Abstract: Techniques for vector database indexing are described. In an example, a plurality of unstructured data is obtained. Vector embeddings are performed on each unstructured data from amongst the plurality of unstructured data to generate a plurality of vectors. Each vector from amongst the plurality of vectors is indicative of attributes of the unstructured data from amongst the plurality of unstructured data. The plurality of unstructured data and the plurality of vectors is stored in the vector database. A vector index corresponding to the plurality of vectors based on a first indexing mechanism is generated where the vector index includes at least one cluster of vectors with similar attributes. An index mapping is performed to store the vector index into a relational database.

Abstract: A clustering system for synchronously clustering work items is provided. The clustering system comprises a user interface configured to enable a user to define one or more parameters. The clustering system further includes a clustering module communicatively coupled to the user interface and configured to retrieve a plurality of work items based on the one or more parameters, retrieve a plurality of embeddings corresponding to the plurality of work items and generate a plurality of work group by clustering embeddings with vector similarities. Each work group comprises a set of homogenous work items, in other words similar work items.