This project is an AI-powered research assistant designed to help users efficiently navigate and understand scientific papers. It combines cutting-edge technologies to provide a seamless experience for researchers and humans.
- Next.js: A React framework for building server-side rendered and statically generated web applications.
- TypeScript: A typed superset of JavaScript that compiles to plain JavaScript.
- Prisma: An open-source database toolkit for Node.js and TypeScript.
- LlamaIndex: A data framework for LLM-based applications to ingest, structure, and access private or domain-specific data.
- Shadcn/UI: A collection of re-usable components built with Radix UI and Tailwind CSS, providing accessible and customizable UI elements.
- Trigger.dev: A platform for building and managing background jobs and workflows.
- Pinecone: A vector database for storing and searching high-dimensional vectors, ideal for semantic search and AI applications.
- Sequin: Sequin is a tool for capturing changes and streaming data out of your Postgres database.
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Article Metadata Management: The system can store and retrieve metadata about scientific articles, including titles, authors, publication dates, and abstracts.
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PDF Processing: The application can load and process PDF files of scientific articles, extracting relevant information for further analysis.
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Text Summarization: Utilizing advanced natural language processing techniques, the system can generate concise summaries of scientific articles.
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Similar Article Recommendations: The application can suggest related articles based on content similarity, helping users discover relevant research.
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Interactive User Interface: A responsive and user-friendly interface allows users to easily navigate through articles, read summaries, and interact with the system.
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Background Processing: Leveraging Trigger.dev, the system can handle computationally intensive tasks asynchronously, ensuring a smooth user experience.
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Data Persistence: Using Prisma, the application efficiently manages and stores data in a structured database.
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API Integration: The system provides RESTful API endpoints for various functionalities, such as retrieving article summaries and metadata.
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Scalable Architecture: Built with Next.js, the application is designed to be scalable and performant, capable of handling a large number of requests and users.
This AI-powered research assistant aims to streamline the process of scientific literature review, making it easier for humans to stay up-to-date with the latest developments in their fields of study.