Showing results for Category: Vector Database from Semantic Kernel

We are thrilled to announce the General Availability (GA) of our Memory packages for .NET, Java, and Python! The Semantic Kernel team and our partners have been working hard to allow you to quickly connect to vector stores and make it easy for you to do embeddings and data retrieval between stores. Transforming Data Management with Vector Stores...

Hello again Java AI developers! We are announcing the 1.3.0 release of the Semantic Kernel for Java. You can find our updates on the GitHub repository and the artifacts on Maven Central. This release will mark the final experimental version of our Vector Store functionality. Vector search is now available across all our supported vector store...

This article will help readers to create their own connectors for Kernel Memory. It will introduce fundamental concepts of Kernel Memory and Elasticsearch and will show some practical use cases of how to use the interface IMemoryDb. The complete source code for the connector is located in the GitHub repository FreeMindLabs.KernelMemory.Elasticse...

The Emerging LLM Stack In a recent interview, the co-founder of Cohere stated - “For the first time in the history of our planet, we have machines that we’ve created that can use our language.” A bold statement indeed, but it couldn’t be more accurate and we’ve all witnessed the incredible power of said machines. It would be an understatement to s...

Every system needs to be able to add AI to its workflow to empower the users to complete their task much faster.  The Semantic Kernel team and community have been working hard to create a Java based kernel to support Java developers to unleash AI into their apps.  I will walk you through this journey below. We will review the following concepts ...

We're excited to announce integration of Azure AI Search with Semantic Kernel, available in both C# and Python. This integration follows the existing Semantic Memory architecture, making it incredibly easy for developers to add memory to prompts and plugins. This integration unlocks the following key benefits. By leveragi...

To use Chroma with Semantic Kernel, visit the sample notebook on the Semantic Kernel GitHub repo. And if you like the repo, please give it a star! In this interview with Jeff Huber, CEO and co-founder of Chroma, a leading AI-native vector database, Jeff discusses how Chroma bridges the gap between AI models and production by leveraging embed...

To use Weaviate with Semantic Kernel, visit the sample notebook on the Semantic Kernel GitHub repo. And if you like the repo, please give it a star! In this interview, Bob van Luijt, CEO and co-founder of Weaviate, shares his journey from starting an internet business at a young age to co-founding Weaviate. Bob discusses the inspiration be...

Interview with Qdrant CEO and co-founder Andre Zayarni Semantic Kernel now works seamlessly with the open-source vector database Qdrant — just launch an instance of Qdrant on Azure with one click and you're good to go. You can learn more about Qdrant's history in this interview with the CEO and co-founder, Andre Zayarni: SK: What moment in yo...

A key component of leveraging machine learning large languages models (LLM) for natural language processing (NLP) in applications for chat search like ChatGPT, ranking and recommendation engines, anomaly detection, and semantic search is the ability to leverage massive amounts of unstructured data for search. But where would we save and retrie...