doc: fix headings and indenting (#748)

* doc: fix headings and indenting * only one H1 header (for title) is allowed * fix indenting under ordered lists Signed-off-by: David B. Kinder <[email protected]>
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diff --git a/ChatQnA/benchmark/README.md b/ChatQnA/benchmark/README.md
@@ -4,15 +4,15 @@ This folder contains a collection of Kubernetes manifest files for deploying the
 
 By following this guide, you can run benchmarks on your deployment and share the results with the OPEA community.
 
-# Purpose
+## Purpose
 
 We aim to run these benchmarks and share them with the OPEA community for three primary reasons:
 
 - To offer insights on inference throughput in real-world scenarios, helping you choose the best service or deployment for your needs.
 - To establish a baseline for validating optimization solutions across different implementations, providing clear guidance on which methods are most effective for your use case.
 - To inspire the community to build upon our benchmarks, allowing us to better quantify new solutions in conjunction with current leading llms, serving frameworks etc.
 
-# Metrics
+## Metrics
 
 The benchmark will report the below metrics, including:
 
@@ -27,9 +27,9 @@ The benchmark will report the below metrics, including:
 
 Results will be displayed in the terminal and saved as CSV file named `1_stats.csv` for easy export to spreadsheets.
 
-# Getting Started
+## Getting Started
 
-## Prerequisites
+### Prerequisites
 
 - Install Kubernetes by following [this guide](https://github.com/opea-project/docs/blob/main/guide/installation/k8s_install/k8s_install_kubespray.md).
 
@@ -38,7 +38,7 @@ Results will be displayed in the terminal and saved as CSV file named `1_stats.c
 - Install Python 3.8+ on the master node for running the stress tool.
 - Ensure all nodes have a local /mnt/models folder, which will be mounted by the pods.
 
-## Kubernetes Cluster Example
+### Kubernetes Cluster Example
 
 ```bash
 $ kubectl get nodes
@@ -49,7 +49,7 @@ k8s-work2           Ready    <none>          35d   v1.29.6
 k8s-work3           Ready    <none>          35d   v1.29.6
 ```
 
-## Manifest preparation
+### Manifest preparation
 
 We have created the [BKC manifest](https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA/benchmark) for single node, two nodes and four nodes K8s cluster. In order to apply, we need to check out and configure some values.
 
@@ -75,7 +75,7 @@ find . -name '*.yaml' -type f -exec sed -i "s#\$(EMBEDDING_MODEL_ID)#${EMBEDDING
 find . -name '*.yaml' -type f -exec sed -i "s#\$(RERANK_MODEL_ID)#${RERANK_MODEL_ID}#g" {} \;
 ```
 
-## Benchmark tool preparation
+### Benchmark tool preparation
 
 The test uses the [benchmark tool](https://github.com/opea-project/GenAIEval/tree/main/evals/benchmark) to do performance test. We need to set up benchmark tool at the master node of Kubernetes which is k8s-master.
 
@@ -88,7 +88,7 @@ source stress_venv/bin/activate
 pip install -r requirements.txt
 ```
 
-## Test Configurations
+### Test Configurations
 
 Workload configuration:
 
@@ -119,19 +119,19 @@ Number of test requests for different scheduled node number:
 
 More detailed configuration can be found in configuration file [benchmark.yaml](./benchmark.yaml).
 
-## Test Steps
+### Test Steps
 
-### Single node test
+#### Single node test
 
-#### 1. Preparation
+##### 1. Preparation
 
 We add label to 1 Kubernetes node to make sure all pods are scheduled to this node:
 
 ```bash
 kubectl label nodes k8s-worker1 node-type=chatqna-opea
 ```
 
-#### 2. Install ChatQnA
+##### 2. Install ChatQnA
 
 Go to [BKC manifest](https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA/benchmark/tuned/with_rerank/single_gaudi) and apply to K8s.
 
@@ -141,9 +141,9 @@ cd GenAIExamples/ChatQnA/benchmark/tuned/with_rerank/single_gaudi
 kubectl apply -f .
 ```
 
-#### 3. Run tests
+##### 3. Run tests
 
-##### 3.1 Upload Retrieval File
+###### 3.1 Upload Retrieval File
 
 Before running tests, upload a specified file to make sure the llm input have the token length of 1k.
 
@@ -174,7 +174,7 @@ curl -X POST "http://${cluster_ip}:6007/v1/dataprep" \
      -F "files=@./upload_file_no_rerank.txt"
 ```
 
-##### 3.2 Run Benchmark Test
+###### 3.2 Run Benchmark Test
 
 We copy the configuration file [benchmark.yaml](./benchmark.yaml) to `GenAIEval/evals/benchmark/benchmark.yaml` and config `test_suite_config.user_queries` and `test_suite_config.test_output_dir`.
 
@@ -191,11 +191,11 @@ cd GenAIEval/evals/benchmark
 python benchmark.py
 ```
 
-#### 4. Data collection
+##### 4. Data collection
 
 All the test results will come to this folder `/home/sdp/benchmark_output/node_1` configured by the environment variable `TEST_OUTPUT_DIR` in previous steps.
 
-#### 5. Clean up
+##### 5. Clean up
 
 ```bash
 # on k8s-master node
@@ -204,17 +204,17 @@ kubectl delete -f .
 kubectl label nodes k8s-worker1 node-type-
 ```
 
-### Two node test
+#### Two node test
 
-#### 1. Preparation
+##### 1. Preparation
 
 We add label to 2 Kubernetes node to make sure all pods are scheduled to this node:
 
 ```bash
 kubectl label nodes k8s-worker1 k8s-worker2 node-type=chatqna-opea
 ```
 
-#### 2. Install ChatQnA
+##### 2. Install ChatQnA
 
 Go to [BKC manifest](https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA/benchmark/tuned/with_rerank/two_gaudi) and apply to K8s.
 
@@ -224,7 +224,7 @@ cd GenAIExamples/ChatQnA/benchmark/tuned/with_rerank/two_gaudi
 kubectl apply -f .
 ```
 
-#### 3. Run tests
+##### 3. Run tests
 
 We copy the configuration file [benchmark.yaml](./benchmark.yaml) to `GenAIEval/evals/benchmark/benchmark.yaml` and config `test_suite_config.user_queries` and `test_suite_config.test_output_dir`.
 
@@ -241,29 +241,29 @@ cd GenAIEval/evals/benchmark
 python benchmark.py
 ```
 
-#### 4. Data collection
+##### 4. Data collection
 
 All the test results will come to this folder `/home/sdp/benchmark_output/node_2` configured by the environment variable `TEST_OUTPUT_DIR` in previous steps.
 
-#### 5. Clean up
+##### 5. Clean up
 
 ```bash
 # on k8s-master node
 kubectl delete -f .
 kubectl label nodes k8s-worker1 k8s-worker2 node-type-
 ```
 
-### Four node test
+#### Four node test
 
-#### 1. Preparation
+##### 1. Preparation
 
 We add label to 4 Kubernetes node to make sure all pods are scheduled to this node:
 
 ```bash
 kubectl label nodes k8s-master k8s-worker1 k8s-worker2 k8s-worker3 node-type=chatqna-opea
 ```
 
-#### 2. Install ChatQnA
+##### 2. Install ChatQnA
 
 Go to [BKC manifest](https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA/benchmark/tuned/with_rerank/four_gaudi) and apply to K8s.
 
@@ -273,7 +273,7 @@ cd GenAIExamples/ChatQnA/benchmark/tuned/with_rerank/four_gaudi
 kubectl apply -f .
 ```
 
-#### 3. Run tests
+##### 3. Run tests
 
 We copy the configuration file [benchmark.yaml](./benchmark.yaml) to `GenAIEval/evals/benchmark/benchmark.yaml` and config `test_suite_config.user_queries` and `test_suite_config.test_output_dir`.
 
@@ -290,11 +290,11 @@ cd GenAIEval/evals/benchmark
 python benchmark.py
 ```
 
-#### 4. Data collection
+##### 4. Data collection
 
 All the test results will come to this folder `/home/sdp/benchmark_output/node_4` configured by the environment variable `TEST_OUTPUT_DIR` in previous steps.
 
-#### 5. Clean up
+##### 5. Clean up
 
 ```bash
 # on k8s-master node

diff --git a/ChatQnA/docker/aipc/README.md b/ChatQnA/docker/aipc/README.md
@@ -173,97 +173,97 @@ OLLAMA_HOST=${host_ip}:11434 ollama run $OLLAMA_MODEL
 
 1. TEI Embedding Service
 
-```bash
-curl ${host_ip}:6006/embed \
-    -X POST \
-    -d '{"inputs":"What is Deep Learning?"}' \
-    -H 'Content-Type: application/json'
-```
+   ```bash
+   curl ${host_ip}:6006/embed \
+       -X POST \
+       -d '{"inputs":"What is Deep Learning?"}' \
+       -H 'Content-Type: application/json'
+   ```
 
 2. Embedding Microservice
 
-```bash
-curl http://${host_ip}:6000/v1/embeddings\
-  -X POST \
-  -d '{"text":"hello"}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:6000/v1/embeddings\
+     -X POST \
+     -d '{"text":"hello"}' \
+     -H 'Content-Type: application/json'
+   ```
 
 3. Retriever Microservice  
    To validate the retriever microservice, you need to generate a mock embedding vector of length 768 in Python script:
 
-```bash
-export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
-curl http://${host_ip}:7000/v1/retrieval \
-  -X POST \
-  -d '{"text":"What is the revenue of Nike in 2023?","embedding":"'"${your_embedding}"'"}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
+   curl http://${host_ip}:7000/v1/retrieval \
+     -X POST \
+     -d '{"text":"What is the revenue of Nike in 2023?","embedding":"'"${your_embedding}"'"}' \
+     -H 'Content-Type: application/json'
+   ```
 
 4. TEI Reranking Service
 
-```bash
-curl http://${host_ip}:8808/rerank \
-    -X POST \
-    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-    -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:8808/rerank \
+       -X POST \
+       -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
+       -H 'Content-Type: application/json'
+   ```
 
 5. Reranking Microservice
 
-```bash
-curl http://${host_ip}:8000/v1/reranking\
-  -X POST \
-  -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:8000/v1/reranking\
+     -X POST \
+     -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
+     -H 'Content-Type: application/json'
+   ```
 
 6. Ollama Service
 
-```bash
-curl http://${host_ip}:11434/api/generate -d '{"model": "llama3", "prompt":"What is Deep Learning?"}'
-```
+   ```bash
+   curl http://${host_ip}:11434/api/generate -d '{"model": "llama3", "prompt":"What is Deep Learning?"}'
+   ```
 
 7. LLM Microservice
 
-```bash
-curl http://${host_ip}:9000/v1/chat/completions\
-  -X POST \
-  -d '{"query":"What is Deep Learning?","max_new_tokens":17,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:9000/v1/chat/completions\
+     -X POST \
+     -d '{"query":"What is Deep Learning?","max_new_tokens":17,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
+     -H 'Content-Type: application/json'
+   ```
 
 8. MegaService
 
-```bash
-curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
-     "messages": "What is the revenue of Nike in 2023?", "model": "'"${OLLAMA_MODEL}"'"
-     }'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
+        "messages": "What is the revenue of Nike in 2023?", "model": "'"${OLLAMA_MODEL}"'"
+        }'
+   ```
 
 9. Dataprep Microservice（Optional）
 
-If you want to update the default knowledge base, you can use the following commands:
+   If you want to update the default knowledge base, you can use the following commands:
 
-Update Knowledge Base via Local File Upload:
+   Update Knowledge Base via Local File Upload:
 
-```bash
-curl -X POST "http://${host_ip}:6007/v1/dataprep" \
-     -H "Content-Type: multipart/form-data" \
-     -F "files=@./nke-10k-2023.pdf"
-```
+   ```bash
+   curl -X POST "http://${host_ip}:6007/v1/dataprep" \
+        -H "Content-Type: multipart/form-data" \
+        -F "files=@./nke-10k-2023.pdf"
+   ```
 
-This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.
+   This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.
 
-Add Knowledge Base via HTTP Links:
+   Add Knowledge Base via HTTP Links:
 
-```bash
-curl -X POST "http://${host_ip}:6007/v1/dataprep" \
-     -H "Content-Type: multipart/form-data" \
-     -F 'link_list=["https://opea.dev"]'
-```
+   ```bash
+   curl -X POST "http://${host_ip}:6007/v1/dataprep" \
+        -H "Content-Type: multipart/form-data" \
+        -F 'link_list=["https://opea.dev"]'
+   ```
 
-This command updates a knowledge base by submitting a list of HTTP links for processing.
+   This command updates a knowledge base by submitting a list of HTTP links for processing.
 
 ## 🚀 Launch the UI