How to Build a Full-Stack RAG Powered Smart Web Searching AI Tool using Tavily, Langchain & Mistral

In this video, we craft a full-stack application from the ground up, utilizing the Tavily Search API for fast, accurate, and RAG-optimized AI-enhanced search results. We also briefly discuss the integration of the Retrieval-Augmented Generation (RAG) technique and harness the power of the #mistral model as our Large Language Model (LLM) #llm , which runs on the #Groq LPU for unmatched processing speed and efficiency. The AI stacks are seamlessly orchestrated using the langchain Python package. #langchain

Additionally, we will also develop a dynamic app featuring a Python #FastAPI backend and a #Reactjs frontend, all facilitated by the databutton online platform. The frontend is designed with the potential for further enhancements and expansion. This tutorial is presented as a demo app, with minimal video edits for clarity.

Tavily - https://docs.tavily.com
LangChain Tavily retreiver - https://python.langchain.com/docs/int...
groq - https://wow.groq.com/about-us/
mistral ai - https://mistral.ai
Databutton - https://www.databutton.io

Similar video -    • Build your own RAG (retrieval augment...  

Access to Databutton - Join here - https://databutton.typeform.com/to/GO...

Related videos on databutton workflow
Part 1 -    • Building Full stack App with No code ...  
Part 2 -    • AI Agents at work - Building No Code ...  

Blogs
Blog 1 - Building Next-Gen Apps with AI Agents -   / building-next-gen-apps-with-ai-agents  
Blog 2 - Prototyping Ideas to Full Stack GenAI Web Apps for PoCs and Internal Tools -   / prototyping-ideas-to-full-stack-genai-web-...