/grain-brain

Primary LanguagePython

GrainBrain

Overview

GrainBrain is a chatbot project designed to provide answers based on scientific articles. It uses a mix of advanced NLP techniques and AI models to process, understand, and retrieve information from a variety of text documents. The project uses several technologies, including Streamlit for the user interface, gpt4all for language model, the LangChain framework for text processing, and FAISS for vector storage.

Getting Started

  1. Clone the repository:

    git clone https://github.com/nikogamulin/grain-brain

  2. Download model files: The model files can be downloaded from the provided link. After downloading, place the model files into the models folder in your local clone of the repository.

  3. Install Dependencies: Install all the required dependencies for the project. It is recommended to create a virtual environment first.

    python -m venv env source env/bin/activate # On Windows use `env\Scripts\activate` pip install -r requirements.txt

Usage

  1. Add your data: Put your PDF documents (articles or other body of knowledge) into the data/raw/articles folder.

  2. Parse the articles: Run articles_pdf_parser.py to transform the articles into .txt files.

    python articles_pdf_parser.py

    This will create text files from your PDFs and store them in data/raw/articles_txt.

  3. Build FAISS vector store: After generating text files, run faiss_vectorstore_builder.py to create the index.

    python faiss_vectorstore_builder.py

  4. Run the application: After the index has been built, run the Streamlit application.

    streamlit run app.py

Notes

Please note that index files are not included due to article sharing restrictions.

Contributing

If you would like to contribute, please fork the repository and use a feature branch. Pull requests are warmly welcome.

Links

Licensing

The code in this project is licensed under MIT license.