Dockerbot Functionality and Application Architecture

In this example, the Dockerbot answers the question and provides links to the video with timestamps, which may contain more information about the answer. The dockerbot service takes the question, turns it into an embedding using the text-embedding-3-small model, queries the Pinecone database to find similar embeddings, and then passes that context into the gpt-4-turbo-preview to generate an answer. 3. Select the first link to see what information it provides. Based on the previous example, select [https://www.youtube.com/watch?v=yaQZFhrW0fU&t=553s](https://www.youtube.com/watch?v=yaQZFhrW0fU&t=553s). In the example link, you can see that the section of video perfectly answers the question, "What is a sugar cookie?". ## Explore the application architecture The following image shows the application's high-level service architecture, which includes: - yt-whisper: A local service, ran by Docker Compose, that interacts with the remote OpenAI and Pinecone services. - dockerbot: A local service, ran by Docker Compose, that interacts with the remote OpenAI and Pinecone services. - OpenAI: A remote third-party service. - Pinecone: A remote third-party service.

This section explains how the dockerbot service uses embeddings, Pinecone database queries, and the GPT-4 Turbo model to answer questions. The architecture consists of yt-whisper and dockerbot local services, which interact with remote OpenAI and Pinecone services. Yt-whisper downloads audio, transcribes it, computes embeddings, and saves them into Pinecone, while dockerbot computes question embeddings, completes chats with the question and provided transcriptions, retrieves relevant transcriptions, and provides links to specific video timestamps to offer more context.