The Overseer part In truth, I'm less familiar with and it's full feature set, But at a quick glance it looks like it would be similar to ombi (https://ombi.io/). I know for a fact that you can set up OMBI to let users just type in stuff and it will grab it for them. I didn't like that and opted to manually approve requests. Source: 11 months ago

Now add Ombi so that shared plex users have the ability to request content by themselves. Source: about 1 year ago

Now pipe that all through ombi and you will live like a king! Also, don't forget to add your watchlist from plex to radarr and sonarr. Source: about 1 year ago

If what you're looking for is a request handled from the user's you give access to. Then you should check out Ombi. Ombi. Source: over 1 year ago

I use Jellyfin to host my media and Ombi for users to request media. Ombi sends user requests to Radarr for movies, and Sonarr for TV shows. Source: over 1 year ago

We will use the OpenAI embeddings API to convert the content of the blog posts into vector embeddings. You will need to sign up for an API key on the OpenAI website to use the API. You will need to provide your credit card information as there is a cost associated with using the API. You can review the pricing on the OpenAI website. There are alternatives to generate embeddings. Hugging Face provides... - Source: dev.to / 5 days ago

Hugging-face 🤗 is a repository to host all the LLM models available in the world. https://huggingface.co/. - Source: dev.to / 13 days ago

HuggingFaceEmbeddings is a function that we use for converting our documents to vector which is called embedding, you can use any embedding model from huggingface, it will load the model on your local computer and create embeddings(you can use external api/service to create embeddings), then we just pass this to context and create index and store them into folder so we can reuse them and don't need to recalculate it. - Source: dev.to / about 1 month ago

The only requirement for this tutorial is to have an Hugging Face account. In order to get it:. - Source: dev.to / about 2 months ago

Finally, you'll need to download a compatible language model and copy it to the ~/llama.cpp/models directory. Head over to Hugging Face and search for a GGUF-formatted model that fits within your device's available RAM. I'd recommend starting with TinyLlama-1.1B. - Source: dev.to / about 2 months ago