276 lines
12 KiB
Markdown
276 lines
12 KiB
Markdown
# Cursor Azure GPT-5
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A service that allows Cursor to use Azure GPT-5 deployments by:
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- Adapting incoming Cursor **completions API** requests to the **Responses API**
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- Forwarding the requests to Azure
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- Adapting outgoing Azure **Responses API** streams into **completions API** streams
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This project originates from Cursor's lack of support for Azure models that are only served through the **Responses API**. It will hopefully become obsolete as Cursor continues to improve its model support.
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## Feature highlights
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- Switching between `high`/`medium`/`low` reasoning effort levels by selecting different models in Cursor.
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- Configuring different _reasoning summary_ levels.
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- Displaying _reasoning summaries_ in Cursor natively, like any other reasoning model.
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- Production-ready, so you can share the service among different users in an organization.
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- When running from a terminal, [rich](https://github.com/Textualize/rich) logging of the model's context on every request, including Markdown rendering, syntax highlighting, tool calls/outputs, and more.
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Upcoming features:
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- Multimodal: Will be implemented as soon as better testing is in place and there is demand (PRs welcome).
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- Multiple models simultaneously: Even Cursor's Azure configuration only supports a single deployment at a time. It would be fairly easy to implement support for multiple models in this service, covering even more needs.
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- Full test coverage: See the [Testing](#testing) section for an explanation of the current low coverage.
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Feel free to create or vote on any [project issues](https://github.com/gabrii/Cursor-Azure-GPT-5/issues), and star the project to show your support.
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## Quick start
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If you prefer to deploy the service (for example, to allow multiple members of your team to use it), check the [Production](#production) section, as the project comes with production-ready containers using `supervisord` and `gunicorn`.
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### 1. Service configuration
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Make a copy of the file `.env.example` as `.env` and update the following flags as needed:
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| Flag | Description | Default |
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| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------ | ----------- |
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| `SERVICE_API_KEY` | Arbitrary API key to protect your service. Set it to a random string. | `change-me` |
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| `AZURE_BASE_URL` | Your Azure OpenAI endpoint base URL (no trailing slash), e.g. `https://<resource>.openai.azure.com`. | required |
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| `AZURE_API_KEY` | Azure OpenAI API key. | required |
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| `AZURE_DEPLOYMENT` | Name of the Azure model deployment to use. | `gpt-5` |
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| `AZURE_VERBOSITY_LEVEL` | Hint the model to be more or less expansive in its replies. Use either `high` / `medium` / `low` | `medium` |
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| `AZURE_SUMMARY_LEVEL` | Set to `none` to disable summaries. You might have to disable them if your organization hasn't been approved for this feature. | `detailed` |
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Alternatively, you can pass them through the environment where you run the application.
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<details>
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<summary>Optional Configuration</summary>
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| Flag | Description | Default |
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| ------------------- | ---------------------------------------------------------------------- | -------------------- |
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| `AZURE_API_VERSION` | Azure OpenAI Responses API version to call. | `2025-04-01-preview` |
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| `AZURE_TRUNCATION` | Truncation strategy for long inputs. | `auto` |
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| `FLASK_ENV` | Flask environment. Use `development` for dev or `production` for prod. | `production` |
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| `RECORD_TRAFFIC` | Toggle writing request/response traffic to `recordings/` | `off` |
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</details>
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### 2. Exposing the service
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<details>
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<summary>Why do I have to?</summary>
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> Since Cursor routes requests through its external prompt-building service rather than directly from the IDE to your API, your custom endpoint must be publicly reachable on the Internet.
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>
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> Consider using Cloudflare because its tunnels are free and require no account.
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</details>
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[Install `cloudflared`](https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/downloads/) and run:
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```bash
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cloudflared tunnel --url http://localhost:8080
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```
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Copy the URL of your tunnel from the output of the command. It looks something like this:
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```text
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+----------------------------------------------------+
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| Your quick Tunnel has been created! Visit it at: |
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| https://foo-bar.trycloudflare.com |
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+----------------------------------------------------+
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```
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Then paste it into _Cursor Settings > Models > API Keys > OpenAI API Key > Override OpenAI Base URL_:
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### 3. Configuring Cursor
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In addition to updating the OpenAI Base URL, you need to:
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1. Set _OpenAI API Key_ to the value of `SERVICE_API_KEY` in your `.env`
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2. Ensure the toggles for both options are **on**, as shown in the previous image.
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3. Add the custom models called exactly `gpt-high`, `gpt-medium`, and `gpt-low`, as shown in the previous image. You can also create `gpt-minimal` for minimal reasoning effort. You don't need to remove other models.
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<details>
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<summary>Additional steps if you face this error:
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<img src="assets/cursor_invalid_model.jpg" alt="The model does not work with your current plan or api key" width="100%">
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</summary>
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> This is a bug on Cursor's side when custom models edit files in **∞ Agent** mode. Regardless of the model, and even if `edit_file` is working correctly, Cursor may show this pop-up and interrupt generation after the first `edit_file` function call.
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>
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> This only happens when using model names Cursor has not allowlisted or prepared for, such as `gpt-high`. However, we can't use the standard model names such as `gpt-5-high` because Cursor does not route those to custom OpenAI Base URLs.
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>
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> For now, this bug can be bypassed by using the Custom Modes beta
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>
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> In the near future, either the bug in Agent mode will be fixed or those two remaining functions will be added to Custom Modes—or, even better, Azure support will improve enough to render this project obsolete.
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4. Enable Custom Modes Beta in _Cursor Settings > Chat_: 
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5. Create a custom mode:
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<img src="assets/cursor_custom_mode.gif" alt="Fix for cursor BYOK from azure" width="200">
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</details>
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### 4. Running the service
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To run the production version of the app:
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```bash
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docker compose up flask-prod
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```
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> For instructions on how to run locally without Docker, and the different development commands, see the [Development](#development) section.
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## Development
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### Running locally
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<details><summary>Expand</summary>
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#### Bootstrap your local environment
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```bash
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python -m venv .venv
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pip install -r requirements/dev.txt
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```
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#### Running the development server
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```bash
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flask run -p 8080
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```
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#### Running the production server*
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```bash
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export FLASK_ENV=production
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export FLASK_DEBUG=0
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export LOG_LEVEL=info
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flask run -p 8080
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```
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This will only run the Flask server with the production settings. For a closer approximation of the production server running with `supervisord` and `gunicorn`, check [Running with Docker](#running-with-docker).
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#### Running tests
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```bash
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flask test
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```
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To run only specific tests, you can use the pytest `-k` argument:
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```bash
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flask test -k ...
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```
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#### Running linter
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```bash
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flask lint
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```
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The `lint` command will attempt to fix any linting/style errors in the code. If you only want to know if the code will pass CI and do not wish for the linter to make changes, add the `--check` argument.
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```bash
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flask lint --check
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```
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</details>
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### Running with Docker
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<details><summary>Expand</summary>
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#### Running the development server
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```bash
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docker compose up flask-dev
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```
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#### Running the production server
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```bash
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docker compose up flask-prod
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```
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This image runs the server through `supervisord` and `gunicorn`. See the [Production](#production) section for more details.
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When running flask-prod, the production flags are set in `docker-compose.yml`:
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```yml
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FLASK_ENV: production
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FLASK_DEBUG: 0
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LOG_LEVEL: info
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GUNICORN_WORKERS: 4
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```
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The list of `environment:` variables in the `docker-compose.yml` file takes precedence over any variables specified in `.env`.
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#### Running tests
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```bash
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docker compose run --rm manage test
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```
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To run only specific tests, you can use the pytest `-k` argument:
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```bash
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docker compose run --rm manage test -k ...
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```
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#### Running linter
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```bash
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docker compose run --rm manage lint
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```
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The `lint` command will attempt to fix any linting/style errors in the code. If you only want to know if the code will pass CI and do not wish for the linter to make changes, add the `--check` argument.
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```bash
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docker compose run --rm manage lint --check
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```
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</details>
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## Testing
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To make the generation of test fixtures easier, the `RECORD_TRAFFIC` flag has been added, which creates files with all the incoming/outgoing traffic between this service and Cursor/Azure in the directory `recordings/`
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To avoid violating Cursor's intellectual property, a redaction layer removes any sensitive data, such as: system prompts, tool names, tool descriptions, and any context containing scaffolding from Cursor's prompt-building service.
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Therefore, recorded traffic can be published under `tests/recordings/` to be used as test fixtures while remaining MIT-licensed.
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## Production
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<details><summary>Expand</summary>
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### Configure server
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You might want to review and modify the following configuration files:
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| File | Description |
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| --------------------------------------- | --------------------------------------------------------------------------------------------------------------- |
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| `supervisord/gunicorn.conf` | Supervisor program config for Gunicorn (bind :5000, gevent; workers/log level from env; logs to stdout/stderr). |
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| `supervisord/supervisord_entrypoint.sh` | Container entrypoint that execs supervisord (prepends it when args start with -). |
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| `supervisord/supervisord.conf` | Main Supervisord config: socket, logging, nodaemon; includes conf.d program configs. |
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### Build, tag, and push the image
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```bash
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docker compose build flask-prod
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docker tag app-production your-tag
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docker push your-tag
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```
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</details> |