Excergic/Acupuncture-RAG-App by Dhaivat JambudiaExcergic/Acupuncture-RAG-App by Dhaivat Jambudia

Excergic/Acupuncture-RAG-App

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Dhaivat Jambudia
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Simple RAG System

🩺 Acupuncture Clinical Advisor - Retrieval-Augmented Generation (RAG)

📌 Overview

The Acupuncture Clinical Advisor is a Retrieval-Augmented Generation (RAG) application that provides evidence-based acupuncture treatment recommendations. It utilizes LangChain, FAISS, and Groq's Llama3-70B model to analyze clinical guidelines and generate structured treatment plans.

🏗️ Project Structure

├── main.py                 # Streamlit-based user interface
├── rag_notebook.ipynb      # Notebook for processing PDFs and creating FAISS index
├── requirements.txt        # Dependencies for the project
├── faiss_index/            # FAISS vector store directory
├── Acupuncture_PDFs/       # Directory containing clinical guideline PDFs
└── README.md               # Project documentation

🚀 Features

Clinical Query Processing: Users can input acupuncture-related clinical questions.
PDF-based Knowledge Base: Extracts information from acupuncture guidelines.
FAISS Vector Store: Efficient retrieval of relevant knowledge.
LLM-powered Analysis: Uses Groq's Llama3-70B to generate professional responses.
Streamlit UI: Simple and interactive interface for clinical consultation.

📥 Installation

1️⃣ Clone the Repository

git clone https://github.com/yourusername/acupuncture-rag.git
cd acupuncture-rag

2️⃣ Create a Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

🔑 API Key Configuration

Create a .env file in the root directory and add your GROQ API key:
GROQ_API_KEY=your_api_key_here

📚 Preparing the Knowledge Base

1️⃣ Process Clinical Guidelines (PDFs)

Place your acupuncture guideline PDFs inside the Acupuncture_PDFs/ directory. Then, run the rag_notebook.ipynb script to create a FAISS vector store:
python rag_notebook.py

2️⃣ Start the Streamlit Application

streamlit run main.py

📌 Usage

Open the web UI.
Enter a clinical question related to acupuncture.
Click "Generate Treatment Plan".
View structured recommendations including:
Diagnosis criteria
Treatment protocol
Acupuncture points
Safety considerations

🛠️ Technologies Used

Python
Streamlit (UI)
LangChain (RAG pipeline)
FAISS (Vector database)
Groq Llama3-70B (LLM for response generation)
HuggingFace Sentence-Transformers (Embeddings)
PyPDFLoader (PDF processing)

📝 License

MIT License. Feel free to use and improve this project!

🤝 Contributing

If you want to contribute, fork this repository and submit a pull request. Feedback and improvements are always welcome!

📧 Contact

For any inquiries, reach out via:
X (Twitter): @dhaivat00
Instagram: dhaivatjambudia
Or open an issue on GitHub.

1 like · 9 views · Sep 15, 2025

Personal Assistant for Acupuncture Treatment for who are practicing or Studying Acupuncture and Experienced Doctors

Timeline

Aug 26, 2025 - Aug 30, 2025

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