Building a Medical RAG Chatbot with BioMistral LLM!
Heerthi Raja H
Computer Vision | CV/Robotics Enthusiast | Sharing my lessons | Learning and building in public!
Building a Medical RAG Chatbot with BioMistral LLM: A Step-by-Step Guide
Generative AI and Retrieval-Augmented Generation (RAG) are transforming the way we process information. I recently built a Medical RAG Chatbot powered by the BioMistral Open Source LLM. The chatbot uses a heart health document as its knowledge base to provide accurate, domain-specific answers to user queries. Here's a detailed walkthrough of how I designed and implemented this project.
Project Overview
The Medical RAG Chatbot is designed to answer queries related to heart health by retrieving the most relevant information from a medical PDF and generating human-like responses using a language model. The integration of a retriever (for document search) and an open-source LLM ensures the chatbot provides accurate and context-aware answers.
Step 1: Setting Up the Environment
The first step involves setting up Google Colab and installing the necessary libraries.
1.1 Mounting Google Drive
I stored the dataset (heart health PDF) and the BioMistral model in Google Drive. To access these files in Colab, I mounted the drive:
1.2 Installing Required Libraries
The project requires libraries such as LangChain for chaining components, Sentence Transformers for embeddings, and ChromaDB for storing vectorized data.
Step 2: Loading and Preparing the Data
The heart health document was processed to extract text and split it into manageable chunks for retrieval.
2.1 Loading the PDF
Using LangChain's PyPDFDirectoryLoader, I loaded the heart health PDF from Google Drive.
The docs variable contains the extracted text, with each document representing a page from the PDF.
2.2 Splitting the Text
To ensure efficient retrieval, the text was split into smaller, overlapping chunks using a text splitter.
Step 3: Creating the Vector Store
The vector store is a database that stores embeddings (numerical representations of text) for similarity searches.
3.1 Generating Embeddings
I used PubMedBERT from Sentence Transformers to generate domain-specific embeddings for the text.
3.2 Building the Vector Store
The embeddings were stored in ChromaDB, a high-performance vector database.
3.3 Testing the Search
To validate the setup, I queried the vector store to retrieve relevant chunks of text.
This step ensures the vector store retrieves the correct context for any user query.
Step 4: Loading the BioMistral LLM
The BioMistral-7B LLM was used for generating responses. This open-source model is lightweight enough to run on Google Colab with a T4 GPU.
Key parameters:
Step 5: Integrating the RAG Chain
To combine retrieval and generation, I used LangChain's RetrievalQA mechanism.
5.1 Building the Chain
A custom prompt was designed to guide the model's responses.
The final RAG Chain links the retriever, prompt, and LLM:
Step 6: Building the Chat Interface
To make the chatbot interactive, I implemented a simple command-line interface.
This interface allows users to ask medical questions, retrieve context from the PDF, and receive responses generated by the BioMistral LLM.
Sample Interactions
Query: What are the diseases that affect heart health?
Answer: High blood pressure, coronary artery disease, congestive heart failure, arrhythmia, and cardiomyopathy.
Query: What are the preventive measures?
Answer: Regular hand washing, avoiding close contact with sick individuals, staying informed about public health updates, and maintaining a healthy lifestyle.
PC - data science basics
Key Takeaways
This project showcases the potential of RAG in creating practical, real-world applications. Here’s what I learned:
This chatbot is a step toward leveraging AI for reliable medical assistance. If you’re interested in building something similar, start small, and experiment with different datasets and LLMs. Happy coding!
That's about it for this article.
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Wishing you good health and a prosperous journey into the world of AI!
Best regards,
Heerthi Raja H