HealO.ai -

About

The goal is to create a healthcare diagnosis chatbot that assists users by analyzing their symptoms, utilizing databases that contain real doctor-patient conversations. The solution involves leveraging task-specific datasets, Retrieval-Augmented Generation (RAG) frameworks, vector databases, and quantized LLM fine-tuning (QLoRA). The system aims to improve LLM accuracy by providing it with external accurate data, based on the user's needs and to reduce LLM hallucinations via finetuning.

Demo Video

HealO.ai Demonstration video

Libraries and Frameworks Used

• Python: Primary language used for backend
• LangChain: To generate templates for few-shot prompting
• PyTorch: Primary ML framework used
• bitsandbytes: For QLoRA fine-tuning

Steps for setting up the Project

To set-up the project, follow the below commands:

• Run python install -r requirement.txt from LLM_model folder.
• Run python3 main.py from LLM_model folder to spawn the server for interacting with LLM model.
• Run npm run from frontend folder to start the frontend.

---

To test the backend using cli:

• Run python install -r requirement.txt from LLM_model folder.
• Run python3 test_prompt_executor.py from LLM_model folder to start the cli tool for interacting with LLM model.