This RAG chatbot is a simple design for non-technical users. Done with streamlit the UI is minimalistic yet content. This is made as part of the Information Retrieval project presentation.
- Text Embedding
- Vector Indexing
- User-Friendly Replies
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
What things you need to install the software and how to install them:
Python libraries as listed in the requirements.txt file
Follow these detailed steps to get a development environment running:
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Clone the repository:
git clone https://github.com/SiddhuSiddharth/MindQuest.git cd MindQuest -
Import specific requirements:
virtualenv -p <python3.xx.xx> <virtualenvname> pip install -r requirements.txt
This was the command used to create virtual environment using specific py version. Create a virtual environment and install all libraries. For the best results we recommend you use python 3.10.
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Run the application:
streamlit run Streamlit.py
Now connect your code with the model and run it.
Navigate to http://localhost:8501/ in your web browser to access the user interface.
This project is built using the Streamlit for the UI and Python for the Models and embeddings. The embeddings function is performed using distilbert-base-uncase with the vector index database being CromeDB. The Model used here is Cohere command-xlarge-nightly.
- 20PD28 Siddharth Subramanian
- 20PD30 Sree Aditya G S
- 20PD07 Devavarapu Atchutha Manga Satya Prasad
- We thank our mentor Dr. Sridevi U K for her guidance and support in building this chatbot.