Chat with csv files using Langchain & OpenAI| Tutorial:53

#ai #llm #langchain #openai #learntocode2023

🚀 **Welcome to Total Technology Zone!** 🌟

**Tutorial #53: Chat with Your CSV Files Using LangChain and Streamlit!**

Hey tech enthusiasts, it’s Ronnie here, and today we’re diving into an exciting tutorial that’s all about bringing your CSV files to life! Ever wondered how you can interact with your data in a more dynamic way? In this tutorial, we’re exploring how to upload CSV files and chat with them using the power of LangChain and Streamlit.

🔍 **What’s on the agenda?**
1. **Building a Streamlit web app:** Step-by-step guidance on creating a user-friendly interface.
2. **Uploading and Reading CSV Files:** Learn how to seamlessly upload your data and get it ready for interaction.
3. **Creating an Interactive Chat:** Implement a chat box where users can ask questions directly to their uploaded CSV data.
4. **Leveraging LangChain and OpenAI:** Discover how to use LangChain coupled with OpenAI's GPT models to process and respond to user queries in real-time.

🏏 **Case Study - IPL Data Analysis:** We’ll be applying these techniques on IPL (Indian Premier League) data spanning from 2008 to 2017 to showcase real-world application. From finding the team with the most wins to uncovering the venue hosting the most matches, we cover it all!

💡 **Why watch this tutorial?**
- **Hands-on Learning:** Follow along with code examples in VS Code to build your own data chat application.
- **Insights into LangChain and Streamlit:** Dive deep into how these powerful tools can be utilized for innovative data interaction.
- **Real-World Application:** See how this technology can be applied to analyze and interact with real datasets like the IPL data.

👉 **Ready to transform how you interact with data?** Join me in this coding journey and unlock new ways to engage with your datasets. Don’t forget to upload your CSVs and start chatting away!

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Thank you for your continued support, and remember, happy learning! See you in the next tutorial. 👋
Make sure to replace `[GitHub Repository with Source Code]` with the actual link to your GitHub repository containing the tutorial's source code and `[Download IPL Data for Practice]` with the link where viewers can download the IPL dataset used in the tutorial. This description is designed to be engaging, informative, and provides a clear overview of what viewers can expect from the video, encouraging both learning and interaction.

[SatyendraJaiswalsattu]

Very helpful.. one question
Can we convert our csv to vector and then apply our prompt.

Coz as per my understanding this bot is like taking to my dataframe ..

What will happen if we are asking any prompt where few calculations are required from the dataset ..
Any comments??