Document loaders have a load() method that loads data from the configured source and returns it as documents. They may also have a lazy_load() method for loading data into memory as and when they are needed.

LlamaHub is a library of data loaders, readers, and tools created by the LlamaIndex community. It provides utilities to easily connect LLMs to diverse knowledge sources.

Vector databases can be used to store and serve machine learning models and their corresponding embeddings. The primary application is similarity search (also semantic search),

Stochastic parrots refers to LLMs that can produce convincing language but lack any true comprehension of the meaning behind words.

maintain the Transformers Python library, which is used for NLP tasks, includes implementations of state-of-the-art and popular models like Mistral 7B, BERT, and GPT-2, and is compatible with PyTorch, TensorFlow, and JAX.

DocArray as our in-memory vector storage. DocArray provides various features like advanced indexing, comprehensive serialization protocols, a unified Pythonic interface, and more. Further, it offers efficient and intuitive handling of multimodal data for tasks such as natural language processing, computer vision, and audio processing.

a chatbot, ChatLangChain, that can answer questions about the LangChain documentation.

in models with between 2 and 7 billion parameters, new capabilities emerge such as the ability to generate different creative text in formats like poems, code, scripts, musical pieces, emails, and letters, and to answer even open-ended and challenging questions in an informative way.

Hugging Face offer various other libraries within their ecosystem, including Datasets for dataset processing, Evaluate for model evaluation, Simulate for simulation, and Gradio for machine learning demos.