🔍Your First Retrieval Augmented Generation QA Application!

Steps to Run:

1. Create a Python 3.11 environment
2. pip install jupyter so you can run the notebook
3. pip install -r requirements.txt to install all the requirements

Build 🏗️

Run the notebook and observe the WandB dashboard.

Ship 🚢

• Add one of the following "extras" to the RAQA pipeline:
  • Allow it to work with PDF files
  • Implement a new distance metric
  • Add metadata support to the vector database
• Make a simple diagram of the RAQA process
• Run the notebook
• Record a Loom walking through the notebook, the questions in the notebook, your addition, and a WandB trace.

Share 🚀

• Show your App in a loom video and explain the diagram
• Make a social media post about your final application and tag @AIMakerspace
• Share 3 lessons learned
• Share 3 lessons not learned

Here's a template to get your post started!

🚀 Exciting News! 🎉

I just built and shipped my very first Retrieval Augmented Generation QA Application using Chainlit and the OpenAI API! 🤖💼 

🔍 Three Key Takeaways:
1️⃣ The power of combining traditional search methods with state-of-the-art generative models is mind-blowing. 🧠✨
2️⃣ Collaboration and leveraging community resources like AI Makerspace can greatly accelerate the learning curve. 🌱📈
3️⃣ Dive deep, keep iterating, and never stop learning. Each project brings a new set of challenges and equally rewarding lessons. 🔄📚

A huge shoutout to the @AI Makerspace for their invaluable resources and guidance. 🙌

Looking forward to more AI-driven adventures! 🌟 Feel free to connect if you'd like to chat more about it! 🤝

#OpenAI #Chainlit #AIPowered #Innovation #TechJourney