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]>

README.md

@@ -16,31 +16,31 @@ The GenAIInfra repository is organized under four main directories, which includ
 
 ## Prerequisite
 
-GenAIInfra uses Kubernetes as the cloud native infrastructure. Please follow the steps below to prepare the Kubernetes environment.
+GenAIInfra uses Kubernetes as the cloud native infrastructure. Follow the steps below to prepare the Kubernetes environment.
 
 ### Setup Kubernetes cluster
 
-Please follow [Kubernetes official setup guide](https://kubernetes.io/docs/setup/) to setup Kubernetes. We recommend to use Kubernetes with version >= 1.27.
+Follow [Kubernetes official setup guide](https://kubernetes.io/docs/setup/) to setup Kubernetes. We recommend to use Kubernetes with version >= 1.27.
 
 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/).
 
 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.
 
-### (Optional) To run GenAIInfra on [Intel Gaudi](https://habana.ai/products/) product:
+### (Optional) To run GenAIInfra on [Intel Gaudi](https://habana.ai/products/) product
 
 The following steps are optional. They're only required if you want to run the workloads on Intel Gaudi product.
 
-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.
+1. Check the [support matrix](https://docs.habana.ai/en/latest/Support_Matrix/Support_Matrix.html) to make sure that environment meets the requirements.
 
 2. [Install Intel Gaudi software stack](https://docs.habana.ai/en/latest/Installation_Guide/Bare_Metal_Fresh_OS.html#driver-fw-install-bare).
 
 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.
 
-NOTE: Please make sure you configure the appropriate container runtime based on the type of container runtime you installed during Kubernetes setup.
+   NOTE: Make sure you configure the appropriate container runtime based on the type of container runtime you installed during Kubernetes setup.
 
 4. [Install Intel Gaudi device plugin for Kubernetes](https://docs.habana.ai/en/latest/Orchestration/Gaudi_Kubernetes/Device_Plugin_for_Kubernetes.html).
 
-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.
+   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.
 
 ## Usages
 

helm-charts/common/asr/README.md

@@ -4,7 +4,7 @@ Helm chart for deploying asr microservice.
 
 asr depends on whisper, you should set ASR_ENDPOINT endpoints before start.
 
-## (Option1): Installing the chart separately:
+## (Option1): Installing the chart separately
 
 First, you need to install the whisper chart, please refer to the [whisper](../whisper) chart for more information.
 
@@ -19,7 +19,7 @@ helm dependency update
 helm install asr . --set ASR_ENDPOINT=${ASR_ENDPOINT}
 ```
 
-## (Option2): Installing the chart with dependencies automatically:
+## (Option2): Installing the chart with dependencies automatically
 
 ```console
 cd GenAIInfra/helm-charts/common/asr

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

@@ -4,7 +4,7 @@ Helm chart for deploying data-prep microservice.
 
 data-prep will use redis and tei service, please specify the endpoints.
 
-## (Option1): Installing the chart separately:
+## (Option1): Installing the chart separately
 
 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.
 
@@ -20,7 +20,7 @@ helm dependency update
 helm install data-prep . --set REDIS_URL=${REDIS_URL} --set TEI_EMBEDDING_ENDPOINT=${TEI_EMBEDDING_ENDPOINT}
 ```
 
-## (Option2): Installing the chart with dependencies automatically:
+## (Option2): Installing the chart with dependencies automatically
 
 ```console
 cd GenAIInfra/helm-charts/common/data-prep

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

@@ -4,7 +4,7 @@ Helm chart for deploying embedding microservice.
 
 embedding-usvc depends on TEI, set TEI_EMBEDDING_ENDPOINT.
 
-## (Option1): Installing the chart separately:
+## (Option1): Installing the chart separately
 
 First, you need to install the tei chart, please refer to the [tei](../tei) chart for more information.
 
@@ -19,7 +19,7 @@ helm dependency update
 helm install embedding-usvc . --set TEI_EMBEDDING_ENDPOINT=${TEI_EMBEDDING_ENDPOINT}
 ```
 
-## (Option2): Installing the chart with dependencies automatically:
+## (Option2): Installing the chart with dependencies automatically
 
 ```console
 cd GenAIInfra/helm-charts/common/embedding-usvc

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

@@ -4,7 +4,7 @@ Helm chart for deploying LLM microservice.
 
 guardrails-usvc depends on TGI, you should set TGI_LLM_ENDPOINT as tgi endpoint.
 
-## (Option1): Installing the chart separately:
+## (Option1): Installing the chart separately
 
 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.
 
@@ -21,7 +21,7 @@ helm dependency update
 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
 ```
 
-## (Option2): Installing the chart with dependencies automatically:
+## (Option2): Installing the chart with dependencies automatically
 
 ```console
 cd GenAIInfra/helm-charts/common/guardrails-usvc

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

@@ -4,7 +4,7 @@ Helm chart for deploying LLM microservice.
 
 llm-uservice depends on TGI, you should set TGI_LLM_ENDPOINT as tgi endpoint.
 
-## (Option1): Installing the chart separately:
+## (Option1): Installing the chart separately
 
 First, you need to install the tgi chart, please refer to the [tgi](../tgi) chart for more information.
 
@@ -20,7 +20,7 @@ helm dependency update
 helm install llm-uservice . --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --set TGI_LLM_ENDPOINT=${TGI_LLM_ENDPOINT} --wait
 ```
 
-## (Option2): Installing the chart with dependencies automatically:
+## (Option2): Installing the chart with dependencies automatically
 
 ```console
 cd GenAIInfra/helm-charts/common/llm-uservice

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

@@ -4,7 +4,7 @@ Helm chart for deploying reranking microservice.
 
 reranking-usvc depends on teirerank, set the TEI_RERANKING_ENDPOINT as teirerank endpoint.
 
-## (Option1): Installing the chart separately:
+## (Option1): Installing the chart separately
 
 First, you need to install the teirerank chart, please refer to the [teirerank](../teirerank) chart for more information.
 
@@ -19,7 +19,7 @@ helm dependency update
 helm install reranking-usvc . --set TEI_RERANKING_ENDPOINT=${TEI_RERANKING_ENDPOINT}
 ```
 
-## (Option2): Installing the chart with dependencies automatically:
+## (Option2): Installing the chart with dependencies automatically
 
 ```console
 cd GenAIInfra/helm-charts/common/reranking-usvc

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

@@ -4,11 +4,11 @@ Helm chart for deploying Retriever microservice.
 
 retriever-usvc depends on redis and tei, you should set these endpoints before start.
 
-## (Option1): Installing the chart separately:
+## (Option1): Installing the chart separately
 
-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.
+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.
 
-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`.
+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`.
 
 To install retriever-usvc chart, run the following:
 
@@ -20,7 +20,7 @@ helm dependency update
 helm install retriever-usvc . --set REDIS_URL=${REDIS_URL} --set TEI_EMBEDDING_ENDPOINT=${TEI_EMBEDDING_ENDPOINT}
 ```
 
-## (Option2): Installing the chart with dependencies automatically:
+## (Option2): Installing the chart with dependencies automatically
 
 ```console
 cd GenAIInfra/helm-charts/common/retriever-usvc

helm-charts/common/tts/README.md

@@ -4,7 +4,7 @@ Helm chart for deploying tts microservice.
 
 tts depends on speecht5, you should set TTS_ENDPOINT endpoints before start.
 
-## (Option1): Installing the chart separately:
+## (Option1): Installing the chart separately
 
 First, you need to install the speecht5 chart, please refer to the [speecht5](../speecht5) chart for more information.
 
@@ -19,7 +19,7 @@ helm dependency update
 helm install tts . --set TTS_ENDPOINT=${TTS_ENDPOINT}
 ```
 
-## (Option2): Installing the chart with dependencies automatically:
+## (Option2): Installing the chart with dependencies automatically
 
 ```console
 cd GenAIInfra/helm-charts/common/tts

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

@@ -4,7 +4,7 @@ Helm chart for deploying Web Retriever microservice.
 
 Web retriever depends on tei, you should set TEI_EMBEDDING_ENDPOINT endpoints before start.
 
-## (Option1): Installing the chart separately:
+## (Option1): Installing the chart separately
 
 First, you need to install the tei chart, please refer to the [tei](../tei) chart for more information.
 
@@ -21,7 +21,7 @@ export GOOGLE_CSE_ID="yourownid"
 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}
 ```
 
-## (Option2): Installing the chart with dependencies automatically:
+## (Option2): Installing the chart with dependencies automatically
 
 ```console
 cd GenAIInfra/helm-charts/common/web-retriever

microservices-connector/README.md

@@ -3,8 +3,8 @@
 This repo defines the GenAI Microservice Connector(GMC) for OPEA projects. GMC can be used to compose and adjust GenAI pipelines dynamically
 on kubernetes. It can leverage the microservices provided by [GenAIComps](https://github.com/opea-project/GenAIComps) and external services to compose GenAI pipelines. External services might be running in a public cloud or on-prem by providing an URL and access details such as an API key and ensuring there is network connectivity. It also allows users to adjust the pipeline on the fly like switching to a different Large language Model(LLM), adding new functions into the chain(like adding guardrails),etc. GMC supports different types of steps in the pipeline, like sequential, parallel and conditional.
 
-Please refer to [usage_guide](./usage_guide.md) for sample use cases.
-Please refer to [chatqna_use_cases](./config/samples/ChatQnA/use_cases.md) for more ChatQnA use cases.
+Refer to [usage_guide](./usage_guide.md) for sample use cases.
+Refer to [chatqna_use_cases](./config/samples/ChatQnA/use_cases.md) for more ChatQnA use cases.
 
 ## Description
 
@@ -31,10 +31,10 @@ Istio Service Mesh can also be leveraged to facilicate communication between mic
 
 ### Introduction
 
-There are `2` components in this repo:
+There are two components in this repo:
 
-- 1. `manager`: controller manager to handle GMC CRD
-- 2. `router`: route the traffic among the microservices defined in GMC
+- `manager`: controller manager to handle GMC CRD
+- `router`: route the traffic among the microservices defined in GMC
 
 ### GMC build
 
@@ -75,10 +75,10 @@ There are 3 steps for deploying GMC on K8s cluster as below:
 
 **Deploy GMC NOTE:**
 
-- Before installting the manifests, please replace your own huggingface tokens
-- `MOUNT_DIR` is the `hostPath` to save the models on you cluster hosts, so please make sure it is ready on every node of the k8s nodes and the microservices have enough permission to access it.
+- Before installting the manifests, replace your own huggingface tokens
+- `MOUNT_DIR` is the `hostPath` to save the models on you cluster hosts, so make sure it is ready on every node of the k8s nodes and the microservices have enough permission to access it.
 - The `SYSTEM_NAMESPACE` should keep the same with the namespace defined in `gmc-manager.yaml` and `gmc-manager-rbac.yaml`
-- The configmap name `gmcyaml` is defined in gmcmanager deployment Spec. Please modify accordingly if you want
+- The configmap name `gmcyaml` is defined in gmcmanager deployment Spec. Modify accordingly if you want
   use a different name for the configmap
 
 ```sh
@@ -119,13 +119,13 @@ make undeploy
 
 #### Deploy via helm chart
 
-Please refer to [helm chart README](./helm/README.md) for deploying GMC using helm chart.
+Refer to [helm chart README](./helm/README.md) for deploying GMC using helm chart.
 
 ### Next Step
 
-Please refer to [usage_guide](./usage_guide.md) for sample use cases.
-Please refer to [advanced_use_cases](./config/samples/ChatQnA/use_cases.md) for more use cases based on ChatQnA example.
+Refer to [usage_guide](./usage_guide.md) for sample use cases.
+Refer to [advanced_use_cases](./config/samples/ChatQnA/use_cases.md) for more use cases based on ChatQnA example.
 
 ### Troubleshooting guide
 
-Please refer to this [troubleshooting_guide](./troubleshooting_guide.md) for identifying GMC Custom Resource issues.
+Refer to this [troubleshooting_guide](./troubleshooting_guide.md) for identifying GMC Custom Resource issues.

microservices-connector/config/samples/ChatQnA/use_cases.md

@@ -29,7 +29,7 @@ For Gaudi:
 - tgi-service: ghcr.io/huggingface/tgi-gaudi:1.2.1
 
 > [NOTE]  
-> Please refer to [Xeon README](https://github.com/opea-project/GenAIExamples/blob/main/ChatQnA/docker/xeon/README.md) or [Gaudi README](https://github.com/opea-project/GenAIExamples/blob/main/ChatQnA/docker/gaudi/README.md) to build the OPEA images. These too will be available on Docker Hub soon to simplify use.
+> Refer to [Xeon README](https://github.com/opea-project/GenAIExamples/blob/main/ChatQnA/docker/xeon/README.md) or [Gaudi README](https://github.com/opea-project/GenAIExamples/blob/main/ChatQnA/docker/gaudi/README.md) to build the OPEA images. These too will be available on Docker Hub soon to simplify use.
 
 ## Deploy ChatQnA pipeline
 
@@ -138,9 +138,8 @@ This involves deploying the ChatQnA custom resource. You can use `chatQnA_datapr
    chatqa   http://router-service.chatqa.svc.cluster.local:8080      10/0/10    3m
    ```
 
-> [NOTE]
-
-Comparing with `General ChatQnA with preset RAG data`, there should be `10` microservices, the extra one is the microservice of `dataprep`.
+   > [NOTE]
+   > Comparing with `General ChatQnA with preset RAG data`, there should be `10` microservices, the extra one is the microservice of `dataprep`.
 
 4. Deploy a client pod to test the application
 
@@ -162,9 +161,8 @@ Comparing with `General ChatQnA with preset RAG data`, there should be `10` micr
    kubectl exec "$CLIENT_POD" -n chatqa -- curl -s --no-buffer $accessUrl  -X POST  '{"text":"What are the key features of Intel Gaudi?","parameters":{"max_new_tokens":100, "do_sample": true}}' -H 'Content-Type: application/json'
    ```
 
-> [NOTE]
-
-You can remove your ChatQnA pipeline by executing standard Kubernetes kubectl commands to remove a custom resource. Verify it was removed by executing kubectl get pods in the chatqa namespace.
+   > [NOTE]
+   > You can remove your ChatQnA pipeline by executing standard Kubernetes kubectl commands to remove a custom resource. Verify it was removed by executing kubectl get pods in the chatqa namespace.
 
 ### ChatQnA supports multiple LLM models
 
@@ -191,9 +189,8 @@ This involves deploying the ChatQnA custom resource. You can use `chatQnA_switch
    switch   http://router-service.switch.svc.cluster.local:8080   15/0/15   83s
    ```
 
-> [NOTE]
-
-Comparing with `General ChatQnA with preset RAG data`, there should be `15` microservices, the extra are the microservices for different embedding models and LLM models.
+   > [NOTE]
+   > Comparing with `General ChatQnA with preset RAG data`, there should be `15` microservices, the extra are the microservices for different embedding models and LLM models.
 
 4. Deploy a client pod to test the application
 
@@ -217,6 +214,5 @@ Comparing with `General ChatQnA with preset RAG data`, there should be `15` micr
    kubectl exec "$CLIENT_POD" -n switch -- curl -s --no-buffer $accessUrl  -X POST  -d '{"text":"What are the key features of Intel Gaudi?", "model-id":"llama", "embedding-model-id":"small", "parameters":{"max_new_tokens":50, "do_sample": true}}' -H 'Content-Type: application/json'
    ```
 
-> [NOTE]
-
-Showing as above, user can switch the LLM models in runtime by changing the request body, such as adding `"model-id":"llama"` in request body to use the Llama model or changing it into `"model-id":"intel"` to use the Intel model.
+   > [NOTE]
+   > Showing as above, user can switch the LLM models in runtime by changing the request body, such as adding `"model-id":"llama"` in request body to use the Llama model or changing it into `"model-id":"intel"` to use the Intel model.