---
title: GenAI video transcription and chat
linkTitle: Video transcription and chat
description: Explore a generative AI video analysis app that uses Docker, OpenAI, and Pinecone.
keywords: python, generative ai, genai, llm, whisper, pinecone, openai, whisper
summary: |
  Learn how to build and deploy a generative AI video analysis and
  transcription bot using Docker.
tags: [ai]
aliases:
  - /guides/use-case/genai-video-bot/
params:
  time: 20 minutes
---

## Overview

This guide presents a project on video transcription and analysis using a set of
technologies related to the
[GenAI Stack](https://www.docker.com/blog/introducing-a-new-genai-stack/).

The project showcases the following technologies:

- [Docker and Docker Compose](#docker-and-docker-compose)
- [OpenAI](#openai-api)
- [Whisper](#whisper)
- [Embeddings](#embeddings)
- [Chat completions](#chat-completions)
- [Pinecone](#pinecone)
- [Retrieval-Augmented Generation](#retrieval-augmented-generation)

> **Acknowledgment**
>
> This guide is a community contribution. Docker would like to thank
> [David Cardozo](https://www.davidcardozo.com/) for his contribution
> to this guide.

## Prerequisites

- You have an [OpenAI API Key](https://platform.openai.com/api-keys).

  > [!NOTE]
  >
  > OpenAI is a third-party hosted service and [charges](https://openai.com/pricing) may apply.

- You have a [Pinecone API Key](https://app.pinecone.io/).
- You have installed the latest version of [Docker Desktop](/get-started/get-docker.md). Docker adds new features regularly and some parts of this guide may work only with the latest version of Docker Desktop.
- You have a [Git client](https://git-scm.com/downloads). The examples in this section use a command-line based Git client, but you can use any client.

## About the application

The application is a chatbot that can answer questions from a video. In
addition, it provides timestamps from the video that can help you find the sources used to answer your question.

## Get and run the application

1. Clone the sample application's repository. In a terminal, run the following
   command.

   ```console
   $ git clone https://github.com/Davidnet/docker-genai.git
   ```

   The project contains the following directories and files:

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

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

   ```text
   #----------------------------------------------------------------------------
   # 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
   ```

3. Build and run the application. In a terminal, change directory to your
   `docker-genai` directory and run the following command.

   ```console
   $ docker compose up --build
   ```

   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 2 services in the terminal.

   In the logs, you'll see the services are exposed on ports `8503` and `8504`.
   The two services are complimentary 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 following section explores this service.

## Using the yt-whisper service

The yt-whisper service is a YouTube video processing service that uses the OpenAI