Build a Medical RAG App using BioMistral, Qdrant, and Llama.cpp

AI Anytime
21 Feb 202434:05

TLDRIn this AI Anytime video, the host guides viewers through the process of building a Medical RAG (Retrieval Augmented Generation) application using BioMistral, a specialized 7 billion parameter medical language model. The tutorial covers selecting a domain-specific embedding model, utilizing Qdrant as a self-hosted vector database, and employing Llama.cpp for CPU-based orchestration. The video also includes a live demo of the app retrieving information from medical documents and generating human-like responses, emphasizing privacy and the avoidance of third-party data sharing.

Takeaways

  • 😀 The video introduces a Retrieval-Augmented Generation (RAG) app for the medical domain, leveraging the BioMistral model.
  • 🔬 BioMistral is a 7 billion parameter medical domain-specific language model that has shown promising results compared to larger models.
  • 📚 The model is particularly suitable for medical data, as it's trained on PubMed Central data, emphasizing the importance of domain-specific models.
  • 💡 The video emphasizes the need to choose domain-specific embedding models, such as the PubMed B embedding model, for optimal performance in specialized domains.
  • 🛠️ Lama CPP is used for model inference, allowing for CPU-based operations and making it accessible to a broader audience without GPU requirements.
  • 🌐 Qdrant is chosen as the self-hosted vector database, allowing for local and private data storage, which is crucial for handling sensitive medical information.
  • 🔍 The app demonstrates the process of retrieving relevant medical information from documents using a combination of vector databases and language models.
  • 📝 The script outlines a step-by-step guide on building the RAG app, from selecting the right models to setting up the infrastructure and coding the application.
  • 🔗 The video mentions the availability of the code on GitHub, encouraging viewers to access, modify, and use the project for their own purposes.
  • 🔎 The video concludes with a live demo of the app, showcasing its ability to retrieve and generate responses to medical queries, emphasizing the practical application of the discussed technology.

Q & A

  • What is the purpose of the video?

    -The purpose of the video is to guide viewers on how to implement a Retrieval-Augmented Generation (RAG) application for the medical domain using BioMistral, Qdrant, and LangChain.

  • What is BioMistral and why is it significant for this project?

    -BioMistral is a large language model specific to the medical domain, with 7 billion parameters. It is significant for this project because it has been trained on PubMed Central data, making it suitable for medical domain-specific tasks.

  • Why is the presenter advising against comparing BioMistral with larger models?

    -The presenter advises against comparing BioMistral with larger models because they serve different purposes. While larger models are general-purpose, BioMistral is fine-tuned for specific tasks in the medical domain, making direct comparisons less relevant.

  • What role does the embedding model play in this project?

    -The embedding model, in this case, PubmedBERT, is used to create vectors of the documents. These vectors are then used by the retrieval system to find relevant information from the medical texts.

  • Why is Qdrant chosen as the vector database for this application?

    -Qdrant is chosen as the vector database because it is a self-hosted, open-source solution that allows for local deployment, ensuring data privacy and control over the infrastructure.

  • What is the advantage of using LangChain and Llama.cpp for this project?

    -LangChain and Llama.cpp are used for orchestration and inference, respectively. They allow for efficient handling of the RAG process and enable the application to run on CPU, which can be beneficial for cost and accessibility reasons.

  • How does the presenter demonstrate the functionality of the biomedical RAG app?

    -The presenter demonstrates the functionality by asking questions related to medical topics, such as 'tell me about motor symptom management,' and showing how the app retrieves and generates responses using the underlying technology stack.

  • What are the system requirements for running the RAG app as described in the video?

    -The system requirements include having a model like BioMistral, an embedding model like PubmedBERT, a vector database like Qdrant, and the necessary software stack including Docker for Qdrant, and Python with the required libraries for LangChain and Llama.cpp.

  • How does the video address the issue of data privacy and security in the context of the RAG app?

    -The video emphasizes the importance of data privacy and security by showcasing a self-hosted solution where the data remains on-premise, and no sensitive information is shared with third parties.

  • What are the potential use cases for the biomedical RAG app discussed in the video?

    -Potential use cases for the biomedical RAG app include building chatbots for retrieving medical literature, assisting in disease symptom management, and providing information on various health-related topics.

Outlines

00:00

🚀 Introduction to Building a Domain-Specific RAG App

The video introduces a project to build a Retrieval-Augmented Generation (RAG) app for the medical domain using a new large language model called Biom Mistral. This model, released recently, is specialized for medical data and has 7 billion parameters. It performs well compared to other models, including GPT-3.5 Turbo. The video aims to guide viewers through the process of building a domain-specific RAG app using open-source technology, emphasizing the importance of selecting the right embedding model and the benefits of using a domain-specific model like Biom Mistral, which has been trained on PubMed Central data.

05:01

🛠️ Setting Up the Technical Stack for the RAG App

The video explains the technical setup for the RAG app, which includes using the PermID embedding model due to its fine-tuning on medical literature, and the self-hosted vector database Qand. The presenter demonstrates how to set up the application locally using open-source tools and frameworks like Lang Chain and Lama CPP, ensuring data privacy and reducing costs. A live demo is provided, showing the app's ability to retrieve information from medical documents and generate human-like responses.

10:03

💻 Coding the RAG App: Ingesting Documents and Creating Vectors

The presenter walks through the coding process for the RAG app, starting with ingesting documents and creating vectors using the sentence Transformers library. The video covers the installation of Docker and setting up the Qand vector database. It also discusses the choice of embedding models, emphasizing the importance of domain-specific models for tasks like contract management or healthcare. The process of defining the embedding model, setting up the URL for the vector database, and creating the vectors is detailed.

15:04

🌐 Building the FastAPI Backend for the RAG App

The video continues with the development of the FastAPI backend for the RAG app. It covers the initialization of the FastAPI app, the setup of static and templates folders, and the creation of HTML templates for the user interface. The presenter also discusses the use of the Fetch technique in vanilla JavaScript for the front-end interaction and the structure of the app.py file, which includes the definition of API endpoints and the integration of the large language model for inference.

20:04

🔍 Implementing Retrieval and Inference in the RAG App

The presenter details the implementation of the retrieval and inference mechanisms in the RAG app. This includes defining the local path for the Biom Mistral model, setting up the Lama CPP tool for faster inference, and creating a prompt template for the model to generate responses. The video also covers the configuration of the retriever algorithm to connect with the Qand vector database and retrieve relevant document chunks based on user queries.

25:05

📝 Testing the RAG App and Demonstrating Its Capabilities

The video concludes with a live test of the RAG app, showcasing its ability to answer questions about medical topics using the configured models and databases. The presenter asks questions related to HIV antibody tests and cancer categories, demonstrating the app's retrieval and synthesis of information from medical documents. The video highlights the importance of context and source document visibility for traceability and concludes with a call to action for feedback and further exploration of the project on GitHub.

Mindmap

Keywords

💡RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is a machine learning paradigm that combines the capabilities of retrieval and generative models. In the context of the video, RAG is used to develop an application for the medical domain. The retrieval component is responsible for finding relevant information from a dataset, while the generative component uses this information to create human-like responses. The video demonstrates how RAG can be implemented using a specific large language model tailored for medical information.

💡BioMistral

BioMistral is a large language model (LLM) mentioned in the video that is specialized for the medical domain. With 7 billion parameters, it is designed to handle and generate medical-related content. The video discusses how BioMistral outperforms other models in specific medical tasks, showcasing its effectiveness in a RAG application for retrieving and generating medical information.

💡PubMed

PubMed is a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. In the video, models like BioMistral are trained on data from PubMed Central, which is why the script mentions relying on PubMed B embedding models. These models are fine-tuned to better understand medical nomenclature, making them suitable for tasks like generating responses to medical queries.

💡Llama.cpp

Llama.cpp is a tool used in the video for orchestrating the interaction between different components of the RAG application. It is part of the LangChain ecosystem and is used to manage the workflow, including the retrieval of information from the vector database and the generation of responses by the LLM. The video emphasizes using Llama.cpp for CPU-based operations, indicating its efficiency in handling LLM inference without the need for GPU acceleration.

💡Qdrant

Qdrant is a self-hosted vector database mentioned in the video, which is used to store and manage the vectors generated from medical documents. It plays a crucial role in the RAG application by providing fast and efficient retrieval of relevant document chunks based on the user's query. The video demonstrates setting up Qdrant locally, which allows for complete control over the data and privacy.

💡Embedding Model

An embedding model in the context of the video is used to convert text into numerical representations, or vectors, that capture the semantic meaning of the text. The script specifies using a domain-specific embedding model like PubMed B because it is fine-tuned on medical literature, which aligns with the medical domain focus of the RAG application. These embeddings are then stored in a vector database for retrieval.

💡LangChain

LangChain is an open-source framework that provides tools for building applications involving language models. In the video, LangChain is used in conjunction with Llama.cpp for orchestrating the RAG application. It offers a way to connect different components such as the vector database, embedding models, and the large language model to create a coherent workflow for generating responses.

💡Quantized Model

A quantized model, as discussed in the video, is a version of a larger model that has been optimized for size and speed by reducing the precision of its weights. The video mentions using a quantized version of BioMistral, which is smaller in size and can run efficiently on CPU, making it a sustainable choice for enterprises concerned about compute costs and carbon emissions.

💡Domain-Specific Model

A domain-specific model is tailored to a particular area of knowledge, in this case, the medical domain. The video emphasizes the importance of using domain-specific models like BioMistral and PubMed B embeddings because they have been trained on medical data and thus better understand the terminology and context of medical information. This specialization allows for more accurate and relevant responses in a RAG application.

💡Sustainability

Sustainability in the context of the video refers to the environmental impact of running large AI models, particularly in terms of energy consumption and carbon emissions. The video discusses the benefits of using smaller, quantized models like BioMistral's 4-bit quantized version, which can run efficiently on CPU and reduce the carbon footprint associated with AI computations.

Highlights

Introduction to building a medical RAG (Retrieval-Augmented Generation) app using BioMistral, Qdrant, and Llama.cpp

BioMistral is a new 7 billion parameter medical domain-specific large language model

Comparison of BioMistral with other models like GPD 3.5 Turbo and the importance of domain-specific models

Recommendation to use domain-specific embedding models like PubMed B for better medical nomenclature understanding

Utilization of Llama.cpp for CPU-based large language model inference

Choice of Qdrant as a self-hosted vector database for local and private data storage

Demonstration of the biomedical RAG app built with open-source tech, ensuring data privacy and no third-party reliance

Explanation of the retrieval process using Qdrant Vector DB and its integration with the LLM

Tutorial on setting up the Qdrant Vector Database using Docker

Guide to creating document embeddings using sentence Transformers and the PubMed B model

Instructions on downloading and using the quantized BioMistral model for efficient computation

Details on setting up the FastAPI application for creating RESTful APIs

Integration of the retrieval QA chain using Lang Chain for generating responses from the LLM

Live demo of the RAG app retrieving information from medical documents and generating human-like responses

Discussion on the importance of showing context and source documents for tracability in medical information retrieval

Encouragement for viewers to build upon the project, implement chat memory, and explore further improvements

Invitation for feedback and further discussion on the utility of BioMistral and other open-source models in medical applications

Conclusion and call to action for viewers to subscribe, like, and share the video for more content on AI and medical applications