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feat: optimize error raise (#820)

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takatost 2023-08-13 00:59:36 +08:00 committed by GitHub
parent 2f179d61dc
commit 1bd0a76a20
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4 changed files with 20 additions and 8 deletions
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2
api/core/callback_handler/llm_callback_handler.py
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@ -96,4 +96,4 @@ class LLMCallbackHandler(BaseCallbackHandler):
) )
self.conversation_message_task.save_message(llm_message=self.llm_message, by_stopped=True) self.conversation_message_task.save_message(llm_message=self.llm_message, by_stopped=True)
else: else:
logging.exception(error) logging.debug("on_llm_error: %s", error)

13
api/core/generator/llm_generator.py
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@ -2,6 +2,7 @@ import logging
from langchain.schema import OutputParserException from langchain.schema import OutputParserException
from core.model_providers.error import LLMError
from core.model_providers.model_factory import ModelFactory from core.model_providers.model_factory import ModelFactory
from core.model_providers.models.entity.message import PromptMessage, MessageType from core.model_providers.models.entity.message import PromptMessage, MessageType
from core.model_providers.models.entity.model_params import ModelKwargs from core.model_providers.models.entity.model_params import ModelKwargs
@ -120,8 +121,10 @@ class LLMGenerator:
try: try:
output = model_instance.run(prompts) output = model_instance.run(prompts)
questions = output_parser.parse(output.content) questions = output_parser.parse(output.content)
except Exception: except LLMError:
logging.exception("Error generating suggested questions after answer") questions = []
except Exception as e:
logging.exception(e)
questions = [] questions = []
return questions return questions
@ -157,10 +160,12 @@ class LLMGenerator:
try: try:
output = model_instance.run(prompts) output = model_instance.run(prompts)
rule_config = output_parser.parse(output.content) rule_config = output_parser.parse(output.content)
except LLMError as e:
raise e
except OutputParserException: except OutputParserException:
raise ValueError('Please give a valid input for intended audience or hoping to solve problems.') raise ValueError('Please give a valid input for intended audience or hoping to solve problems.')
except Exception: except Exception as e:
logging.exception("Error generating prompt") logging.exception(e)
rule_config = { rule_config = {
"prompt": "", "prompt": "",
"variables": [], "variables": [],

6
api/core/model_providers/providers/azure_openai_provider.py
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@ -283,7 +283,11 @@ class AzureOpenAIProvider(BaseModelProvider):
if obfuscated: if obfuscated:
credentials['openai_api_key'] = encrypter.obfuscated_token(credentials['openai_api_key']) credentials['openai_api_key'] = encrypter.obfuscated_token(credentials['openai_api_key'])
return credentials return {
'openai_api_base': credentials['openai_api_base'],
'openai_api_key': credentials['openai_api_key'],
'base_model_name': credentials['base_model_name']
}
else: else:
if hosted_model_providers.azure_openai: if hosted_model_providers.azure_openai:
return { return {

7
api/tasks/generate_conversation_summary_task.py
View File

@ -6,6 +6,7 @@ from celery import shared_task
from werkzeug.exceptions import NotFound from werkzeug.exceptions import NotFound
from core.generator.llm_generator import LLMGenerator from core.generator.llm_generator import LLMGenerator
from core.model_providers.error import LLMError
from extensions.ext_database import db from extensions.ext_database import db
from models.model import Conversation, Message from models.model import Conversation, Message
@ -42,5 +43,7 @@ def generate_conversation_summary_task(conversation_id: str):
end_at = time.perf_counter() end_at = time.perf_counter()
logging.info(click.style('Conversation summary generated: {} latency: {}'.format(conversation_id, end_at - start_at), fg='green')) logging.info(click.style('Conversation summary generated: {} latency: {}'.format(conversation_id, end_at - start_at), fg='green'))
except Exception: except LLMError:
logging.exception("generate conversation summary failed") pass
except Exception as e:
logging.exception(e)