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| One Shot Ask | [PlainJavaAskApproach](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/ask/approaches/PlainJavaAskApproach.java) | Use Azure AI Search and Java OpenAI APIs. It first retrieves top documents from search and use them to build a prompt. Then, it uses OpenAI to generate an answer for the user question.Several search retrieval options are available: Text, Vector, Hybrid. When Hybrid and Vector are selected an additional call to OpenAI is required to generate embeddings vector for the question. | :white_check_mark: | :x: |
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| Chat | [PlainJavaChatApproach](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/chat/approaches/PlainJavaChatApproach.java) | Use Azure AI Search and Java OpenAI APIs. It first calls OpenAI to generate a search keyword for the chat history and then answer to the last chat question.Several search retrieval options are available: Text, Vector, Hybrid. When Hybrid and Vector are selected an additional call to OpenAI is required to generate embeddings vector for the chat extracted keywords. | :white_check_mark: | :x: |
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| Chat | [PlainJavaChatApproach](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/chat/approaches/PlainJavaChatApproach.java) | Use Azure AI Search and Java OpenAI APIs. It first calls OpenAI to generate a search keyword for the chat history and then answer to the last chat question. Several search retrieval options are available: Text, Vector, Hybrid. When Hybrid and Vector are selected an additional call to OpenAI is required to generate embeddings vector for the chat extracted keywords. | :white_check_mark: | :x: |
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| One Shot Ask | [JavaSemanticKernelWithMemoryApproach](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/ask/approaches/semantickernel/JavaSemanticKernelWithMemoryApproach.java) | Use Java Semantic Kernel framework with built-in MemoryStore for embeddings similarity search. A semantic function [RAG.AnswerQuestion](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/resources/semantickernel/Plugins/RAG/AnswerQuestion/config.json) is defined to build the prompt using Memory Store vector search results.A customized version of SK built-in [CognitiveSearchMemoryStore](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/ask/approaches/semantickernel/memory/CustomAzureCognitiveSearchMemoryStore.java) is used to map index fields populated by the documents ingestion process. | :x: | This approach is currently disabled within the UI, memory feature will be available in the next java Semantic Kernel GA release |
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| One Shot Ask | [JavaSemanticKernelChainsApproach](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/ask/approaches/semantickernel/JavaSemanticKernelChainsApproach.java) | Use Java Semantic Kernel framework with semantic and native functions chaining. It uses an imperative style for AI orchestration through semantic kernel functions chaining. [InformationFinder.SearchFromQuestion](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/retrieval/semantickernel/CognitiveSearchPlugin.java) native function and [RAG.AnswerQuestion](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/resources/semantickernel/Plugins/RAG/AnswerQuestion/config.json) semantic function are called sequentially. Several search retrieval options are available: Text, Vector, Hybrid. | :x: | :white_check_mark: |
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| Chat | [JavaSemanticKernelWithMemoryApproach](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/chat/approaches/semantickernel/JavaSemanticKernelWithMemoryChatApproach.java) | Use Java Semantic Kernel framework with built-in MemoryStore for embeddings similarity search. A semantic function [RAG.AnswerConversation](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/resources/semantickernel/Plugins/RAG/AnswerQuestion/config.json) is defined to build the prompt using Memory Store vector search results. A customized version of SK built-in [CognitiveSearchMemoryStore](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/ask/approaches/semantickernel/memory/CustomAzureCognitiveSearchMemoryStore.java) is used to map index fields populated by the documents ingestion process. | :x: | :x: This approach is currently disabled within the UI, memory feature will be available in the next java Semantic Kernel GA release |
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| Chat | [JavaSemanticKernelChainsApproach](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/chat/approaches/semantickernel/JavaSemanticKernelChainsChatApproach.java) | Use Java Semantic Kernel framework with semantic and native functions chaining. It uses an imperative style for AI orchestration through semantic kernel functions chaining. [InformationFinder.SearchFromConversation](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/java/com/microsoft/openai/samples/rag/retrieval/semantickernel/CognitiveSearchPlugin.java) native function and [RAG.AnswerConversation](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/resources/semantickernel/Plugins/RAG/AnswerConversation/config.json) semantic function are called sequentially. Several search retrieval options are available: Text, Vector, Hybrid. | :x: | :white_check_mark: |
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## Production Deployment on Azure Applciation Landing Zones
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## Production Deployment on Azure Application Landing Zones
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This sample is designed to get you started quickly and let you experiment with Java intelligent Apps RAG architectures on Azure.For production deployment, you can use the [Azure Application Landing Zones (LZA)](https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/app-platform/ready) to deploy the solution maintaining best practices for security, monitoring, networking and operational excellence.
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This sample is designed to get you started quickly and let you experiment with Java intelligent Apps RAG architectures on Azure.For production deployment, you can use the [Azure Application Landing Zones (LZA)](https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/app-platform/ready) to deploy the solution maintaining best practices for security, monitoring, networking and operational excellence.
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Check the [chat-with-your-data-lza-app-accelerator](https://github.com/dantelmomsft/chat-with-your-data-java-lza-app-accelerator) to see how you can deploy this solution on App Service LZA or Azure Container Apps LZA.
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