[DOC] update ChatQnA README (#201)

Signed-off-by: Wang, Xigui <[email protected]>

ChatQnA/README.md

@@ -1,14 +1,14 @@
 # ChatQnA Application
 
-Chatbots are the most widely adopted use case for leveraging the powerful chat and reasoning capabilities of large language models (LLM). The retrieval augmented generation (RAG) architecture is quickly becoming the industry standard for developing chatbots because it combines the benefits of a knowledge base (via a vector store) and generative models to reduce hallucinations, maintain up-to-date information, and leverage domain-specific knowledge.
+Chatbots are the most widely adopted use case for leveraging the powerful chat and reasoning capabilities of large language models (LLMs). The retrieval augmented generation (RAG) architecture is quickly becoming the industry standard for chatbots development. It combines the benefits of a knowledge base (via a vector store) and generative models to reduce hallucinations, maintain up-to-date information, and leverage domain-specific knowledge.
 
-RAG bridges the knowledge gap by dynamically fetching relevant information from external sources, ensuring that responses generated remain factual and current. At the heart of this architecture are vector databases, instrumental in enabling efficient and semantic retrieval of information. These databases store data as vectors, allowing RAG to swiftly access the most pertinent documents or data points based on semantic similarity.
+RAG bridges the knowledge gap by dynamically fetching relevant information from external sources, ensuring that responses generated remain factual and current. The core of this architecture are vector databases, which are instrumental in enabling efficient and semantic retrieval of information. These databases store data as vectors, allowing RAG to swiftly access the most pertinent documents or data points based on semantic similarity.
 
 ChatQnA architecture shows below:
 
 ![architecture](https://i.imgur.com/lLOnQio.png)
 
-This ChatQnA use case performs RAG using LangChain, Redis vectordb and Text Generation Inference on Intel Gaudi2 or Intel XEON Scalable Processors. The Intel Gaudi2 accelerator supports both training and inference for deep learning models in particular for LLMs. Please visit [Habana AI products](https://habana.ai/products) for more details.
+This ChatQnA use case performs RAG using LangChain, Redis VectorDB and Text Generation Inference on Intel Gaudi2 or Intel XEON Scalable Processors. The Intel Gaudi2 accelerator supports both training and inference for deep learning models in particular for LLMs. Please visit [Habana AI products](https://habana.ai/products) for more details.
 
 # Deploy ChatQnA Service
 

ChatQnA/docker-composer/gaudi/README.md

@@ -72,7 +72,7 @@ cd GenAIExamples/ChatQnA/ui/
 docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
 ```
 
-Then run the command `docker images`, you will have the following 7 Docker Images:
+Then run the command `docker images`, you will have the following 8 Docker Images:
 
 1. `opea/embedding-tei:latest`
 2. `opea/retriever-redis:latest`
@@ -109,7 +109,7 @@ export LLM_SERVICE_HOST_IP=${host_ip}
 export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/chatqna"
 ```
 
-Note: Please replace with `host_ip` with you external IP address, do not use localhost.
+Note: Please replace with `host_ip` with you external IP address, do **NOT** use localhost.
 
 ### Start all the services Docker Containers
 
@@ -132,7 +132,7 @@ curl ${host_ip}:8090/embed \
 2. Embedding Microservice
 
 ```bash
-curl http://${host_ip}:6000/v1/embeddings\
+curl http://${host_ip}:6000/v1/embeddings \
   -X POST \
   -d '{"text":"hello"}' \
   -H 'Content-Type: application/json'
@@ -149,10 +149,10 @@ embedding = [random.uniform(-1, 1) for _ in range(768)]
 print(embedding)
 ```
 
-Then substitute your mock embedding vector for the `${your_embedding}` in the following cURL command:
+Then substitute your mock embedding vector for the `${your_embedding}` in the following `curl` command:
 
 ```bash
-curl http://${host_ip}:7000/v1/retrieval\
+curl http://${host_ip}:7000/v1/retrieval \
   -X POST \
   -d '{"text":"test", "embedding":${your_embedding}}' \
   -H 'Content-Type: application/json'
@@ -170,7 +170,7 @@ curl http://${host_ip}:8808/rerank \
 5. Reranking Microservice
 
 ```bash
-curl http://${host_ip}:8000/v1/reranking\
+curl http://${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'
@@ -188,7 +188,7 @@ curl http://${host_ip}:8008/generate \
 7. LLM Microservice
 
 ```bash
-curl http://${host_ip}:9000/v1/chat/completions\
+curl http://${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'