How to implement Async on ChatOpenAI?

[ArslanKAS]

I'm able to run the code and get a summary for a row in a dataset. But since the llm isn't async right now so I've to wait a lot for the summaries. I tried to turn it into an async function but I can't find the async substitute for the ChatOpenAI function.

Can anyone help me on how I can turn it into an Async function using ChatOpenAI (gpt-3.5 turbo).

  import asyncio
  async def summary_main(index, row, prompt, df):

    df_row = row.copy()
    # OpenAI summary function
    async def openai_summary(index, prompt):
        prompt_message = f"""
        {prompt}
            call summaries: ```{df_row["summary"]}```
        """

        chat = ChatOpenAI(model="gpt-3.5-turbo-0301",temperature=0.5, max_tokens=1200)
        chat([HumanMessage(content="""
                                I'm an employee by the company. I'll provide you with a summary. \
                                Your job is to produce executive summary out of it.
                        """)])
        messages = [
            SystemMessage(content="""
                    You will be provided with a summary by the user. \ 
                    Please provide an executive summary of it with more context and detail in 6 lines.'
                """,),
            HumanMessage(content=row["summary"])
        ]
        responses = await chat(messages)
        print(responses.content)

async def generate_concurrently(df, prompt):
    tasks = []
    for index, row in df.head(5).iterrows():
        task = summary_main(index, row, prompt, df)
        tasks.append(task)
        if len(tasks) == 50:  # Run the tasks in batches of 50
            await asyncio.gather(*tasks)  # Wait for the batch to complete
            tasks = []  # Reset the tasks list for the next batch

    if tasks:  # Run the remaining tasks in the last batch
        await asyncio.gather(*tasks)

df_summary = await generate_concurrently(summaries_df_sample, prompt)

Thank you