Build a Medical RAG App using BioMistral, Qdrant, and Llama.cpp
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
🚀 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.
🛠️ 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.
💻 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.
🌐 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.
🔍 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.
📝 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)
💡BioMistral
💡PubMed
💡Llama.cpp
💡Qdrant
💡Embedding Model
💡LangChain
💡Quantized Model
💡Domain-Specific Model
💡Sustainability
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
