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Generative AI with Google Cloud - Building Applications Using PaLM 2 and LangChain

Google Cloud Events via YouTube

Overview

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Learn how to build and operationalize generative AI applications by combining Google Cloud's PaLM 2 language model with the LangChain framework in this comprehensive technical session. Explore the fundamentals of Google Cloud's AI & ML portfolio, understand typical LLM usage patterns, and master key LangChain concepts including Chains, Agents, Memory, Models, Prompts, and Indexes. Follow along with hands-on demonstrations covering the creation of simple generative apps, memory-enabled chat applications, and retrieval augmented generation implementations. Gain practical insights through three detailed demos that showcase developer steps and best practices for integrating PaLM 2 with LangChain. Conclude with an interactive Q&A session addressing common implementation challenges and advanced use cases. Perfect for developers looking to leverage Google Cloud's next-generation language model capabilities while taking advantage of LangChain's flexibility and ease of use.

Syllabus

Introduction to Google Cloud's AI & ML portfolio
Typical usage of LLMs
Common patterns using LLMs
Introduction to LangChain
Where does LangChain fit in the LLM stack?
LangChain concepts: Chains, Agents, Memory, Models, Prompts, and Indexes
Why use Agents?
Implementing Agents
Embeddings
Demos and developer steps
1 Simple generative app
2 Memory-enabled chat app
3 Retrieval augmented generation
Q&A

Taught by

Google Cloud Events

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