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.

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),

prompt-guided technique called Chain of Density (CoD) to incrementally increase the information density of GPT-4 generated summaries while controlling length.

We can perform simple vector arithmetic with these vectors, for example, the vector for king minus man plus the vector for woman gives us a vector that comes close to queen.

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.

The first step in training an LLM is tokenization. This process involves building a vocabulary, which maps tokens to unique numerical representations so that they can be processed by the model, given that LLMs are mathematical functions that require numerical inputs and outputs.

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.

In LangChain, we can also extract information from the conversation as facts and store these by integrating a knowledge graph as the memory.

milvus is immensely popular; however, other libraries such as qdrant, weviate, and chroma have been catching up.