Update retrieve and reranking README.md

README.md

@@ -34,7 +34,7 @@ The initially supported microservices are described in the below table. More mic
 			<td><a href="./comps/embeddings/README.md">Embedding</a></td>
             <td><a href="https://www.langchain.com">LangChain</a></td>
 			<td><a href="https://huggingface.co/BAAI/bge-large-en-v1.5">BAAI/bge-large-en-v1.5</a></td>
-			<td><a href="https://github.com/huggingface/tei-gaudi">TEI-Habana</a></td>
+			<td><a href="https://github.com/huggingface/tei-gaudi">TEI-Gaudi</a></td>
 			<td>Gaudi2</td>
 			<td>Embedding on Gaudi2</td>
 		</tr>
@@ -58,7 +58,7 @@ The initially supported microservices are described in the below table. More mic
 			<td><a href="./comps/reranks/README.md">Reranking</a></td>
             <td><a href="https://www.langchain.com">LangChain</a></td>
 			<td><a href="https://huggingface.co/BAAI/bge-reranker-large">BAAI/bge-reranker-large</a></td>
-			<td><a href="https://github.com/huggingface/tei-gaudi">TEI-Habana</a></td>
+			<td><a href="https://github.com/huggingface/tei-gaudi">TEI-Gaudi</a></td>
 			<td>Gaudi2</td>
 			<td>Reranking on Gaudi2</td>
 		</tr>

comps/reranks/README.md

@@ -1,10 +1,10 @@
 # Reranking Microservice
 
-The Reranking Microservice, fueled by Rerank models, stands as a straightforward yet immensely potent tool for semantic search. When provided with a query and a collection of documents, Rerank swiftly indexes the documents based on their semantic relevance to the query, arranging them from most to least pertinent. This microservice significantly enhances overall accuracy. In a text retrieval system, either a dense embedding model or a sparse lexical search index is often employed to retrieve relevant text documents based on the input. However, a reranking model can further refine this process by rearranging potential candidates into a final, optimized order.
+The Reranking Microservice, fueled by reranking models, stands as a straightforward yet immensely potent tool for semantic search. When provided with a query and a collection of documents, reranking swiftly indexes the documents based on their semantic relevance to the query, arranging them from most to least pertinent. This microservice significantly enhances overall accuracy. In a text retrieval system, either a dense embedding model or a sparse lexical search index is often employed to retrieve relevant text documents based on the input. However, a reranking model can further refine this process by rearranging potential candidates into a final, optimized order.
 
 # 🚀Start Microservice with Python
 
-To start the Reranking microservice, you need to install python packages first.
+To start the Reranking microservice, you must first install the required python packages.
 
 ## Install Requirements
 
@@ -72,7 +72,7 @@ docker compose -f docker_compose_reranking.yaml up -d
 ## Check Service Status
 
 ```bash
-curl http://localhost:8000/v1/health_check\
+curl http://localhost:8000/v1/health_check \
   -X GET \
   -H 'Content-Type: application/json'
 ```

comps/retrievers/README.md

@@ -1,26 +1,27 @@
 # Retriever Microservice
 
-This retriever microservice is a highly efficient search service designed for handling and retrieving embedding vectors. It operates by receiving an embedding vector as input and conducting a similarity search against vectors stored in a vectordb database. Users must specify the vectordb's URL and the index name, and the service searches within that index to find documents with the highest similarity to the input vector.
+This retriever microservice is a highly efficient search service designed for handling and retrieving embedding vectors. It operates by receiving an embedding vector as input and conducting a similarity search against vectors stored in a VectorDB database. Users must specify the VectorDB's URL and the index name, and the service searches within that index to find documents with the highest similarity to the input vector.
 
-The service primarily utilizes measures of similarity in vector space to rapidly retrieve documents that are contentually similar. This vector-based retrieval approach is particularly suited for handling large datasets, offering fast and accurate search results that significantly enhance the efficiency and quality of information retrieval.
+The service primarily utilizes similarity measures in vector space to rapidly retrieve contentually similar documents. The vector-based retrieval approach is particularly suited for handling large datasets, offering fast and accurate search results that significantly enhance the efficiency and quality of information retrieval.
 
 Overall, this microservice provides robust backend support for applications requiring efficient similarity searches, playing a vital role in scenarios such as recommendation systems, information retrieval, or any other context where precise measurement of document similarity is crucial.
 
 # 🚀Start Microservice with Python
 
-To start the retriever microservice, you need to install python packages first.
+To start the retriever microservice, you must first install the required python packages.
 
 ## Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## Setup Vectordb Service
+## Setup VectorDB Service
 
-You need to setup your own vectordb service (Redis in this example), and ingest your knowledge documents into the vector database.
+You need to setup your own VectorDB service (Redis in this example), and ingest your knowledge documents into the vector database.
 
-As for Redis, you could start a docker container using the following commands. Remember to ingest data into it manually.
+As for Redis, you could start a docker container using the following commands.
+Remember to ingest data into it manually.
 
 ```bash
 docker run -d --name="redis-vector-db" -p 6379:6379 -p 8001:8001 redis/redis-stack:7.2.0-v9
@@ -70,14 +71,14 @@ docker compose -f docker_compose_retriever.yaml up -d
 ## Check Service Status
 
 ```bash
-curl http://localhost:7000/v1/health_check\
+curl http://localhost:7000/v1/health_check \
   -X GET \
   -H 'Content-Type: application/json'
 ```
 
 ## Consume Embedding Service
 
-To consume the retriever microservice, you need to generate a mock embedding vector of length 768 in Python script:
+To consume the Retriever Microservice, you need to generate a mock embedding vector of length 768 in Python script:
 
 ```Python
 import random
@@ -88,7 +89,7 @@ print(embedding)
 Then substitute your mock embedding vector for the `${your_embedding}` in the following cURL command:
 
 ```bash
-curl http://${your_ip}:7000/v1/retrieval\
+curl http://${your_ip}:7000/v1/retrieval \
   -X POST \
   -d '{"text":"What is the revenue of Nike in 2023?","embedding":${your_embedding}}' \
   -H 'Content-Type: application/json'