Full-stack application tutorial, where we build an AI-powered search application from the ground up. Leverages the cutting-edge capabilities of the Tavily Search API for fast, accurate, and RAG-optimized AI-enhanced search results. Through this tutorial, we explore the integration of advanced AI models and techniques, including the Retrieval-Augmented Generation (RAG) technique and the Mistral model mixtral-8x7b-32768 as the Large Language Model (LLM), running on the Groq LPU for unparalleled processing speed and efficiency.
- Tavily Search API: Utilize for fast and accurate AI-enhanced search results.
- Mistral Model: Leverage as the LLM, running on the Groq LPU.
- LangChain Python Package: Orchestrate AI stacks seamlessly.
- Databutton Platform: Facilitate development - Build with a Python FastAPI backend and a React.js frontend.
The app was developed using the Databutton platform - the Capability and UI Builder Agents avialble within the tool generated all the code. The comprehensive walkthrough of building this application is detailed in the tutorial video. From setting up the project environment to integrating the Tavily Search API and deploying the full-stack application, every step is explained. This tutorial also includes a demonstration of the app's minimal architecture.
