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Copy file name to clipboardExpand all lines: README.md
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@@ -12,6 +12,10 @@ GenAIExamples are designed to give developers an easy entry into generative AI,
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[GenAIEval](https://github.com/opea-project/GenAIEval) measures service performance metrics such as throughput, latency, and accuracy for GenAIExamples. This feature helps users compare performance across various hardware configurations easily.
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## Documentation
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The GenAIExamples [documentation](https://opea-project.github.io/latest/examples/index.html) contains a comprehensive guide on all available examples including architecture, deployment guides, and more. Information on GenAIComps, GenAIInfra, and GenAIEval can also be found there.
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## Getting Started
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GenAIExamples offers flexible deployment options that cater to different user needs, enabling efficient use and deployment in various environments. Here’s a brief overview of the three primary methods: Python startup, Docker Compose, and Kubernetes.
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### Deployment Guide
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Deployment are based on released docker images by default, check [docker image list](./docker_images_list.md) for detailed information. You can also build your own images following instructions.
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Deployment is based on released docker images by default, check [docker image list](./docker_images_list.md) for detailed information. You can also build your own images following instructions.
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#### Prerequisite
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#### Deploy Examples
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> **Note**: Check for [sample guides](https://opea-project.github.io/latest/examples/index.html) first for your use case. If it is not available, then refer to the table below.
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| Use Case | Docker Compose<br/>Deployment on Xeon | Docker Compose<br/>Deployment on Gaudi | Docker Compose<br/>Deployment on ROCm | Kubernetes with Helm Charts | Kubernetes with GMC |
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