doppelganger's Introduction

doppelgänger

Problem: open-source maintainers spend a lot of time managing duplicate/related (doppelgänger) issues & pull requests
Solution: doppelgänger compares newly submitted issues/PRs against existing ones to automatically flag duplicate/related (doppelgänger) issues/PRs

Topics: vector db, github, open-source, embedding search, rag, similarity scores

Screen.Recording.2024-04-27.at.4.57.11.PM.mov

Setup

Clone this repository to your local machine:

git clone https://github.com/dannyl1u/doppelganger.git
cd doppelganger

Build Docker image and run:

docker build -t doppelganger . && docker run --name doppelganger doppelganger

Create a virtual environment and install dependencies:
- python -m venv venv
- source venv/bin/activate # Use venv\Scripts\activate on Windows
- pip install -r requirements.txt
Run the Flask server:
```
python app.py
```
Configure a GitHub Webhook:
- Go to your GitHub repository settings
- Navigate to "Webhooks" and click "Add webhook"
- Enter the following details:
  - Payload URL: https://your-public-url/webhook
  - Content type: application/json
  - Which events would you like to trigger this webhook?: Select "Let me select individual events" and check "Issues" and "Pull requests"
- Click "Add webhook"

Notes

To make your Flask server publicly accessible, consider using a tool like ngrok to expose it to the internet during development.
Ensure proper security measures for the webhook endpoint to avoid unauthorized access or potential attacks.

Star History

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doppelganger's Issues

feat: store issues/prs in vector db

chromadb

feat: setup github webhook for submitted issues/prs

feat: determine similarity threshold based on user activity

feat: determine THRESHOLD based on user activity

feat: load existing issues into bot "memory"

determine the threshold based on user

feat: store issues/prs in vector db

feat: determine THRESHOLD based on user activity

feat: openai api compatible model presets (profiles)

Is your feature request related to a problem? Please describe.
As the modelfiles from the hub are pretty good, it would be nice if we could use also them with external models from openAI api compatible services.

Describe the solution you'd like
Option A:
When adding a modelfile, we could check that it should use a model from the external provider and select it.
Option B:
When selecting a modelfile, we should be able to exchange then the actual model to one from the external service.

Describe alternatives you've considered
Setting the default prompt according to the modelfile works for now, just the profile pick and name is wrong and the extra settings.

Additional context
My ollama instance has limited vram available. External services can provide bigger models. I think it would be good if the same characters would be usable when using an external service.