diff --git a/ChatQnA/benchmark/README.md b/ChatQnA/benchmark/README.md
index 52c7764b8..b666e8ce4 100644
--- a/ChatQnA/benchmark/README.md
+++ b/ChatQnA/benchmark/README.md
@@ -4,7 +4,7 @@ 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:
 
@@ -12,7 +12,7 @@ We aim to run these benchmarks and share them with the OPEA community for three
 - 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:
 
@@ -119,11 +119,11 @@ 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:
 
@@ -131,7 +131,7 @@ We add label to 1 Kubernetes node to make sure all pods are scheduled to this no
 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
@@ -204,9 +204,9 @@ 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:
 
@@ -214,7 +214,7 @@ We add label to 2 Kubernetes node to make sure all pods are scheduled to this no
 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`.
 
@@ -241,11 +241,11 @@ 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_2` configured by the environment variable `TEST_OUTPUT_DIR` in previous steps.
 
-#### 5. Clean up
+##### 5. Clean up
 
 ```bash
 # on k8s-master node
@@ -253,9 +253,9 @@ 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:
 
@@ -263,7 +263,7 @@ We add label to 4 Kubernetes node to make sure all pods are scheduled to this no
 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
index 25a12dd16..3d4bfebb8 100644
--- 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
 
diff --git a/ChatQnA/docker/gaudi/README.md b/ChatQnA/docker/gaudi/README.md
index 0d39b9297..2b49a33f4 100644
--- a/ChatQnA/docker/gaudi/README.md
+++ b/ChatQnA/docker/gaudi/README.md
@@ -157,22 +157,22 @@ cd ../../..
 
 Then run the command `docker images`, you will have the following 8 Docker Images:
 
-1. `opea/embedding-tei:latest`
-2. `opea/retriever-redis:latest`
-3. `opea/reranking-tei:latest`
-4. `opea/llm-tgi:latest` or `opea/llm-vllm:latest` or `opea/llm-vllm-ray:latest`
-5. `opea/tei-gaudi:latest`
-6. `opea/dataprep-redis:latest`
-7. `opea/chatqna:latest` or `opea/chatqna-guardrails:latest` or `opea/chatqna-without-rerank:latest`
-8. `opea/chatqna-ui:latest`
+- `opea/embedding-tei:latest`
+- `opea/retriever-redis:latest`
+- `opea/reranking-tei:latest`
+- `opea/llm-tgi:latest` or `opea/llm-vllm:latest` or `opea/llm-vllm-ray:latest`
+- `opea/tei-gaudi:latest`
+- `opea/dataprep-redis:latest`
+- `opea/chatqna:latest` or `opea/chatqna-guardrails:latest` or `opea/chatqna-without-rerank:latest`
+- `opea/chatqna-ui:latest`
 
 If Conversation React UI is built, you will find one more image:
 
-9. `opea/chatqna-conversation-ui:latest`
+- `opea/chatqna-conversation-ui:latest`
 
 If Guardrails docker image is built, you will find one more image:
 
-10. `opea/guardrails-tgi:latest`
+- `opea/guardrails-tgi:latest`
 
 ## 🚀 Start MicroServices and MegaService
 
@@ -274,190 +274,190 @@ For validation details, please refer to [how-to-validate_service](./how_to_valid
 
 1. TEI Embedding Service
 
-```bash
-curl ${host_ip}:8090/embed \
-    -X POST \
-    -d '{"inputs":"What is Deep Learning?"}' \
-    -H 'Content-Type: application/json'
-```
+   ```bash
+   curl ${host_ip}:8090/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 consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
-is determined by the embedding model.
-Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
+   To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
+   is determined by the embedding model.
+   Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
 
-Check the vecotor dimension of your embedding model, set `your_embedding` dimension equals to it.
+   Check the vecotor dimension of your embedding model, set `your_embedding` dimension equals to it.
 
-```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\":\"test\",\"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://${host_ip}:7000/v1/retrieval \
+     -X POST \
+     -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
+     -H 'Content-Type: application/json'
+   ```
 
 4. TEI Reranking Service
 
-> Skip for ChatQnA without Rerank pipeline
+   > Skip for ChatQnA without Rerank pipeline
 
-```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
 
-> Skip for ChatQnA without Rerank pipeline
+   > Skip for ChatQnA without Rerank pipeline
 
-```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. LLM backend Service
 
-In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
+   In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
 
-Try the command below to check whether the LLM serving is ready.
+   Try the command below to check whether the LLM serving is ready.
 
-```bash
-docker logs ${CONTAINER_ID} | grep Connected
-```
+   ```bash
+   docker logs ${CONTAINER_ID} | grep Connected
+   ```
 
-If the service is ready, you will get the response like below.
+   If the service is ready, you will get the response like below.
 
-```log
-2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
-```
+   ```
+   2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
+   ```
 
-Then try the `cURL` command below to validate services.
+   Then try the `cURL` command below to validate services.
 
-```bash
-#TGI Service
-curl http://${host_ip}:8005/generate \
-  -X POST \
-  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   #TGI Service
+   curl http://${host_ip}:8005/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
-```bash
-#vLLM Service
-curl http://${host_ip}:8007/v1/completions \
-  -H "Content-Type: application/json" \
-  -d '{
-  "model": "${LLM_MODEL_ID}",
-  "prompt": "What is Deep Learning?",
-  "max_tokens": 32,
-  "temperature": 0
-  }'
-```
+   ```bash
+   #vLLM Service
+   curl http://${host_ip}:8007/v1/completions \
+     -H "Content-Type: application/json" \
+     -d '{
+     "model": "${LLM_MODEL_ID}",
+     "prompt": "What is Deep Learning?",
+     "max_tokens": 32,
+     "temperature": 0
+     }'
+   ```
 
-```bash
-#vLLM-on-Ray Service
-curl http://${host_ip}:8006/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -d '{"model": "${LLM_MODEL_ID}", "messages": [{"role": "user", "content": "What is Deep Learning?"}]}'
-```
+   ```bash
+   #vLLM-on-Ray Service
+   curl http://${host_ip}:8006/v1/chat/completions \
+     -H "Content-Type: application/json" \
+     -d '{"model": "${LLM_MODEL_ID}", "messages": [{"role": "user", "content": "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://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
-     "messages": "What is the revenue of Nike in 2023?"
-     }'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
+        "messages": "What is the revenue of Nike in 2023?"
+        }'
+   ```
 
 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.
 
-Also, you are able to get the file/link list that you uploaded:
+   Also, you are able to get the file/link list that you uploaded:
 
-```bash
-curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
-     -H "Content-Type: application/json"
-```
+   ```bash
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
+        -H "Content-Type: application/json"
+   ```
 
-Then you will get the response JSON like this. Notice that the returned `name`/`id` of the uploaded link is `https://xxx.txt`.
-
-```json
-[
-  {
-    "name": "nke-10k-2023.pdf",
-    "id": "nke-10k-2023.pdf",
-    "type": "File",
-    "parent": ""
-  },
-  {
-    "name": "https://opea.dev.txt",
-    "id": "https://opea.dev.txt",
-    "type": "File",
-    "parent": ""
-  }
-]
-```
+   Then you will get the response JSON like this. Notice that the returned `name`/`id` of the uploaded link is `https://xxx.txt`.
+
+   ```json
+   [
+     {
+       "name": "nke-10k-2023.pdf",
+       "id": "nke-10k-2023.pdf",
+       "type": "File",
+       "parent": ""
+     },
+     {
+       "name": "https://opea.dev.txt",
+       "id": "https://opea.dev.txt",
+       "type": "File",
+       "parent": ""
+     }
+   ]
+   ```
 
-To delete the file/link you uploaded:
+   To delete the file/link you uploaded:
 
-```bash
-# delete link
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "https://opea.dev.txt"}' \
-     -H "Content-Type: application/json"
-
-# delete file
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "nke-10k-2023.pdf"}' \
-     -H "Content-Type: application/json"
-
-# delete all uploaded files and links
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "all"}' \
-     -H "Content-Type: application/json"
-```
+   ```bash
+   # delete link
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+        -d '{"file_path": "https://opea.dev.txt"}' \
+        -H "Content-Type: application/json"
+
+   # delete file
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+        -d '{"file_path": "nke-10k-2023.pdf"}' \
+        -H "Content-Type: application/json"
+
+   # delete all uploaded files and links
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+        -d '{"file_path": "all"}' \
+        -H "Content-Type: application/json"
+   ```
 
 10. Guardrails (Optional)
 
diff --git a/ChatQnA/docker/gaudi/how_to_validate_service.md b/ChatQnA/docker/gaudi/how_to_validate_service.md
index 0e58491eb..fb039fad9 100644
--- a/ChatQnA/docker/gaudi/how_to_validate_service.md
+++ b/ChatQnA/docker/gaudi/how_to_validate_service.md
@@ -78,7 +78,7 @@ Check the log of container by:
 
 View the logs of `ghcr.io/huggingface/tgi-gaudi:1.2.1`
 
-#docker logs 05c40b636239 -t
+`docker logs 05c40b636239 -t`
 
 ```
 ...
diff --git a/ChatQnA/docker/gpu/README.md b/ChatQnA/docker/gpu/README.md
index 022cfdfae..eee076bf8 100644
--- a/ChatQnA/docker/gpu/README.md
+++ b/ChatQnA/docker/gpu/README.md
@@ -140,147 +140,147 @@ docker compose up -d
 
 1. TEI Embedding Service
 
-```bash
-curl ${host_ip}:8090/embed \
-    -X POST \
-    -d '{"inputs":"What is Deep Learning?"}' \
-    -H 'Content-Type: application/json'
-```
+   ```bash
+   curl ${host_ip}:8090/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 consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
-is determined by the embedding model.
-Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
+   To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
+   is determined by the embedding model.
+   Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
 
-Check the vecotor dimension of your embedding model, set `your_embedding` dimension equals to it.
+   Check the vecotor dimension of your embedding model, set `your_embedding` dimension equals to it.
 
-```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\":\"test\",\"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://${host_ip}:7000/v1/retrieval \
+     -X POST \
+     -d "{\"text\":\"test\",\"embedding\":${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. TGI Service
 
-In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
+   In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
 
-Try the command below to check whether the TGI service is ready.
+   Try the command below to check whether the TGI service is ready.
 
-```bash
-docker logs ${CONTAINER_ID} | grep Connected
-```
+   ```bash
+   docker logs ${CONTAINER_ID} | grep Connected
+   ```
 
-If the service is ready, you will get the response like below.
+   If the service is ready, you will get the response like below.
 
-```log
-2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
-```
+   ```
+   2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
+   ```
 
-Then try the `cURL` command below to validate TGI.
+   Then try the `cURL` command below to validate TGI.
 
-```bash
-curl http://${host_ip}:8008/generate \
-  -X POST \
-  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 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://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
-     "messages": "What is the revenue of Nike in 2023?"
-     }'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
+        "messages": "What is the revenue of Nike in 2023?"
+        }'
+   ```
 
 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.
 
-Also, you are able to get the file list that you uploaded:
+   Also, you are able to get the file list that you uploaded:
 
-```bash
-curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
-     -H "Content-Type: application/json"
-```
+   ```bash
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
+        -H "Content-Type: application/json"
+   ```
 
-To delete the file/link you uploaded:
+   To delete the file/link you uploaded:
 
-```bash
-# delete link
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "https://opea.dev"}' \
-     -H "Content-Type: application/json"
-
-# delete file
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "nke-10k-2023.pdf"}' \
-     -H "Content-Type: application/json"
-
-# delete all uploaded files and links
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "all"}' \
-     -H "Content-Type: application/json"
-```
+   ```bash
+   # delete link
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+        -d '{"file_path": "https://opea.dev"}' \
+        -H "Content-Type: application/json"
+
+   # delete file
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+        -d '{"file_path": "nke-10k-2023.pdf"}' \
+        -H "Content-Type: application/json"
+
+   # delete all uploaded files and links
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+        -d '{"file_path": "all"}' \
+        -H "Content-Type: application/json"
+   ```
 
 ## 🚀 Launch the UI
 
diff --git a/ChatQnA/docker/xeon/README.md b/ChatQnA/docker/xeon/README.md
index c736c674a..a28128da0 100644
--- a/ChatQnA/docker/xeon/README.md
+++ b/ChatQnA/docker/xeon/README.md
@@ -269,182 +269,182 @@ docker compose -f compose_vllm.yaml up -d
 
 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 consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
-is determined by the embedding model.
-Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
+   To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
+   is determined by the embedding model.
+   Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
 
-Check the vecotor dimension of your embedding model, set `your_embedding` dimension equals to it.
+   Check the vector dimension of your embedding model, set `your_embedding` dimension equals to it.
 
-```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\":\"test\",\"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://${host_ip}:7000/v1/retrieval \
+     -X POST \
+     -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
+     -H 'Content-Type: application/json'
+   ```
 
 4. TEI Reranking Service
 
-> Skip for ChatQnA without Rerank pipeline
+   > Skip for ChatQnA without Rerank pipeline
 
-```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
 
-> Skip for ChatQnA without Rerank pipeline
+   > Skip for ChatQnA without Rerank pipeline
 
-```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. LLM backend Service
 
-In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
+   In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
 
-Try the command below to check whether the LLM serving is ready.
+   Try the command below to check whether the LLM serving is ready.
 
-```bash
-docker logs ${CONTAINER_ID} | grep Connected
-```
+   ```bash
+   docker logs ${CONTAINER_ID} | grep Connected
+   ```
 
-If the service is ready, you will get the response like below.
+   If the service is ready, you will get the response like below.
 
-```log
-2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
-```
+   ```
+   2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
+   ```
 
-Then try the `cURL` command below to validate services.
+   Then try the `cURL` command below to validate services.
 
-```bash
-# TGI service
-curl http://${host_ip}:9009/generate \
-  -X POST \
-  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   # TGI service
+   curl http://${host_ip}:9009/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
-```bash
-# vLLM Service
-curl http://${host_ip}:9009/v1/completions \
-  -H "Content-Type: application/json" \
-  -d '{"model": "Intel/neural-chat-7b-v3-3", "prompt": "What is Deep Learning?", "max_tokens": 32, "temperature": 0}'
-```
+   ```bash
+   # vLLM Service
+   curl http://${host_ip}:9009/v1/completions \
+     -H "Content-Type: application/json" \
+     -d '{"model": "Intel/neural-chat-7b-v3-3", "prompt": "What is Deep Learning?", "max_tokens": 32, "temperature": 0}'
+   ```
 
 7. LLM Microservice
 
-This service depends on above LLM backend service startup. It will be ready after long time, to wait for them being ready in first startup.
+   This service depends on above LLM backend service startup. It will be ready after long time, to wait for them being ready in first startup.
 
-```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://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
-     "messages": "What is the revenue of Nike in 2023?"
-     }'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
+        "messages": "What is the revenue of Nike in 2023?"
+        }'
+   ```
 
 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 [nke-10k-2023.pdf](https://github.com/opea-project/GenAIComps/blob/main/comps/retrievers/langchain/redis/data/nke-10k-2023.pdf) Upload:
+   Update Knowledge Base via Local File [nke-10k-2023.pdf](https://github.com/opea-project/GenAIComps/blob/main/comps/retrievers/langchain/redis/data/nke-10k-2023.pdf) 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.
 
-Also, you are able to get the file list that you uploaded:
+   Also, you are able to get the file list that you uploaded:
 
-```bash
-curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
-     -H "Content-Type: application/json"
-```
+   ```bash
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
+        -H "Content-Type: application/json"
+   ```
 
-Then you will get the response JSON like this. Notice that the returned `name`/`id` of the uploaded link is `https://xxx.txt`.
-
-```json
-[
-  {
-    "name": "nke-10k-2023.pdf",
-    "id": "nke-10k-2023.pdf",
-    "type": "File",
-    "parent": ""
-  },
-  {
-    "name": "https://opea.dev.txt",
-    "id": "https://opea.dev.txt",
-    "type": "File",
-    "parent": ""
-  }
-]
-```
+   Then you will get the response JSON like this. Notice that the returned `name`/`id` of the uploaded link is `https://xxx.txt`.
+
+   ```json
+   [
+     {
+       "name": "nke-10k-2023.pdf",
+       "id": "nke-10k-2023.pdf",
+       "type": "File",
+       "parent": ""
+     },
+     {
+       "name": "https://opea.dev.txt",
+       "id": "https://opea.dev.txt",
+       "type": "File",
+       "parent": ""
+     }
+   ]
+   ```
 
-To delete the file/link you uploaded:
+   To delete the file/link you uploaded:
 
-The `file_path` here should be the `id` get from `/v1/dataprep/get_file` API.
+   The `file_path` here should be the `id` get from `/v1/dataprep/get_file` API.
 
-```bash
-# delete link
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "https://opea.dev.txt"}' \
-     -H "Content-Type: application/json"
-
-# delete file
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "nke-10k-2023.pdf"}' \
-     -H "Content-Type: application/json"
-
-# delete all uploaded files and links
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "all"}' \
-     -H "Content-Type: application/json"
-```
+   ```bash
+   # delete link
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+        -d '{"file_path": "https://opea.dev.txt"}' \
+        -H "Content-Type: application/json"
+
+   # delete file
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+        -d '{"file_path": "nke-10k-2023.pdf"}' \
+        -H "Content-Type: application/json"
+
+   # delete all uploaded files and links
+   curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+        -d '{"file_path": "all"}' \
+        -H "Content-Type: application/json"
+   ```
 
 ## 🚀 Launch the UI
 
diff --git a/ChatQnA/docker/xeon/README_qdrant.md b/ChatQnA/docker/xeon/README_qdrant.md
index a03b563b2..d8f0c9de6 100644
--- a/ChatQnA/docker/xeon/README_qdrant.md
+++ b/ChatQnA/docker/xeon/README_qdrant.md
@@ -224,119 +224,119 @@ docker compose -f compose_qdrant.yaml up -d
 
 1. TEI Embedding Service
 
-```bash
-curl ${host_ip}:6040/embed \
-    -X POST \
-    -d '{"inputs":"What is Deep Learning?"}' \
-    -H 'Content-Type: application/json'
-```
+   ```bash
+   curl ${host_ip}:6040/embed \
+       -X POST \
+       -d '{"inputs":"What is Deep Learning?"}' \
+       -H 'Content-Type: application/json'
+   ```
 
 2. Embedding Microservice
 
-```bash
-curl http://${host_ip}:6044/v1/embeddings\
-  -X POST \
-  -d '{"text":"hello"}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:6044/v1/embeddings\
+     -X POST \
+     -d '{"text":"hello"}' \
+     -H 'Content-Type: application/json'
+   ```
 
 3. Retriever Microservice
 
-To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
-is determined by the embedding model.
-Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
+   To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector
+   is determined by the embedding model.
+   Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768.
 
-Check the vecotor dimension of your embedding model, set `your_embedding` dimension equals to it.
+   Check the vecotor dimension of your embedding model, set `your_embedding` dimension equals to it.
 
-```bash
-export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
-curl http://${host_ip}:6045/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://${host_ip}:6045/v1/retrieval \
+     -X POST \
+     -d '{"text":"What is the revenue of Nike in 2023?","embedding":"'"${your_embedding}"'"}' \
+     -H 'Content-Type: application/json'
+   ```
 
 4. TEI Reranking Service
 
-```bash
-curl http://${host_ip}:6041/rerank \
-    -X POST \
-    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-    -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:6041/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://${host_ip}:6046/v1/reranking\
-  -X POST \
-  -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:6046/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. TGI Service
 
-In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
+   In first startup, this service will take more time to download the model files. After it's finished, the service will be ready.
 
-Try the command below to check whether the TGI service is ready.
+   Try the command below to check whether the TGI service is ready.
 
-```bash
-docker logs ${CONTAINER_ID} | grep Connected
-```
+   ```bash
+   docker logs ${CONTAINER_ID} | grep Connected
+   ```
 
-If the service is ready, you will get the response like below.
+   If the service is ready, you will get the response like below.
 
-```log
-2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
-```
+   ```
+   2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
+   ```
 
-Then try the `cURL` command below to validate TGI.
+   Then try the `cURL` command below to validate TGI.
 
-```bash
-curl http://${host_ip}:6042/generate \
-  -X POST \
-  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:6042/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 7. LLM Microservice
 
-```bash
-curl http://${host_ip}:6047/v1/chat/completions\
-  -X POST \
-  -d '{"query":"What is Deep Learning?","max_new_tokens":17,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:6047/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://${host_ip}:8912/v1/chatqna -H "Content-Type: application/json" -d '{
-     "messages": "What is the revenue of Nike in 2023?"
-     }'
-```
+   ```bash
+   curl http://${host_ip}:8912/v1/chatqna -H "Content-Type: application/json" -d '{
+        "messages": "What is the revenue of Nike in 2023?"
+        }'
+   ```
 
 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://${host_ip}:6043/v1/dataprep" \
-     -H "Content-Type: multipart/form-data" \
-     -F "files=@./your_file.pdf"
-```
+   ```bash
+   curl -X POST "http://${host_ip}:6043/v1/dataprep" \
+        -H "Content-Type: multipart/form-data" \
+        -F "files=@./your_file.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://${host_ip}:6043/v1/dataprep" \
-     -H "Content-Type: multipart/form-data" \
-     -F 'link_list=["https://opea.dev"]'
-```
+   ```bash
+   curl -X POST "http://${host_ip}:6043/v1/dataprep" \
+        -H "Content-Type: multipart/form-data" \
+        -F 'link_list=["https://opea.dev"]'
+   ```
 
 ## 🚀 Launch the UI
 
diff --git a/ChatQnA/kubernetes/manifests/README.md b/ChatQnA/kubernetes/manifests/README.md
index 4f44b65cd..cb4ff7e12 100644
--- a/ChatQnA/kubernetes/manifests/README.md
+++ b/ChatQnA/kubernetes/manifests/README.md
@@ -3,9 +3,9 @@
 > [NOTE]
 > The following values must be set before you can deploy:
 > HUGGINGFACEHUB_API_TOKEN
-
+>
 > You can also customize the "MODEL_ID" if needed.
-
+>
 > You need to make sure you have created the directory `/mnt/opea-models` to save the cached model on the node where the ChatQnA workload is running. Otherwise, you need to modify the `chatqna.yaml` file to change the `model-volume` to a directory that exists on the node.
 
 ## Deploy On Xeon
diff --git a/CodeGen/docker/gaudi/README.md b/CodeGen/docker/gaudi/README.md
index a563f416b..5c440cade 100644
--- a/CodeGen/docker/gaudi/README.md
+++ b/CodeGen/docker/gaudi/README.md
@@ -110,29 +110,29 @@ docker compose up -d
 
 1. TGI Service
 
-```bash
-curl http://${host_ip}:8028/generate \
-  -X POST \
-  -d '{"inputs":"Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception.","parameters":{"max_new_tokens":256, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:8028/generate \
+     -X POST \
+     -d '{"inputs":"Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception.","parameters":{"max_new_tokens":256, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 2. LLM Microservices
 
-```bash
-curl http://${host_ip}:9000/v1/chat/completions\
-  -X POST \
-  -d '{"query":"Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception.","max_new_tokens":256,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:9000/v1/chat/completions\
+     -X POST \
+     -d '{"query":"Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception.","max_new_tokens":256,"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'
+   ```
 
 3. MegaService
 
-```bash
-curl http://${host_ip}:7778/v1/codegen -H "Content-Type: application/json" -d '{
-     "messages": "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."
-     }'
-```
+   ```bash
+   curl http://${host_ip}:7778/v1/codegen -H "Content-Type: application/json" -d '{
+        "messages": "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."
+        }'
+   ```
 
 ## 🚀 Launch the Svelte Based UI
 
diff --git a/CodeGen/docker/xeon/README.md b/CodeGen/docker/xeon/README.md
index 74b5a2b7c..d5b988064 100644
--- a/CodeGen/docker/xeon/README.md
+++ b/CodeGen/docker/xeon/README.md
@@ -113,29 +113,29 @@ docker compose up -d
 
 1. TGI Service
 
-```bash
-curl http://${host_ip}:8028/generate \
-  -X POST \
-  -d '{"inputs":"Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception.","parameters":{"max_new_tokens":256, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:8028/generate \
+     -X POST \
+     -d '{"inputs":"Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception.","parameters":{"max_new_tokens":256, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 2. LLM Microservices
 
-```bash
-curl http://${host_ip}:9000/v1/chat/completions\
-  -X POST \
-  -d '{"query":"Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception.","max_new_tokens":256,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:9000/v1/chat/completions\
+     -X POST \
+     -d '{"query":"Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception.","max_new_tokens":256,"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'
+   ```
 
 3. MegaService
 
-```bash
-curl http://${host_ip}:7778/v1/codegen -H "Content-Type: application/json" -d '{
-     "messages": "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."
-     }'
-```
+   ```bash
+   curl http://${host_ip}:7778/v1/codegen -H "Content-Type: application/json" -d '{
+        "messages": "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."
+        }'
+   ```
 
 ## 🚀 Launch the UI
 
diff --git a/CodeGen/kubernetes/manifests/README.md b/CodeGen/kubernetes/manifests/README.md
index 87d6490f8..9a4383983 100644
--- a/CodeGen/kubernetes/manifests/README.md
+++ b/CodeGen/kubernetes/manifests/README.md
@@ -3,9 +3,9 @@
 > [NOTE]
 > The following values must be set before you can deploy:
 > HUGGINGFACEHUB_API_TOKEN
-
+>
 > You can also customize the "MODEL_ID" if needed.
-
+>
 > You need to make sure you have created the directory `/mnt/opea-models` to save the cached model on the node where the CodeGen workload is running. Otherwise, you need to modify the `codegen.yaml` file to change the `model-volume` to a directory that exists on the node.
 > Alternatively, you can change the `codegen.yaml` to use a different type of volume, such as a persistent volume claim.
 
diff --git a/CodeGen/kubernetes/manifests/xeon/ui/README.md b/CodeGen/kubernetes/manifests/xeon/ui/README.md
index dce34d335..01ed0becf 100644
--- a/CodeGen/kubernetes/manifests/xeon/ui/README.md
+++ b/CodeGen/kubernetes/manifests/xeon/ui/README.md
@@ -7,30 +7,32 @@ You can use react-codegen.yaml to deploy CodeGen with reactUI.
 kubectl apply -f react-codegen.yaml
 ```
 
-## Prerequisites for Deploying CodeGen with ReactUI:
+## Prerequisites for Deploying CodeGen with ReactUI
 Before deploying the react-codegen.yaml file, ensure that you have the following prerequisites in place:
 
 1. Kubernetes installation: Make sure that you have Kubernetes installed.
 2. Configuration Values: Set the following values in react-codegen.yaml before proceeding with the deployment:
-    #### a. HUGGINGFACEHUB_API_TOKEN (Your HuggingFace token to download your desired model from HuggingFace):
+
+    a. HUGGINGFACEHUB_API_TOKEN (Your HuggingFace token to download your desired model from HuggingFace):
     ```
     # You may set the HUGGINGFACEHUB_API_TOKEN via method:
     export HUGGINGFACEHUB_API_TOKEN="YourOwnToken"
     cd GenAIExamples/CodeGen/kubernetes/manifests/xeon/ui/
     sed -i "s/insert-your-huggingface-token-here/${HUGGINGFACEHUB_API_TOKEN}/g" react-codegen.yaml
     ```
-    #### b. Set the proxies based on your network configuration
+    b. Set the proxies based on your network configuration
     ```
     # Look for http_proxy, https_proxy, no_proxy key and fill up the value with your proxy configuration.
     ```
 3. MODEL_ID and model-volume (OPTIONAL): You may as well customize the "MODEL_ID" to use different model and model-volume for the volume to be mounted.
 4. After completing these, you can proceed with the deployment of the react-codegen.yaml file.
 
-## Verify Services:
+## Verify Services
 Make sure all the pods are running, you should see total of 4 pods running:
-1. codegen
-2. codegen-llm-uservice
-3. codegen-react-ui
-4. codegen-tgi
+
+- codegen
+- codegen-llm-uservice
+- codegen-react-ui
+- codegen-tgi
 
 You may open up the UI by using the codegen-react-ui endpoint in the browser.
diff --git a/CodeTrans/docker/gaudi/README.md b/CodeTrans/docker/gaudi/README.md
index 5faf97b78..5b8c00b07 100755
--- a/CodeTrans/docker/gaudi/README.md
+++ b/CodeTrans/docker/gaudi/README.md
@@ -98,37 +98,37 @@ docker compose up -d
 
 1. TGI Service
 
-```bash
-curl http://${host_ip}:8008/generate \
-  -X POST \
-  -d '{"inputs":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:","parameters":{"max_new_tokens":17, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 2. LLM Microservice
 
-```bash
-curl http://${host_ip}:9000/v1/chat/completions\
-  -X POST \
-  -d '{"text":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:"}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:9000/v1/chat/completions\
+     -X POST \
+     -d '{"text":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:"}' \
+     -H 'Content-Type: application/json'
+   ```
 
 3. MegaService
 
-```bash
-curl http://${host_ip}:7777/v1/codetrans \
-    -H "Content-Type: application/json" \
-    -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'
-```
+   ```bash
+   curl http://${host_ip}:7777/v1/codetrans \
+       -H "Content-Type: application/json" \
+       -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'
+   ```
 
 4. Nginx Service
 
-```bash
-curl http://${host_ip}:${NGINX_PORT}/v1/codetrans \
-    -H "Content-Type: application/json" \
-    -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'
-```
+   ```bash
+   curl http://${host_ip}:${NGINX_PORT}/v1/codetrans \
+       -H "Content-Type: application/json" \
+       -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'
+   ```
 
 ## 🚀 Launch the UI
 
diff --git a/CodeTrans/docker/xeon/README.md b/CodeTrans/docker/xeon/README.md
index 03f144b22..b4c0830fd 100755
--- a/CodeTrans/docker/xeon/README.md
+++ b/CodeTrans/docker/xeon/README.md
@@ -106,37 +106,37 @@ docker compose up -d
 
 1. TGI Service
 
-```bash
-curl http://${host_ip}:8008/generate \
-  -X POST \
-  -d '{"inputs":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:","parameters":{"max_new_tokens":17, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 2. LLM Microservice
 
-```bash
-curl http://${host_ip}:9000/v1/chat/completions\
-  -X POST \
-  -d '{"query":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:"}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:9000/v1/chat/completions\
+     -X POST \
+     -d '{"query":"    ### System: Please translate the following Golang codes into  Python codes.    ### Original codes:    '\'''\'''\''Golang    \npackage main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n    '\'''\'''\''    ### Translated codes:"}' \
+     -H 'Content-Type: application/json'
+   ```
 
 3. MegaService
 
-```bash
-curl http://${host_ip}:7777/v1/codetrans \
-    -H "Content-Type: application/json" \
-    -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'
-```
+   ```bash
+   curl http://${host_ip}:7777/v1/codetrans \
+       -H "Content-Type: application/json" \
+       -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'
+   ```
 
 4. Nginx Service
 
-```bash
-curl http://${host_ip}:${NGINX_PORT}/v1/codetrans \
-    -H "Content-Type: application/json" \
-    -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'
-```
+   ```bash
+   curl http://${host_ip}:${NGINX_PORT}/v1/codetrans \
+       -H "Content-Type: application/json" \
+       -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n    fmt.Println(\"Hello, World!\");\n}"}'
+   ```
 
 ## 🚀 Launch the UI
 
diff --git a/CodeTrans/kubernetes/manifests/README.md b/CodeTrans/kubernetes/manifests/README.md
index 709d6ea0f..5edc148cb 100644
--- a/CodeTrans/kubernetes/manifests/README.md
+++ b/CodeTrans/kubernetes/manifests/README.md
@@ -3,9 +3,9 @@
 > [NOTE]
 > The following values must be set before you can deploy:
 > HUGGINGFACEHUB_API_TOKEN
-
+>
 > You can also customize the "MODEL_ID" if needed.
-
+>
 > You need to make sure you have created the directory `/mnt/opea-models` to save the cached model on the node where the CodeTrans workload is running. Otherwise, you need to modify the `codetrans.yaml` file to change the `model-volume` to a directory that exists on the node.
 
 ## Required Models
diff --git a/DocSum/docker/gaudi/README.md b/DocSum/docker/gaudi/README.md
index cf48ca885..fe765ecb0 100644
--- a/DocSum/docker/gaudi/README.md
+++ b/DocSum/docker/gaudi/README.md
@@ -98,29 +98,29 @@ docker compose up -d
 
 1. TGI Service
 
-```bash
-curl http://${your_ip}:8008/generate \
-  -X POST \
-  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${your_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 2. LLM Microservice
 
-```bash
-curl http://${your_ip}:9000/v1/chat/docsum \
-  -X POST \
-  -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${your_ip}:9000/v1/chat/docsum \
+     -X POST \
+     -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
+     -H 'Content-Type: application/json'
+   ```
 
 3. MegaService
 
-```bash
-curl http://${host_ip}:8888/v1/docsum -H "Content-Type: application/json" -d '{
-     "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
-     }'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/docsum -H "Content-Type: application/json" -d '{
+        "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
+        }'
+   ```
 
 ## 🚀 Launch the Svelte UI
 
diff --git a/DocSum/docker/xeon/README.md b/DocSum/docker/xeon/README.md
index 7a84d47d5..751837495 100644
--- a/DocSum/docker/xeon/README.md
+++ b/DocSum/docker/xeon/README.md
@@ -107,35 +107,36 @@ docker compose up -d
 
 1. TGI Service
 
-```bash
-curl http://${your_ip}:8008/generate \
-  -X POST \
-  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${your_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 2. LLM Microservice
 
-```bash
-curl http://${your_ip}:9000/v1/chat/docsum \
-  -X POST \
-  -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${your_ip}:9000/v1/chat/docsum \
+     -X POST \
+     -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
+     -H 'Content-Type: application/json'
+   ```
 
 3. MegaService
 
-```bash
-curl http://${host_ip}:8888/v1/docsum -H "Content-Type: application/json" -d '{
-     "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
-     }'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/docsum -H "Content-Type: application/json" -d '{
+        "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
+        }'
+   ```
 
 Following the validation of all aforementioned microservices, we are now prepared to construct a mega-service.
 
 ## 🚀 Launch the UI
 
 Open this URL `http://{host_ip}:5173` in your browser to access the svelte based frontend.
+
 Open this URL `http://{host_ip}:5174` in your browser to access the React based frontend.
 
 ### Svelte UI
diff --git a/DocSum/kubernetes/manifests/README.md b/DocSum/kubernetes/manifests/README.md
index c47e4b39f..ba0d012f8 100644
--- a/DocSum/kubernetes/manifests/README.md
+++ b/DocSum/kubernetes/manifests/README.md
@@ -3,9 +3,9 @@
 > [NOTE]
 > The following values must be set before you can deploy:
 > HUGGINGFACEHUB_API_TOKEN
-
+>
 > You can also customize the "MODEL_ID" and "model-volume"
-
+>
 > You need to make sure you have created the directory `/mnt/opea-models` to save the cached model on the node where the DocSum workload is running. Otherwise, you need to modify the `docsum.yaml` file to change the `model-volume` to a directory that exists on the node.
 
 ## Deploy On Xeon
diff --git a/DocSum/kubernetes/manifests/xeon/ui/README.md b/DocSum/kubernetes/manifests/xeon/ui/README.md
index 4f7a2ad97..a1fffd4b7 100644
--- a/DocSum/kubernetes/manifests/xeon/ui/README.md
+++ b/DocSum/kubernetes/manifests/xeon/ui/README.md
@@ -7,30 +7,31 @@ You can use react-docsum.yaml to deploy Docsum with reactUI.
 kubectl apply -f react-docsum.yaml
 ```
 
-## Prerequisites for Deploying DocSum with ReactUI:
+## Prerequisites for Deploying DocSum with ReactUI
 Before deploying the react-docsum.yaml file, ensure that you have the following prerequisites in place:
 
 1. Kubernetes installation: Make sure that you have Kubernetes installed.
 2. Configuration Values: Set the following values in react-docsum.yaml before proceeding with the deployment:
-    #### a. HUGGINGFACEHUB_API_TOKEN (Your HuggingFace token to download your desired model from HuggingFace):
-    ```
-    # You may set the HUGGINGFACEHUB_API_TOKEN via method:
-    export HUGGINGFACEHUB_API_TOKEN="YourOwnToken"
-    cd GenAIExamples/DocSum/kubernetes/manifests/xeon/ui/
-    sed -i "s/insert-your-huggingface-token-here/${HUGGINGFACEHUB_API_TOKEN}/g" react-docsum.yaml
-    ```
-    #### b. Set the proxies based on your network configuration
-    ```
-    # Look for http_proxy, https_proxy, no_proxy key and fill up the value with your proxy configuration.
-    ```
+    a. HUGGINGFACEHUB_API_TOKEN (Your HuggingFace token to download your desired model from HuggingFace):
+       ```
+       # You may set the HUGGINGFACEHUB_API_TOKEN via method:
+       export HUGGINGFACEHUB_API_TOKEN="YourOwnToken"
+       cd GenAIExamples/DocSum/kubernetes/manifests/xeon/ui/
+       sed -i "s/insert-your-huggingface-token-here/${HUGGINGFACEHUB_API_TOKEN}/g" react-docsum.yaml
+       ```
+    b. Set the proxies based on your network configuration
+       ```
+       # Look for http_proxy, https_proxy, no_proxy key and fill up the value with your proxy configuration.
+       ```
 3. MODEL_ID and model-volume (OPTIONAL): You may as well customize the "MODEL_ID" to use different model and model-volume for the volume to be mounted.
 4. After completing these, you can proceed with the deployment of the react-docsum.yaml file.
 
-## Verify Services:
+## Verify Services
 Make sure all the pods are running, you should see total of 4 pods running:
-1. docsum
-2. docsum-llm-uservice
-3. docsum-react-ui
-4. docsum-tgi
+
+- docsum
+- docsum-llm-uservice
+- docsum-react-ui
+- docsum-tgi
 
 You may open up the UI by using the docsum-react-ui endpoint in the browser.
diff --git a/FaqGen/docker/gaudi/README.md b/FaqGen/docker/gaudi/README.md
index 79410bfbb..f07fbed11 100644
--- a/FaqGen/docker/gaudi/README.md
+++ b/FaqGen/docker/gaudi/README.md
@@ -99,29 +99,29 @@ docker compose up -d
 
 1. TGI Service
 
-```bash
-curl http://${your_ip}:8008/generate \
-  -X POST \
-  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${your_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 2. LLM Microservice
 
-```bash
-curl http://${host_ip}:9000/v1/faqgen \
-  -X POST \
-  -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:9000/v1/faqgen \
+     -X POST \
+     -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
+     -H 'Content-Type: application/json'
+   ```
 
 3. MegaService
 
-```bash
-curl http://${host_ip}:8888/v1/faqgen -H "Content-Type: application/json" -d '{
-     "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
-     }'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/faqgen -H "Content-Type: application/json" -d '{
+        "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
+        }'
+   ```
 
 ## 🚀 Launch the UI
 
diff --git a/FaqGen/docker/xeon/README.md b/FaqGen/docker/xeon/README.md
index cbe3a726b..8265ff02e 100644
--- a/FaqGen/docker/xeon/README.md
+++ b/FaqGen/docker/xeon/README.md
@@ -98,31 +98,31 @@ docker compose up -d
 
 1. TGI Service
 
-```bash
-curl http://${host_ip}:8008/generate \
-  -X POST \
-  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 2. LLM Microservice
 
-```bash
-curl http://${host_ip}:9000/v1/faqgen \
-  -X POST \
-  -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:9000/v1/faqgen \
+     -X POST \
+     -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
+     -H 'Content-Type: application/json'
+   ```
 
 3. MegaService
 
-```bash
-curl http://${host_ip}:8888/v1/faqgen -H "Content-Type: application/json" -d '{
-     "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
-     }'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/faqgen -H "Content-Type: application/json" -d '{
+        "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
+        }'
+   ```
 
-Following the validation of all aforementioned microservices, we are now prepared to construct a mega-service.
+   Following the validation of all aforementioned microservices, we are now prepared to construct a mega-service.
 
 ## 🚀 Launch the UI
 
diff --git a/FaqGen/kubernetes/manifests/xeon/ui/README.md b/FaqGen/kubernetes/manifests/xeon/ui/README.md
index 8a0da45c0..a3817e695 100644
--- a/FaqGen/kubernetes/manifests/xeon/ui/README.md
+++ b/FaqGen/kubernetes/manifests/xeon/ui/README.md
@@ -7,26 +7,26 @@ You can use react-faqgen.yaml to deploy FaqGen with reactUI.
 kubectl apply -f react-faqgen.yaml
 ```
 
-## Prerequisites for Deploying FaqGen with ReactUI:
+## Prerequisites for Deploying FaqGen with ReactUI
 Before deploying the react-faqgen.yaml file, ensure that you have the following prerequisites in place:
 
 1. Kubernetes installation: Make sure that you have Kubernetes installed.
 2. Configuration Values: Set the following values in react-faqgen.yaml before proceeding with the deployment:
-    #### a. HUGGINGFACEHUB_API_TOKEN (Your HuggingFace token to download your desired model from HuggingFace):
-    ```
-    # You may set the HUGGINGFACEHUB_API_TOKEN via method:
-    export HUGGINGFACEHUB_API_TOKEN="YourOwnToken"
-    cd GenAIExamples/FaqGen/kubernetes/manifests/xeon/ui/
-    sed -i "s/insert-your-huggingface-token-here/${HUGGINGFACEHUB_API_TOKEN}/g" react-faqgen.yaml
-    ```
-    #### b. Set the proxies based on your network configuration
-    ```
-    # Look for http_proxy, https_proxy, no_proxy key and fill up the value with your proxy configuration.
-    ```
+    a. HUGGINGFACEHUB_API_TOKEN (Your HuggingFace token to download your desired model from HuggingFace):
+       ```
+       # You may set the HUGGINGFACEHUB_API_TOKEN via method:
+       export HUGGINGFACEHUB_API_TOKEN="YourOwnToken"
+       cd GenAIExamples/FaqGen/kubernetes/manifests/xeon/ui/
+       sed -i "s/insert-your-huggingface-token-here/${HUGGINGFACEHUB_API_TOKEN}/g" react-faqgen.yaml
+       ```
+    b. Set the proxies based on your network configuration
+       ```
+       # Look for http_proxy, https_proxy, no_proxy key and fill up the value with your proxy configuration.
+       ```
 3. MODEL_ID and model-volume (OPTIONAL): You may as well customize the "MODEL_ID" to use different model and model-volume for the volume to be mounted.
 4. After completing these, you can proceed with the deployment of the react-faqgen.yaml file.
 
-## Verify Services:
+## Verify Services
 Make sure all the pods are running, you should see total of 4 pods running:
 1. faqgen
 2. faqgen-llm-uservice
diff --git a/ProductivitySuite/docker/ui/react/README.md b/ProductivitySuite/docker/ui/react/README.md
index 0ad0d23c3..97fae9be1 100644
--- a/ProductivitySuite/docker/ui/react/README.md
+++ b/ProductivitySuite/docker/ui/react/README.md
@@ -35,7 +35,8 @@ Here're some of the project's features:
 ### CODEGEN
 
 - Generate code: generate the corresponding code based on the current user's input.
-  ###### Screen Shot
+
+  Screen Shot
   ![project-screenshot](../../../assets/img/codegen.png)
 
 ### DOC SUMMARY
@@ -59,7 +60,7 @@ Here're some of the project's features:
 
 ![project-screenshot](../../../assets/img/faq_generator.png)
 
-## 🛠️ Get it Running:
+## 🛠️ Get it Running
 
 1. Clone the repo.
 
diff --git a/ProductivitySuite/docker/xeon/README.md b/ProductivitySuite/docker/xeon/README.md
index 82b024c52..0c1da5490 100644
--- a/ProductivitySuite/docker/xeon/README.md
+++ b/ProductivitySuite/docker/xeon/README.md
@@ -285,202 +285,202 @@ Please refer to [keycloak_setup_guide](keycloak_setup_guide.md) for more detail
 
 9. CodeGen LLM Microservice
 
-```bash
-curl http://${host_ip}:9001/v1/chat/completions\
-  -X POST \
-  -d '{"query":"def print_hello_world():"}' \
-  -H 'Content-Type: application/json'
-```
-
-11. DocSum LLM Microservice
-
-```bash
-curl http://${host_ip}:9002/v1/chat/docsum\
-  -X POST \
-  -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5"}' \
-  -H 'Content-Type: application/json'
-```
-
-12. FAQGen LLM Microservice
-
-```bash
-curl http://${host_ip}:9003/v1/faqgen\
-  -X POST \
-  -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5"}' \
-  -H 'Content-Type: application/json'
-```
-
-13. ChatQnA MegaService
-
-```bash
-curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
-     "messages": "What is the revenue of Nike in 2023?"
-     }'
-```
-
-14. FAQGen MegaService
-
-```bash
-curl http://${host_ip}:8889/v1/faqgen -H "Content-Type: application/json" -d '{
-     "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
-     }'
-```
-
-15. DocSum MegaService
-
-```bash
-curl http://${host_ip}:8890/v1/docsum -H "Content-Type: application/json" -d '{
-     "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
-     }'
-```
-
-16. CodeGen MegaService
-
-```bash
-curl http://${host_ip}:7778/v1/codegen -H "Content-Type: application/json" -d '{
-     "messages": "def print_hello_world():"
-     }'
-```
-
-17. Dataprep Microservice
-
-If you want to update the default knowledge base, you can use the following commands:
-
-Update Knowledge Base via Local File Upload:
-
-```bash
-curl -X POST "http://${host_ip}:6007/v1/dataprep" \
-     -H "Content-Type: multipart/form-data" \
-     -F "files=@./nke-10k-2023.pdf"
-```
-
-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:
-
-```bash
-curl -X POST "http://${host_ip}:6007/v1/dataprep" \
-     -H "Content-Type: multipart/form-data" \
-     -F 'link_list=["https://opea.dev"]'
-```
-
-This command updates a knowledge base by submitting a list of HTTP links for processing.
-
-Also, you are able to get the file list that you uploaded:
-
-```bash
-curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
-     -H "Content-Type: application/json"
-```
-
-To delete the file/link you uploaded:
-
-```bash
-# delete link
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "https://opea.dev.txt"}' \
-     -H "Content-Type: application/json"
-
-# delete file
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "nke-10k-2023.pdf"}' \
-     -H "Content-Type: application/json"
-
-# delete all uploaded files and links
-curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-     -d '{"file_path": "all"}' \
-     -H "Content-Type: application/json"
-```
-
-18. Prompt Registry Microservice
-
-If you want to update the default Prompts in the application for your user, you can use the following commands:
+   ```bash
+   curl http://${host_ip}:9001/v1/chat/completions\
+     -X POST \
+     -d '{"query":"def print_hello_world():"}' \
+     -H 'Content-Type: application/json'
+   ```
 
-```bash
-curl -X 'POST' \
-  http://{host_ip}:6015/v1/prompt/create \
-  -H 'accept: application/json' \
-  -H 'Content-Type: application/json' \
-  -d '{
-    "prompt_text": "test prompt", "user": "test"
-}'
-```
+10. DocSum LLM Microservice
 
-Retrieve prompt from database based on user or prompt_id
+    ```bash
+    curl http://${host_ip}:9002/v1/chat/docsum\
+      -X POST \
+      -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5"}' \
+      -H 'Content-Type: application/json'
+    ```
 
-```bash
-curl -X 'POST' \
-  http://{host_ip}:6015/v1/prompt/get \
-  -H 'accept: application/json' \
-  -H 'Content-Type: application/json' \
-  -d '{
-  "user": "test"}'
-
-curl -X 'POST' \
-  http://{host_ip}:6015/v1/prompt/get \
-  -H 'accept: application/json' \
-  -H 'Content-Type: application/json' \
-  -d '{
-  "user": "test", "prompt_id":"{prompt_id returned from save prompt route above}"}'
-```
+11. FAQGen LLM Microservice
 
-Delete prompt from database based on prompt_id provided
+    ```bash
+    curl http://${host_ip}:9003/v1/faqgen\
+      -X POST \
+      -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5"}' \
+      -H 'Content-Type: application/json'
+    ```
 
-```bash
-curl -X 'POST' \
-  http://{host_ip}:6015/v1/prompt/delete \
-  -H 'accept: application/json' \
-  -H 'Content-Type: application/json' \
-  -d '{
-  "user": "test", "prompt_id":"{prompt_id to be deleted}"}'
-```
+12. ChatQnA MegaService
 
-19. Chat History Microservice
+    ```bash
+    curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
+         "messages": "What is the revenue of Nike in 2023?"
+         }'
+    ```
 
-To validate the chatHistory Microservice, you can use the following commands.
+13. FAQGen MegaService
 
-Create a sample conversation and get the message ID.
+    ```bash
+    curl http://${host_ip}:8889/v1/faqgen -H "Content-Type: application/json" -d '{
+         "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
+         }'
+    ```
 
-```bash
-curl -X 'POST' \
-  http://${host_ip}:6012/v1/chathistory/create \
-  -H 'accept: application/json' \
-  -H 'Content-Type: application/json' \
-  -d '{
-  "data": {
-    "messages": "test Messages", "user": "test"
-  }
-}'
-```
+14. DocSum MegaService
 
-Retrieve the conversation based on user or conversation id
+    ```bash
+    curl http://${host_ip}:8890/v1/docsum -H "Content-Type: application/json" -d '{
+         "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
+         }'
+    ```
+
+15. CodeGen MegaService
+
+    ```bash
+    curl http://${host_ip}:7778/v1/codegen -H "Content-Type: application/json" -d '{
+         "messages": "def print_hello_world():"
+         }'
+    ```
 
-```bash
-curl -X 'POST' \
-  http://${host_ip}:6012/v1/chathistory/get \
-  -H 'accept: application/json' \
-  -H 'Content-Type: application/json' \
-  -d '{
-  "user": "test"}'
-
-curl -X 'POST' \
-  http://${host_ip}:6012/v1/chathistory/get \
-  -H 'accept: application/json' \
-  -H 'Content-Type: application/json' \
-  -d '{
-  "user": "test", "id":"{Conversation id to retrieve }"}'
-```
+16. Dataprep Microservice
 
-Delete Conversation from database based on conversation id provided.
+    If you want to update the default knowledge base, you can use the following commands:
 
-```bash
-curl -X 'POST' \
-  http://${host_ip}:6012/v1/chathistory/delete \
-  -H 'accept: application/json' \
-  -H 'Content-Type: application/json' \
-  -d '{
-  "user": "test", "id":"{Conversation id to Delete}"}'
-```
+    Update Knowledge Base via Local File Upload:
+
+    ```bash
+    curl -X POST "http://${host_ip}:6007/v1/dataprep" \
+         -H "Content-Type: multipart/form-data" \
+         -F "files=@./nke-10k-2023.pdf"
+    ```
+
+    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:
+
+    ```bash
+    curl -X POST "http://${host_ip}:6007/v1/dataprep" \
+         -H "Content-Type: multipart/form-data" \
+         -F 'link_list=["https://opea.dev"]'
+    ```
+
+    This command updates a knowledge base by submitting a list of HTTP links for processing.
+
+    Also, you are able to get the file list that you uploaded:
+
+    ```bash
+    curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
+         -H "Content-Type: application/json"
+    ```
+
+    To delete the file/link you uploaded:
+
+    ```bash
+    # delete link
+    curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+         -d '{"file_path": "https://opea.dev.txt"}' \
+         -H "Content-Type: application/json"
+
+    # delete file
+    curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+         -d '{"file_path": "nke-10k-2023.pdf"}' \
+         -H "Content-Type: application/json"
+
+    # delete all uploaded files and links
+    curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
+         -d '{"file_path": "all"}' \
+         -H "Content-Type: application/json"
+    ```
+
+17. Prompt Registry Microservice
+
+    If you want to update the default Prompts in the application for your user, you can use the following commands:
+
+    ```bash
+    curl -X 'POST' \
+      http://{host_ip}:6015/v1/prompt/create \
+      -H 'accept: application/json' \
+      -H 'Content-Type: application/json' \
+      -d '{
+        "prompt_text": "test prompt", "user": "test"
+    }'
+    ```
+
+    Retrieve prompt from database based on user or prompt_id
+
+    ```bash
+    curl -X 'POST' \
+      http://{host_ip}:6015/v1/prompt/get \
+      -H 'accept: application/json' \
+      -H 'Content-Type: application/json' \
+      -d '{
+      "user": "test"}'
+
+    curl -X 'POST' \
+      http://{host_ip}:6015/v1/prompt/get \
+      -H 'accept: application/json' \
+      -H 'Content-Type: application/json' \
+      -d '{
+      "user": "test", "prompt_id":"{prompt_id returned from save prompt route above}"}'
+    ```
+
+    Delete prompt from database based on prompt_id provided
+
+    ```bash
+    curl -X 'POST' \
+      http://{host_ip}:6015/v1/prompt/delete \
+      -H 'accept: application/json' \
+      -H 'Content-Type: application/json' \
+      -d '{
+      "user": "test", "prompt_id":"{prompt_id to be deleted}"}'
+    ```
+
+18. Chat History Microservice
+
+    To validate the chatHistory Microservice, you can use the following commands.
+
+    Create a sample conversation and get the message ID.
+
+    ```bash
+    curl -X 'POST' \
+      http://${host_ip}:6012/v1/chathistory/create \
+      -H 'accept: application/json' \
+      -H 'Content-Type: application/json' \
+      -d '{
+      "data": {
+        "messages": "test Messages", "user": "test"
+      }
+    }'
+    ```
+
+    Retrieve the conversation based on user or conversation id
+
+    ```bash
+    curl -X 'POST' \
+      http://${host_ip}:6012/v1/chathistory/get \
+      -H 'accept: application/json' \
+      -H 'Content-Type: application/json' \
+      -d '{
+      "user": "test"}'
+
+    curl -X 'POST' \
+      http://${host_ip}:6012/v1/chathistory/get \
+      -H 'accept: application/json' \
+      -H 'Content-Type: application/json' \
+      -d '{
+      "user": "test", "id":"{Conversation id to retrieve }"}'
+    ```
+
+    Delete Conversation from database based on conversation id provided.
+
+    ```bash
+    curl -X 'POST' \
+      http://${host_ip}:6012/v1/chathistory/delete \
+      -H 'accept: application/json' \
+      -H 'Content-Type: application/json' \
+      -d '{
+      "user": "test", "id":"{Conversation id to Delete}"}'
+    ```
 
 ## 🚀 Launch the UI
 
@@ -528,7 +528,8 @@ Here're some of the project's features:
 ### CODEGEN
 
 - Generate code: generate the corresponding code based on the current user's input.
-  ###### Screen Shot
+
+  Screen Shot
   ![project-screenshot](../../assets/img/codegen.png)
 
 ### DOC SUMMARY
diff --git a/ProductivitySuite/kubernetes/manifests/README.md b/ProductivitySuite/kubernetes/manifests/README.md
index cfb02c45b..11dd0acd9 100644
--- a/ProductivitySuite/kubernetes/manifests/README.md
+++ b/ProductivitySuite/kubernetes/manifests/README.md
@@ -16,7 +16,7 @@ In ProductivitySuite, it consists of following pipelines/examples and components
 - keycloak
 ```
 
-## Prerequisites for Deploying ProductivitySuite with ReactUI:
+## Prerequisites for Deploying ProductivitySuite with ReactUI
 To begin with, ensure that you have following prerequisites in place:
 
 1. Kubernetes installation: Make sure that you have Kubernetes installed.
diff --git a/README.md b/README.md
index a42df4a66..55a53ed1a 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,3 @@
-<div align="center">
-
 # Generative AI Examples
 
 [![version](https://img.shields.io/badge/release-0.9-green)](https://github.com/opea-project/GenAIExamples/releases)
@@ -7,8 +5,6 @@
 
 ---
 
-<div align="left">
-
 ## Introduction
 
 GenAIComps-based Generative AI examples offer streamlined deployment, testing, and scalability. All examples are fully compatible with Docker and Kubernetes, supporting a wide range of hardware platforms such as Gaudi, Xeon, and other hardwares.
diff --git a/Translation/docker/gaudi/README.md b/Translation/docker/gaudi/README.md
index 04ec63453..16a22e976 100644
--- a/Translation/docker/gaudi/README.md
+++ b/Translation/docker/gaudi/README.md
@@ -71,28 +71,28 @@ docker compose up -d
 
 1. TGI Service
 
-```bash
-curl http://${host_ip}:8008/generate \
-  -X POST \
-  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 2. LLM Microservice
 
-```bash
-curl http://${host_ip}:9000/v1/chat/completions \
-  -X POST \
-  -d '{"query":"Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:9000/v1/chat/completions \
+     -X POST \
+     -d '{"query":"Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"}' \
+     -H 'Content-Type: application/json'
+   ```
 
 3. MegaService
 
-```bash
-curl http://${host_ip}:8888/v1/translation -H "Content-Type: application/json" -d '{
-     "language_from": "Chinese","language_to": "English","source_language": "我爱机器翻译。"}'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/translation -H "Content-Type: application/json" -d '{
+        "language_from": "Chinese","language_to": "English","source_language": "我爱机器翻译。"}'
+   ```
 
 Following the validation of all aforementioned microservices, we are now prepared to construct a mega-service.
 
diff --git a/Translation/docker/xeon/README.md b/Translation/docker/xeon/README.md
index 915e2b3a4..25204094d 100644
--- a/Translation/docker/xeon/README.md
+++ b/Translation/docker/xeon/README.md
@@ -79,28 +79,28 @@ docker compose up -d
 
 1. TGI Service
 
-```bash
-curl http://${host_ip}:8008/generate \
-  -X POST \
-  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
 
 2. LLM Microservice
 
-```bash
-curl http://${host_ip}:9000/v1/chat/completions \
-  -X POST \
-  -d '{"query":"Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"}' \
-  -H 'Content-Type: application/json'
-```
+   ```bash
+   curl http://${host_ip}:9000/v1/chat/completions \
+     -X POST \
+     -d '{"query":"Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"}' \
+     -H 'Content-Type: application/json'
+   ```
 
 3. MegaService
 
-```bash
-curl http://${host_ip}:8888/v1/translation -H "Content-Type: application/json" -d '{
-     "language_from": "Chinese","language_to": "English","source_language": "我爱机器翻译。"}'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/translation -H "Content-Type: application/json" -d '{
+        "language_from": "Chinese","language_to": "English","source_language": "我爱机器翻译。"}'
+   ```
 
 Following the validation of all aforementioned microservices, we are now prepared to construct a mega-service.
 
diff --git a/VisualQnA/README.md b/VisualQnA/README.md
index 910deda2a..a00c0602e 100644
--- a/VisualQnA/README.md
+++ b/VisualQnA/README.md
@@ -18,7 +18,7 @@ This example guides you through how to deploy a [LLaVA-NeXT](https://github.com/
 ![llava screenshot](./assets/img/llava_screenshot1.png)
 ![llava-screenshot](./assets/img/llava_screenshot2.png)
 
-# Required Models
+## Required Models
 
 By default, the model is set to `llava-hf/llava-v1.6-mistral-7b-hf`. To use a different model, update the `LVM_MODEL_ID` variable in the [`set_env.sh`](./docker/gaudi/set_env.sh) file.
 
@@ -28,13 +28,13 @@ export LVM_MODEL_ID="llava-hf/llava-v1.6-mistral-7b-hf"
 
 You can choose other llava-next models, such as `llava-hf/llava-v1.6-vicuna-13b-hf`, as needed.
 
-# Deploy VisualQnA Service
+## Deploy VisualQnA Service
 
 The VisualQnA service can be effortlessly deployed on either Intel Gaudi2 or Intel XEON Scalable Processors.
 
 Currently we support deploying VisualQnA services with docker compose.
 
-## Setup Environment Variable
+### Setup Environment Variable
 
 To set up environment variables for deploying VisualQnA services, follow these steps:
 
@@ -65,7 +65,7 @@ To set up environment variables for deploying VisualQnA services, follow these s
    source ./docker/xeon/set_env.sh
    ```
 
-## Deploy VisualQnA on Gaudi
+### Deploy VisualQnA on Gaudi
 
 Refer to the [Gaudi Guide](./docker/gaudi/README.md) to build docker images from source.
 
@@ -78,7 +78,7 @@ docker compose up -d
 
 > Notice: Currently only the **Habana Driver 1.16.x** is supported for Gaudi.
 
-## Deploy VisualQnA on Xeon
+### Deploy VisualQnA on Xeon
 
 Refer to the [Xeon Guide](./docker/xeon/README.md) for more instructions on building docker images from source.
 
diff --git a/VisualQnA/docker/gaudi/README.md b/VisualQnA/docker/gaudi/README.md
index 4b9f055b8..5e0571121 100644
--- a/VisualQnA/docker/gaudi/README.md
+++ b/VisualQnA/docker/gaudi/README.md
@@ -91,34 +91,34 @@ Follow the instructions to validate MicroServices.
 
 1. LLM Microservice
 
-```bash
-http_proxy="" curl http://${host_ip}:9399/v1/lvm -XPOST -d '{"image": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "prompt":"What is this?"}' -H 'Content-Type: application/json'
-```
+   ```bash
+   http_proxy="" curl http://${host_ip}:9399/v1/lvm -XPOST -d '{"image": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "prompt":"What is this?"}' -H 'Content-Type: application/json'
+   ```
 
 2. MegaService
 
-```bash
-curl http://${host_ip}:8888/v1/visualqna -H "Content-Type: application/json" -d '{
-     "messages": [
-      {
-        "role": "user",
-        "content": [
-          {
-            "type": "text",
-            "text": "What'\''s in this image?"
-          },
-          {
-            "type": "image_url",
-            "image_url": {
-              "url": "https://www.ilankelman.org/stopsigns/australia.jpg"
-            }
-          }
-        ]
-      }
-    ],
-    "max_tokens": 300
-    }'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/visualqna -H "Content-Type: application/json" -d '{
+        "messages": [
+         {
+           "role": "user",
+           "content": [
+             {
+               "type": "text",
+               "text": "What'\''s in this image?"
+             },
+             {
+               "type": "image_url",
+               "image_url": {
+                 "url": "https://www.ilankelman.org/stopsigns/australia.jpg"
+               }
+             }
+           ]
+         }
+       ],
+       "max_tokens": 300
+       }'
+   ```
 
 ## 🚀 Launch the UI
 
diff --git a/VisualQnA/docker/xeon/README.md b/VisualQnA/docker/xeon/README.md
index 73e30ab96..d5afda219 100644
--- a/VisualQnA/docker/xeon/README.md
+++ b/VisualQnA/docker/xeon/README.md
@@ -133,34 +133,34 @@ Follow the instructions to validate MicroServices.
 
 1. LLM Microservice
 
-```bash
-http_proxy="" curl http://${host_ip}:9399/v1/lvm -XPOST -d '{"image": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "prompt":"What is this?"}' -H 'Content-Type: application/json'
-```
+   ```bash
+   http_proxy="" curl http://${host_ip}:9399/v1/lvm -XPOST -d '{"image": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "prompt":"What is this?"}' -H 'Content-Type: application/json'
+   ```
 
 2. MegaService
 
-```bash
-curl http://${host_ip}:8888/v1/visualqna -H "Content-Type: application/json" -d '{
-     "messages": [
-      {
-        "role": "user",
-        "content": [
-          {
-            "type": "text",
-            "text": "What'\''s in this image?"
-          },
-          {
-            "type": "image_url",
-            "image_url": {
-              "url": "https://www.ilankelman.org/stopsigns/australia.jpg"
-            }
-          }
-        ]
-      }
-    ],
-    "max_tokens": 300
-    }'
-```
+   ```bash
+   curl http://${host_ip}:8888/v1/visualqna -H "Content-Type: application/json" -d '{
+        "messages": [
+         {
+           "role": "user",
+           "content": [
+             {
+               "type": "text",
+               "text": "What'\''s in this image?"
+             },
+             {
+               "type": "image_url",
+               "image_url": {
+                 "url": "https://www.ilankelman.org/stopsigns/australia.jpg"
+               }
+             }
+           ]
+         }
+       ],
+       "max_tokens": 300
+       }'
+   ```
 
 ## 🚀 Launch the UI
 
diff --git a/VisualQnA/kubernetes/manifests/README.md b/VisualQnA/kubernetes/manifests/README.md
index 9973b15e7..aa92531f3 100644
--- a/VisualQnA/kubernetes/manifests/README.md
+++ b/VisualQnA/kubernetes/manifests/README.md
@@ -2,7 +2,7 @@
 
 > [NOTE]
 > You can also customize the "LVM_MODEL_ID" if needed.
-
+>
 > You need to make sure you have created the directory `/mnt/opea-models` to save the cached model on the node where the visualqna workload is running. Otherwise, you need to modify the `visualqna.yaml` file to change the `model-volume` to a directory that exists on the node.
 
 ## Deploy On Xeon