How to reduce the time taken by the ChatOpenAI to perform a prompt based call using LLMchain?

[jais001]

Hi,
I am using the LLMChain with ChatOpenAI, to perform prompt calls. It takes 30 seconds to get the result. Is there a way to reduce the time taken by OpenAI calls?
Provided below are my configurations:
The prompt will be a query of max_tokens size = 3000
llm = ChatOpenAI(
model_name='gpt-4',
temperature=0,
openai_api_key=openai_key
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
data = llm_chain.run(query)

[dosubot[bot]]

🤖

Hi,

To reduce the time taken by the ChatOpenAI to perform a prompt-based call using LLMChain, you can consider the following approaches:

1. Reduce the maximum token size: The max_tokens parameter in the BaseOpenAI class is set to 256 by default. If you're currently using a maximum token size of 3000, reducing this number could potentially speed up the response time. However, this might also affect the quality or length of the generated text.

2. Use streaming: The streaming parameter in the BaseOpenAI class is set to False by default. If you set this to True, the results will be streamed as they are generated, which might help to decrease the response time. However, note that streaming is not compatible with generating multiple completions (n > 1) or generating the "best" completion (best_of > 1).

3. Adjust the batch size: The batch_size parameter in the BaseOpenAI class is set to 20 by default. If you're making multiple requests at once, adjusting this number could potentially affect the response time. However, the optimal batch size can depend on various factors, so you might need to experiment to find the best value.

4. Use async calls: The acompletion_with_retry function is an asynchronous version of the completion_with_retry function. If you're making multiple requests at once, using async calls could potentially speed up the response time by allowing the requests to be made concurrently.

5. Use tiktoken: Tiktoken is used to count the number of tokens in documents to constrain them to be under a certain limit. This can help to avoid unnecessary API calls for documents that are too large. You can specify a model name to use with tiktoken in the BaseOpenAI class.

Please note that these are general suggestions and the actual impact on response time can depend on various factors, including the specific prompt and model you're using, as well as the server load and network latency.

You can find more details in the LangChain codebase.

I hope this helps! If you have any other questions, feel free to ask.

Best,
Dosu

Sources

• libs/langchain/langchain/llms/openai.py

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