Enhancing Training Search Experience using Azure AI Video Indexer

Training and development are vital for Companies often have vast archives of training videos, webinars, and recorded sessions. However, efficiently finding the right training content from video archives can be challenging. This example demonstrates how Azure Video indexer, combined with Azure Open , can make finding relevant video content easier for learners and trainers. Organizations struggle with the following issues in managing their training video archives:

  • Difficulty in locating specific content: Employees often spend excessive time searching for specific training modules or information within hundreds of training videos.
  • Inconsistent search results:
  • Poor user experience: The lack of contextually relevant search results can degrade the overall user experience, reducing the effectiveness of training programs.

The Solution

Creating a video archive search solution using Azure Video Indexer can address these challenges effectively. Here's how:

  1. Indexing training videos: Use Azure Video Indexer to extract metadata, such as transcripts, keywords, Object Character Recognition OCR and detect peopleobjects.
  2. Advanced search capabilities: including exact time stamps in the video.
  3. Enhanced retrieval: Use the RAG (retrieval-augmented generation) pattern to retrieve relevant segments from the training videos and generate detailed, informative responses using Large Language Models (LLM). This ensures that users not only find the right video but also get specific answers to their queries.


  • Improved Search: Employees can easily find specific training content, enhancing their learning experience.
  • Time Efficiency: Reduces the time spent searching for information, allowing employees to focus more on learning and development.
  • Contextual Relevance: Delivers accurate and contextually relevant search results, improving the overall effectiveness of training programs.
  • Enhanced User Experience: Provides seamless and intuitive search experience, increasing user satisfaction and engagement.

General Implementation Steps

Below is a general description of the implementation steps.

Visit the Azure AI Video Indexer sample repository on GitHub for a detailed guide on implement the solution.

Step 1: Data Indexing with Video Indexer

  • Extract Metadata: Use Azure AI Video Indexer to analyze and extract metadata from your training videos. This includes transcripts, keywords, OCR and other relevant data.
  • Index Metadata: Index the extracted metadata using Azure AI Search or other Vector DB to create a searchable database.

Step 2: Configure Azure OpenAI Service

  • Set Up ChatGPT: Configure the Azure OpenAI Service to access the ChatGPT model, enabling natural language understanding and generation capabilities.
  • Integrate with Search: Connect the Azure OpenAI Service to your indexed data, allowing it to process and respond to user queries.

Step 3: Develop Search Interface

  • User Interface: Create a user-friendly interface where employees can input their queries. This interface should support natural language queries and provide clear, concise search results.
  • Query Processing: Implement query processing using the RAG pattern. Retrieve relevant video segments from the indexed data and use ChatGPT to generate detailed responses.

Example Scenario

For example, an auto dealer sales representative wants to learn more about a new car model, Lux XS, to prepare for an upcoming sales event.   The representative quickly accesses the Video Q&A internal portal and asks, “What are the engine specifications of the Lux XS model?” The response provides a list of training videos and the time stamps of relevant content.  They can click on items in the list and view the exact spot in the video.

Want to explore Video Indexer and stay up to date on all releases? Here are some helpful resources:


This article was originally published by Microsoft's Azure AI Services Blog. You can find the original article here.