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

# Dedicated Embeddings

> Generate text embedding vectors using your Friendli Dedicated Endpoint. Convert text into dense vector representations for search and similarity.

Creates an embedding vector representing the input text.

To request successfully, it is mandatory to enter a **Personal API Key** (e.g. flp\_XXX) value in the **Bearer Token** field.
Refer to the [authentication section](/openapi/introduction#authentication) on our introduction page to learn how to acquire this variable and [visit here](https://friendli.ai/suite/~/setting/keys) to generate your API Key.


## OpenAPI

````yaml https://github.com/friendliai/friendli-openapi/raw/refs/heads/main/openapi.yaml post /dedicated/v1/embeddings
openapi: 3.1.0
info:
  title: Friendli Suite API Reference
  description: This is an OpenAPI reference of Friendli Suite API.
  termsOfService: https://friendli.ai/terms-of-service
  contact:
    name: FriendliAI Support Team
    email: support@friendli.ai
  version: 0.1.0
servers:
  - url: https://api.friendli.ai
security: []
tags:
  - name: Serverless.Chat
  - name: Serverless.ToolAssistedChat
  - name: Serverless.Messages
  - name: Serverless.ChatRender
  - name: Serverless.Completions
  - name: Serverless.Token
  - name: Serverless.Audio
  - name: Serverless.Model
  - name: Serverless.Knowledge
  - name: Dedicated.Chat
  - name: Dedicated.Messages
  - name: Dedicated.ChatRender
  - name: Dedicated.Completions
  - name: Dedicated.Embeddings
  - name: Dedicated.TextClassification
  - name: Dedicated.Token
  - name: Dedicated.Image
  - name: Dedicated.Audio
  - name: Dedicated.Endpoint
  - name: Container.Chat
  - name: Container.Messages
  - name: Container.Completions
  - name: Container.TextClassification
  - name: Container.Token
  - name: Container.Image
  - name: Container.Audio
  - name: Cost
  - name: Dataset
  - name: File
paths:
  /dedicated/v1/embeddings:
    post:
      tags:
        - Dedicated.Embeddings
      summary: Embeddings
      description: Creates an embedding vector representing the input text.
      operationId: dedicatedEmbeddings
      parameters:
        - name: X-Friendli-Team
          in: header
          required: false
          schema:
            anyOf:
              - type: string
              - type: 'null'
            description: ID of team to run requests as (optional parameter).
            title: X-Friendli-Team
          description: ID of team to run requests as (optional parameter).
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/DedicatedEmbeddingsBody'
      responses:
        '200':
          description: Successfully generated embeddings.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/DedicatedEmbeddingsSuccess'
              examples:
                Example:
                  value:
                    id: embd-26a1e10db1311bc2adb488d2d205288b
                    model: (endpoint-id)
                    object: list
                    data:
                      - index: 0
                        object: embedding
                        embedding:
                          - 0.0023064255
                          - -0.009327292
                          - -0.0028842222
                    usage:
                      prompt_tokens: 26
                      completion_tokens: 0
                      total_tokens: 26
                    created: 1735722153
        '422':
          description: Unprocessable Entity
      security:
        - token: []
components:
  schemas:
    DedicatedEmbeddingsBody:
      properties:
        model:
          type: string
          title: Model
          description: >-
            ID of target endpoint. If you want to send request to specific
            adapter, use the format "YOUR_ENDPOINT_ID:YOUR_ADAPTER_ROUTE".
            Otherwise, you can just use "YOUR_ENDPOINT_ID" alone.
          examples:
            - (endpoint-id)
        input:
          anyOf:
            - type: string
            - items:
                type: string
              type: array
            - type: 'null'
          title: Input
          description: >-
            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.


            Either `input` or `tokens` field is required.
          examples:
            - The food was delicious and the waiter...
        tokens:
          anyOf:
            - items:
                type: integer
              type: array
            - type: 'null'
          title: Tokens
          description: |-
            The tokenized prompt (i.e., input tokens).

            Either `input` or `tokens` field is required.
        encoding_format:
          anyOf:
            - type: string
              enum:
                - float
                - base64
            - type: 'null'
          title: Encoding Format
          description: >-
            The format to return the embeddings in. Can be either `float` or
            [`base64`](https://pypi.org/project/pybase64/).
          default: float
      type: object
      required:
        - model
      title: DedicatedEmbeddingsBody
      example:
        encoding_format: float
        input: The food was delicious and the waiter...
        model: (endpoint-id)
    DedicatedEmbeddingsSuccess:
      properties:
        id:
          type: string
          title: Id
          description: A unique ID of the embeddings.
        model:
          anyOf:
            - type: string
            - type: 'null'
          title: Model
          description: >-
            The model to generate the embeddings. For dedicated endpoints, it
            returns the endpoint ID.
        object:
          type: string
          const: list
          title: Object
          description: The object type, which is always set to `list`.
        data:
          items:
            $ref: '#/components/schemas/EmbeddingObject'
          type: array
          title: Data
          description: A list of embedding objects.
        usage:
          $ref: '#/components/schemas/TextUsage'
        created:
          type: integer
          title: Created
          description: >-
            The Unix timestamp (in seconds) for when the embeddings were
            created.
      type: object
      required:
        - id
        - object
        - data
        - usage
        - created
      title: DedicatedEmbeddingsSuccess
    EmbeddingObject:
      properties:
        index:
          type: integer
          title: Index
          description: The index of the embedding in the list of embeddings.
        object:
          type: string
          const: embedding
          title: Object
          description: The object type, which is always set to `embedding`.
        embedding:
          anyOf:
            - items:
                type: number
              type: array
            - type: string
              format: binary
          title: Embedding
          description: >-
            The embedding vector, which is a list of floats or a base64-encoded
            string. The length of vector depends on the model.
      type: object
      required:
        - index
        - object
        - embedding
      title: EmbeddingObject
    TextUsage:
      properties:
        prompt_tokens:
          type: integer
          title: Prompt Tokens
          description: Number of tokens in the prompt.
          examples:
            - 5
        completion_tokens:
          type: integer
          title: Completion Tokens
          description: Number of tokens in the generated completions.
          examples:
            - 7
        total_tokens:
          type: integer
          title: Total Tokens
          description: >-
            Total number of tokens used in the request (`prompt_tokens` +
            `completion_tokens`).
          examples:
            - 12
        prompt_tokens_details:
          anyOf:
            - $ref: '#/components/schemas/PromptTokensDetails'
            - type: 'null'
          description: Breakdown of tokens used in the prompt.
      type: object
      required:
        - prompt_tokens
        - completion_tokens
        - total_tokens
      title: TextUsage
    PromptTokensDetails:
      properties:
        cached_tokens:
          anyOf:
            - type: integer
            - type: 'null'
          title: Cached Tokens
          description: Cached tokens present in the prompt.
      type: object
      title: PromptTokensDetails
  securitySchemes:
    token:
      type: http
      description: >-
        When using Friendli Suite API for inference requests, you need to
        provide a **Personal API Key** for authentication and authorization
        purposes.


        For more detailed information, please refer
        [here](https://friendli.ai/docs/openapi/introduction#authentication).
      scheme: bearer

````