BGE models on the HuggingFace are one of the best open-source embedding models. BGE model is created by the Beijing Academy of Artificial Intelligence (BAAI). Huggingface Endpoints · HuggingFaceEmbeddings · ChatHuggingFace

Compute query embeddings using a HuggingFace transformer model. Parameters: text (str) – The text to embed. Returns: Embeddings for the text. Return type ...

HuggingFace sentence_transformers embedding models. To use, you should have the sentence_transformers python package installed.

14 мая 2024 г. · This new Python package is designed to bring the power of the latest development of Hugging Face into LangChain and keep it up to date.

We can also access embedding models via the Hugging Face Inference API, which does not require us to install sentence_transformers and download models locally.

HuggingFace sentence_transformers embedding models. To use, you should have the sentence_transformers python package installed.

This notebook demonstrates how you can build an advanced RAG (Retrieval Augmented Generation) for answering a user's question about a specific knowledge base.

Build context-aware reasoning applications. Contribute to langchain-ai/langchain development by creating an account on GitHub.

29 апр. 2024 г. · LangChain simplifies the process of accessing Hugging Face embeddings by encapsulating the complexities within its intuitive interface. Let's ...

This code is a Python function that loads documents from a directory and returns a list of dictionaries containing the name of each document and its chunks.