Liu stressed the importance of a purpose-built vector database, as opposed to more work-around solutions like a vector search library, because of its database functions. Enter vectors, a geometric representation of a data object used in ML that may indicate a variety of attributes, including origin, direction, and magnitude.Ī vector database, then, is purpose-built to store, index, and query large quantities of embeddings-a low-dimension space into which you can translate high-dimensional vectors for simplified ML operations. Liu offered clarity on the relationship between unstructured data, vectors, and vector databases ultimately, the surge of unstructured data-or any data that does not conform to a predefined data model-necessitated a system to easily categorize and depict that data for more simplistic consumption and management. These exciting chat solutions, however, require a robust support network of data models that underpin the tool with the accuracy needed for optimal ML outcomes.įrank Liu, ML architect at Zilliz, joined DBTA’s webinar, “Vector Databases Have Entered the Chat-How ChatGPT Is Fueling the Need for Specialized Vector Storage,” to explore how purpose-built vector databases are the key to successfully integrating with chat solutions, as well as present explanatory information on how autoregressive LMs, unstructured data, vectors, and vector databases intersect. With ChatGPT dominating the space of conversational AI and rapid, helpful response turnout, as well as OpenAI’s open source retrieval plugins for the revolutionary tool, ChatGPT will begin to permeate a variety of solutions to bring people and information closer than ever.
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