Create your own Medical Chatbot with Llama 2!

Table of Contents

1. Introduction
2. Overview of the Llama2 Model
3. Building a Medical Board using Llama2
4. Preprocessing the Data
5. Loading the Model
6. Creating the Prompt Template
7. Setting a Custom Prompt
8. Retrieval QA Chain
9. Setting Up Chainlit
10. Final Result

1. Introduction

In this article, we will explore how to build a medical board using the Llama2 model. Llama2 is an open-source large language model that has gained popularity in the open-source community due to its ability to run on compute-limited devices like CPUs. We will demonstrate how to take a quantized model from Hugging Face and build a custom chatbot on top of it using your own data and knowledge base.

2. Overview of the Llama2 Model

Llama2 was released by Meta AI and has been widely adopted by the open-source community. It has undergone quantization and optimizations to make it suitable for running on CPU machines. In this article, we will Show You how to use Llama2 or any other open-source language model to build your own medical board.

3. Building a Medical Board using Llama2

To build a medical board using Llama2, we first need to preprocess the data. We will use the Line Chain library for this task, which provides functions for text splitting, document loading, and more. Once the data is prepared, we can use the Sentence Transformers library to Create embeddings for our text. These embeddings will be stored in a vector store like FastText. We can then load the Llama2 model and set up a retrieval QA chain to retrieve responses from the model.

4. Preprocessing the Data

Before building the medical board, we need to preprocess the data. This involves splitting the text into chunks using a text splitter and loading the documents using a document loader. Line Chain provides various loaders for different file formats like PDF and text. Once the documents are loaded, we can create embeddings using the Sentence Transformers library.

5. Loading the Model

To load the Llama2 model, we need to use the C Transformers library, which is a Python binding for the Transformer model implemented in C++. By loading the quantized model from Hugging Face using C Transformers, we can run it efficiently on a CPU machine.

6. Creating the Prompt Template

To provide a customizable prompt for the medical board, we will create a prompt template using the Prompt Template function provided by the Line Chain library. The template will include placeholders for the Context and the user's query.

7. Setting a Custom Prompt

By setting a custom prompt for the medical board, we can personalize the response given by the model. We will use the custom prompt template function and pass the necessary input variables to create a unique prompt for each user query.

8. Retrieval QA Chain

The retrieval QA chain is responsible for retrieving the Relevant information from the knowledge base using the Llama2 model. We will set up the retrieval QA chain using the Line Chain library, specifying the model, chain Type, and retriever. This chain will handle the retrieval of answers Based on the user's query.

9. Setting Up Chainlit

To create a user-friendly interface for the medical board, we will use Chainlit, an open-source Python library. Chainlit makes it easy to build conversational interfaces on top of language models. We will use the provided decorators, such as on_chat_start, on_message, and on_settings, to handle the different stages of the conversation.

10. Final Result

By combining all the components, we can create a powerful medical board that interacts with the user and provides relevant and helpful answers based on their queries. The user can simply input their question, and the system will retrieve the necessary information from the knowledge base using the Llama2 model.

In conclusion, we have demonstrated how to build a medical board using the Llama2 model. The use of the Line Chain library, C Transformers, and Chainlit allows us to create a personalized and user-friendly conversational interface. By following the step-by-step process outlined in this article, you can create your own chatbot using Llama2 or any other large language model.

Highlights

• Build a medical board using the Llama2 model
• Preprocess data using the Line Chain library
• Load the model using C Transformers
• Create a custom prompt template for personalized responses
• Implement a retrieval QA chain to retrieve information from the knowledge base
• Set up Chainlit for a user-friendly conversational interface

FAQ

Q: What is Llama2?

A: Llama2 is an open-source large language model released by Meta AI. It has gained popularity for its ability to run on compute-limited devices like CPUs.

Q: Can I use a different language model instead of Llama2?

A: Yes, you can use any other open-source language model of your choice, such as GPT-3 or BERT.

Q: How can I customize the prompt for the medical board?

A: You can use the Prompt Template function provided by the Line Chain library to create a custom prompt that includes placeholders for the context and the user's query.

Q: Is it possible to add additional functionalities to the medical board?

A: Yes, you can add more functionalities to the medical board by extending the code provided in this article. You can explore the Line Chain and Chainlit documentation for more options.

Q: Can I deploy the medical board on a GPU machine?

A: Yes, you can deploy the medical board on a GPU machine by modifying the code accordingly and ensuring that the necessary dependencies are met.