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--- |
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title: LISA Demo |
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emoji: ⚡ |
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colorFrom: yellow |
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colorTo: red |
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sdk: gradio |
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sdk_version: 4.44.1 |
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app_file: app.py |
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pinned: false |
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startup_duration_timeout: 2h |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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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. |
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DEMO: https://huggingface.co/spaces/Kadi-IAM/LISA |
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### Installation |
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1. Clone the Repository: |
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```bash |
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git clone "link of this repo" |
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cd LISA |
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``` |
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2. Install Dependencies: |
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```bash |
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pip install -r requirements.txt |
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``` |
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3. Set enviroment variables (Huggingface for downloading model weights, Tavily for web search and Groq for LLMs): |
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```bash |
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export HUGGINGFACEHUB_API_TOKEN=your_api_key_here |
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export TAVILY_API_KEY=your_api_key_here |
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export GROQ_API_KEY=your_api_key_here |
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``` |
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4. Set Up the Knowledge Base |
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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. |
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```bash |
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python preprocess_documents.py |
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``` |
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4. Running LISA |
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Once setup is complete, run the following command to launch LISA: |
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```bash |
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python app.py |
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``` |
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### About |
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For more information on our work in intelligent research data management systems, please visit [KadiAI](https://kadi.iam.kit.edu/kadi-ai). |
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