This project is an AI-powered Medical Chatbot designed to provide medical information and assistance. It combines Retrieval-Augmented Generation (RAG) techniques, a pre-trained large language model (Phi-3-mini-128k-instruct), and a custom knowledge base to generate relevant and context-specific answers to user queries.
- Retrieval-Augmented Generation (RAG): Integrates a knowledge base for more accurate and contextually relevant answers.
- FastAPI Backend: A fast and efficient API to process user queries and deliver responses.
- Customizable Knowledge Base: Create and manage a domain-specific knowledge base using LangChain.
- AI Model Integration: Utilizes
microsoft/Phi-3-mini-128k-instructfor text generation. - Interactive Query Handling: Users can submit medical-related questions and receive precise answers.
- Histogram Analysis: Visualizes document token lengths for optimized chunking.
- LLM: Microsoft’s
phi-3-mini-128k-instruct - RAG Framework: LangChain for vector search & prompt formatting
- API: FastAPI (Python)
- Frontend: React.js
- Embedding: HuggingFace or OpenAI embeddings
- Vector Store: FAISS / ChromaDB (pluggable)
- Text data is preprocessed using LangChain's Document and split into chunks for efficient retrieval using the
RecursiveCharacterTextSplitter.
- A user query is matched with relevant chunks from the knowledge base using vector similarity search (e.g.,
KNOWLEDGE_VECTOR_DATABASE.similarity_search).
- The retrieved context and user query are formatted into a prompt using a pre-defined RAG template.
- The formatted prompt is passed to the Phi-3 model to generate a concise and contextually relevant answer.
- The FastAPI backend processes incoming user queries, retrieves context, formats the RAG prompt, and returns the AI-generated response.
This project was bootstrapped with Create React App.
In the project directory, you can run:
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Open http://localhost:3000 to view it in your browser.
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See the section about running tests for more information.
Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.
The build is minified and the filenames include the hashes.
Your app is ready to be deployed!
See the section about deployment for more information.
Note: this is a one-way operation. Once you eject, you can't go back!
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cccde44 (Initial commit for Medical Chatbot project)