What you'll Learn
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Gain a solid foundation in the LangGraph framework.
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Engage in practical sessions to build a planning Agent for Deep Research & Structured Report Generation.
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Customize report structures and content to fit specific needs.
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Implement parallel execution to speed up web research and section writing.
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Integrate Tavily for comprehensive web searches to enhance report quality.
Course curriculum
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1
End to End RAG Application Development
- Project Introduction and Architecture- Build a Planning Agent for Deep Research Structured Report Generation
- Project Implementation - Project Setup -Part-1
- Project Implementation - Part II - Create Utility Functions
- Project Implementation -Part III - Create Report Planner Node
- Project Implementation Part- IV - Create Section Builder Sub-Agent
- Project Implementation - Part V - Create Format Sections Node
- Project Implementation - Part VI - Create Final Section Writer Node
- Project Implementation - Part VII - Create Compile Final Report Node
- Project Implementation Part VIII - Create Planning Agent
- Reading Resources
- Handouts
About the Instructor
Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya
Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence.
Who Should Enroll?
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Professionals: Individuals looking to enhance their expertise in AI and explore advanced frameworks for building corrective RAG systems.
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Aspiring Students: For those on their journey to mastering AI, ready to delve into advanced concepts and make a significant impact in the tech world.
FAQ
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What is LangGraph, and why is it essential for building advanced AI agents?
LangGraph is a graph-based framework designed for building complex, dynamic AI agents. It enables efficient workflow management and decision-making, making it ideal for advanced AI applications.
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Do I need prior experience with AI agents to take this course?
A basic understanding of AI agents and Python programming is recommended. Familiarity with tools like LangChain may be helpful.
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Will I receive a certificate upon completion?
Yes, you will receive a certificate of completion after successfully finishing the course and assessments.
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How does LangGraph differ from Autogen and CrewAI?
LangGraph uses graph-based architectures for scalable workflows and dynamic decision-making, ideal for complex tasks. Autogen focuses on multi-agent orchestration but requires more programming, while CrewAI simplifies team-based agent tasks but lacks advanced workflow optimization.
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Will this course cover real-world applications of LangGraph?
Yes, the course includes practical exercises to to Build a Planning Agent for Deep Research & Structured Report Generation.
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What makes LangGraph suitable for building scalable AI systems?
Its graph-based design allows for modular, dynamic workflows and conditional routing, making it ideal for building scalable, efficient AI systems capable of handling intricate tasks.