In an era where the need to exploit and analyze data is increasing, vector databases emerge as a vital tool enabling artificial intelligence to handle massive amounts of information. The Azure Data Explorer system, known as “Kusto,” is one of the advanced solutions that makes it possible to effectively store and manage data representations, or what is referred to as “embeddings.” In this article, we will explore how to use Kusto as a vector database, detailing the necessary steps to upload and store vector data extracted through the OpenAI API, in addition to conducting targeted search operations based on data similarity using advanced techniques. Stay tuned to explore this topic that attracts the attention of researchers and developers in the field of artificial intelligence and data analysis!
Using Kusto as a Vector Database
Kusto, also known as Azure Data Explorer, is an important tool in the field of artificial intelligence, allowing users to process and analyze large amounts of data quickly and efficiently. This section discusses how to use Kusto as a vector database, including storing vectors extracted from AI models and performing advanced searches using cosine similarity techniques. This process is crucial, especially in contexts like natural language processing and deep learning, where models require processing data in vector form to reflect complex features and characteristics.
Effectively using Kusto as a vector database requires setting up an appropriate environment that includes a Kusto server and other elements like the OpenAI API keys. The first step is to prepare the data, which is done by uploading pre-prepared vectors from sources such as embedded Wikipedia articles. After uploading the data, we must organize it further in the database in the form of tables containing the vectors and other information.
This opens up new types of queries, such as improving information retrieval based on concept-similar criteria, making searches more accurate and efficient. For example, if you have a collection of stored Wikipedia articles, you can use Kusto to perform a search for similar articles that contain matching content based on the vector present in the database.
Setting Up the OpenAI API and Retrieving Vectors
Once Kusto is set up as a database, the next step is to set up the API keys for the OpenAI federation that will be used to generate vectors from texts. The user must ensure they are using the correct model for embedding representation, and in this case, the “text-embedding-3-small” model is often preferred. This process not only requires obtaining the keys but also setting the correct permissions and a number of libraries in Python, such as the “openai” library, which enables you to connect to the OpenAI service and perform quick manipulations.
Once the API code is set up correctly, you can send texts to query and apply the embedding model to transform these texts into vector structures. This allows you to prepare the data so that you can integrate it into the Kusto database for later use in searching and advanced computational comparisons.
It is also important to note that performance is greatly affected by how these applications are set up and used together. For example, when sending a text for inquiry, this process will return vectors that can be used in cosine similarity techniques to verify how connected the stored texts are to the query. This collaboration between OpenAI and Kusto enhances the overall power of the system, providing information faster and improving the accuracy of search results.
Performing Semantic Search Using Kusto
Semantic search is one of the main uses of Kusto as a vector database, where high efficiency in data analysis and text understanding leads to accurate and effective responses. This approach relies on cosine similarity comparisons between the stored vectors and the vector resulting from the query, allowing users to find related information more precisely.
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Search is implemented through Kusto, where a query is created using information from the database along with the vector resulting from the user’s query. Kusto uses a special function known as “series-cosine-similarity-fl” to enhance search effectiveness. This function allows for accelerated vector comparisons and identifies the 10 most similar items based on the angular distance between vectors. These methods reflect the idea of logical understanding of information rather than relying solely on literal data.
Through semantic searches, users can conduct queries like “places of worship” or “unfortunate events in history”, resulting in the retrieval of relevant articles from the database, encouraging deep immersion in complex subjects. The practical applications of this process are numerous, from supporting decision-making in businesses to improving user experience across applications and websites.
Source link: https://cookbook.openai.com/examples/vector_databases/kusto/getting_started_with_kusto_and_openai_embeddings
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