Customer Case Study: How to use Elasticsearch Vector Store Connector for Microsoft Semantic Kernel for AI Agent development
Table of Contents
- Introduction
- Setting up the Project
- Creating Data Model with Semantic Kernel Attributes
- Using the Semantic Kernel Service
- Sending Queries to the AI Chat Completion Service
- Possibilities with Elasticsearch
- What's Next?
1. Introduction
The Elastic team introduces the Semantic Kernel Elasticsearch Vector Store connector for Microsoft Semantic Kernel, making it easy for developers to use Elasticsearch as a vector store in building AI agents. The blog focuses on building a RAG application using Azure OpenAI, Elasticsearch, and Semantic Kernel (.net).
2. Setting up the Project
Add required NuGet packages and directives to the project, and set up configurations and secrets using a configuration provider.
3. Creating Data Model with Semantic Kernel Attributes
Define the data model and use Semantic Kernel attributes to specify the storage model schema and search hints for text search.
4. Using the Semantic Kernel Service
Utilize the service to create collections, ingest demo records, and simplify the use of a vector store with method calls.
5. Sending Queries to the AI Chat Completion Service
Send the final prompt/query to the AI chat completion service and receive accurate responses based on the data model attributes.
6. Possibilities with Elasticsearch
Explore other possibilities with Elasticsearch, such as semantic_text mapping, semantic reranking, advanced RAG techniques, and building local agents using Elasticsearch vector store.
7. What's Next?
Learn how to easily integrate Elasticsearch with Semantic Kernel for advanced search features in .NET applications while leveraging the abstractions provided by Semantic Kernel.