README.md

metadata

title: LISA Demo
emoji: ⚡
colorFrom: yellow
colorTo: red
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: false
startup_duration_timeout: 2h

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

LISA (Lithium Ion Solid-state Assistant) is a question-and-answer (Q&A) research assistant designed for efficient knowledge management with a primary focus on battery science, yet versatile enough to support broader scientific domains. Built on a Retrieval-Augmented Generation (RAG) architecture, LISA uses advanced Large Language Models (LLMs) to provide reliable, detailed answers to research questions.

DEMO: https://huggingface.co/spaces/Kadi-IAM/LISA

Installation

1. Clone the Repository:

   git clone "link of this repo"
   cd LISA
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Set enviroment variables (Huggingface for downloading model weights, Tavily for web search and Groq for LLMs):

   export HUGGINGFACEHUB_API_TOKEN=your_api_key_here
   export TAVILY_API_KEY=your_api_key_here
   export GROQ_API_KEY=your_api_key_here
   

4. Set Up the Knowledge Base Populate the knowledge base with relevant documents or research papers. Ensure that documents are in a format (pdf or xml) compatible with the RAG pipeline. By default documents should be located at data/documents. After running the following comand, some caches files are saved into data/db. ATTENTION: pickle is used to save these caches, be careful with potential security risks.

   python preprocess_documents.py
   

5. Running LISA Once setup is complete, run the following command to launch LISA:

   python app.py
   

About

For more information on our work in intelligent research data management systems, please visit KadiAI.