Providing proper step logs #40 #42
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Providing proper step logs in the Microbot and openAi_api file
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| def _create_llm(self): | ||
| if self.model_provider == ModelProvider.OPENAI: | ||
| if self.model_provider in [ModelProvider.OPENAI, ModelProvider.OPENAI_STANDARD]: |
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ModelProvider is an internal class. To use ModelProvider.OPENAI_STANDARD, You need to introduce that enum in the constants.py file.
| print(f"\n{'='*80}") | ||
| print(f"🚀 TASK STARTED: {task}") | ||
| print(f"{'='*80}") |
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Let's convert all print statements into INFO log statements. To avoid being unresponsive in the console while running, add a WARNING log to mention Log Level is above INFO. So, no output will be printed here during normal operation. Please wait patiently.
It gives us better control over the output and log stream. Particularly useful to run in quiet mode.
| return_value = {} | ||
| while self._validate_llm_response(return_value) is False: | ||
| response = self.ai_client.responses.create( | ||
| response = self.ai_client.chat.completions.create( |
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Our intention to use responses api over chat_completion api is to depending on the model itself to maintain the context using store=true. We're yet to invest and implement that feature in detail. So, I would recommend you to stick with responses api itself as much as possible.
If you still wish to stick with chat_completion, please create a new class OpenAIChatCompletionAPI class and implement your changes. When introducing a new such class, please create one Abstract class and implement it in both of the API classes (Later we'll move to Factory design patter or Pydantic based models).
We'll have provision to include appropriate API class in CustomBots. So, the user can choose their required API class based on the model they use.
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Provided proper step logs in the Microbot and openAi_api file, Do check and let me know if anything is needed.