{"id":1484,"date":"2023-11-13T05:00:57","date_gmt":"2023-11-13T13:00:57","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/semantic-kernel\/?p=1484"},"modified":"2023-11-13T08:29:55","modified_gmt":"2023-11-13T16:29:55","slug":"building-ai-applications-with-microsoft-semantic-kernel-and-mongodb-atlas-vector-search","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/agent-framework\/building-ai-applications-with-microsoft-semantic-kernel-and-mongodb-atlas-vector-search\/","title":{"rendered":"Building AI Applications with Microsoft Semantic Kernel and MongoDB Atlas Vector Search"},"content":{"rendered":"

As part of this guest blogpost by the MongoDB Atlas Vector Search team, we are excited to announce native support for MongoDB Atlas Vector Search<\/a>\u00a0in\u00a0Microsoft Semantic Kernel<\/a>. With this integration, users can bring the power of LLMs (large language models) to their proprietary data securely, and build generative AI applications using RAG (retrieval-augmented generation) with programming languages like Python and C#. The accompanying tutorial will walk you through an example.<\/p>\n

What is MongoDB Atlas Vector Search?<\/h2>\n

MongoDB Atlas Vector Search is a fully managed service that simplifies the process of effectively indexing high-dimensional vector embedding data within MongoDB and being able to perform fast vector similarity searches.<\/p>\n

Embedding refers to the representation of words, phrases, or other entities as dense vectors in a continuous vector space. It’s designed to ensure that words with similar meanings are grouped closer together. This method helps computer models better understand and process language by recognizing patterns and relationships between words and is what allows us to search by semantic meaning.<\/p>\n

When data is converted into numeric vector embeddings using encoding models, these embeddings can be stored directly alongside their respective source data within the MongoDB database. This co-location of vector embeddings and the original data not only enhances the efficiency of queries but also eliminates potential synchronization issues. By avoiding the need to maintain separate databases or synchronization processes for the source data and its embeddings, MongoDB provides a seamless and integrated data retrieval experience.<\/p>\n

This consolidated approach streamlines database management and allows for intuitive and sophisticated semantic searches, making the integration of AI-powered experiences easier.<\/p>\n

Microsoft Semantic Kernel and MongoDB<\/h2>\n

This combination enables developers to build AI-powered intelligent applications using MongoDB Atlas Vector Search and large language models from providers like OpenAI, Azure OpenAI, and Hugging Face.<\/p>\n

Despite all their incredible capabilities, LLMs have a knowledge cutoff date and often need to be augmented with proprietary, up-to-date information for the particular business that an application is being built for. This \u201clong-term memory for LLM\u201d capability for AI-powered intelligent applications is typically powered by leveraging\u00a0vector embeddings<\/a>. Semantic Kernel allows for storing and retrieving this vector context for AI apps using the memory plugin (which now has support for MongoDB Atlas Vector Search).<\/p>\n

Tutorial<\/h2>\n

Atlas Vector Search<\/a>\u00a0is integrated in this tutorial to provide a way to interact with our memory store that was created through our MongoDB and Semantic Kernel\u00a0connector<\/a>.<\/p>\n

This tutorial takes you through how to use Microsoft Semantic Kernel to properly upload and embed documents into your MongoDB Atlas cluster, and then conduct queries using Microsoft Semantic Kernel as well, all in Python!<\/p>\n

Pre-requisites<\/h2>\n