Exploring Conversations with Data using LLama3 LLM (Chat With Your Data) 

Welcome to Exploring Conversations with Data using LLama3 LLM!
In this video, you'll learn how to build a powerful data analysis app using Python, PandasAI, and the LLaMA3 Large Language Model.
We'll guide you through the process of creating an interactive application with Streamlit, where users can input their questions and interact with the dataset seamlessly.
Additionally, You can connect this application to a Relational Database Management System (RDMS) for more robust data interactions.
You'll also learn how to obtain and use the necessary groq_api_key to integrate the LLaMA3 model effectively.
In this session, we'll cover the following key points:
✨ Introduction to Chat with Data using LLaMA3 LLM
📚 Importing essential libraries including Langchain and their applications
🔑 Obtaining the groq_api_key
📊 Reading and configuring datasets with SmartDataFrame using PandasAI
💬 Interacting with your dataset
🌐 Creating a web-based solution with Streamlit to chat with your dataset
📝 Storing the history of questions
📈 Displaying visualizations generated by LLM on Streamlit
🔍 Querying different data points
🛠️ Explaining the script and its uses
Time Stamp:
00:00 Introduction to Chat with Data using LLama3
02:00 Importing the necessary packages
03:00 Overview of Groq and API Key setup
03:55 Loading the dataset and configuration into PandasAI
04:58 Submitting a query
06:00 Setting up the Streamlit interface
09:00 Querying with LLM on Streamlit
10:02 Adding charts and saving history on Streamlit
14:20 Queries with the LLM model
17:50 Detailed code explanation
19:20 PandasAI log
Source Data & Script: github.com/AbhisheakSaraswat/...
Get ready to dive into the world of data analysis and conversational AI. Follow along and by the end of this session, you'll have a fully functional app ready for your data exploration needs
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