GloVe is a widely used method for generating word embeddings using co-occurrence statistics from text corpora. These embeddings are commonly used as input to vector databases for semantic search and other vector-based information retrieval tasks.

Word2vec is a popular machine learning technique for generating vector embeddings based on the distributional properties of words in large corpora. It is directly relevant to vector databases as it produces the high-dimensional vector representations stored and indexed by these databases for vector search and similarity tasks.

Gensim is a Python library for topic modeling and vector space modeling, providing tools to generate high-dimensional vector embeddings from text data. These embeddings can be stored and efficiently searched in vector databases, making Gensim directly relevant to vector search use cases.

spaCy is an industrial-strength NLP library in Python that provides advanced tools for generating word, sentence, and document embeddings. These embeddings are commonly stored and searched in vector databases for NLP and semantic search applications.

Applied book on using deep learning for search, including dense vector representations, semantic search, and neural ranking, all directly relevant to building applications on top of vector databases.

Healthsearch is an open-source demo application that uses Weaviate as a vector database to retrieve supplement products based on user-written reviews and queries, illustrating real-world semantic product search over vector embeddings.