Exploring AI Integration in Projects 😃

Hello everybody! I've always been fascinated by the potential of integrating AI into my projects. Recently, I followed a fantastic tutorial on freeCodeCamp.org, guiding me through the process of incorporating AI tooling from OpenAI into my projects.

Tutorial Reference:

• Title: Building a Smart SaaS Application by freeCodeCamp.org

Project Overview:

I embarked on building a Software as a Service (SaaS) application that empowers users to upload PDF files and leverage an AI assistant for answering questions based on the content of these files.

You can explore the project on my GitHub repository, and I'm actively working on enhancing its features and functionality. 🤓💻 Feel free to check it out, contribute, or provide feedback!

Tech Stack:

• Next.js: React framework by Vercel for full-stack web applications.
• Vercel: Cloud hosting provider for rapid deployment.
• Clerk: Facilitates user management and login, streamlining development.
• DrizzleORM: Typescript Object-relational mapper for database operations.
• NeonDb: Cloud hosting for fully managed serverless Postgres databases.
• AWS S3: Object storage for user-uploaded PDFs.

AI Tech Stack:

• PineConeDb: Vector database for storing embeddings from the open-ai text-embedding-ada-002 model.
• LangChain: Framework for developing applications powered by language models, used for loading PDF files.
• OpenAI: Utilized for vector embedding and chat-gpt 3.5 turbo for interactive communication.
• Vercel AI SDK: Open-source library for building conversational UIs.
• Vercel Edge runtime: Enables the execution of edge functions with Web AI capabilities.
• Retrieval Augmented Generation: Meta AI's method addressing knowledge-intensive tasks.

Explore the links for detailed insights into each technology and its role in the project. Happy coding! 🚀