Podcast Generator
This notebook demonstrates an automated podcast generation system implemented using LangGraph, Azure OpenAI, and Google's Gemini model. The system is designed to generate content for podcasts based on given topics, including keyword generation and structure planning. At the end full podcast will be generated purely based on topic given. Finally extensive (web) based function tool search is also used to augment the needed information for the topic.
Automated content generation systems can significantly reduce the workload for podcast creators while providing structured and relevant content. This implementation showcases how advanced AI models and graph-based workflows can be combined to create a sophisticated system that considers multiple aspects of podcast planning and content creation. Special focus is set on (web) research aspect..
What you'll learn
- 1State Management: Using TypedDict to define and manage the state of each customer interaction.
- 2Query Categorization: Classifying customer queries into Technical, Billing, or General categories.
- 3Sentiment Analysis: Determining the emotional tone of customer queries.
- 4Response Generation: Creating appropriate responses based on the query category and sentiment.
- 5Escalation Mechanism: Automatically escalating queries with negative sentiment to human agents.
- 6Workflow Graph: Utilizing LangGraph to create a flexible and extensible workflow.
About this tutorial
This hands-on Jupyter notebook is part of GenAI Agents, a free open-source repository by Nir Diamant covering ai agents techniques with runnable code examples and detailed explanations.
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