#Reader

Reader transforms URLs into LLM-compatible inputs and offers an advanced web search function for updated knowledge access. It includes features like adaptive crawling and image reading, supported by a stable, free API.

This library enables efficient handling of TFRecord files in Python, compatible with both uncompressed and GZIP compressed formats. It offers IterableDataset support for PyTorch, facilitating optimal data loading, and underscores the importance of index files for multi-worker environments. It provides tools for managing both finite and infinite datasets, integrates shuffling, and supports transformations including image decoding and sequence padding. Its compatibility with tf.train.Example and tf.train.SequenceExample ensures flexible data management.