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@@ -71,7 +71,7 @@ The accelerator presented here provides several options, for example:
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* A backend with support for 'custom' and 'On Your Data' [conversation flows](./docs/conversation_flow_options_test.md)
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* Advanced prompt engineering capabilities
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* An admin site for ingesting/inspecting/configuring your dataset on the fly
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* Push or Pull model for data ingestion: See [integrated vectorization](./docs/integrated_vectorization.md) documentation for more details
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* Push or Pull model for data ingestion: See [integrated vectorization](./docs/integrated_vectorization_test.md) documentation for more details
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* Running a Retrieval Augmented Generation (RAG) solution locally
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*Have you seen [ChatGPT + Enterprise data with Azure OpenAI and AI Search demo](https://github.com/Azure-Samples/azure-search-openai-demo)? If you would like to experiment: Play with prompts, understanding RAG pattern different implementation approaches, see how different features interact with the RAG pattern and choose the best options for your RAG deployments, take a look at that repo.

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