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rom212andEvan Mattson
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### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Sample notebook 06-memory-and-embeddings demonstrated how semantic kernel lets users add memory to their kernels and also work around ever-growing contexts that eventually exceed the maximum token limit. It did so by using the in RAM VolatileMemoryStore and teased the possibility of offloading this to an external vector store. With this pull request, we want to provide guidance and repeatable steps so that users can actually perform semantic search by leveraging an external vector store, Azure AI Search (formerly known as Azure Cognitive Search ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The changes consist in new cells added at the tail end of the existing notebook. We register a new memory store, external this time and show case how the rest of the code can be reused as-is. We add the capability of providing access credentials to Azure AI Search in environment variables as well. ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [ ] The code builds clean without any errors or warnings - [ ] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [ ] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone 😄 --------- Co-authored-by: Evan Mattson <[email protected]>