Build and Run a Medical Chatbot using Llama 2 on CPU Machine: All Open Source

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The better you write prompts, the better responses you will get.

Key Ideas

• 🤖 Build a medical chatbot using Llama 2, leveraging open-source tools like LangChain and Chainlit for retrieval and response generation.

• 💻 The chatbot runs on CPU machines using a quantized model from Hugging Face, making it accessible without GPU resources.

• 📚 The knowledge base is derived from the Gale Encyclopedia of Medicine, with embeddings created using Sentence Transformers.

• 🏗️ The architecture involves preprocessing data, creating embeddings, storing them in a vector database, and using retrieval chains for responses.

• 🧠 C Transformers are used to load the quantized model, and vector stores like Chroma DB or Qdrant handle embeddings with similarity algorithms.

• ✍️ Custom prompt templates are developed to ensure accurate and relevant responses from the chatbot.

• 🔍 The chatbot uses a retrieval QA chain to source responses from the knowledge base, ensuring accuracy by returning source documents.

• 💬 Chainlit is used to build the conversational interface, manage user interactions, and handle asynchronous calls.

• 🖥️ The chatbot interface is built with Bootstrap and Chainlit in FastAPI, allowing for conversational retrieval memory and follow-up questions.

• 🔧 The chatbot can run locally on CPU machines, with the entire code available on GitHub for users to access and customize.

📃 Video Mini Summary

TL;DR

💨 The video shows how to build a medical chatbot using Llama 2 on a CPU machine. It utilizes a quantized model from Hugging Face, with data sourced from the Gale Encyclopedia of Medicine.

The chatbot is constructed with LangChain, Chainlit, and Sentence Transformers for embeddings, which are stored in a vector database. It operates on Ubuntu/Linux with a minimum of 13 GB RAM. The interface is powered by Chainlit and FastAPI, allowing for conversational memory and retrieval QA. The code is available on GitHub.

Building a Medical Chatbot with Llama 2 and Open-Source Tools

🤖 The video demonstrates how to build a medical chatbot using Llama 2. The chatbot is powered by LangChain for retrieval and Chainlit for the conversational interface. The chatbot is designed to answer medical queries based on a custom knowledge base, which is created from a large medical document. The process involves setting up a simple interface where users can ask questions, and the chatbot retrieves relevant information from the knowledge base.

Running on CPU with Quantized Models

💻 The chatbot runs on CPU machines using a quantized model from Hugging Face, making it accessible for users without GPU resources. The quantized model used is Llama 2 7B, which has been optimized to run efficiently on limited hardware. This allows users to deploy the chatbot on their own infrastructure without relying on external APIs or expensive hardware.

"Not everybody owns a GPU or VRAM, right? Not everybody has that."

Knowledge Base from Gale Encyclopedia of Medicine

📚 The knowledge base for the chatbot is built from the Gale Encyclopedia of Medicine, a 600+ page document. The data is processed into embeddings using Sentence Transformers, specifically the all-MiniLM-L6-v2 model. These embeddings are then used to retrieve relevant information when users ask questions.

Architecture: Preprocessing, Embeddings, and Retrieval

🏗️ The architecture involves several key steps:

1. Preprocessing the data using LangChain loaders and splitters.
2. Creating embeddings with Sentence Transformers.
3. Storing the embeddings in a vector database.
4. Using retrieval chains to fetch responses based on user queries.

This setup ensures that the chatbot can efficiently retrieve and return accurate information from the knowledge base.

C Transformers and Vector Stores for Efficient Retrieval

🧠 To load the quantized model, the video uses C Transformers, which are Python bindings for models implemented in C/C++. For storing embeddings, vector stores like Chroma DB or Qdrant are mentioned, though the video primarily uses FAISS for this project. These vector stores use similarity algorithms like cosine similarity to match user queries with relevant data.

Custom Prompt Templates for Accurate Responses

✍️ A custom prompt template is developed to ensure that the chatbot provides accurate and relevant responses. The prompt instructs the model to use specific pieces of information from the knowledge base and avoid making up answers if it doesn't know something. This helps maintain the reliability of the responses.

"If you don't know the answer, just say that you don't know."

Retrieval QA Chain Ensures Source Accuracy

🔍 The chatbot uses a retrieval QA chain, which ensures that responses are sourced directly from the knowledge base. This chain retrieves documents from the vector store and passes them through the language model to generate answers. The chatbot also returns the source documents alongside the answers, ensuring transparency and accuracy.

Chainlit Powers the Conversational Interface

💬 The conversational interface is built using Chainlit, which allows for easy management of user interactions and asynchronous calls. Chainlit provides features like tracking function calls (e.g., retrieval QA, document chains) and managing user sessions, making it ideal for building chatbots that require real-time interaction.

FastAPI and Bootstrap for Enhanced User Experience

🖥️ The chatbot interface is built using Bootstrap and integrated with FastAPI, allowing for a smooth user experience. The interface supports conversational retrieval memory, enabling users to ask follow-up questions based on previous interactions. This makes the chatbot more interactive and user-friendly.

Local Deployment with Customizable Code

🔧 The entire chatbot can be run locally on a CPU machine, with all code available on GitHub for users to access and customize. This makes it easy for developers to adapt the chatbot to their own needs, whether they want to use different data sources or modify the interface.

Conclusion

🌚 The chatbot is designed to run on CPU machines, making it accessible to users without GPUs. It employs quantized models, embeddings, and vector stores to effectively process medical queries.

The system is built with open-source tools like LangChain and Chainlit, offering a conversational interface that retains memory for faster responses. The chatbot's performance depends on CPU speed, and the code is available for customization. Future improvements will address hallucinations and privacy concerns.