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What is a vector database?
- A vector database is a system that stores and organizes data using vectors, which are mathematical representations of unstructured data.
- These specialized databases make use of vector embeddings and numerical representations to efficiently manage and query large sets of complex high-dimensional data that traditional databases struggle with.
- They play an important role in AI and ML applications, which increasingly rely on processing and managing large amounts of unstructured data, such as text, images, videos, and geospatial coordinates.
- The databases convert unstructured data into vector embeddings, enabling advanced indexing techniques for efficient deletion, retrieval, updating, and filtering operations.
- Advanced indexing techniques such as approximate neighbour search and hierarchical small navigable world are used to ensure rapid search operations.
- These databases offer cutting-edge performance, scalability, and cost-effectiveness compared to traditional relational databases, while streamlining AI model output and simplifying the management of new data over time.
- Vector databases work by using a similarity search to locate vectors that reside in the vicinity of a query vector within a multidimensional space.
- Query execution involves applying a similarity metric to find a vector that is as similar as possible to the query, rather than searching for exact matches as in traditional relational databases.
- The indexing phase is critical to efficient vector retrieval and involves selecting a suitable machine learning model for generating vector embeddings based on the data type, converting data into embeddings, and saving relevant metadata.
- The querying phase runs queries using the same model that was used to store data in the database, retrieves similar results, and applies metadata filters to narrow down the search output.
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