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feat: optimize error raise (#820)
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2f179d61dc
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@ -96,4 +96,4 @@ class LLMCallbackHandler(BaseCallbackHandler):
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)
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self.conversation_message_task.save_message(llm_message=self.llm_message, by_stopped=True)
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else:
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logging.exception(error)
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logging.debug("on_llm_error: %s", error)
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@ -2,6 +2,7 @@ import logging
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from langchain.schema import OutputParserException
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from core.model_providers.error import LLMError
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from core.model_providers.model_factory import ModelFactory
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from core.model_providers.models.entity.message import PromptMessage, MessageType
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from core.model_providers.models.entity.model_params import ModelKwargs
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@ -120,8 +121,10 @@ class LLMGenerator:
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try:
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output = model_instance.run(prompts)
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questions = output_parser.parse(output.content)
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except Exception:
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logging.exception("Error generating suggested questions after answer")
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except LLMError:
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questions = []
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except Exception as e:
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logging.exception(e)
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questions = []
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return questions
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@ -157,10 +160,12 @@ class LLMGenerator:
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try:
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output = model_instance.run(prompts)
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rule_config = output_parser.parse(output.content)
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except LLMError as e:
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raise e
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except OutputParserException:
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raise ValueError('Please give a valid input for intended audience or hoping to solve problems.')
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except Exception:
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logging.exception("Error generating prompt")
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except Exception as e:
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logging.exception(e)
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rule_config = {
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"prompt": "",
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"variables": [],
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@ -283,7 +283,11 @@ class AzureOpenAIProvider(BaseModelProvider):
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if obfuscated:
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credentials['openai_api_key'] = encrypter.obfuscated_token(credentials['openai_api_key'])
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return credentials
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return {
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'openai_api_base': credentials['openai_api_base'],
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'openai_api_key': credentials['openai_api_key'],
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'base_model_name': credentials['base_model_name']
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}
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else:
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if hosted_model_providers.azure_openai:
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return {
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@ -6,6 +6,7 @@ from celery import shared_task
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from werkzeug.exceptions import NotFound
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from core.generator.llm_generator import LLMGenerator
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from core.model_providers.error import LLMError
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from extensions.ext_database import db
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from models.model import Conversation, Message
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@ -42,5 +43,7 @@ def generate_conversation_summary_task(conversation_id: str):
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end_at = time.perf_counter()
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logging.info(click.style('Conversation summary generated: {} latency: {}'.format(conversation_id, end_at - start_at), fg='green'))
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except Exception:
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logging.exception("generate conversation summary failed")
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except LLMError:
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pass
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except Exception as e:
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logging.exception(e)
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