Update tutorials/how-to-implement-rag-generativeapis/index.mdx

Co-authored-by: SamyOubouaziz <[email protected]>

Author: bene2k1

tutorials/how-to-implement-rag-generativeapis/index.mdx

@@ -290,15 +290,15 @@ for r in rag_chain.stream("Your question"):
     print(r, end="", flush=True)
     time.sleep(0.1)
 ```
-- LLM initialization: We initialize the ChatOpenAI instance using the endpoint and API key from the environment variables, along with the specified model name.
+- LLM initialization: we initialize the ChatOpenAI instance using the endpoint and API key from the environment variables, along with the specified model name.
 
-- Prompt setup: The prompt is pulled from the hub using a pre-defined template, ensuring consistent query formatting.
+- Prompt setup: the prompt is pulled from the hub using a predefined template, ensuring consistent query formatting.
 
-- Retriever configuration: We set up the retriever to access the vector store, allowing the RAG system to retrieve relevant information based on the query.
+- Retriever configuration: we set up the retriever to access the vector store, allowing the RAG system to retrieve relevant information based on the query.
 
-- RAG chain construction: We create the RAG chain, which connects the retriever, prompt, LLM, and output parser in a streamlined workflow.
+- RAG chain construction: we create the RAG chain, which connects the retriever, prompt, LLM, and output parser in a streamlined workflow.
 
-- Query execution: Finally, we stream the output of the RAG chain for a specified question, printing each response with a slight delay for better readability.
+- Query execution: finally, we stream the output of the RAG chain for a specified question, printing each response with a slight delay for better readability.
 
 ### Query the RAG system with your own prompt template