📚 Research Paper Summariser

Quickly finds latest research paper of user's chosen domain and generates summary according to user's current level of knowledge in real-time from publicly available research paper archives arXiv | Cornell University.

Demo

The arXiv API is used to search latest research paper in user's chosen domain. You can see that the LLM App enables AI-powered summary for current and latest research paper pdfs (i.e. unstructured documents) of various subjects like physics, biology, computer science, etc. and indexes input data in real-time just after files are found and stored on local storage.

How to run the tool

There are 3 ways to run the app:

Run with Conda with a Linux VM or WSL (for windows users) or simply on linux/macOS terminal (for linux/macOS users)

1. Clone the repository on your system.

2. Create a .env file with following variables:

OPENAI_API_TOKEN={OPENAI_API_KEY}

HOST=0.0.0.0

PORT=8080

EMBEDDER_LOCATOR=text-embedding-ada-002

EMBEDDING_DIMENSION=1536

MODEL_LOCATOR=gpt-3.5-turbo

MAX_TOKENS=200

TEMPERATURE=0.0

RESEARCH_PAPER_FOLDER_PATH={REPLACE_WITH_RESEARCH_PAPER_RELATIVE_PATH}

3. From the project root folder, open your terminal and run WSL if you will be using it.

4. Create a conda environment

5. Activate the conda environment

6. Install the app dependencies

Install the required packages:

pip  install  --upgrade  -r  requirements.txt

7. Run the Pathway API

You start the application by running main.py:

python  main.py

8. Run Streamlit UI

You can run the UI separately by running Streamlit app

streamlit run ui.py command. It connects to the Pathway's backend API automatically and you will see the UI frontend is running on your browser.

Run with Docker

1. Create .env file in the root directory of the project, copy and paste the below config. Replace the OPENAI_API_TOKEN configuration value with your key {OPENAI_API_KEY} and replace RESEARCH_PAPER_LOCAL_FOLDER_PATH with a path where Research Paper folder is located {REPLACE_WITH_RESEARCH_PAPER_FOLDER_PATH}. Other properties are optional to change and be default.

OPENAI_API_TOKEN={OPENAI_API_KEY}

HOST=0.0.0.0

PORT=8080

EMBEDDER_LOCATOR=text-embedding-ada-002

EMBEDDING_DIMENSION=1536

MODEL_LOCATOR=gpt-3.5-turbo

MAX_TOKENS=200

TEMPERATURE=0.0

RESEARCH_PAPER_FOLDER_PATH={REPLACE_WITH_RESEARCH_PAPER_RELATIVE_PATH}

2. From the project root folder, open your terminal and run docker compose up.

3. Navigate to localhost:8501 on your browser when docker installion is successful.

Run from the source

Prerequisites

1. Make sure that Python 3.10 or above installed on your machine.

2. Download and Install Pip to manage project packages.

3. Create an OpenAI account and generate a new API Key: To access the OpenAI API, you will need to create an API Key. You can do this by logging into the OpenAI website and navigating to the API Key management page.

Then, follow the easy steps to install and get started using the app.

Step 1: Clone the repository

Next, navigate to the project folder:

cd  Weekly-Research-Paper-Summariser

Step 2: Set environment variables

Create .env file in the root directory of the project, copy and paste the below config, and replace the {OPENAI_API_KEY} configuration value with your key.

OPENAI_API_TOKEN={OPENAI_API_KEY}

HOST=0.0.0.0

PORT=8080

EMBEDDER_LOCATOR=text-embedding-ada-002

EMBEDDING_DIMENSION=1536

MODEL_LOCATOR=gpt-3.5-turbo

MAX_TOKENS=200

TEMPERATURE=0.0

RESEARCH_PAPER_FOLDER_PATH={REPLACE_WITH_RESEARCH_PAPER_RELATIVE_PATH}

Replace RESEARCH_PAPER_FOLDER_PATH with your local Research Paper folder path and optionally, you customize other values.

Step 3 (Optional): Create a new virtual environment

Create a new virtual environment in the same folder and activate that environment:

python  -m  venv  pw-env && source  pw-env/bin/activate

Step 4: Install the app dependencies

Install the required packages:

pip  install  --upgrade  -r  requirements.txt

Step 5: Run the Pathway API

You start the application by running main.py:

python  main.py

Step 6: Run Streamlit UI

You can run the UI separately by running Streamlit app

streamlit run ui.py command. It connects to the Pathway's backend API automatically and you will see the UI frontend is running on your browser.