Kusto as a Vector database for AI embeddings

Azure Data Explorer, commonly known as Kusto, is an innovative cloud-based service from Microsoft that provides advanced data analytics capabilities. It is designed to handle large volumes of diverse data, making it an ideal platform for real-time analysis and insights. One of the key strengths of Kusto is its ability to work with unstructured data through its dynamic data type, which can accommodate arrays and property bags. This flexibility makes Kusto an excellent choice for storing and querying vector data.

In the context of machine learning and data science, vectors are often used to represent complex data points, such as embeddings generated by language models like those offered by OpenAI. These embeddings capture the semantic essence of texts, images, or other data types and are crucial for tasks like semantic search, where the goal is to find the most relevant information based on meaning rather than exact keyword matches.

Kusto's dynamic data type is perfectly suited for storing these vector representations. By allowing users to augment vector data with additional metadata in separate columns, Kusto enhances the richness and utility of the stored information. Moreover, Kusto's built-in function, series_cosine_similarity_fl, facilitates efficient vector similarity searches, enabling users to quickly and accurately identify similar items within large datasets.

To showcase the power of Kusto in combination with OpenAI embeddings, a compelling demo scenario has been crafted. The scenario involves precomputed embeddings generated by OpenAI's API, which are then stored in Kusto. Users can input raw text queries, which are converted into embeddings using OpenAI's API, and then use Kusto's cosine similarity search to find the most semantically relevant entries in the stored dataset.

This integration of Kusto and OpenAI embeddings opens up a world of possibilities for semantic search applications. Whether you are looking to enhance your knowledge management system, build a sophisticated recommendation engine, or simply explore the vast landscape of data with a semantic lens, Kusto and OpenAI provide the tools you need.

To get started with this powerful combination, a detailed Notebook is provided, guiding users through the process of using precomputed embeddings, storing them in Kusto, and performing semantic searches. This hands-on approach is designed to familiarize users with the capabilities of both Kusto and OpenAI embeddings, providing a solid foundation for building advanced analytics solutions.

Dive into the world of advanced analytics with Azure Data Explorer and OpenAI embeddings, and discover the potential of semantic search in handling and deriving insights from large-scale data. Get started for free with Kusto and unlock the power of real-time analytics and vector database capabilities.