GenAI Stack: Building a Video Analysis and Transcription Chatbot | Docker

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GenAI Stack: Building a Video Analysis and Transcription Chatbot | Docker

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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 a video transcription and chat project that leverages the GenAI Stack, along with seamless integration provided by Docker, to streamline video content processing and understanding. 

High-level architecture 

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 Pinecone to store and retrieve the embeddings of video transcriptions. 

Two-part illustration showing “yt-whisper” process on the left, which involves downloading audio, transcribing it using whisper (an audio transcription system), computing embeddings (mathematical representations of the audio features), and saving those embeddings into pinecone. On the right side (labeled "dockerbot"), the process includes computing a question embedding, completing a chat with the question combined with provided transcriptions and knowledge, and retrieving relevant transcriptions.
Figure 1: Schematic diagram outlining a two-component system for processing and interacting with video data.

The application’s high-level service architecture includes the following:

  • yt-whisper: A local service, run by Docker Compose, that interacts with the remote OpenAI and Pinecone services. Whisper is an automatic speech recognition (ASR) system developed by OpenAI, representing a significant milestone in AI-driven speech processing. Trained on an extensive dataset of 680,000 hours of multilingual and multitask supervised data sourced from the web, Whisper demonstrates remarkable robustness and accuracy in English speech recognition. 
  • Dockerbot: A local service, run by Docker Compose, that interacts with the remote OpenAI and Pinecone services. The service takes the question of a user, computes a corresponding embedding, and then finds the most relevant transcriptions in the video knowledge database. The transcriptions are then presented to an LLM, which takes the transcriptions and the question and tries to provide an answer based on this information.
  • OpenAI: The OpenAI API provides an LLM service, which is known for its cutting-edge AI and machine learning technologies. In this application, OpenAI’s technology is used to generate transcriptions from audio (using the Whisper model) and to create embeddings for text data, as well as to generate responses to user queries (using GPT and chat completions).
  • Pinecone: A vector database service optimized for similarity search, used for building and deploying large-scale vector search applications. In this application, Pinecone is employed to store and retrieve the embeddings of video transcriptions, enabling efficient and relevant search functionality within the application based on user queries.

Getting started

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.

Clone the repository 

The next step is to clone the repository:

git clone https://github.com/dockersamples/docker-genai.git

The project contains the following directories and files:

├── docker-genai/
│ ├── docker-bot/
│ ├── yt-whisper/
│ ├── .env.example
│ ├── .gitignore
│ ├── LICENSE
│ ├── README.md
│ └── docker-compose.yaml

Specify your API keys

In the /docker-genai directory, create a text file called .env, and specify your API keys inside. The following snippet shows the contents of the .env.example file that you can refer to as an example.

#-------------------------------------------------------------
# OpenAI
#-------------------------------------------------------------
OPENAI_TOKEN=your-api-key # Replace your-api-key with your personal API key

#-------------------------------------------------------------
# Pinecone
#--------------------------------------------------------------
PINECONE_TOKEN=your-api-key # Replace your-api-key with your personal API key

Build and run the application

In a terminal, change directory to your docker-genai directory and run the following command:

docker compose up --build

Next, Docker Compose builds and runs the application based on the services defined in the docker-compose.yaml 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 8503 and 8504. The two services are complementary to each other.

The yt-whisper service is running on port 8503. This service feeds the Pinecone database with videos that you want to archive in your knowledge database. The next section explores the yt-whisper service.

Using yt-whisper

The yt-whisper 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 yt-whisper service at http://localhost:8503. Once the application appears, specify a YouTube video URL in the URL field and select Submit. The example shown in Figure 2 uses a video from David Cardozo.

Screenshot showing example of processed content with "download transcription" option for a video from david cardozo on how to "develop ml interactive gpu-workflows with visual studio code, docker and docker hub. "
Figure 2: A web interface showcasing processed video content with a feature to download transcriptions.

Submitting a video

The yt-whisper service downloads the audio of the video, then uses Whisper to transcribe it into a WebVTT (*.vtt) 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.

Accessing Dockerbot chat service

You can now access the Dockerbot chat service on port 8504 and ask questions about the videos as shown in Figure 3.

Screenshot of dockerbot interaction with user asking a question about nvidia containers and dockerbot responding with links to specific timestamps in the video.
Figure 3: Example of a user asking Dockerbot about NVIDIA containers and the application giving a response with links to specific timestamps in the video.

Conclusion

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. 

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