Get Started with LangChain & Hugging Face with Langsmith Tracing in 5 mins

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In this video, we'll explore how to leverage the power of LangChain and Hugging Face to build your own AI applications, completely free of OpenAI costs. We will also briefly use Langsmith to do tracing of our LLM calls.

Chapters:
0:00 - Introduction:
We'll kick things off by introducing the concept of generative AI and discussing the advantages of using open-source models like those available on Hugging Face.
0:30 - Setting Up:
Learn how to create accounts on Hugging Face and LangChain and obtain the necessary API keys for accessing the platforms and enabling key features like tracing.
1:30 - Choosing Your Model:
Explore the vast library of free, open-source generative AI models on Hugging Face. We'll discuss different model types and functionalities, helping you find the perfect one for your project.
2:30 - Building with LangChain:
Get hands-on with LangChain! We'll demonstrate how to load your chosen model and use LangChain's intuitive framework to build a simple generative AI application.
4:00 - Observability with LangSmith:
Understand the importance of observability in AI development and learn how LangSmith, a tool within LangChain, provides insights into your model's performance and behavior.
5:00 - Next Steps & Inspiration:
We'll wrap up by sharing resources and ideas to further your generative AI journey, encouraging you to explore and build your own innovative applications.
This video is perfect for:
Developers eager to explore generative AI without incurring OpenAI costs.
Anyone curious about LangChain and Hugging Face and their capabilities.
Individuals interested in building their own AI applications using open-source models.
Join us and unlock the potential of free generative AI!

#huggingface #langchain #opensourceai #aiapplications #python #aidevelopment #jupyternotebook #llmops #langsmith

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A great way of explaining things, thumbs up!

nikta
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Why does my notebook code gives “false” instead of true?

eternal_dread