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Model Overview

This project provides an MLflow-compatible model that acts as a proxy to an external backend service. Instead of performing local machine learning inference, the model forwards user queries to a backend API and returns structured responses.

Request Format​

The model expects input in JSON format following the MLflow scoring protocol.

Example Request​

{
  "inputs": [
    {
      "question": "Which documents mention institutions or universities?",
      "dataset": "research_production",
      "agent": "domain"
    }
  ]
}

Field Descriptions​

  • inputs (array) – A list of one or more query objects (each representing a single request).
  • question (string) – The user’s natural language query.
  • dataset (string) – Specifies which dataset to use (e.g. research_production, ai_ecosystem).
  • agent (string) – Selects which backend agent handles the request (e.g. domain, data_exploration).

Response Format​

Example Response​

{
  "content": {
    "images": [],
    "text": "Documents mentioning institutions or universities are found in Articles 37, 56, and 68."
  },
  "explanation": {
    "code": "",
    "sources": [
      {
        "article": "Article 37",
        "page": 20,
        "title": "Law on Gender Equality",
        "url": "https://example.com/doc1.pdf"
      }
    ]
  }
}

Field Descriptions​

  • content (object) – The main user-facing answer.

    • text (string) – The generated natural language response.
    • images (array) – Optional list of images related to the answer.

  • explanation (object) – Supporting details for transparency and traceability.

    • code (string) – Optional code used to produce the answer (mainly for data queries).

    • sources (array) – List of references backing the answer:

      • article (string) – Referenced article or section.
      • page (number) – Page number in the document.
      • title (string) – Document title.
      • url (string) – Link to the source document.

Architecture​

Client → MLflow → ModelMapper → Model → External API

Usage​

mlflow models serve -m "models:/social_policy_making_model/1" --env-manager local -p 5001