vector-provision-optio VS yt-fts

If you're getting started with Weaviate, these two are probably what you need:

1. Wizard to create a docker-compose file: https://weaviate.io/developers/weaviate/installation/docker-... (e.g. choose the embedding model)

2. Sample notebook showing how to index items using the python library: https://github.com/weaviate-tutorials/vector-provision-optio...

I've been trying to solve a problem with implementing semantic search on my YouTube search engine yt-fts (https://github.com/NotJoeMartinez/yt-fts). I've managed to substantially speed up search results by storing subtitle embeddings in Chroma. But a bigger problem has been with how to properly segment the text in a way that accounts for the duration and context of word embeddings while returning precise time stamps. This a blog post exploring what I've tried so far.

Hey everyone, I just wanted to share my latest project yt-fts it's a command line tool written in python that allows you to search YouTube videos for keywords. It downloads video transcripts with yt-dlp, parses command line input with click and integrates full text/semantic search with sqlite and openAI python libraries.

Thank you! I was able to integrate this into the project[1]. I'm also looking into using your openai-to-sqlite[2] library for semantic search.

[1]https://github.com/NotJoeMartinez/yt-fts/pull/25

If you've ever needed to search a large YouTube channel for keywords, yt-fts (YouTube Full Text Search) is a simple python script that uses yt-dlp to scrape all of a youtube channels subtitles and load them into an sqlite database that is searchable from the command line. It allows you to query a channel for specific key word or phrase and will generate time stamped youtube urls to the video containing the keyword. You can also export these search queries to a csv.