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Hide planner approach pending creating a stable response
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‎README.md

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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 Cognitive 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 cognitive 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 Cognitive 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 cognitive 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: | :white_check_mark: |
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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.Search](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/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 cognitive search retrieval options are available: Text, Vector, Hybrid. | :x: | :white_check_mark: |
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| One Shot Ask | [JavaSemanticKernelPlannerApproach](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/JavaSemanticKernelPlannerApproach.java) | Use Java Semantic Kernel framework with built-in Planner for functions orchestration. It uses a declarative style for AI orchestration through the built-in SequentialPlanner. SequentialPlanner call OpenAI to generate a plan for answering a question using available skills/plugins: [InformationFinder](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/CognitiveSearchPlugin.java) and [RAG](https://github.com/Azure-Samples/azure-search-openai-demo-java/blob/main/app/backend/src/main/resources/semantickernel/Plugins/RAG/AnswerQuestion/config.json). Several cognitive search retrieval options are available: Text, Vector, Hybrid. | :x: | :white_check_mark: |
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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.Search](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/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 cognitive search retrieval options are available: Text, Vector, Hybrid. | :x: | :white_check_mark: |
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The plain Java Open AI sdk based implementations are stable. Java Semantic Kernel based implementations are still experimental and it will be consolidated as soon as Java Semantic Kernel beta version will be released. Below a brief description of the SK integration status:
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| RAG Approach | Status |
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|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------|
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| [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) | :white_check_mark: |
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| [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) | :white_check_mark: |
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| [JavaSemanticKernelPlannerApproach](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/JavaSemanticKernelPlannerApproach.java) | :x: break after upgrading to sk 0.2.9.alpha |
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## Getting Started
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‎app/frontend/src/pages/oneshot/OneShot.tsx

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label="Semantic Kernel mode"
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options={[
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{ key: "chains", text: "Function Chaining", selected: skMode == SKMode.Chains, data: SKMode.Chains },
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{ key: "planner", text: "Planner", selected: skMode == SKMode.Planner, data: SKMode.Planner }
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// TODO: Enable this once the planner is ready
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{ key: "planner", text: "Planner", selected: skMode == SKMode.Planner, data: SKMode.Planner, disabled: true }
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]}
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required
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onChange={onSKModeChange}

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