🍕 RepoQuery 🍕

A REST service to answer user-queries about public GitHub repositories

🔎 The Project

RepoQuery is an early-beta project, that uses recursive OpenAI function calling paired with semantic search using sentence-transformers/all-MiniLM-L6-v2 to index and answer user queries about public GitHub repositories.

📬 Service Endpoints

Note: Since the service returns responses as SSEs, a REST client like Postman is recommended. Download it here. The Postman web client doesn't support requests to localhost.

Endpoint	Method	Description
/	GET	Redirects to the configured redirect URL.
/embed	POST	Generate and store embeddings for a GitHub repository.
/query	POST	Perform a query on the API with a specific question related to a repository.
/collection	GET	Check if a repository has been indexed.

1. /embed

Parameters

The parameters are passed as a JSON object in the request body:

• owner (string, required): The owner of the repository.
• name (string, required): The name of the repository.
• branch (string, required): The name of the branch.

Response

The request is processed by the server and responses are sent as Server-sent events(SSE). The event stream will contain events with optional data.

Example

curl --location 'localhost:3000/embed' \
--header 'Content-Type: application/json' \
--data '{
    "owner": "open-sauced",
    "name": "ai",
    "branch": "beta"
}'

2. /query

Parameters

The parameters are passed as a JSON object in the request body:

• query (string, required): The question or query you want to ask.
• repository (object, required): Information about the repository for which you want to get the answer.
  • owner (string, required): The owner of the repository.
  • name (string, required): The name of the repository.
  • branch (string, required): The name of the branch.

Response

The request is processed by the server and responses are sent as Server-sent events(SSE). The event stream will contain events with optional data.

Example

curl --location 'localhost:3000/query' \
--header 'Content-Type: application/json' \
--data '{
    "query": "How is the PR description being generated using AI?",
    "repository": {
        "owner": "open-sauced",
        "name": "ai",
        "branch": "beta"
    }
}'

3. /collection

Parameters

• owner (string, required): The owner of the repository.
• name (string, required): The name of the repository.
• branch (string, required): The name of the branch.

Response

This endpoint returns an OK status code if the repository has been indexed by the service.

Example

curl --location 'localhost:3000/embed?owner=open-sauced&name=ai&branch=beta'

🧪 Running Locally

To run the project locally, there are a few prerequisites:

• The Rust toolchain
• The Onnx Runtime. Will be downloaded and installed automatically when building the project.
• Docker to run the QdrantDB instance.
• make for easy automation and development workflow

Once, the above requirements are satisfied, you can run the project like so:

Environment variables

The project requires the following environment variables to be set.

• OPENAI_API_KEY. To authenticate requests to OpenAI.

Database setup

Start Docker and run the following commands to spin-up a Docker container with a QdrantDB image.

docker pull qdrant/qdrant
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant

The database dashboard will be accessible at localhost:6333/dashboard, the project communicates with the DB on port 6334.

Running the project

Run the following command to install the dependencies and run the project on port 3000.

cargo run --release

This command will build and run the project with optimizations enabled(Highly recommended).

🐳 Docker container

The repo-query engine can also be run locally via a docker container and includes all the necessary dependencies.

To build the container tagged as open-sauced-repo-query:latest, run:

make local-image

Then, you can start the repo-query service with:

docker run --env-file ./.env -p 3000:3000 open-sauced-repo-query

There's also a docker-compose.yaml file that can be used to start both qdrant and the repo-query engine together.

To build the image and then start the services, run:

make up

Attributions

sentence-transformers/all-MiniLM-L6-v2.

@inproceedings{reimers-2019-sentence-bert,
  title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
  author = "Reimers, Nils and Gurevych, Iryna",
  booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
  month = "11",
  year = "2019",
  publisher = "Association for Computational Linguistics",
  url = "https://arxiv.org/abs/1908.10084",
}

🤝 Contributing

We encourage you to contribute to OpenSauced! Please check out the Contributing guide for guidelines about how to proceed.

We have a commit utility called @open-sauced/conventional-commit that helps you write your commits in a way that is easy to understand and process by others.

🍕 Community

Got Questions? Join the conversation in our Discord.
Find Open Sauced videos and release overviews on our YouTube Channel.

⚖️ LICENSE

MIT © Open Sauced