1- Lets Learn About Langchain-What We Will Learn And Demo Projects

Summary

Krishak introduces an updated Lang Chain series on his YouTube channel, focusing on building generative AI applications using both paid and open-source LLM APIs. He plans to cover everything from scratch to advanced, demonstrating end-to-end projects, deployment, and ecosystem utilization. The series will also explore custom output functions, data injection techniques, vector embeddings, and local LLM model execution with AMA.

Takeaways

• 😀 Krishak introduces an updated Lang chain series aimed at covering updates and teaching how to build generative AI applications.
• 🔍 The series will cover content from scratch to advanced, focusing on using both paid and open-source LLM APIs and models.
• 🛠️ Krishak emphasizes the importance of the Lang chain ecosystem for deployment and will demonstrate its use throughout the series.
• 📚 Documentation will be a key part of the series, with Krishak using a diagram to simplify complex concepts for beginners.
• 🔑 Projects will incorporate Lang Smith for monitoring, debugging, evaluation, and annotation, and Lang Serve for deployment.
• 🤖 The series will explore cognitive architectures, chains, agent retrieval strategies, and the Lang chain community for third-party integration.
• 📝 Custom output functions will be taught, allowing users to tailor responses from LLM models to fit their specific product needs.
• 📈 Data injection techniques for various formats like CSV and PDF will be discussed, along with vector embeddings using both paid and open-source APIs.
• 💻 AMA (Align Machine) will be highlighted as a crucial library for running LLM models locally, requiring a high-configuration system.
• 🔧 Lang chain core will delve into the Lang chain expression language, covering techniques like paralyzation, fallback, tracing, and composition.
• 🚀 Krishak will demonstrate the entire ecosystem in action, including monitoring and debugging, with practical examples and projects.

Q & A

What is the main aim of the updated Lang Chain series?

The main aim of the updated Lang Chain series is to cover the new updates from Lang Chain and demonstrate how to build generative AI-powered applications using both paid LLM APIs and open-source LLM models.

What will the series cover besides building AI-powered applications?

The series will also cover creating end-to-end projects and using the Lang Chain ecosystem for deployment purposes.

Why does Krishak emphasize the importance of documentation in the video?

Krishak emphasizes documentation to help viewers understand how to use Lang Chain's documentation effectively and to provide clarity on the concepts and components involved in the projects.

What example does Krishak give to explain the usage of Lang Smith and Lang Server?

Krishak explains that Lang Smith is used for monitoring, debugging, evaluation, and annotation, while Lang Server is used for deployment with respect to a REST API. These components will be used in each project and technique demonstrated in the series.

What are the three main components of Lang Chain mentioned in the video?

The three main components mentioned are cognitive architectures (chains, agents, retrieval strategies), Lang Chain Community (for third-party integration), and model IO retrieval and agent tooling.

How does Krishak plan to demonstrate the usage of Lang Chain components?

Krishak plans to demonstrate the usage by combining prompt templates, chains, and custom output functions for specific tasks, as well as showing data injection techniques and vector embeddings using both paid and open-source LLM APIs.

What is AMA, and why is it important in the series?

AMA is a library that helps run large language models locally. It is important because it allows viewers to execute LLM models without needing high-end cloud-based APIs, provided they have a good system configuration.

What kind of project does Krishak showcase as an example using AMA and Lang Chain?

Krishak showcases a simple chatbot project created with Streamlit, which demonstrates the use of Llama 2 model executed locally via AMA, integrated with Lang Smith for monitoring and debugging.

What does Krishak demonstrate with the simple chatbot project?

He demonstrates how to execute LLM models locally, interact with the chatbot, and monitor the LLM calls using Lang Smith, highlighting response times, latency, and other details.

What does Krishak promise to cover in the next video of the series?

In the next video, Krishak promises to cover environment setup, creating API keys, using open-source LLM models, and various other foundational aspects needed to start building projects with Lang Chain.