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Vector databases have built-in search capability that quickly delivers optimized and relevant results, especially with complex data sets such as image, video, and audio.
Learn how vector databases enable advanced AI applications, semantic search, and efficient data retrieval for unstructured datasets.
Vector databases and search aren’t new, but vectorization is essential for generative AI and working with LLMs. Here's what you need to know.
Commitment to Open-Source Ecosystems “As data infrastructure for unstructured data, Milvus is revolutionary because it processes vector embeddings and not just strings.
Vector databases, which catalog and structure data, are crucial to LLMs. Here are seven startups that are building them.
CockroachDB’s approach tackles the complex problem of distributed vector indexing. The company’s new C-SPANN vector index uses the SPANN algorithm, which is based on Microsoft research.
Vector Back Office is a transportation management solution to help carriers and shippers streamline data entry.
We look at the use of vector data in AI, how vector databases work, plus vector embedding, the challenges for storage of vector data and the key suppliers of vector database products ...
The Vector Flow platform leverages AI algorithms to aggregate data and automate security functions related to physical identity and access operations, security operation center (SOC) automation ...