10. About The Series
524.9.2. Optimizing Code for Performance
21. Introduction: 1.1. Learning Objectives
534.9.3. Handling Rate Limits and API Restrictions
32. Generative AI Workflow
544.10. Extending LangChain with Advanced Techniques
42.1. Introduction to the Vertex AI Studio
554.10.1. Leveraging Data Augmentation for Improved Model Performance
52.2. Generative AI
564.10.2. Enhancing Model Responsiveness with Dynamic Prompting
62.3. Foundation Models: 2.3.1. How Foundation Models Power Your Applications
574.10.3. Adapting Model Interactions Based on User Feedback
73. The Gemini Multimodal
584.11. Deploying LangChain Projects
83.1. Gemini Business Use Cases
594.11.1. Preparing Your LangChain Project for Deployment
93.2. How to Interact with Gemini Multimodal
604.11.2. Deployment Options (Local, Cloud, and Hybrid)
103.2.1. Starting with a Prompt
614.11.3. Monitoring and Maintaining Deployed Applications
113.2.2. Anatomy of a Prompt
624.12. Advanced Use Cases and Applications
123.3. Language Capabilities: Prompt Design Modes
634.12.1. Using LangChain for NLP Tasks
133.3.1. Free-form Prompt Design: Hands-on Practice 1
644.12.2. AI-Driven Customer Support Solutions
143.3.2. Structured Prompt Design: Hands-on Practice 2
654.12.3. Building AI-Powered Research Tools
153.3.3. Practical Examples for Understanding Model Parameters (Temperature, Top K & Top P)
664.13. LangChain with Vertex AI Studio
163.4. Model Tuning
674.13.1. Introduction to Vertex AI and LangChain Integration
173.4.1. How to Customize & Tune a GenAI Model
684.13.2. Setting Up LangChain Projects in Vertex AI Studio
183.4.2. How to Start a Tuning Job in the Vertex AI Studio
694.13.3. Model Deployment and Workflow Automation in Vertex AI Studio: Deploy, Monitor & Evaluate
193.5. Wrap up
704.13.4. Optimizing LangChain with Google’s Vertex AI Tools
204. LangChain for Generative AI
714.14. Real-World Projects and Case Studies
214.1. Introduction to LangChain and Generative AI: 4.1.2. Setting Up Your Environment for LangChain Development
724.14.1. Case Study: Automating Content Creation
224.2. Core Concepts and Architecture
734.14.2. Hands-On Project: Developing a Sophisticated Smart Personal Assistant Using LangChain
234.2.1. Understanding Chains in LangChain
744.14.3. Real-World Challenges and Solutions
244.2.2. Modules: The Building Blocks of LangChain
754.15. Wrap-Up: Taking Your Skills to the Next Level
254.2.3. Key Components: Models, Prompts, and Parsers
764.16. Overview of Upcoming Parts of the Series
264.3. Models and Prompt Design
774.16.1. Hugging Face: Expanding Your Model Toolkit
274.3.1. Choosing the Right Model for Your Needs
784.16.2. Vector Databases: Efficient Data Storage and Retrieval for AI
284.3.2. Prompt Design Fundamentals
794.16.3. Llama Index: Building and Optimizing AI-Driven Knowledge Bases
294.3.3. Advanced Prompting Techniques for Custom Responses
804.16.4. More LLM Generative AI Projects and Deployment Techniques
304.4. Building Chains and Workflows
815. Practice Exercises & Projects
314.4.1. Creating Simple Chains
825.1. Multiple Choice Questions, Set 1
324.4.2. Designing Multi-Step Workflows
835.2. Correct Answers
334.4.3. Handling Errors and Fallbacks in Chains
845.3. Multiple Choice Questions, Set 2
344.5. Integrating APIs with LangChain
855.4. Correct Answers
354.5.1. API Integration Basics
865.5. Practice Project 1: Getting Started with Gemini Multimodal in Vertex AI Studio
364.5.2. Leveraging External APIs for Enhanced Functionality
875.5.1. Project Objective
374.5.3. Using Web Scraping and Other Data Sources
885.5.2. Project Overview
384.6. Memory Management in LangChain
895.5.3. Steps to Complete Project
394.6.1. Introduction to Memory in AI Models
905.5. Practice Project 2: Generating Creative Text Prompts with Gemini Multimodal and Vertex AI Studio
404.6.2. Implementing State Management in LangChain
915.5.1. Project Objective
414.6.3. Best Practices for Memory Optimization
925.5.2. Prerequisites
424.7. User Input and Parsing Responses
935.5.3. Steps to Complete Project
434.7.1. Capturing and Validating User Input
945.6. Practice Project 3: Building an Interactive FAQ Chatbot with LangChain and OpenAI API
444.7.2. Parsing Model Outputs
955.6.1. Project Objective
454.7.3. Using Parsers for Structured Data Handling
965.6.2. Project Overview
464.8. Custom Modules and Extensions
975.6.3. Prerequisites
474.8.1. Writing Custom Modules in LangChain
985.6.4. Steps to Complete the Project
484.8.2. Extending LangChain’s Capabilities with Plugins
995.6.5. Expected Outcome
494.8.3. Using OpenAI Plugins and Integrations
1005.6.6. Next Steps
504.9. Debugging and Optimization
1015.7. How to Get Additional Help & Support
514.9.1. Debugging Common LangChain Errors
102References
