Question Explorer helps students expand their knowledge by finding 10 different questions similar to their own. Using advanced language processing and vector similarity search, it provides a diverse range of questions on the same topic. Explore new ideas and deepen your understanding with Question Explorer.
This document presents the formal architecture for the Similar-Question-Finder application. The application is designed to help students expand their knowledge by finding 10 different questions similar to their own, using advanced language processing and vector similarity search techniques.
Framework: React, Angular, or Vue.js
Purpose: Build a responsive and interactive web application to provide a user-friendly interface for students to enter their questions
Framework: Flask (Python)
Purpose: Develop a RESTful API to accept questions from the frontend, process them, and return 10 similar questions, enabling separation of concerns and better scalability and maintainability
Library: Hugging Face Transformers or SpaCy
Model: Pre-trained models like BERT or GPT
Purpose: Leverage state-of-the-art NLP techniques to process the input question and convert it into a vector representation for efficient similarity search
Database: Weaviate
Purpose: Utilize Weaviate, a RESTful vector database, to store and manage the vector representations of questions and enable efficient similarity search for rapid retrieval of the most similar questions
Database: PostgreSQL, MySQL, MongoDB, or Amazon DynamoDB
Purpose: Store other application-related data that does not require vector similarity search in a robust and scalable database for quick retrieval
Service: Redis or Memcached
Purpose: Implement caching mechanisms to store the results of recent queries, reducing the load on NLP and vector similarity search components and improving response times for users
Platform: Amazon Web Services (AWS)
Infrastructure as Code: Terraform
Deployment Services: AWS Elastic Beanstalk or AWS Lambda
Purpose: Deploy and scale the application in a reliable and efficient manner on a leading cloud platform, utilizing Terraform to manage the infrastructure consistently and reproducibly
By following this architecture, the Similar-Question-Finder application will be built on a robust, scalable, and maintainable foundation that leverages modern NLP techniques and vector similarity search with Weaviate to provide a valuable service to its users.