Gensim is an open-source Python library designed for natural language processing (NLP) tasks. It specializes in topic modeling and document similarity analysis, making it easier to extract insights from large text corpora. Gensim uses efficient algorithms to handle large datasets, allowing users to work with data that may not fit into memory. One of Gensim's key features is its ability to create vector representations of words and documents, which helps in understanding relationships between them. It supports various models, including Word2Vec, Doc2Vec, and Latent Dirichlet Allocation (LDA), enabling users to perform complex analyses with relative ease.