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| "## Pre-requisites\n", | ||
| "## Prerequisites\n", | ||
| "1. Please follow the [README](../README.md#configure-azure-ai-service-resource) to create the essential resource needed for this sample.\n", | ||
| "2. Install the required packages." | ||
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| "## File to Analyze\n", | ||
| "\n", | ||
| "Use the following variable to specify the file to analyze. For this tutorial, we will examine a short example video. To try your own videos, replace the file path accordingly. For longer videos, you may need to adjust the `timeout` variable in `poll_result`." | ||
| "Use the variable below to specify the file to analyze. For this tutorial, we examine a short example video. To try your own videos, please replace the file path accordingly. For longer videos, you may need to adjust the `timeout` variable in `poll_result`." | ||
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| "source": [ | ||
| "# 1. Automated Chapter Discovery\n", | ||
| "\n", | ||
| "We will first use Content Understanding and allow the service to interact with an LLM model, enabling it to apply its own reasoning to segment the video into meaningful chapters. It will provide concise descriptions with timestamps.\n", | ||
| "First, we use Content Understanding and allow the service to interact with an LLM model, enabling it to apply its own reasoning to segment the video into meaningful chapters. It provides concise descriptions with timestamps.\n", | ||
| "\n", | ||
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| "The custom analyzer schema template for this approach is [video_chapters_dynamic.json](../analyzer_templates/video_chapters_dynamic.json). This file defines the schema and configuration for a custom video analyzer that uses AI to dynamically generate chapters and scenes based on its understanding of the video content. The service analyzes the video and identifies the most meaningful chapter divisions, providing start and end timestamps for each. The configuration section allows flexible segmentation, enabling the model to reason about the content and organize chapters appropriately.\n", | ||
| "\n", | ||
| "In this example, we will use the utility class `AzureContentUnderstandingClient` to load the analyzer schema from the template file and submit it to the Azure Content Understanding service. Then, we will analyze the video and generate the desired chapter and scene structure." | ||
| "In this example, we use the utility class `AzureContentUnderstandingClient` to load the analyzer schema from the template file and submit it to the Azure Content Understanding service. Then, we analyze the video and generate the desired chapter and scene structure." | ||
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| "\n", | ||
| "The custom analyzer schema template for this approach is [video_chapters_structured.json](../analyzer_templates/video_chapters_structured.json). This file defines the schema and configuration for a custom video analyzer. In this example, it specifies how a video should be segmented into chapters and scenes, including three chapter types: **\"Topic Introduction\"**, **\"Details About the Work Done\"**, and **\"Conclusion or Results\"**. Each segment contains a list of scenes, with each scene described by a short description, start timestamp, and end timestamp. The configuration section controls segmentation behavior and other analysis options, while the fieldSchema section outlines the expected structure of the output, ensuring chapters and scenes are clearly organized and non-overlapping.\n", | ||
| "\n", | ||
| "We will also be using the utility class `AzureContentUnderstandingClient` to load the analyzer schema from the template file and submit it to the Azure Content Understanding service. Then, we will analyze the video and generate the desired chapter and scene structure." | ||
| "We will also use the utility class `AzureContentUnderstandingClient` to load the analyzer schema from the template file and submit it to the Azure Content Understanding service. Then, we will analyze the video and generate the desired chapter and scene structure." | ||
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