StatGPT Backend

This repository contains code for StatGPT backend, which implements APIs and main logic of the StatGPT application.

More information about StatGPT and its architecture can be found in the documentation repository.

Technological stack

Application is written in Python 3.11 and uses the following main technologies:

Technology	Purpose
AI DIAL SDK	SDK for building applications on top of AI DIAL platform
FastAPI	Web framework for API development
SQLAlchemy	ORM for database operations
LangChain	LLM application framework
Pydantic	Data validation and settings
sdmx1	SDMX data handling and provider connections

Project structure

• statgpt/admin — backend of the administrator part which allows the user to add and update data.
• statgpt/common — common code used in the statgpt.admin and statgpt.app applications.
• statgpt/app — main application that generates response using LLMs and based on data prepared by statgpt.admin.
• statgpt/cli — command-line interface (CLI) for managing various aspects of StatGPT.
• tests - unit and integration tests.
• docker - Dockerfiles for building docker images.

Environment variables

The applications are configured using environment variables. The environment variables are described in the following files:

• Common environment variables - used in both applications
• Admin Backend environment variables
• Main App environment variables
• CLI environment variables

Local Setup

Pre-requisites

1. Install Make

• MacOS - should be already installed
• Windows
• Windows, using Chocolatey
• Make sure that make is in the PATH (run which make).

2. Install Python 3.11

Direct installation:

• MacOS, using Homebrew - brew install python@3.11
• Windows or MacOS, using official repository
• Windows, using Chocolatey
• Make sure that python3 or python3.11 is in the PATH and works properly (run python3.11 --version).

Alternative: use pyenv:

• pyenv allows to manage different python versions on the same machine
• execute following from the repository root folder:

  pyenv install 3.11
  pyenv local 3.11  # use Python 3.11 for the current project

3. Install Poetry

Recommended way - system-wide, independent of any particular python venv:

• MacOS - recommended way to install poetry is to use pipx
• Windows - recommended way to install poetry is to use official installer
• Make sure that poetry is in the PATH and works properly (run poetry --version).

4. Install Docker Engine and Docker Compose suitable for your OS

Since Docker Desktop requires a paid license for commercial use, you can use one of the following alternatives:

• Docker Engine and Docker Compose on Linux
• Rancher Desktop on Windows or MacOS

5. Install GNU gettext (for localization)

Required for localization commands (make extract_messages, make update_messages, make compile_messages):

• MacOS - brew install gettext
• Linux/WSL - sudo apt install gettext
• Windows (native) - Install via Chocolatey: choco install gettext

Verify installation: which xgettext msgmerge msgfmt

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Setup

1. Clone the repository

2. Create venv (python virtual environment)

Create python virtual environment, using poetry:

make init_venv

If you see the following error: Skipping virtualenv creation, as specified in config file., it means venv was not created because poetry is configured not to create a new virtual environment. You can fix this:

• Either by updating poetry config:
  • poetry config --local virtualenvs.create true (local config)
  • or poetry config virtualenvs.create true (global config)
• or by creating venv manually: python -m venv .venv

3. Activate venv

For Mac / Linux:

source .venv/bin/activate

For Windows:

.venv/Scripts/Activate

4. Install required python packages

The following will install basic and dev dependencies:

make install_dev

5. Create .env file in the root of the project

You can copy the template file and fill values for secrets manually:

cp .env.template .env

The Environment variables section provides links to pages with detailed information about environment variables.

6. Create dial/core/config/config.json file by running python script

Not implemented yet, TODO: create a script that generates config based on .env variables

Run StatGPT locally

1. Run the DIAL using docker compose:

   docker compose up -d

2. Apply alembic migrations:

   make db_migrate

3. Run Admin backend (if you want to initialize or update data):

   make statgpt_admin

4. Run StatGPT application:

   make statgpt_app

5. Initialize sample content (optional):

   # Run CLI and initialize sample client
   make statgpt_cli

   Then in the CLI:

   statgpt> content init --client-id sample -y
   statgpt> channel reindex -c statgpt-sample --mode all
   

   Wait till reindexing is finished (check status using channel status command in CLI). After that run deduplication:

   statgpt> channel deduplicate -c statgpt-sample
   

   See CLI documentation for more commands.

Admin MCP (Beta)

The Admin application includes an optional MCP (Model Context Protocol) server for dataset onboarding assistance. It provides tools and prompts for coding agents such as Cursor and Claude Code.

Note: This feature is optional and disabled by default. It requires installing additional dependencies and enabling via environment variable.

See MCP setup instructions for details.

Utils for Development

1. Format the code

make format

2. Run linters

make lint

3. Pre-Commit Hooks

To automatically apply black and isort on each commit, enable PreCommit Hooks:

make install_pre_commit_hooks

This command will set up the git hook scripts.

4. Create a new alembic migration:

(!) It is critical to note that autogenerate is not intended to be perfect. It is always necessary to manually review and correct the candidate migrations that autogenerate produces.

(!) After creating a new migration, it is necessary to update the ALEMBIC_TARGET_VERSION in the statgpt/common/config/version.py file to the new version.

make db_autogenerate MESSAGE="Your message"

or:

alembic -c alembic.ini revision --autogenerate -m "Your message"

5. Undo last alembic migration

make db_downgrade

6. Localization (i18n)

The project uses GNU gettext for internationalizing dataset formatters. Use these commands when working with translations:

Workflow:

1. Extract translatable strings - Run after adding/modifying strings marked with _() in formatter code:

   make extract_messages

   This creates/updates the locales/dataset.pot template file.

2. Review changes to locales/dataset.pot file - check git diff. There should be no unexpected changes (removals, additions) - they sometimes happen on Windows platforms.

3. Update translation files - Run to sync .po files with the new template:

   make update_messages

   This updates en/LC_MESSAGES/dataset.po and uk/LC_MESSAGES/dataset.po with new strings.

4. Fill missing translations in .po files. Either manually or using coding agent.

5. Compile translations - Run after translating strings in .po files to generate binary .mo files:

   make compile_messages

   Or use the shorthand: make locales

Note: All commands require GNU gettext to be installed (see Prerequisites).

Run Tests

• Run all tests (unit and integration):

  make test

• Run only unit tests:

  make test_unit

• Run only integration tests:

  make test_integration

⚠️ WARNING: Integration tests require a database and Elasticsearch instance. Consider using separate test instances instead of the ones from docker-compose.yml because tests truncate tables during execution, which may result in DATA LOSS. Configure TEST_DATABASE_* environment variables accordingly. See Common environment variables for details.