AzureChatOpenAI ainvoke take along time?

[vsvn-ThuyTQ]

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Example Code

class PricingCalcHandler(BaseCallbackHandler):
    def __init__(self, request: Request = None) -> None:
        super().__init__()
        self.request = request
    def on_llm_start(
        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
    ) -> Any:
        logger.debug(f"on_llm_start {serialized}")
        
    def on_llm_end(self, llm_result: LLMResult, *, run_id: UUID, parent_run_id: UUID , **kwargs: Any) -> Any:
        try:
            logger.debug(f"run_id {run_id} llm_result {llm_result}")
            if self.request and llm_result:
                logger.info(f'run id {run_id} save pricing!')
        except Exception as e:
            logger.error(e)
def llm_with_callback(request: Request = None):
    pricing_handler = PricingCalcHandler(request)
    return AzureChatOpenAI(
        azure_deployment = os.environ.get('AZURE_OPENAI_DEPLOYMENT'),
        azure_endpoint=os.environ.get('AZURE_OPENAI_ENDPOINT'),
        api_key = os.environ.get('AZURE_OPENAI_KEY'),
        api_version="2023-09-01-preview",
        cache=config.USE_CACHE_LLM,
        model_kwargs = {"seed": Constants.GPT_SEED},
        max_retries=3,
        temperature=0,
        callbacks=[pricing_handler]
    ) 
 llm = llm_with_callback(request=self._request)
 self.feature_prompt = self.feature_prompt_template.format(content=bookmarks_as_string)
 features_raw = await llm.ainvoke(self.feature_prompt)

Description

I don't understand why when I run invoke, I wait for 1 hour without receiving a response from gpt. When I debug I realized that gpt only runs until on_llm_start and stays here for about 1 hour with no signs of stopping.

System Info

Langchain
AzureOpenAI

[dosubot[bot]]

The issue you're encountering with the ainvoke method hanging during execution could be due to several factors, including deadlocks, blocking operations within your asynchronous code, or issues with the callback not being triggered properly. Here are some steps to diagnose and potentially resolve the issue:

1. Check for Blocking Operations: Ensure there are no blocking calls in your asynchronous code path. Mixing synchronous and asynchronous code improperly can lead to deadlocks that prevent the event loop from progressing.

2. Debug Logging: Increase the logging level to capture more detailed information about the execution flow and any errors or warnings. This can provide insights into where the process might be hanging.

3. Error Handling: Implement comprehensive error handling around the streaming API calls to catch and log any exceptions. This can help identify if an error in the streaming response is causing the issue.

4. Simplify Code for Testing: Temporarily simplify your code to a basic streaming request without additional logic. This can help isolate the problem by determining if the issue persists in a more controlled environment.

5. Review Callback Implementation: Ensure that the on_llm_start and other callbacks are implemented correctly and are being triggered as expected. If these callbacks do not get triggered due to a lack of incoming tokens or an error, it could appear as if the process is hanging.

6. Configuration and API Issues: Verify your configuration for the OpenAI API, including API keys, model selection, and request parameters. Incorrect configuration can lead to failed requests or empty responses.

If after following these steps the issue remains unresolved, it might be beneficial to consult the documentation or seek further assistance with a more detailed error description or behavior observation. This approach will help in narrowing down the potential causes and finding a resolution more efficiently.

Details

Sources

• libs/community/langchain_community/llms/openai.py
• libs/community/langchain_community/callbacks/llmonitor_callback.py
• libs/community/langchain_community/chat_models/azure_openai.py
• libs/partners/openai/langchain_openai/chat_models/azure.py
• libs/langchain/langchain/utilities/asyncio.py
• docs/docs/modules/agents/how_to/max_time_limit.ipynb
• libs/core/langchain_core/runnables/base.py

About Dosu
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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

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[vsvn-ThuyTQ]

@dosu

[dosubot[bot]]

Given the details you've provided, it seems like the issue might be related to the Azure deployment or network configuration, especially since the process hangs at on_llm_start without proceeding. Here are some specific steps to address potential Azure-related issues:

1. Azure Deployment Configuration: Verify that your Azure deployment settings for the OpenAI service are correctly configured. Incorrect deployment settings can lead to requests not reaching the OpenAI service.

2. Network Configuration and Firewalls: Check if there are any network configuration issues or firewalls that might be blocking the requests to Azure OpenAI. Sometimes, corporate or personal firewalls can prevent outgoing requests from reaching the service.

3. Azure Service Limits: Ensure you're not hitting any service limits on Azure OpenAI that could prevent your requests from being processed. Azure services have limits on the number of concurrent requests and total requests per period.

4. API Key and Endpoint Verification: Double-check that the API key and endpoint in your environment variables are correct and have the necessary permissions to invoke the OpenAI service.

5. Azure Support and Logs: If possible, consult Azure support or check the Azure logs for any error messages or warnings that could indicate what's causing the request to hang. Azure logs can provide detailed information about the request lifecycle and potential issues.

6. Timeouts and Retry Logic: Implement or adjust timeouts and retry logic in your code. Sometimes, network issues or temporary service disruptions can cause requests to hang, and having a timeout with retry logic can help mitigate these issues.

7. Simplify the Request: As a test, try simplifying your request to the Azure OpenAI service to see if a more basic request succeeds. This can help determine if the issue is with the specific request or a broader configuration or network issue.

If these steps do not resolve the issue, it might be helpful to create a minimal reproducible example and reach out to Azure support for more direct assistance, as they can provide insights specific to your Azure environment and service usage.

Details

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

[v-Neethan]

Hi @vsvn-ThuyTQ ,

Is this issue resolved?