https://github.com/darren277/githubtoslack
Webhook relay station between GitHub and Slack.
https://github.com/darren277/githubtoslack
abstract-factory-pattern flask github slack webhooks
Last synced: 9 months ago
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Webhook relay station between GitHub and Slack.
- Host: GitHub
- URL: https://github.com/darren277/githubtoslack
- Owner: darren277
- Created: 2021-08-26T12:44:43.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-03-25T13:11:37.000Z (9 months ago)
- Last Synced: 2025-03-25T13:25:11.595Z (9 months ago)
- Topics: abstract-factory-pattern, flask, github, slack, webhooks
- Language: Python
- Homepage:
- Size: 164 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
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README
# About
This is a relay station for webhooks, currently predominantly GitHub and Slack.
This is also my first attempt at using an abstract factory pattern for easier extensibility.
This is a work in progress.
**Please note:** LLM tool calls are very experimental at the moment.
# Usage
1. Configure webhook for a GitHub repository (see Notes below).
2. Configure webhook for a Slack channel.
Check out the following resource for details:
https://api.slack.com/messaging/webhooks
3. Clone repository.
4. Create a `.env` file for:
- `SLACK_RELAY_ENDPOINT`: The endpoint for all Slack messages (ex: "https://hooks.slack.com/services/{STRING1}/{STRING2}/{STRING3}").
- `GITHUB_REPO`: Repository to monitor for changes (ex: "https://github.com/darren277/githubtoslack/").
- `PORT`: Flask port.
5. Create a virtual environment.
- For example, on Ubuntu:
1. `python3 -m venv venv`.
2. `source venv/bin/activate`.
6. Install dependencies.
1. `pip3 install -r requirements.txt`.
7. Run `python3 app.py`.
## Slack to GitHub
Provision a personal access token from GitHub Developer Settings.
Configure the slash command from https://api.slack.com/apps//slash-commands.
`/githubissue Fix user login bug`.
## LLM Endpoint
Don't forget to install RabbitMQ:
```shell
sudo apt-get install rabbitmq-server
sudo systemctl start rabbitmq-server
sudo systemctl enable rabbitmq-server
```
To troubleshoot, check the logs:
```shell
celery -A tasks.celery worker --loglevel=info
```
# Notes
Mine is deployed on an AWS EC2 instance so that GitHub has a remote URL to send to.
Safely testing locally would almost require a tunneling service like ngrok.
## LLM Use Cases
### Structured Outputs
I encountered an interesting scenario today. I was originally intending to use `pydantic` to define a structured model for OpenProject `WorkPackage` objects (i.e. Tasks, etc) that would be passed directly into the OpenAI chat completions API call. What occured instead, however, was I discovered a certain complication due to having multiple fundamentally different kinds of tool calls available, making structured output definitions trickier.
So what happened instead, was that I wound up creating a third tool call option, wherein I simply use a `pydantic` builtin method to convert the input to the desired structured output. It is experimental for the time being, so we'll see how it works.
### Multiple Tools
It turns out that incorporating numerous fundamentally different kinds of tool calls can make things a bit messy.
For example, I found that the LLM was using tools that I really did not see as all that relevant for the given query.
### Example Task Creation
As of right now, it unfortunately needs a pretty precise definition of some attributes in order to behave consistently. This is something I will experiment with as I go.
Slack slash command example: `/llm create the following task: Title: "Fix User Sign-Up Issue". Start date: 2025-02-20. Due date: 2025-02-28. Estimated time: 5 hours.`.
# TODO
- Programmatically list endpoints.
- Proper exception handling and logging.
- Add more webhook types.
## OpenProject API
Consider validating attributes before sending request for a cleaner error experience.
```json
{
"_type":"Error",
"errorIdentifier":"urn:openproject-org:api:v3:errors:MultipleErrors",
"message":"Multiple field constraints have been violated.",
"_embedded":
{
"errors":
[
{
"_type":"Error",
"errorIdentifier":"urn:openproject-org:api:v3:errors:PropertyConstraintViolation",
"message":"Project can't be blank.",
"_embedded":{"details":{"attribute":"project"}}
},
{
"_type":"Error",
"errorIdentifier":"urn:openproject-org:api:v3:errors:PropertyConstraintViolation",
"message":"Type can't be blank.",
"_embedded":{"details":{"attribute":"type"}}
}
]
}
}
```
## SurrealDB
To run: `docker run --rm --pull always -p $(EXT_PORT):$(INT_PORT) surrealdb/surrealdb:latest start`.
I will be using `EXT_PORT=8011` and `INT_PORT=8011` for both the external and internal port, but you can change this to whatever you like.
To run (hardcoded port): `docker run --rm --pull always -p 8011:8011 surrealdb/surrealdb:latest start --bind 0.0.0.0:8011 --user root --pass root`.
### Migrations
To migrate the Wiki data (for vector-based RAG search):
Step 1: Export Wiki as an HTML file from OpenProject.
Step 2: Run the migration script (HTML filename is currently hardcoded. Will convert to a CLI argument later).
```shell
python3 migrations.py
```
Note that you have to use the same embedding model for both the migration and the search queries.