No matter how labor-intensive and hard to edit your files are, DocHub delivers a simple way to change them. You can change any part in your HWPML with no effort. Whether you need to tweak a single component or the entire document, you can entrust this task to our powerful tool for fast and quality results.
Additionally, it makes sure that the final form is always ready to use so that you can get on with your tasks without any delays. Our all-encompassing group of features also comes with pro productivity tools and a catalog of templates, enabling you to take full advantage of your workflows without losing time on recurring activities. On top of that, you can gain access to your papers from any device and integrate DocHub with other solutions.
DocHub can handle any of your document management activities. With a great deal of features, you can create and export paperwork however you want. Everything you export to DocHub’s editor will be stored safely for as long as you need, with rigid protection and information safety frameworks in place.
Check DocHub today and make managing your paperwork easier!
what is going on guys welcome back in this video today weamp;#39;re going to learn how to use large language models locally to create embeddings and to store these embeddings in a vector store in order to be able to do similarity search for example for recommender systems so let us get right into it all right so weamp;#39;re going to learn how to use large language models locally to create embeddings and embeddings are basically just representations in v VOR space of some given data so for example you can have a collection of news articles or titles of news articles and you can embed them into Vector space meaning that one title is then represented as a vector of a certain size all the vectors have the same size and all these vectors can then be stored in a vector database or a vector store and then you can perform similarity search to find the most similar uh articles or article titles given a new article title which can be very useful for recommender systems because you can
