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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.
FastText is an open-source library by Facebook for efficient learning of word representations and text classification. It generates high-dimensional vector embeddings used in vector databases for tasks like semantic search and document clustering.
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.
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.
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.
Sentence-Transformers is a Python library for creating state-of-the-art sentence, text, and image embeddings. It enables the conversion of text into high-dimensional numerical vectors that capture semantic meaning, which is essential for tasks like semantic search and Retrieval Augmented Generation (RAG), often leveraging vector databases.
This project is open-source and distributed under a defined license, as indicated by the presence of a LICENSE file within its repository.