{"id":28154338,"url":"https://github.com/yuniko-software/kernel-memory-ecommerce-sample","last_synced_at":"2025-09-14T16:50:13.352Z","repository":{"id":267527339,"uuid":"901300109","full_name":"yuniko-software/kernel-memory-ecommerce-sample","owner":"yuniko-software","description":"Kernel Memory. 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Your feedback and contributions are greatly appreciated.\n\n## Introduction\n\nThis repository contains a sample .NET project demonstrating the use of [Kernel Memory](https://github.com/microsoft/kernel-memory) for semantic search and Retrieval-Augmented Generation (RAG) on a small commercial products dataset. \nIt mimics an **e-shop** environment where users can search for products, and the application retrieves the most relevant results.\n\nThe project features a [serverless setup](https://microsoft.github.io/kernel-memory/serverless) of Kernel Memory, with services embedded directly in the .NET application. \nYou can run this sample using either **Postgres** (with [pgVector](https://github.com/pgvector/pgvector)) or [Qdrant](https://github.com/qdrant/qdrant) as the vector database.\n\nThis sample uses **OpenAI's `gpt-4o-mini`** as the language model and **`text-embedding-ada-002`** as the embedding model. Other models are also supported; check the [Kernel Memory repository](https://github.com/microsoft/kernel-memory) for all supported models.\n\n## Setup\n\n1. **Configure API Key**:\n   \n   Open the `appsettings.json` file in the project root and insert your API token under `KernelMemory:Services:OpenAI:APIKey`. This key is required to authenticate with the OpenAI services used in the sample.\n\n   ```json\n   {\n     \"KernelMemory\": {\n       \"Services\": {\n         \"OpenAI\": {\n           \"APIKey\": \"your-api-key-here\"\n         }\n       }\n     }\n   }\n   ```\n   \n2. **Run the Application**:\n   \n   To start the services, run `docker-compose up -d` **from the repository root**.\n\n   Alternatively, you can run the application through the `docker-compose` startup project directly from your IDE ([Visual Studio](https://learn.microsoft.com/en-us/visualstudio/containers/tutorial-multicontainer?view=vs-2022)/Rider/VS Code)\n\n3. **Ingest Sample Dataset**:\n   \n   After the application is running, open your browser and navigate to [http://localhost:9000](http://localhost:9000).\n   From there, you can ingest the sample dataset located at `/utils/dataset/products.csv` ([link](./utils/dataset/products.csv))\n\n## Accessing the Application\n\n- [http://localhost:9000](http://localhost:9000) - Application UI\n- [http://localhost:9000/swagger](http://localhost:9000/swagger) - Swagger API Documentation\n- [http://localhost:5341](http://localhost:5341) - Seq (structured logs, traces). Default login: `admin` Default password: `password`\n- [http://localhost:6333/dashboard](http://localhost:6333/dashboard) - Qdrant Dashboard\n\n## Contribution\n\nFeel free to open discussions, submit pull requests, or share suggestions to help improve the project! The authors are very friendly and open to feedback and contributions.\n\n\n   \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyuniko-software%2Fkernel-memory-ecommerce-sample","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyuniko-software%2Fkernel-memory-ecommerce-sample","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyuniko-software%2Fkernel-memory-ecommerce-sample/lists"}