Here you find the vector data that you would need if you like to play with my latest baby.It is called Prompt Quill and it will help you make nice prompts more easy. Its not just a dull Prompt helper you might know, it is the world's first RAG workflow feeded with more than 4.9 Million Prompts I did take from Civitai and other sources with their permission.The data is now prepared to deliver negative prompts as well as models that might work well with the generated prompt. Also there is a one click installer to all versions, also all versions now support image generation with the created prompts, so its really getting time you start trying it =)Find the sources you need here: <a target="_blank" rel="ugc" href="https://github.com/osi1880vr/prompt_quill">https://github.com/osi1880vr/prompt_quill</a>If you like it please leave a thumbs up =)You like to contribute, just raise an PR on github.There is new features, it now allows for a deep dive into the context aka found prompts from the vector store, you can run batches of prompt generation and you can enter your prompt in your native language, it will then get translated into english and processed from there, the translated prompt will also be shown in the output so you can have an idea what it translated to.You like to get in contact PM me here or find me on discord: <a target="_blank" rel="ugc" href="https://discord.gg/Krn9UutdGH">https://discord.gg/Krn9UutdGH</a>One last thing, I'm very interested to get this thing to life on some server, if you interested in sponsoring a long term hosting solution please lets talk :)

Prompt Quill Vector Data

This is the latest version of the data for Prompt Quill. It has two major changes compared to the older versions, first it uses a new embedding model with now 768 vectors. Further it also has two indexes that allow to also do full text search in the data. We use that as additional filters. Don't be scared when you hear full text search. The response time is still in the 5-7 ms range, last but not least I also added more data so we now got 3.98M prompts in the dataset. Why use a different embedding model? We did some tests at the time we added the additional data. The new data gave us already more detailed and more to the point images, then as an experiment we used the larger embedding model and we found its a difference like day and night. The new embedding model did find much more matching context data compared to the olde model so that in the end the resulting prompts became even more to the point. So we decided to accept the growth in space the data uses as the results are so much better.

Files with SHA256: 1f379e048f338155dc4bc57b1c132675ad25a4ccb3b434e9d56fef032817a1f9