https://github.com/seatedro/arxival

https://github.com/seatedro/arxival

Last synced: 5 days ago
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• Host: GitHub
• URL: https://github.com/seatedro/arxival
• Owner: seatedro
• Created: 2025-01-03T16:41:17.000Z (19 days ago)
• Default Branch: master
• Last Pushed: 2025-01-06T03:43:45.000Z (16 days ago)
• Last Synced: 2025-01-06T04:25:50.271Z (16 days ago)
• Language: Python
• Homepage: https://arxival.xyz
• Size: 301 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# arXival 📚

![ArXival Banner](./arxival_header.png)

a research paper answering engine focused on openly accessible ML papers. give it a query, get back ai-generated responses with citations and figures from relevant papers.

## what's under the hood 🔧

### data sources

- arxiv + semantic scholar api for paper metadata and pdfs

- paper content processed from pdfs using pymupdf

### tech stack

- **frontend**: next.js 15 + app router, tailwind, shadcn/ui

- **backend**: fastapi + uvicorn

- **vector store**: chromadb (running on cloud)

- **llm**: openai gpt 4o-mini

- **embeddings**: openai text-embedding-3-large

- **storage**: cloudflare r2 for extracted figures

## running locally 🚀

1. clone the repo:

```bash

git clone https://github.com/seatedro/arxival.git

cd arxival

```

2. set up backend:

```bash

cd server

python -m venv .venv

source .venv/bin/activate  # or `.venv\Scripts\activate` on windows

pip install -r requirements.txt

```

3. set up env vars:

```bash

# backend (.env in server/)

OPENAI_API_KEY=your_key

PINECONE_API_KEY=your_token

PINECONE_HOST=your_server

R2_ENDPOINT=your_endpoint

R2_ACCESS_KEY_ID=your_key

R2_SECRET_ACCESS_KEY=your_key

```

4. start the backend:

```bash

python run.py

```

5. set up frontend:

```bash

cd ui

npm install

```

6. set up frontend env:

```bash

# frontend (.env in ui/)

NEXT_PUBLIC_BACKEND_URL=http://localhost:8000

```

7. start the frontend:

```bash

npm run dev

```

8. (optional) ingest some papers:

```bash

cd ../server

python cli_batch.py --query "machine learning" --max-papers 50

```

hit up `http://localhost:3000` and you're good to go! 🎉