paper-summariser

Summarises and queries research papers using Retrieval Augmented Generation using LLAMA2.

retriever module

• Language Model: Llama2
• VectorDB: ChromaDB
• Chains: Langchain
• File format: PDF

cli usage

cd retriver
python cli.py -f file_path -pdir persist_dir 
# -pdir is optional and only needed if you want to store the 
# vectors on disk

The cli provides a shell to run queries via langchain. Type /exit to quit the shell.

REST api

cd retriever
uvicorn api:app --host 0.0.0.0 --port 8000

Endpoints

• / : root
• /upload : upload file as multipart POST request
• /query: send query as json, POST

For detailed API docs, check http://0.0.0.0:8000/docs.

web frontend

cd web
npm install
npm run start

The web frontend runs on http://localhost:4200.

Docker

sudo docker compose up
# or, docker compose up
# depending on your OS

Due to image definition, the ollama container does not recognise the ollama command, which one would otherwise use to pull models. To circumvent this issue, run the pull_llama2.sh script once docker compose is done building images and is running all the services.

chmod +x scripts/pull_llama2.sh
./scripts/pull_llama2.sh
# this will take a while depending on your internet connection

Once up and running, you can access the web service as usual at http://localhost:4200. The REST api can be accessed at http://localhost:9000. If you wish to change these port numbers, edit the docker-compose.yml file accordingly.

env setup

conda

cd retriever
conda env create -f environment.yml

venv

cd retriever
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

TODO

• Keep a history of documents in the vector database
• Show error message in the web ui instead of File has been uploaded
• A progress bar while embeddings are being loaded, it works fast on GPUs but for CPUs, takes a while
• Pass API url to web as an ENV var. Currently it's hard coded.