Building a Video Analysis and Transcription Chatbot with the GenAI Stack

Videos are full of valuable information, but tools are often needed to help find it. From educationa

Videos are full of valuable information, but tools are often needed to help find it. From educational institutions seeking to analyze lectures and tutorials to businesses aiming to understand customer sentiment in video reviews, transcribing and understanding video content is crucial for informed decision-making and innovation. Recently, advancements in AI/ML technologies have made this task more accessible than ever.  Developing GenAI technologies with Docker opens up endless possibilities for unlocking insights from video content. By leveraging transcription, embeddings, and large language models (LLMs), organizations can gain deeper understanding and make informed decisions using diverse and raw data such as videos.  In this article, we’ll dive into that leverages the , along with seamless integration provided by Docker, to streamline video content processing and understanding.  The application’s architecture is designed to facilitate efficient processing and analysis of video content, leveraging cutting-edge AI technologies and containerization for scalability and flexibility. Figure 1 shows an overview of the architecture, which uses to store and retrieve the embeddings of video transcriptions.  The application’s high-level service architecture includes the following: To get started, complete the following steps: The application is a chatbot that can answer questions from a video. Additionally, it provides timestamps from the video that can help you find the sources used to answer your question. The next step is to clone the repository: The project contains the following directories and files: In the directory, create a text file called , and specify your API keys inside. The following snippet shows the contents of the file that you can refer to as an example. In a terminal, change directory to your directory and run the following command: Next, Docker Compose builds and runs the application based on the services defined in the file. When the application is running, you’ll see the logs of two services in the terminal. In the logs, you’ll see the services are exposed on ports and . The two services are complementary to each other. The service is running on port . This service feeds the Pinecone database with videos that you want to archive in your knowledge database. The next section explores the service. The service is a YouTube video processing service that uses the OpenAI Whisper model to generate transcriptions of videos and stores them in a Pinecone database. The following steps outline how to use the service. Open a browser and access the service at . Once the application appears, specify a YouTube video URL in the URL field and select . The example shown in Figure 2 uses from David Cardozo. The service downloads the audio of the video, then uses Whisper to transcribe it into a ( ) format (which you can download). Next, it uses the “text-embedding-3-small” model to create embeddings and finally uploads those embeddings into the Pinecone database. After the video is processed, a video list appears in the web app that informs you which videos have been indexed in Pinecone. It also provides a button to download the transcript. You can now access the Dockerbot chat service on port and ask questions about the videos as shown in Figure 3. In this article, we explored the exciting potential of GenAI technologies combined with Docker for unlocking valuable insights from video content. It shows how the integration of cutting-edge AI models like Whisper, coupled with efficient database solutions like Pinecone, empowers organizations to transform raw video data into actionable knowledge.  Whether you’re an experienced developer or just starting to explore the world of AI, the provided resources and code make it simple to embark on your own video-understanding projects.