Supercharging AI Solutions with GROQ

Lesson 1

Building a Lightning-Fast Chatbot with Memory Using Groq, Lama3, and Streamlit

In this video, you'll learn how to build a lightning-fast chatbot with memory capabilities using Lama3, Groq, and Streamlit. Groq provides rapid processing similar to GPT but with enhanced speed, while Streamlit offers an easy way to create web applications with minimal code. By following step-by-step instructions, you'll create a chatbot that not only processes user inputs swiftly but also remembers previous interactions within the same session, thanks to Langchain's buffer memory.

You'll learn how to:

• Set up a basic chat interface using Streamlit
• Use Groq’s API to create fast chat completions
• Integrate Langchain to enable short-term memory for chat history
• Build a web-based chatbot with conversational context By the end, you'll have a fully functional chatbot with memory, capable of holding fast and intelligent conversations on a custom website.

Lesson 2

J.A.R.V.I.S-like Assistant

In this tutorial, you'll learn how to create your own voice-activated AI assistant, inspired by Jarvis from Iron Man. We'll use Groq, a lightning-fast alternative to GPT, to process voice commands in real-time. You'll record your voice, convert it to text with DeepGram, and use Groq to generate responses that will be spoken back to you.

You'll learn how to:

• Set up a Python web app with Flask to handle voice input
• Record and transcribe voice data using DeepGram for speech-to-text
• Leverage Groq’s fast processing for answering questions and translating text
• Convert AI-generated responses back to speech
• Build a full cycle for an interactive AI assistant, capable of real-time conversation

By the end, you’ll have built a responsive AI assistant that listens, responds, and performs tasks like a true digital co-pilot.

Lesson 3

Building an Interview Transcription and Query System (Part 1)

In this video, you'll learn how to build an interactive application that transcribes interviews, stores them in a vector database, and allows users to query the content with accurate, contextually relevant responses using Groq's capabilities and Langchain's Retrieval Augmented Generation (RAG).

You'll learn how to:

• Set up a virtual environment and install necessary libraries (Groq, Langchain, Pinecone, etc.)
• Use the Whisper model on the Groq API to convert audio interviews into text transcriptions
• Build a transcription-based chat completion system that answers user queries using the interview content

Lesson 4

Building an Interview Transcription and Query System (Part 2)

In this second part, you'll learn how to store the interviews in a vector database, and allows users to query the content with accurate, contextually relevant responses using Groq's capabilities and Langchain's Retrieval Augmented Generation (RAG).

You'll learn how to:

• Store transcriptions in a Pinecone vector database for efficient data retrieval
• Use Langchain to implement Retrieval Augmented Generation (RAG) for querying the stored interview data