PyLate

Scoring function used in late interaction models like ColBERT that computes query-document relevance by finding maximum similarity between each query token and document tokens, then summing.

Maximum Similarity late interaction function introduced by ColBERT for ranking. Calculates cosine similarity between query and document token embeddings, keeping maximum score per query token for highly effective long-document retrieval.

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.

The Natural Language Toolkit (NLTK) is a leading Python platform for building programs to work with human language data. It provides easy-to-use interfaces to lexical resources like WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.