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

Summiz Holo

Development of a medical chatbot using Llama 2 model, open-source quantization for CPU accessibility, embedding creation from Gale Encyclopedia of Medicine, and hands-on coding with Chainlit

• Development of a medical chatbot using the Llama 2 model, released by Meta AI.
• The open-source community has worked on quantization to enable Llama 2 to run on CPU machines.
• The video demonstrates how to build a chatbot from scratch using a quantized model from Hugging Face.
• The knowledge base for the chatbot is derived from the Gale Encyclopedia of Medicine, which has over 600 pages.
• The creation of embeddings from the knowledge base is facilitated by an open-source embedding model.
• The use of frameworks like Langchain and Chainlit to enable retrieval and response generation in the chatbot.
• The chatbot can run on CPU machines, making it accessible for users without GPU resources.
• Chainlit is introduced as a tool for creating conversational interfaces and chatbots easily.
• The coding process will be emphasized over talking, with a focus on hands-on application development.
• The GitHub repository with code samples will be provided for users to follow along.

Building a Medical Chatbot on Ubuntu/Linux with llama27b Model, C Transformers, Sentence Transformers, and Vector Store Integration

• You can use any operating system to build and run the medical chatbot, but the speaker is using Ubuntu/Linux.
• Minimal requirements to build and run the bot include a model download, with specific mention of needing at least 13 GB of RAM.
• The model to download is llama27b chat ggml q80 pin, credited to Tom Jobbins and available on Hugging Face.
• To run the model on a CPU machine, a quantized model is required, as the standard model from Meta AI won't work.
• C Transformers is necessary for loading the model from Hugging Face, as it provides Python bindings for the Transformer model implemented in C/C++.
• Sentence Transformers will be used for creating embeddings in the chatbot workflow.
• A vector store is needed to store the embeddings, with options like Chroma DB, Fast, and Qdrant available.
• The architecture involves preprocessing data using LangChain, passing it to an embedding model, storing in a vector database, and responding to user prompts.

High-level architecture utilizing vector stores, cosine similarity, Levenshtein algorithms, gamma 2 ggml model, C Transformers, LangChain, document loaders for PDFs, and recursive text splitting

• The architecture discussed is high level and focuses on vector stores and inbuilt similarity algorithms like cosine similarity and Levenshtein.
• Vector stores have features that include no latency issues and the ability to handle metadata.
• The process involves using a quantized model, specifically the gamma 2 ggml model, with C Transformers for loading.
• Sentence Transformers are used for creating embeddings which are passed to the model with the help of LangChain.
• The implementation includes using document loaders for various formats, particularly PDF, and a recursive character text splitter for processing text.
• Hugging Face embeddings can be used for generating embeddings, but there may be compatibility issues, which can be addressed by switching to sentence Transformers.
• The coding process includes defining paths for data and vector stores, and creating a vector database through a directory loader.

Embedding model integration with Sentence Transformer, local vector storage in DB fast path, Python script ingestion of PDF data, LangChain chatbot development with custom prompt templates and retrieval chains

• The process involves using an embedding model from Sentence Transformer to create vectors and store them locally in a vector store, specifically in a folder labeled 'DB fast path.'
• The code for ingesting data is executed with a Python script named 'ingest.py,' which generates embeddings from a large PDF file.
• A model file named 'model.py' is created to write the code for the chatbot, utilizing LangChain for various functionalities.
• Custom prompt templates are being developed to structure the responses of the chatbot, emphasizing accuracy and relevance in answering user questions.
• The integration of various components from LangChain, including retrieval chains and language models, is critical for the chatbot's functionality.

Custom prompt definition for QA retrieval using Langchain, context importance for QA effectiveness, Llama model parameters with C Transformers for performance, retrieval QA chain for sourced responses, and source document accuracy

• Define a custom prompt for QA retrieval using a prompt template function from Langchain.
• The context from the knowledge base is essential for the effectiveness of the QA system.
• Loading the large language model (LLM) using C Transformers for better performance.
• The model type being used is Llama, with specific parameters like max new tokens and temperature.
• The retrieval QA chain is established to ensure responses are sourced from the provided knowledge documents.
• The importance of returning source documents to provide accurate information based on the fed knowledge.

Defining a QA bot with embeddings, integrating custom prompts, utilizing local database loading, and implementing output parsing in Chainlit framework

• The process involves defining a QA bot using embeddings and writing additional functions to support this.
• The retrieval QA chain combines functions and utilizes local database loading.
• A custom QA prompt is integrated into the functions to handle user queries more effectively.
• The implementation of a final output parsing function for user queries is discussed.
• The Chainlit framework is introduced as a powerful tool for building and saving LLM apps with a conversational interface.
• The chat start function initializes the QA bot and manages user interaction through message updates.

Building LLM-based bots with asynchronous message handling, Chainlit interaction, and user session management

• Building LLM-based bots provides an interface and tracking mechanism without extensive coding.
• The use of decorators in the code for message handling and callbacks is emphasized.
• The implementation involves asynchronous calls and handling of user sessions.
• The chatbot utilizes Chainlit for interaction and response management.
• There are conditions to handle the presence or absence of source documents in responses.
• The final output is presented on a user interface, engaging users with a conversational format.
• The process includes running the model using Chainlit on a CPU machine.

FastAPI medical chatbot interface utilizing Bootstrap and ChainLit for conversational memory, retrieval QA, and Llama 2 model with AVX2 support

• An interface built with Bootstrap and powered by ChainLit in FastAPI is being used for the medical chatbot.
• ChainLit allows for a conversational interface and retains previous questions in memory, leading to faster responses.
• The chatbot utilizes retrieval QA to provide answers based on a knowledge base created from embeddings.
• The system can track function calls and the processes running in the background, such as the LM chain.
• The chatbot's performance may vary based on CPU speed and internet connectivity, with responses potentially taking one to two minutes.
• The model is sourced from Llama 2 and enhanced with AVX2 support for faster loading.
• Users can ask follow-up questions and utilize conversational retrieval memory for ongoing queries.

Conversational interface customization with local CPU execution, data management, and GitHub accessibility for medical query demonstrations and hallucination control

• Using stuff documents chain and LLM chain to build a conversational interface.
• Ability to customize the interface, such as changing names in the taskbar.
• Option to clean sources and manage data presentation settings.
• The model can be run locally on CPU machines, catering to users with limited compute power.
• The entire code will be available on GitHub for users to access.
• The demonstration showcases the model's output regarding medical queries, such as treatment for infected Bartholin's gland.
• Future videos will address controlling hallucinations and data protection/privacy in LLM applications.