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augustwester / searchthearxiv

The code powering searchthearxiv.com, a simple semantic search engine for more than 300,000 ML papers on arXiv.
https://searchthearxiv.com
GNU General Public License v3.0
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arxiv deep-learning machine-learning scientific-papers search-engine semantic-search web-app word-embeddings

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searchthearXiv

This repo contains the implementation of searchthearxiv.com, a simple semantic search engine for more than 300,000 ML papers on arXiv (and counting). The code is separated into two parts, app and data. app contains the implementation of both the frontend and backend of the web app, while data is responsible for updating the database at regular intervals using OpenAI and Pinecone. Both app and data contain a Dockerfile for easy deployment to cloud platforms. I don't expect (or encourage) anyone to run a clone of the project on their own (that would be weird), but it might serve as inspiration for people building a similar type of semantic search engine.

In order to run the code, you need to supply the following list of environment variables:

KAGGLE_USERNAME=your_kaggle_username
KAGGLE_API_KEY=your_kaggle_api_key
OPENAI_API_KEY=your_openai_api_key
PINECONE_API_KEY=your_pinecone_api_key
PINECONE_INDEX_NAME=your_pinecone_index_name

The Kaggle username and API key are required to fetch the arXiv metadataset, maintained (and updated weekly) by Cornell University. The OpenAI API key is used to embed new papers using the text-embedding-ada-002 model. The Pinecone API key and index name are used to connect to the index (i.e. vector database) hosted on Pinecone.

If you are only interested in the embeddings, I have published the full dataset on Kaggle. The current size is around 10GB but grows slightly every week as new papers are added.

If, for some reason, you still want to embed the papers on your own, you can run embed.py in data after downloading the metadataset from Kaggle, setting the environment variables, and creating a Pinecone index. If you don't want to use Pinecone, you are free to modify the code however you want. Since the index will initially be empty, the script will embed all ML papers (again, more than 300,000). However, before doing so, it will estimate a price using OpenAI's tiktoken tokenizer and ask you to confirm. You can skip this step by running python3 embed.py --no-confirmation.

If you like searchthearxiv.com and would like to see something improved, feel free to submit a pull request 🤗