Nexla

Nexla Marketing Chat API

The Marketing Chat API from Nexla — 1 operation(s) for marketing chat.

Documentation GitHub OpenAPI

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OpenAPI Specification

openapi: 3.2.0
info:
  title: Nexla GenAI (RAG + MCPaaS) Marketing Chat API
  description: Combined Nexla GenAI RAG API Service and MCPaaS
  version: v0.2.3.3-combined
tags:
- name: Marketing Chat
paths:
  /marketing_chat:
    post:
      summary: Marketing Chat
      description: 'Minimal RAG endpoint: fetch vectors and answer for Marketing Chatbot only.

        - No query rewriting, grading, caching.'
      operationId: marketing_chat_marketing_chat_post
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/MarketingChatRequest'
        required: true
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      tags:
      - Marketing Chat
components:
  schemas:
    HTTPValidationError:
      properties:
        detail:
          items:
            $ref: '#/components/schemas/ValidationError'
          type: array
          title: Detail
      type: object
      title: HTTPValidationError
    ValidationError:
      properties:
        loc:
          items:
            anyOf:
            - type: string
            - type: integer
          type: array
          title: Location
        msg:
          type: string
          title: Message
        type:
          type: string
          title: Error Type
        input:
          title: Input
        ctx:
          type: object
          title: Context
      type: object
      required:
      - loc
      - msg
      - type
      title: ValidationError
    Dataset:
      properties:
        id:
          type: string
          title: Id
          description: The unique identifier for the dataset.
      type: object
      required:
      - id
      title: Dataset
    MarketingChatRequest:
      properties:
        user_query:
          type: string
          title: User Query
          description: Query text to be processed.
        datasets:
          items:
            $ref: '#/components/schemas/Dataset'
          type: array
          title: Datasets
          description: List of datasets to include in the query.
        ai_model:
          type: string
          title: Ai Model
          description: Model identifier to use for processing the query.
        llm_provider:
          type: string
          title: Llm Provider
          description: LLM provider to use for processing the query.
        system_prompt:
          anyOf:
          - type: string
          - type: 'null'
          title: System Prompt
          description: Optional system prompt for model behavior
        messages:
          anyOf:
          - items:
              additionalProperties:
                type: string
              type: object
            type: array
          - type: 'null'
          title: Messages
          description: 'Optional prior chat messages in OpenAI format: list of {role, content} entries.'
      type: object
      required:
      - user_query
      - datasets
      - ai_model
      - llm_provider
      title: MarketingChatRequest