DAY-12 | End to End Medical Chatbot Project | Part -1

Introduction

Welcome to our Genera commentary session! In this session, we will be diving into an exciting project implementation — the N2N Medical Chatbot. Throughout this series, we will integrate various technologies you have learned, such as LangChain, a Vector Database, and the Llama 2 model. Today, we will focus on the architecture overview and perform a notebook experiment using Jupyter Notebook as a development environment.

Project Overview

The medical chatbot we are going to implement has a specific focus — it utilizes custom data sourced from a medical PDF file. This PDF will serve as our knowledge base, allowing the chatbot to provide responses strictly based on the information contained within it. While it’s possible to integrate general internet data, today’s focus will be on demonstrating how to effectively use custom data.

Architecture Overview

In today's architecture discussion, we will explore the following components:

1. Data Integration: Load and integrate a medical PDF into our system. For demonstration, we will use "The Encyclopedia of Medicine," a comprehensive medical book.

2. Data Extraction: Extract essential information from the PDF. This step is crucial as it transforms raw data into a usable format.

3. Chunking: Divide the extracted data into smaller, manageable chunks. This is necessary because our NLP model has limitations on the maximum input size (tokens), which for the Llama 2 model is 496 tokens. By chunking, we ensure that data fits within these limits while retaining context through chunk overlap.

4. Embeddings: Convert the text chunks into vector embeddings, which allows us to represent the text numerically.

5. Semantic Index: Create a semantic index, which classifies our vector data in a way that allows for quicker and more accurate retrieval during query processing.

6. Knowledge Base: Once our embeddings are prepared, we will store them in a vector database, in this case, Pinecone.

Implementation Steps

In today's notebook experiment, we will:

• Import necessary libraries such as LangChain, Pinecone, and the required PDF loader.
• Define functions to load the data and split it into chunks.
• Install and configure the embedding model.
• Create a Pinecone cluster and store our vector embeddings.
• Implement the chatbot's functionality to respond to user queries based on the processed data.

By the end of this session, you will have a firm understanding of how these components work together to create a functional medical chatbot.

Keyword

• Medical Chatbot
• N2N Medical Chatbot
• LangChain
• Vector Database
• Llama 2 Model
• Data Integration
• PDF Data Extraction
• Chunking
• Semantic Index
• Pinecone

FAQ

Q1: What is the main focus of the N2N Medical Chatbot project?
A1: The project focuses on utilizing custom data from a medical PDF to provide responses to user queries.

Q2: What technologies are being integrated in this chatbot project?
A2: The technologies include LangChain for the generative AI framework, Pinecone as the vector database, and the Llama 2 model for natural language processing.

Q3: Why is chunking important in this project?
A3: Chunking is essential because it allows us to divide the extracted data into manageable pieces, ensuring that each piece fits within the model's maximum token limit while retaining context through overlap.

Q4: How does the chatbot learn from the provided data?
A4: The chatbot learns by extracting data from the provided PDF, creating embeddings for that data, and storing them in a vector database, which allows it to retrieve relevant information in response to user queries.

Q5: What will be covered in the next session?
A5: The next session will focus on converting the notebook implementation into modular coding and developing a web app interface for the chatbot.

Thank you for joining Day 12 of our project series. We hope you found this session informative and are excited for the next part of the implementation!