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https://github.com/forward-operators/paperchat

PaperChat - cli, API and ChatGPT plugin to query arXiv's dataset
https://github.com/forward-operators/paperchat

arxiv chatgpt chatgpt-plugin gpt huggingface openai

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PaperChat - cli, API and ChatGPT plugin to query arXiv's dataset

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README 

        
# arxivchat



Welcome to arXivchat!



arXivchat is LLM based software that let's you talk about arXiv published papers in a conversational way.

It works as a cli tool, API provider and ChatGPT plugin.



Made by [Forward Operators](https://fwdoperators.com). We work with some of the smartest people on LLM and ML-related projects.



You are more than welcome to contribute!



## Dependencies

- python >=3.10

- poetry

- chromadb

- langchain

- arxiv



## Architecture



![diagram](./images/diagram.png)



## Setup

Follow these steps to quickly set up and run the arXiv plugin:



- Install Python 3.10, if not already installed.



- Clone the repository: git clone https://github.com/Forward-Operators/arxivchat.git



- Navigate to the cloned repository directory: cd /path/to/arxivchat



- Install poetry: `pip install poetry`



- Create a new virtual environment with Python 3.10: `poetry env use python3.10`



- Activate the virtual environment: `poetry shell`



- Install app dependencies: `poetry install`



Set the required environment variables:



```bash

export DATABASE=

export OPENAI_API_KEY=



# Add the environment variables for your chosen vector DB.



# Pinecone

export PINECONE_API_KEY=

export PINECONE_ENVIRONMENT=

export PINECONE_INDEX=



# Qdrant

export QDRANT_URL=

export QDRANT_PORT=

export QDRANT_GRPC_PORT=

export QDRANT_API_KEY=

export QDRANT_COLLECTION=



# Chroma

export CHROMA_HOST=

export CHROMA_PORT=

export CHROMA_COLLECTION=



# Embeddings

export EMBEDDINGS=

export CUDA_ENABLED= - needed for huggingface



```



Run the API locally: `cd app/; gunicorn --worker-class uvicorn.workers.UvicornWorker --config ./gunicorn_conf.py main:app`



Access the API documentation at http://0.0.0.0:8000/docs and test the API endpoints .



## Ingesting

arXiv has a dataset of almost 2 million publications. it is against arXiv's ToS to fetch too much data from their website (as it creates load)

Fortunately, good people from [kaggle](https://kaggle.com) together with Cornell University create a publicly available dataset that you can use.

The dataset is freely available via Google Cloud Storage buckets and updated weekly.



Now the main issues is - how to get only a subset of that entire dataset if we don't want to ingest over 5 terabytes of pdf files?

Dataset is divided into directories per-month, per-year, so if you'd like to get all publications from September of 2021, you could just run:

`gsutil cp -r gs://arxiv-dataset/arxiv/pdf/2109/ ./local_directory`



If you'd like to get an entire dataset:

`gsutil cp -r gs://arxiv-dataset/arxiv/pdf/  ./a_local_directory/`



But if you want to get only a subset (for a given category and dates) take a look into `download.py` file.



By default ingester is expecting this files to be at `/mnt/dataset/arxiv/pdf` with all pdf files there.



Check out and run `python scripy.py` to ingest data. You can also enable debugging there if something doesn't work.



_TODO: maybe change this to directory loader_

_TODO: implement celery deployment and use worker for ingestion_



## Query

`python cli.py`

![cli.py](./images/cli.png "image Title")



Ask the question about the topic you've fed the database before. Returns information about sources as well, runs continously.

Another option is to use REST API (run `uvicorn main:app --reload --host 0.0.0.0 --port 8000` from the `app` directory) or use it as ChatGPT plugin (after deployment)



## Deployment

There are terraform files in `deployment` directory. Use one that suits you best. There's README file in each of them with instructions.

You can also just build a Docker image and run it wherever you want. The image file is quite big though. 



### GCP

For now can be deployed as Cloud Run using docker image, so it's API only deployment. Data ingestion must be run on other machine (I do recommend GPU-enabled Compute Engines, especially if you'd like to use Hugging Face embeddings and because you can mount datase from Google Storage directly using `gcsfuse`)

Potential [solution](https://cloud.google.com/run/docs/tutorials/network-filesystems-fuse) to use GCS bucket with Cloud Run

### Azure

For now it can be deployed as Container Apps (API only deployment, you need another deployment for ingester)



### AWS

AWS is not supported yet. Coming soon.



## Embeddings



### OpenAI

arxivchat uses `text-embedding-ada-002`  for OpenAI by default, you can change that in `app/tools/factory.py`



### HuggingFace

For now you can use any model that works with [`sentence_transformers`](https://huggingface.co/sentence-transformers).

You can change the model in `app/tools/factory.py`



## ToDO

- [ ] Automount gcs arxiv bucket on deployment.

- [ ] Option to use Azure OpenAI.

- [ ] AWS deployment

- [ ] Add tests

- [ ] Automate ingesting new publications

- [ ] Add more vectostores options

- [ ] Add more embeddings options

- [ ] Support streaming responses

- [ ] Take embeddings model name from .env



## Issues & contribution

If you have any problems please use GitHub issues to report them.



## Contributing

We'd love your help in making arXivchat even better! To contribute, please follow these steps:



- Fork the repo

- Create a new branch

- Commit your changes

- Push the branch to your fork

- Create a new Pull Request



## License

arXivchat is released under the MIT License.