Hugging Face
Replika
LangChain
Mitsuku
Haystack NLP Framework
Cleverbot.io
Kuki
Civitai
Embeddinghub
MindsDB
Lionbridge
Zetane Systems
Adimen
Tamr
SuperDuperDB
Deepnote
Hugging Face EmbeddinghubBased on our record, Hugging Face seems to be a lot more popular than Embeddinghub. While we know about 254 links to Hugging Face, we've tracked only 2 mentions of Embeddinghub. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
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 / 12 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
Featureform is a virtual feature store. It enables data scientists to define, manage, and serve their ML model's features. Featureform sits atop your existing infrastructure and orchestrates it to work like a traditional feature store. By using Featureform, a data science team can solve the organizational problems:. Source: almost 2 years ago
Usually embeddings — dense numerical representations of real-world objects and relationships, expressed as a vector — are stored in database servers such as PostgreSQLEmbedding. However Embeddinghub makes it easier to store your embeddings and load them. You can get started with minimal setup, and it also makes your code look less verbose as compared to, say, building a KNN model using scikit-learn. - Source: dev.to / about 2 years ago
Replika - Your Ai friend
MindsDB - We are an open-source project that enables you to do Machine Learning using SQL directly from the Database.
LangChain - Framework for building applications with LLMs through composability
Lionbridge - Translation productivity platform
Mitsuku - Browser-based, AI chat bot.
Zetane Systems - Powerful software for AI in business & industry