https://github.com/yuniko-software/kernel-memory-ecommerce-sample
Kernel Memory. A sample project demonstrating AI-powered semantic search capabilities tailored for e-commerce websites, leveraging RAG (Retrieval-Augmented Generation) architecture
https://github.com/yuniko-software/kernel-memory-ecommerce-sample
ai-search kernel-memory llm opeanai pgvector qdrant rag semantic-search vector-database
Last synced: 22 days ago
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Kernel Memory. A sample project demonstrating AI-powered semantic search capabilities tailored for e-commerce websites, leveraging RAG (Retrieval-Augmented Generation) architecture
- Host: GitHub
- URL: https://github.com/yuniko-software/kernel-memory-ecommerce-sample
- Owner: yuniko-software
- License: apache-2.0
- Created: 2024-12-10T12:06:50.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-03-17T11:58:13.000Z (7 months ago)
- Last Synced: 2025-05-15T06:13:26.296Z (5 months ago)
- Topics: ai-search, kernel-memory, llm, opeanai, pgvector, qdrant, rag, semantic-search, vector-database
- Language: C#
- Homepage: https://yuniko.software/kernel-memory/
- Size: 249 KB
- Stars: 7
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Kernel Memory: E-commerce Sample
Support the project by giving it a star! Your feedback and contributions are greatly appreciated.
## Introduction
This 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.
It mimics an **e-shop** environment where users can search for products, and the application retrieves the most relevant results.
The project features a [serverless setup](https://microsoft.github.io/kernel-memory/serverless) of Kernel Memory, with services embedded directly in the .NET application.
You 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.
This 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.
## Setup
1. **Configure API Key**:
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.
```json
{
"KernelMemory": {
"Services": {
"OpenAI": {
"APIKey": "your-api-key-here"
}
}
}
}
```
2. **Run the Application**:
To start the services, run `docker-compose up -d` **from the repository root**.
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)
3. **Ingest Sample Dataset**:
After the application is running, open your browser and navigate to [http://localhost:9000](http://localhost:9000).
From there, you can ingest the sample dataset located at `/utils/dataset/products.csv` ([link](./utils/dataset/products.csv))
## Accessing the Application
- [http://localhost:9000](http://localhost:9000) - Application UI
- [http://localhost:9000/swagger](http://localhost:9000/swagger) - Swagger API Documentation
- [http://localhost:5341](http://localhost:5341) - Seq (structured logs, traces). Default login: `admin` Default password: `password`
- [http://localhost:6333/dashboard](http://localhost:6333/dashboard) - Qdrant Dashboard
## Contribution
Feel 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.