A fast C++/Python library for Hierarchical Navigable Small World (HNSW) graph-based vector search, offering high performance for dense vector retrieval with incremental insert support.

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.

OpenAI's tokenizer library for encoding and decoding text into tokens, primarily used for calculating token counts with OpenAI's models and estimating chunk sizes for vector database document processing.

Library built on Sentence Transformers for flexible training, inference, and retrieval with state-of-the-art ColBERT models. Features FastPLAID index for efficient multi-vector late interaction retrieval with 10x storage compression and sub-200ms latency.

Python framework for AI Engineers building high-performance search and recommendation applications that combine structured and unstructured data through vector compute.