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A curated collection of papers and technical blogs focused on vector databases, semantic-based vector search, and approximate nearest neighbor search (ANN Search). These resources are essential for understanding and building large-scale information retrieval systems and 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.

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.

Google Vertex AI offers managed vector search capabilities as part of its AI platform, supporting hybrid and semantic search for text, image, and other embeddings.

Marqo is an open-source neural search engine that leverages vector representations to enable semantic search over textual data. It abstracts vector database complexity and provides a high-level interface for building advanced search applications.

NucliaDB is a commercial vector database that enables semantic and vector search across unstructured data, supporting advanced AI and ML-powered applications.

txtai is an open-source AI framework that provides semantic search and vector database capabilities for language model workflows.

A vector database is a specialized database designed to store, index, and retrieve unstructured data represented as high-dimensional vectors, enabling efficient semantic search, similarity search, and powering applications such as LLM long-term memory, semantic search, and recommendation systems.

Weaviate is an open-source, cloud-native vector database that supports fast semantic search, modular extensions, and graph-like querying, making it an ideal solution for building scalable, modern vector search applications.