Question Answer Generator App using Mistral LLM, Langchain, and FastAPI

Introduction

In this article, we will explore the development of a Question Answer Generator application using the Mistral 7B large language model. Mistral AI has recently launched several models that have outperformed other open-source language models in various evaluation benchmarks. This app can be particularly useful for industries across various fields, including education, academia, and legal practices, where documentation, manuals, and reports are common.

Use Cases

The application we are building automates the generation of question-answer pairs from a knowledge base composed of PDFs or documents. Specifically, this can aid in:

• Education: Automatically generating quizzes and study material for students.
• Legal: Extracting questions from legal documents, contracts, or policy manuals, which can help in compliance and auditing processes.
• Research: Facilitating the creation of insightful questions from research papers or technical documents.

Application Overview

The goal of the app is to allow users to upload documentation, and the app will then generate relevant questions and answers based on the content of the uploaded files.

Here's how the application is structured:

1. Frontend: Built using FastAPI and Jinja2 Templates.
2. Backend: Employing Langchain as an orchestration framework and Mistral 7B as the large language model.
3. Data Processing: Utilizes sentence Transformers for embedding models and handles PDF files using PyPDF loader.

Development Steps

1. Setup Environment: We will configure our folder structure and set up necessary dependencies for FastAPI, Jinja2, and PyPDF loader.

2. Loading the Model: A function (load_llm) will be written to load the Mistral 7B model along with its parameters.

3. File Processing: A function (file_preprocessing) to read the PDF file using PyPDF and split the content for question and answer generation.

4. Generating Questions: Implementing a question generation pipeline using Langchain. We will define prompt templates to instruct the model in generating meaningful questions based on the document.

5. Generating Answers: Following the question generation phase, an answer retrieval mechanism will be implemented that will leverage vector embeddings for matching questions and answers.

6. API Endpoints: The application will have endpoints for uploading the PDF document and generating the questions and answers, enabling communication between the frontend and backend.

7. CSV Export: Finally, the application will save the question-answer pairs as a CSV file for user download.

The code for the application can be found on GitHub; feel free to customize it to suit your needs.

Running the Application

To run the application:

1. Navigate to the directory containing your project.
2. Activate your virtual environment (if applicable).
3. Start the application with the command: python app.py.
4. Access the application through your web browser at localhost:8000 to upload your PDF files and generate question-answer pairs.

Conclusion

The Question Answer Generator app developed with the Mistral LLM, Langchain, and FastAPI showcases the potential of leveraging large language models for automated document interpretation and knowledge extraction. This opens doors to various applications across different industries.

By understanding the app's structure and functionality, you can extend it to meet specific requirements, whether that involves further tuning the model, enhancing the question generation algorithm, or refining the user interface.

---

Keyword

Mistral 7B, question answering, Langchain, FastAPI, PDF loading, sentence Transformers, question generation, answer retrieval, educational tools, legal documentation, automation, API development.

---

FAQ

Q1: What is the purpose of the Question Answer Generator app?
A1: The application automates the generation of question-answer pairs from documentation such as PDFs, useful for education, legal, and research purposes.

Q2: What technologies are used in this app?
A2: The app employs Mistral 7B as the large language model, Langchain for orchestration, and FastAPI for building the web application interface.

Q3: How can I run this application on my local machine?
A3: You can run the application by downloading the code from GitHub, activating your virtual environment, and executing python app.py, then accessing it via localhost:8000.

Q4: Can I customize the app?
A4: Yes, the code is open-source, allowing you to modify it according to your requirements, such as adjusting prompts or enhancing functionality.

Q5: Are there any limitations for large documents?
A5: Yes, larger documents may require more computational resources, and you may face token limitations based on the model's capabilities.