Vertex AI Tutorial #2: Conversation Memory & Generative Config
About this lesson
๐ Welcome to Tutorial #2 on Google Cloud Vertex AI! In this video, I'll show you how to configure Generative AI models and implement conversation memory using Python. ๐ฏ What You'll Learn: โ Understanding GenerationConfig parameters โข Temperature: Control creativity (0.9) โข Top-K: Limit word choices (20) โข Top-P: Probability distribution (0.9) โข Max Output Tokens: Response length (20) โ Implementing Chat Memory โข Why simple generate_content() doesn't remember โข Using start_chat() for conversation context โข Real examples of stateful vs stateless interactions ๐ป Code Covered: - Setting up GenerationConfig in Vertex AI - Generate content with custom parameters - Start chat sessions with memory - Compare: generate_content() vs start_chat() ๐ Useful Links: - Vertex AI Documentation: https://cloud.google.com/vertex-ai/docs - Python Client Library: https://cloud.google.com/python/docs/reference/aiplatform/latest - Tutorial Code: [Add your GitHub link] โฑ๏ธ Timestamps: 0:00 - Introduction 0:30 - What is GenerationConfig? 2:00 - Temperature, Top-K, Top-P explained 4:00 - Max Output Tokens 5:00 - Problem: No conversation memory 6:30 - Solution: start_chat() method 8:00 - Demo: Chat with memory 9:30 - Wrap up ๐จโ๐ป About Me: I'm Mohamed Naji Aboo, and I create tutorials on AI, Machine Learning, and Cloud technologies. Subscribe for more Vertex AI content! ๐ Subscribe for more GCP & AI tutorials! ๐ Like if you found this helpful! ๐ฌ Comment your questions below! #VertexAI #GoogleCloud #GenerativeAI #Python #MachineLearning #AI #GCP #VertexAI #GoogleCloud #GenerativeAI #GCP #Python #AI #MachineLearning #CloudComputing #ArtificialIntelligence #VertexAITutorial
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