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doc: fix headings and indenting (#404)
* remove trailing punctuation on headings * fix indenting within lists * catch some misspellings and remove use of please Signed-off-by: David B. Kinder <[email protected]>
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README.md

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## Prerequisite
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GenAIInfra uses Kubernetes as the cloud native infrastructure. Please follow the steps below to prepare the Kubernetes environment.
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GenAIInfra uses Kubernetes as the cloud native infrastructure. Follow the steps below to prepare the Kubernetes environment.
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### Setup Kubernetes cluster
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Please follow [Kubernetes official setup guide](https://kubernetes.io/docs/setup/) to setup Kubernetes. We recommend to use Kubernetes with version >= 1.27.
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Follow [Kubernetes official setup guide](https://kubernetes.io/docs/setup/) to setup Kubernetes. We recommend to use Kubernetes with version >= 1.27.
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There are different methods to setup Kubernetes production cluster, such as [kubeadm](https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/), [kubespray](https://kubespray.io/), and [more](https://kubernetes.io/docs/setup/production-environment/tools/).
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NOTE: We recommend to use containerd when choosing the container runtime during Kubernetes setup. Docker engine is also verified on Ubuntu 22.04 and above.
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### (Optional) To run GenAIInfra on [Intel Gaudi](https://habana.ai/products/) product:
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### (Optional) To run GenAIInfra on [Intel Gaudi](https://habana.ai/products/) product
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The following steps are optional. They're only required if you want to run the workloads on Intel Gaudi product.
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1. Please check the [support matrix](https://docs.habana.ai/en/latest/Support_Matrix/Support_Matrix.html) to make sure that environment meets the requirements.
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1. Check the [support matrix](https://docs.habana.ai/en/latest/Support_Matrix/Support_Matrix.html) to make sure that environment meets the requirements.
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2. [Install Intel Gaudi software stack](https://docs.habana.ai/en/latest/Installation_Guide/Bare_Metal_Fresh_OS.html#driver-fw-install-bare).
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3. [Install and setup container runtime](https://docs.habana.ai/en/latest/Installation_Guide/Bare_Metal_Fresh_OS.html#set-up-container-usage), based on the container runtime used by Kubernetes.
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NOTE: Please make sure you configure the appropriate container runtime based on the type of container runtime you installed during Kubernetes setup.
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NOTE: Make sure you configure the appropriate container runtime based on the type of container runtime you installed during Kubernetes setup.
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4. [Install Intel Gaudi device plugin for Kubernetes](https://docs.habana.ai/en/latest/Orchestration/Gaudi_Kubernetes/Device_Plugin_for_Kubernetes.html).
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Alternatively, Intel provides a base operator to manage the Gaudi software stack. Please refer to [this file](kubernetes-addons/Intel-Gaudi-Base-Operator/README.md) for details.
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Alternatively, Intel provides a base operator to manage the Gaudi software stack. Refer to [this file](kubernetes-addons/Intel-Gaudi-Base-Operator/README.md) for details.
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## Usages
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helm-charts/common/asr/README.md

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asr depends on whisper, you should set ASR_ENDPOINT endpoints before start.
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## (Option1): Installing the chart separately:
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## (Option1): Installing the chart separately
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First, you need to install the whisper chart, please refer to the [whisper](../whisper) chart for more information.
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helm install asr . --set ASR_ENDPOINT=${ASR_ENDPOINT}
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```
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## (Option2): Installing the chart with dependencies automatically:
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## (Option2): Installing the chart with dependencies automatically
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```console
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cd GenAIInfra/helm-charts/common/asr

helm-charts/common/data-prep/README.md

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data-prep will use redis and tei service, please specify the endpoints.
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## (Option1): Installing the chart separately:
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## (Option1): Installing the chart separately
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First, you need to install the tei and redis-vector-db chart, please refer to the [tei](../tei) and [redis-vector-db](../redis-vector-db) for more information.
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helm install data-prep . --set REDIS_URL=${REDIS_URL} --set TEI_EMBEDDING_ENDPOINT=${TEI_EMBEDDING_ENDPOINT}
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```
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## (Option2): Installing the chart with dependencies automatically:
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## (Option2): Installing the chart with dependencies automatically
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```console
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cd GenAIInfra/helm-charts/common/data-prep

helm-charts/common/embedding-usvc/README.md

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embedding-usvc depends on TEI, set TEI_EMBEDDING_ENDPOINT.
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## (Option1): Installing the chart separately:
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## (Option1): Installing the chart separately
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First, you need to install the tei chart, please refer to the [tei](../tei) chart for more information.
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helm install embedding-usvc . --set TEI_EMBEDDING_ENDPOINT=${TEI_EMBEDDING_ENDPOINT}
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```
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## (Option2): Installing the chart with dependencies automatically:
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## (Option2): Installing the chart with dependencies automatically
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```console
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cd GenAIInfra/helm-charts/common/embedding-usvc

helm-charts/common/guardrails-usvc/README.md

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guardrails-usvc depends on TGI, you should set TGI_LLM_ENDPOINT as tgi endpoint.
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## (Option1): Installing the chart separately:
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## (Option1): Installing the chart separately
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First, you need to install the tgi chart, please refer to the [tgi](../tgi) chart for more information. Please use model `meta-llama/Meta-Llama-Guard-2-8B` during installation.
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helm install guardrails-usvc . --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --set SAFETY_GUARD_ENDPOINT=${SAFETY_GUARD_ENDPOINT} --set SAFETY_GUARD_MODEL_ID=${SAFETY_GUARD_MODEL_ID} --wait
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```
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## (Option2): Installing the chart with dependencies automatically:
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## (Option2): Installing the chart with dependencies automatically
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cd GenAIInfra/helm-charts/common/guardrails-usvc

helm-charts/common/llm-uservice/README.md

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llm-uservice depends on TGI, you should set TGI_LLM_ENDPOINT as tgi endpoint.
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## (Option1): Installing the chart separately
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First, you need to install the tgi chart, please refer to the [tgi](../tgi) chart for more information.
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helm install llm-uservice . --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --set TGI_LLM_ENDPOINT=${TGI_LLM_ENDPOINT} --wait
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```
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## (Option2): Installing the chart with dependencies automatically:
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## (Option2): Installing the chart with dependencies automatically
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```console
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cd GenAIInfra/helm-charts/common/llm-uservice

helm-charts/common/reranking-usvc/README.md

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reranking-usvc depends on teirerank, set the TEI_RERANKING_ENDPOINT as teirerank endpoint.
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## (Option1): Installing the chart separately
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First, you need to install the teirerank chart, please refer to the [teirerank](../teirerank) chart for more information.
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helm install reranking-usvc . --set TEI_RERANKING_ENDPOINT=${TEI_RERANKING_ENDPOINT}
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## (Option2): Installing the chart with dependencies automatically:
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## (Option2): Installing the chart with dependencies automatically
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cd GenAIInfra/helm-charts/common/reranking-usvc

helm-charts/common/retriever-usvc/README.md

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retriever-usvc depends on redis and tei, you should set these endpoints before start.
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## (Option1): Installing the chart separately:
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## (Option1): Installing the chart separately
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First, you need to install the tei and redis-vector-db chart, please refer to the [tei](../tei) and [redis-vector-db](../redis-vector-db) for more information.
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First, you need to install the tei and redis-vector-db chart, refer to the [tei](../tei) and [redis-vector-db](../redis-vector-db) for more information.
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After you've deployted the tei and redis-vector-db chart successfully, please run `kubectl get svc` to get the service endpoint and URL respectively, i.e. `http://tei`, `redis://redis-vector-db:6379`.
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After you've deployed the tei and redis-vector-db chart successfully, run `kubectl get svc` to get the service endpoint and URL respectively, i.e. `http://tei`, `redis://redis-vector-db:6379`.
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To install retriever-usvc chart, run the following:
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helm install retriever-usvc . --set REDIS_URL=${REDIS_URL} --set TEI_EMBEDDING_ENDPOINT=${TEI_EMBEDDING_ENDPOINT}
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## (Option2): Installing the chart with dependencies automatically:
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## (Option2): Installing the chart with dependencies automatically
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cd GenAIInfra/helm-charts/common/retriever-usvc

helm-charts/common/tts/README.md

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tts depends on speecht5, you should set TTS_ENDPOINT endpoints before start.
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helm-charts/common/web-retriever/README.md

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Web retriever depends on tei, you should set TEI_EMBEDDING_ENDPOINT endpoints before start.
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helm install web-retriever . --set TEI_EMBEDDING_ENDPOINT=${TEI_EMBEDDING_ENDPOINT} --set GOOGLE_API_KEY=${GOOGLE_API_KEY} --set GOOGLE_CSE_ID=${GOOGLE_CSE_ID}
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