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

Summiz Holo

Developing a CPU-based medical chatbot with Llama 2 and LangChain

• The video demonstrates how to develop a medical chatbot using the Llama 2 model released by Meta AI, focusing on running it on CPU machines.
• The open-source community has worked on quantization and optimizations to make Llama 2 accessible for compute-limited devices.
• The chatbot is built using a quantized model from Hugging Face and incorporates a knowledge base, specifically the Gale Encyclopedia of Medicine.
• The video utilizes frameworks like LangChain and Chainlit to facilitate the retrieval of information and create a conversational interface.
• The chatbot operates without relying on external APIs, allowing for private data handling within a user's infrastructure.
• The video emphasizes a hands-on coding approach, with the intention of guiding viewers through the development process step-by-step.

Building a Medical Chatbot with Llama 2 on CPU Machines

• The video demonstrates how to build and run a medical chatbot using the Llama 2 model on a CPU machine, emphasizing that it can be done on various operating systems, including Ubuntu and Windows.
• The minimal requirements to run the bot include downloading a specific model, the Llama 2 7B chat ggml q80 pin, which is essential for the application.
• The importance of using a quantized model for running on CPU machines is highlighted, as it allows for efficient performance without requiring excessive RAM.
• The process involves using C Transformers, a Python binding in C/C++, to load the model from Hugging Face, which is necessary for CPU compatibility.
• The architecture of the chatbot includes data preprocessing using LangChain, embedding generation with Sentence Transformers, and storage of embeddings in a vector database.
• Various vector stores are mentioned, including Chroma DB and FAISS, with a preference for open-source solutions in the context of the project.
• The workflow involves user prompts being processed through the vector store and the LLM to generate responses, illustrating the interaction between components in the system.

Medical chatbot architecture using vector stores and quantized models

• The architecture of the medical chatbot relies on vector stores and inbuilt similarity algorithms like cosine similarity for efficient data processing without latency issues.
• The implementation involves using a quantized model (gamma 2 ggml) and loading it through C Transformers, rather than traditional Transformers.
• Sentence Transformers are utilized for generating embeddings, which are essential for the vector store functionality.
• The process includes loading documents from various formats (e.g., PDF) using document loaders and splitting text into manageable chunks with a recursive character text splitter.
• The code structure involves defining a data path for storing embeddings and creating a vector database to manage the loaded documents and their embeddings.

Embedding generation, chatbot model creation, and LangChain integration

• The process involves using a sentence transformer to create embeddings from text and storing them in a vector database.
• The script ingest.py is executed to generate embeddings and save them locally in a folder.
• The creation of a model file (model.py) is necessary to write the code for the chatbot.
• Various imports from the LangChain library are utilized, including prompt templates and embeddings.
• The implementation of a retrieval chain is discussed, highlighting the importance of chat history in conversational AI.
• A custom prompt template is created to guide the chatbot's responses, emphasizing the need for accurate answers.

Custom prompt templates, Langchain integration, and LLM performance optimization

• Definition and creation of a custom prompt template for QA retrieval using vector stores.
• Utilization of the Langchain library to implement the prompt template function.
• Loading of the large language model (LLM) using C Transformers for enhanced performance.
• Specification of model parameters such as max new tokens and temperature during model loading.
• Implementation of a retrieval QA chain that integrates the LLM with a database for information retrieval.
• Emphasis on returning source documents from the knowledge fed to the system rather than relying solely on the LLM's base knowledge.
• Importance of crafting effective prompts to improve the quality of responses from the system.

Integrating QA Bots with Retrieval Chains and Local Database Functions

• The process involves defining a QA bot using embeddings and integrating it with a retrieval QA chain.
• Functions are created to load local databases and embeddings, utilizing a language model (LM) and a custom QA prompt.
• The final output parsing function is designed to handle user queries and return responses.
• The Chainlit framework is introduced as a powerful open-source Python package for building and saving LLM applications with a conversational interface.
• The chat start function initializes the QA bot and sends a welcome message to the user, prompting for their query.

LLM-based chatbot with decorators for asynchronous medical query handling

• Building an LLM-based chatbot provides an interface and tracking mechanism for function calls without extensive coding.
• The implementation involves using decorators and asynchronous functions to handle messages and responses.
• The chatbot utilizes a callback handler to stream final answers and manage user interactions.
• The system distinguishes between answers with and without source documents, adjusting responses accordingly.
• The chatbot is designed to run on a CPU machine using Chainlit, facilitating a conversational interface for medical queries.

Bootstrap and FastAPI medical chatbot with retrieval memory and UI modes

• The interface for the medical chatbot is built using Bootstrap and powered by FastAPI, with Chainlit being utilized for the first time.
• The chatbot maintains a memory of previous questions, allowing for faster responses as it does not need to query the model again.
• The chatbot can operate in dark mode and light mode, providing user interface customization options.
• The system uses a retrieval-based question-answering (QA) approach, leveraging embeddings from the Llama 2 model.
• The performance of the chatbot may vary based on CPU processing time and internet speed, with responses potentially taking one to two minutes.
• The chatbot allows for tracking of function calls and the processes being executed in the background.
• Users can ask follow-up questions and utilize conversational retrieval memory for enhanced interaction.

Customizable Llama 2 conversational interface for healthcare response accuracy

• The video discusses building a conversational interface using Llama 2 and C Transformers, highlighting its capabilities for generating responses and managing tasks.
• Users can customize the interface, including changing names and settings like dark mode.
• The model can be run locally on CPU machines, making it accessible for users with limited computing power.
• The video emphasizes the importance of the knowledge fed into the system for generating accurate responses, particularly in a healthcare context.
• Future videos will address controlling hallucinations and ensuring data protection and privacy in large language model applications.