com.openai.unity VS product

I am trying to block the OpenAI website through Defender web filtering for Windows and macOS. It seems to block fine on Edge, but not for Chrome when trying to block the subdomains like chat.openai.com. If we try openai.com then this gets blocked fine on both Chrome and Edge.

NeuralPit platform allows team members to collaborate on projects and seamlessly upload and analyze data in various formats, such as PDF, DOCX, Excel, CSV, video, audio, website URLs, YouTube links, and images. Allow teams to unearth insights, brainstorm ideas, and visualize and interact with data anytime, anywhere. NeuralPit provides small and medium businesses, as well as professionals, with access to features similar to those of Microsoft Copilot and OpenAI, but at significantly lower costs, particularly for teams.

If you have been keeping up to date with the Spring ecosystem, you may have heard about the Spring AI Project, It is currently in its pre-release state but it provides an innovative abstraction toolkit fostering AI integration across applications. The experimental Spring AI project was introduced during the SpringOne conference and allows the creation of AI applications by using common concepts of Spring. Currently, the project integrates Azure OpenAI and OpenAI as AI backends. Use cases like content generation, code generation, semantic search, and summarization are supported by the project.

The official Todoist website can be found at https://todoist.com, and information about OpenAI is available at https://openai.com. The names Todoist and OpenAI as well as related names, marks, emblems, and images are registered trademarks of their respective owners.

A Generative Pre-Trained Transformer (GPT) is a type of Large Language Model (LLM) and they are the hot topic in the technology world this year and many companies are scrambling to add this technology to their products. Creating and training these large models can be a very complex, time consuming and expensive. You may think that you cannot use this technology since it is so complex and expensive but companies like OpenAI have done a ton of work to create useful models and setup platforms exposing APIs to use them. If you have ever used an API where you send some data in, it does some magic behind the scenes and you get some data to use in a response, then you can integrate this cutting edge technology into your application. Let’s take a look at how we can setup a Full stack web app which lets us ask questions sent to OpenAI and stream the response.

OpenAI

We’re excited to walk our first steps toward semantic search. We can’t wait to hear your thoughts on integrating Meilisearch as a vector store. You can give your feedback in this Github discussion.

Looks good in fact! We will eventually let users use third-party API like OpenAi and Hugging Face to compute the embedding of the documents and queries. You can try our first prototype if you want.

Hey @jiripospisil,

Indeed Meilisearch does not offer an aggregation feature yet although it will be possible to get stats for the `min` and `max` values of a faceted field in the next version (v1.1)

Please tell us more about what you mean by aggregation and why it is critical for your use-case by creating a discussion on Github here (https://github.com/meilisearch/product/discussions) or by proposing a new idea on our public portal here (https://roadmap.meilisearch.com) if you don't have a Github account.

Thank you!

If you remove the URLs from indexation, it'll generally save a ton of place and will be much, much faster to index. We are thinking about not indexing URLs by default; you can help us by explaining your use case here -> https://github.com/meilisearch/product/discussions/553

Just a detail, if you're making a `du -sh` on your computer, the size on the disk will stay unchanged because we are doing soft deletion ;). Don't worry. It will be physically deleted after a while if you need it in the future.

If you kept the default configuration of Meilisearch, the maximum size of the HTTP payload is 100Mb (for security). You change it here -> https://docs.meilisearch.com/learn/configuration/instance_op...

addDocumentsInBatches() is just an helper to send your big json array into multiple parts, not absolutely sure you'll need it. (Code -> https://github.com/meilisearch/meilisearch-js/blob/807a6d827...)

I advise you to fill out a discussion on our product repository for us to evaluate your needs, and use case and then see what we plan about that.