Loading...
pymilvus is the official Python SDK for Milvus, allowing developers to interact programmatically with the Milvus vector database. It provides utilities for transforming unstructured data into vector embeddings and supports advanced features such as reranking for optimized search results. The pymilvus[model] variant includes utilities for generating vector embeddings from text using built-in models.
No content provided
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.
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.
A Python library for creating sentence, text, and image embeddings, enabling the conversion of text into high-dimensional numerical vectors that capture semantic meaning. It is essential for tasks like semantic search and Retrieval Augmented Generation (RAG), which often leverage vector databases.
A Python library for generating high-quality sentence, text, and image embeddings. It simplifies the process of converting text into dense vector representations, which are fundamental for similarity search and storage in vector databases.