This video shows how to build a real-time chat application that enhances user experience by streaming responses from language models (LLMs) as they are generated. This video is perfect for both beginners and seasoned developers interested in integrating the power of language models into chat applications using Streamlit and Lang Chain.

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🚀 What You Will Learn:

How to set up your development environment for working with Streamlit and Lang Chain.
Step-by-step guidance on building a chat interface that streams language model responses in real-time.
Implementing the chat history feature to make your chatbot smarter and more contextual.
Tips and tricks for optimizing the performance of your chat application.
Integrating OpenAI models with Lang Chain for dynamic conversation generation.

💻 Technologies Covered:
Streamlit: For creating the front-end of the chat application.
Lang Chain: To interact with language models and handle real-time streaming of responses.
OpenAI Models: Leveraging powerful AI models for generating conversational responses.

🔧 Prerequisites:
Basic understanding of Python programming.
Familiarity with chatbot concepts and language models is helpful but not required.

👨‍💻 For Whom:
This tutorial is designed for software developers, data scientists, and tech enthusiasts interested in building real-time chat applications, integrating language models, or exploring new ways to enhance user engagement through streaming technologies.

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👉 Watch, Learn, and Code Along as we build a real-time chat application from scratch, showcasing the practical implementation of streaming responses for a seamless user experience. Whether you're building for fun or looking to integrate chat functionalities into your projects, this tutorial has you covered.

Timestamps:
0:00 Intro
0:46 What we will build
1:51 Project Setup
4:07 Create Chat UI
9:51 Chat Functionality
14:40 Add Streaming to App
17:28 Conclusion

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