openapi: 3.0.3
info:
  title: Ask Sage Server Admin Training API
  description: 'Ask Sage is an AI-powered platform providing intelligent completions, knowledge management, and workflow automation.


    ## Base URL

    `https://api.asksage.ai`


    ## Authentication

    All endpoints require a valid JWT token passed via the `x-access-tokens` header, unless otherwise noted.


    Obtain a token by authenticating through the User API (`/user/get-token-with-api-key`).


    ## Message Format

    The `message` field in API requests can be either:

    - A single string prompt: `"What is Ask Sage?"`

    - An array of conversation messages: `[{"user": "me", "message": "what is Ask Sage?"}, {"user": "gpt", "message": "Ask Sage is an..."}]`


    ## Key Features

    - **AI Completions** — Query multiple LLM providers with a unified interface

    - **Knowledge Training** — Upload documents, files, and data to build custom datasets

    - **Tabular Data** — Ingest and query structured data (CSV, XLSX) with natural language

    - **Agent Builder** — Create, configure, and execute multi-step AI workflows

    - **Plugins** — Extend capabilities with built-in and custom plugins

    - **MCP Servers** — Connect to Model Context Protocol servers for tool integration'
  version: '2.0'
  contact:
    name: Ask Sage Support
    email: support@asksage.ai
    url: https://asksage.ai
servers:
- url: '{baseUrl}/server'
  description: Ask Sage Server API
  variables:
    baseUrl:
      default: https://api.asksage.ai
      description: API base URL. Use https://api.asksage.ai for production, or your self-hosted instance URL.
security:
- ApiKeyAuth: []
tags:
- name: Training
  description: Model training and content ingestion
paths:
  /train:
    post:
      summary: Train the model
      description: Add new content to the knowledge base
      tags:
      - Training
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              required:
              - content
              properties:
                content:
                  type: string
                  description: Content to train/ingest
                context:
                  type: string
                  description: Additional context for the content
                skip_vectordb:
                  type: boolean
                  default: false
                  description: Skip adding to vector database
                force_dataset:
                  type: string
                  description: Force specific dataset for training
      responses:
        '200':
          description: Training successful
          content:
            application/json:
              schema:
                type: object
                properties:
                  response:
                    type: string
                  embedding:
                    type: string
                  status:
                    type: integer
    get:
      summary: Train the model (GET)
      description: Add new content to the knowledge base
      tags:
      - Training
      responses:
        '200':
          description: Training successful
          content:
            application/json:
              schema:
                type: object
                properties:
                  response:
                    type: string
                  embedding:
                    type: string
                  status:
                    type: integer
  /train-with-file:
    post:
      summary: Train with file
      description: Train the model using file content
      tags:
      - Training
      requestBody:
        required: true
        content:
          multipart/form-data:
            schema:
              type: object
              required:
              - file
              properties:
                file:
                  type: string
                  format: binary
                  description: File to train from
                dataset:
                  type: string
                  description: Dataset to add content to
      responses:
        '200':
          description: File training successful
          content:
            application/json:
              schema:
                type: object
                properties:
                  response:
                    type: string
                  embedding:
                    type: array
                    items:
                      type: string
                  status:
                    type: integer
    get:
      summary: Train with file (GET)
      description: Train the model using file content
      tags:
      - Training
      responses:
        '200':
          description: File training successful
          content:
            application/json:
              schema:
                type: object
                properties:
                  response:
                    type: string
                  embedding:
                    type: array
                    items:
                      type: string
                  status:
                    type: integer
  /train-with-array:
    post:
      summary: Train with array of data
      description: Train the model using an array of content
      tags:
      - Training
      requestBody:
        required: true
        content:
          multipart/form-data:
            schema:
              type: object
              required:
              - data
              - dataset
              properties:
                data:
                  type: string
                  description: JSON array of data to train
                dataset:
                  type: string
                  description: Dataset name
                context:
                  type: string
                  description: Additional context
                filename:
                  type: string
                  description: Source filename
      responses:
        '200':
          description: Array training successful
          content:
            application/json:
              schema:
                type: object
                properties:
                  response:
                    type: string
                  embedding:
                    type: array
                    items:
                      type: array
                      items:
                        type: string
                  status:
                    type: integer
    get:
      summary: Train with array of data (GET)
      description: Train the model using an array of content
      tags:
      - Training
      responses:
        '200':
          description: Array training successful
          content:
            application/json:
              schema:
                type: object
                properties:
                  response:
                    type: string
                  embedding:
                    type: array
                    items:
                      type: array
                      items:
                        type: string
                  status:
                    type: integer
components:
  securitySchemes:
    ApiKeyAuth:
      type: apiKey
      in: header
      name: x-access-tokens
      description: JWT authentication token. Obtain a token by calling the User API endpoint `/user/get-token-with-api-key` with your email and API key.