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RAG pipelines utilize external data to enhance the\r\ncontext provided to Large Language Models (LLMs). While building these\r\npipelines is facilitated by existing tools, evaluating their performance\r\nquantitatively remains a challenge. RAGAS addresses this gap by offering\r\ntools based on cutting-edge research to evaluate LLM-generated text and\r\nprovide valuable insights into the effectiveness of RAG pipelines.\r\n\r\n#### Features to be Implemented\r\n\r\nThe implementation will leverage Kernel Memory to deliver the following\r\nevaluation features:\r\n\r\n- **Faithfulness**: Ensuring the generated text accurately represents\r\nthe source information.\r\n- **Answer Relevancy**: Assessing the pertinence of the answer in\r\nrelation to the query.\r\n- **Context Recall**: Measuring the proportion of relevant context\r\nretrieved.\r\n- **Context Precision**: Evaluating the accuracy of the retrieved\r\ncontext.\r\n- **Context Relevancy**: Determining the relevance of the provided\r\ncontext to the query.\r\n- **Context Entity Recall**: Checking the retrieval of key entities\r\nwithin the context.\r\n- **Answer Semantic Similarity**: Comparing the semantic similarity\r\nbetween the generated answer and the expected answer.\r\n- **Answer Correctness**: Verifying the factual correctness of the\r\ngenerated answers.\r\n\r\n#### Integration\r\n\r\nRAGAS will be integrated into our CI/CD pipeline to enable continuous\r\nperformance monitoring and evaluation of our RAG pipelines. This\r\nintegration will ensure that our RAG systems consistently meet the\r\ndesired performance benchmarks.\r\n\r\n#### Next Steps\r\n\r\n- **Implement evaluation metrics**: Develop the specified evaluation\r\nfeatures using Kernel Memory.\r\n- **Unit tests**: Tests the framework.\r\n- **Integrate with CI/CD**: Configure the evaluation checks to run\r\nautomatically in our CI/CD pipeline.\r\n\r\n---------\r\n\r\nCo-authored-by: Devis Lucato <dluc@users.noreply.github.com>","shortMessageHtmlLink":"Add evaluation root project (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2303905616\" data-permission-text=\"Title is private\" data-url=\"https://github.com/microsoft/kernel-memory/issues/501\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/microsoft/kernel-memory/pull/501/hovercard\" href=\"https://github.com/microsoft/kernel-memory/pull/501\">#501</a>)"}},{"before":"8cc78e693fad2842e43b29cd38e3b073c8ba9bfe","after":"197fbd9793c0da1dc85d28f43af4acc895cd4919","ref":"refs/heads/main","pushedAt":"2024-05-18T18:20:47.000Z","pushType":"pr_merge","commitsCount":1,"pusher":{"login":"dluc","name":"Devis Lucato","path":"/dluc","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/371009?s=80&v=4"},"commit":{"message":"Add batch upsert support for SQL Server Memory DB (#489)\n\nRelated to #485\r\n\r\nImplements batching upsert functionality in SQL Server Memory DB and\r\nupdates documentation to reflect the new feature.\r\n\r\n- **Code Enhancements:**\r\n- Introduces the `IMemoryDbBatchUpsert` interface in\r\n`SqlServerMemory.cs` to support batch upsert operations.\r\n- Adds a new method `BatchUpsertAsync` for handling multiple record\r\nupserts in a batch, improving performance for bulk operations.\r\n- Modifies the `UpsertAsync` method to internally call\r\n`BatchUpsertAsync`, ensuring consistency in handling single and multiple\r\nrecord upserts.\r\n- Implements exception handling for batch upsert scenarios, including\r\nthrowing an `IndexNotFoundException` when attempting to upsert to a\r\nnon-existent index.\r\n\r\n- **Documentation Updates:**\r\n- Updates `README.md` in the SQL Server extension directory to include\r\ninformation about the new batch upsert feature.\r\n- Provides examples on how to use the `BatchUpsertAsync` method for\r\nefficient data insertion or updating.\r\n\r\n\r\n---\r\n\r\nFor more details, open the [Copilot Workspace\r\nsession](https://copilot-workspace.githubnext.com/microsoft/kernel-memory/issues/485?shareId=52eb982e-57e2-421a-adb5-fab759ce9b5c).\r\n\r\n---------\r\n\r\nCo-authored-by: Devis Lucato <dluc@users.noreply.github.com>","shortMessageHtmlLink":"Add batch upsert support for SQL Server Memory DB (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2296465608\" data-permission-text=\"Title is private\" data-url=\"https://github.com/microsoft/kernel-memory/issues/489\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/microsoft/kernel-memory/pull/489/hovercard\" href=\"https://github.com/microsoft/kernel-memory/pull/489\">#489</a>)"}},{"before":"431a36261e1f4c13511bba8e5c2755d8a38ea420","after":"8cc78e693fad2842e43b29cd38e3b073c8ba9bfe","ref":"refs/heads/main","pushedAt":"2024-05-18T01:48:10.000Z","pushType":"pr_merge","commitsCount":1,"pusher":{"login":"dluc","name":"Devis Lucato","path":"/dluc","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/371009?s=80&v=4"},"commit":{"message":"Fix dotnet publish // LLamaSharp conflicts (#500)\n\n## Motivation and Context (Why the change? What's the scenario?)\r\n\r\nLLamaSharp doesn't allow to include multiple backends at once in a\r\npackaged .NET app, requiring to build once for each backend (CPU,\r\nCUDA11, CUDA12, etc). The current code fails to \"publish\" due to\r\nconcurring backends.\r\n\r\nSee also https://github.com/microsoft/kernel-memory/issues/498\r\n\r\n## High level description (Approach, Design)\r\n\r\nSolve the \"publish\" issue by removing all LLamaSharp backends, leaving\r\nit to the end user/developer to choose one.\r\n\r\nThe need to build multiple times, once per LLamaSharp backend, persists,\r\ndue to how LLamaSharp is designed. 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