Enhance Your Biomedical Literature Search with Mixtral 8x7B LLM and Haystack

Enhance Your Biomedical Literature Search with Mixtral 8x7B LLM and Haystack

Table of Contents:

1. Introduction
2. Leveraging Pubmed as a Data Source
3. Building an LLM Augmented qa Tool
4. Understanding MixR Model
5. Using HyStack for Orchestrating the Framework
6. Building the LLM Augmented QA Application
7. Installing the Required Libraries
8. Creating the Pubmed Feature
9. Creating the Document Function
10. Creating the Components and Connections
11. Creating the Ask Function
12. Creating the Gradio Interface
13. Running the Application
14. Conclusion

Introduction

In this video, we will be developing a question-answering (QA) tool for biomedical literature search. We will leverage Pubmed, a free Search Engine for biomedical literature, as our data source. To enhance the question-answering capabilities, we will use an LLM (Large Language Model) as an augmentation layer. The specific LLM we will be using is the MixR model, which is a mixture of experts (Moe) model developed by Mistral AI. To build the application, we will be using the HyStack framework, which is an orchestration framework that allows for seamless integration of different components. The application will provide a user-friendly interface for users to input their queries and retrieve Relevant information from Pubmed using the LLM augmentation. Let's dive into the details of each step.

Leveraging Pubmed as a Data Source

Pubmed is a widely recognized and trusted Search Engine for biomedical literature. It allows users to query recent case studies and other Healthcare-related information. By using Pubmed as our data source, we can access a vast amount of existing information on various health-related topics. This provides a rich foundation for our question-answering tool, as it ensures that users can retrieve up-to-date and reliable information for their research needs.

Building an LLM Augmented QA Tool

To enhance the question-answering capabilities of our tool, we will integrate an LLM (Large Language Model) into the system. Specifically, we will be using the MixR model developed by Mistral AI. LLMs have gained popularity due to their ability to generate human-like responses by leveraging vast amounts of training data. The MixR model combines the expertise of multiple models, allowing it to tackle a wide range of queries effectively. By integrating the LLM into our tool, we ensure that users receive accurate and context-aware answers to their questions.

Understanding MixR Model

The MixR model is a mixture of experts (Moe) model developed by Mistral AI. It combines the expertise of different models that specialize in various areas. This model is especially effective in handling complex queries since different models within the MixR model can be triggered based on the specific query and task at hand. The Atex 7B model, which is part of the Moe model, is especially significant as it combines the expertise of multiple models to provide accurate responses tailored to the user's query and task.

Using HyStack for Orchestrating the Framework

HyStack is an orchestration framework that provides an intuitive way to build and manage applications with diverse components. It allows for seamless integration of different components and simplifies the overall development process. By using HyStack, we ensure that our QA tool is well-structured and easy to maintain. The framework also provides a Pubmed feature that enables direct interaction with the Pubmed search engine, making it an ideal choice for our application.

Building the LLM Augmented QA Application

To develop our LLM augmented QA tool, we will begin by installing the required libraries, including HyStack, Pubmed, and Transformers. These libraries provide the necessary tools and functionalities to build and integrate our components effectively. Once the libraries are installed, we will create the Pubmed feature, which will allow us to connect to the Pubmed database and retrieve relevant information based on user queries.

Installing the Required Libraries

Before building the application, we need to install the required libraries. These include HyStack, Pubmed, and Transformers. HyStack provides the necessary framework for component integration, Pubmed offers access to the Pubmed database, and Transformers provides tools for working with LLMs. Once these libraries are installed, we can proceed with building the application.

Creating the Pubmed Feature

The Pubmed feature serves as the backbone of our QA tool. It connects to the Pubmed search engine and retrieves relevant information based on user queries. We will use the Pubmed class from the Pubmed library to establish this connection. The class requires a tool parameter, which we will set to "Htag 2.0 prototype" to identify our application, and an email parameter, which can be any dummy email address. Once the Pubmed feature is set up, we can proceed with building our components.

Creating the Document Function

The document function plays a crucial role in the Pubmed feature. It extracts the content, abstract, title, and metadata from the retrieved articles. This ensures that we have all the necessary information to generate accurate and comprehensive answers to user queries. By organizing the data in a structured format, we can efficiently process and analyze it using the LLM model.

Creating the Components and Connections

In our LLM augmented QA tool, we will be using several components to facilitate the question-answering process. These components include the keyword Prompt builder, the LLM model, and the Pubmed feature. We will connect these components using the pipeline functionality provided by HyStack. This ensures that the data flows smoothly through the different components, allowing for efficient processing and generation of responses.

Creating the Ask Function

The ask function is the core of our QA tool. It takes user queries as input and generates responses based on the integrated components. Within the ask function, we will use the pipeline functionality to connect the keyword prompt builder, the LLM model, and the Pubmed feature. This ensures that user queries are processed accurately and that relevant information is retrieved from the Pubmed search engine.

Creating the Gradio Interface

Gradio provides a user-friendly interface for our QA tool. It allows users to input their queries and receive real-time responses. We will use the Gradio chatbot interface to create our UI. This interface takes the ask function as input and provides a text box for users to enter their queries. The responses generated by the ask function will be displayed in a Markdown format, providing a clear and organized presentation of the information.

Running the Application

Once all the components and connections are in place, we can run our LLM augmented QA tool. We will use the Gradio launch function to start the application. This function launches a local server and displays the user interface in a web browser. Users can then interact with the application by entering their queries and viewing the generated responses. The application provides a convenient and efficient way to access and retrieve information from Pubmed using LLM augmentation.

Conclusion

In this video, we have successfully developed a question-answering tool for biomedical literature search. By leveraging Pubmed as a data source and integrating an LLM model, we have created an application that provides accurate and context-aware answers to user queries. The use of the HyStack framework and Gradio interface ensures a seamless and user-friendly experience. With this tool, healthcare professionals, researchers, and anyone else in need of valuable and up-to-date information can easily access and retrieve relevant information from Pubmed.