, which is always 'list'
        data:
          type: array
          items:
            $ref: '#/components/schemas/EmbeddingObject'
          description: The list of embeddings generated by the model
        model:
          type: string
          description: The model used for generating the embeddings
        usage:
          allOf:
            - $ref: '#/components/schemas/EmbeddingUsage'
          description: Usage statistics for the embeddings request
      required:
        - data
        - model
    EncodingFormatEnum:
      enum:
        - float
        - base64
      type: string
      description: |-
        * `float` - float
        * `base64` - base64
    EmbeddingObject:
      type: object
      description: Serializer for individual embedding objects.
      properties:
        object:
          type: string
          default: embedding
          description: The object type, which is always 'embedding'
        embedding:
          type: array
          items:
            type: number
            format: double
          description: The embedding vector, which is a list of floats
        index:
          type: integer
          description: The index of the embedding in the list of embeddings
      required:
        - embedding
        - index
    EmbeddingUsage:
      type: object
      description: Serializer for token usage information in embeddings.
      properties:
        prompt_tokens:
          type: integer
          description: The number of tokens in the prompt
        total_tokens:
          type: integer
          description: The total number of tokens used by the request
      required:
        - prompt_tokens
        - total_tokens
  securitySchemes:
    bearerAuth:
      type: http
      scheme: bearer
      description: >-
        Bearer authentication header of the form `Bearer <token>`, where
        `<token>` is your auth token.

````