Windmill (YC S22) is an open source alternative to Retool and a modern Airflow. They provide a developer platform to quickly build production-grade complex workflows and integrations from minimal Python and Typescript scripts. Their one-click integration with Supabase makes it simple to launch new databases, process large quantities of data (maybe even convert them into embeddings), and build internal dashboards. - Source: dev.to / 10 months ago

Every project is a Postgres database, wrapped in a suite of tools like Auth, Storage, Edge Functions, Realtime and Vectors, and encompassed by API middleware and logs. - Source: dev.to / 10 months ago

Since launching our Vector Toolkit a few months ago, the number of AI applications on Supabase has grown - a lot. Hundreds of new databases every week are using pgvector. - Source: dev.to / 10 months ago

Hi everyone, Joshua from Hugging Face (and the creator of Transformers.js) here. Starting with embeddings, we hope to simplify and improve the developer experience when working with embeddings. Supabase already has great support for storage and retrieval of embeddings (thanks to pgvector) [0], so it feels like this collaboration was long overdue! Open-source embedding models are both smaller and more performant... - Source: Hacker News / 10 months 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 / 6 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