The goal of this project is to develop a system that processes YouTube video transcripts, creates embeddings for smaller document chunks, and facilitates user interaction through a chat interface. The system will enable users to ask questions and receive relevant content along with context from the original video, including the URL to the video and the relevant timestamps. This post will be updated as I work through the concepts and start completing tasks. Update #1: To add a little more clarity, I am architecting this in a way where there are components that will be added to a pipeline, and then the pipeline will be run periodically to check for new videos and get the transcript for them. I also need to add a component that checks for the last video id that was processed and use that id to check if there are latest videos. # Components: ## get_youtube_transcript #### Inputs: ``youtube URL`` #### Outputs: ``transcript`` ``message`` ## clean_transcript #### Inputs: ``transcript`` #### Outputs: ``cleaned transcript`` ``message`` ## save_cleaned_transcript #### Input: ``cleaned transcript`` #### Output: ``message`` ## chunk_transcript #### Inputs: ``cleaned transcript`` #### Outputs: ``chunks`` ``message`` ## create_embeddings #### Inputs: ``chunks`` #### Outputs: ``embeddings`` ``message`` ## save_embeddings #### Inputs: ``embeddings`` #### Outputs: ``message``

""" Developers can now build fast speech-to-speech experiences into their applications Today, we're introducing a public beta of the Realtime API, enabling all paid developers to build low-latency, multimodal experiences in their apps. Similar to ChatGPT’s Advanced Voice Mode, the Realtime API supports natural speech-to-speech conversations using the six preset voices(opens in a new window) already supported in the API. """ https://openai.com/index/introducing-the-realtime-api/

When you are working with AI in your code editor, keep all of your logic in one file while you iterate with the AI. It seems to be much faster and better at keeping relevant context that trying to work with code split out into different files.

Just rounded up the installing python and anaconda tutorial, and I deduced the following from Dave breakdown, python is a simpler language to learn when it come any form of development compared to R and other languages especially for beginners, composing of numerous libraries and packages for deployment and Artificial Intelligence. Anaconda on the other hand makes it easy to have an environment where are you package can be created, implemented and deployed without having to bother about application breakage of missing out on file folder and packages as you can emcompas everything you need in Anaconda and build and efficient Artificial intelligence model relating and comparing to a python virtual environment

When starting out your data career it's hard to know what skills to focus on. There are many blog posts offering recommendations, but none of which are backed by data. "Isn't that exactly what a data professional should do?" thought Luke Barousse. So he made datanerd.tech, a data platform giving real-time insight into what the most in-demand data skills are. One downside of the platform was that it aggregated all data, so you were unable to see how skill importance changed over time. After writing a bit of Python and SQL I made a Github pull request doing exactly that. The feature is now live, so feel free to have a look.