mirror of
https://gitee.com/dify_ai/dify.git
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507 lines
21 KiB
Python
507 lines
21 KiB
Python
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import json
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import logging
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import threading
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import time
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import uuid
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from typing import Generator, Union, Any
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from flask import current_app, Flask
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from redis.client import PubSub
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from sqlalchemy import and_
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from core.completion import Completion
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from core.conversation_message_task import PubHandler, ConversationTaskStoppedException
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from core.llm.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, LLMRateLimitError, \
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LLMAuthorizationError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
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from extensions.ext_database import db
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from extensions.ext_redis import redis_client
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from models.model import Conversation, AppModelConfig, App, Account, EndUser, Message
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from services.app_model_config_service import AppModelConfigService
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from services.errors.app import MoreLikeThisDisabledError
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from services.errors.app_model_config import AppModelConfigBrokenError
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from services.errors.completion import CompletionStoppedError
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from services.errors.conversation import ConversationNotExistsError, ConversationCompletedError
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from services.errors.message import MessageNotExistsError
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class CompletionService:
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@classmethod
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def completion(cls, app_model: App, user: Union[Account | EndUser], args: Any,
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from_source: str, streaming: bool = True,
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is_model_config_override: bool = False) -> Union[dict | Generator]:
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# is streaming mode
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inputs = args['inputs']
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query = args['query']
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conversation_id = args['conversation_id'] if 'conversation_id' in args else None
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conversation = None
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if conversation_id:
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conversation_filter = [
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Conversation.id == args['conversation_id'],
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Conversation.app_id == app_model.id,
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Conversation.status == 'normal'
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]
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if from_source == 'console':
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conversation_filter.append(Conversation.from_account_id == user.id)
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else:
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conversation_filter.append(Conversation.from_end_user_id == user.id if user else None)
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conversation = db.session.query(Conversation).filter(and_(*conversation_filter)).first()
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if not conversation:
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raise ConversationNotExistsError()
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if conversation.status != 'normal':
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raise ConversationCompletedError()
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if not conversation.override_model_configs:
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app_model_config = db.session.query(AppModelConfig).get(conversation.app_model_config_id)
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if not app_model_config:
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raise AppModelConfigBrokenError()
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else:
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conversation_override_model_configs = json.loads(conversation.override_model_configs)
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app_model_config = AppModelConfig(
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id=conversation.app_model_config_id,
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app_id=app_model.id,
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provider="",
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model_id="",
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configs="",
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opening_statement=conversation_override_model_configs['opening_statement'],
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suggested_questions=json.dumps(conversation_override_model_configs['suggested_questions']),
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model=json.dumps(conversation_override_model_configs['model']),
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user_input_form=json.dumps(conversation_override_model_configs['user_input_form']),
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pre_prompt=conversation_override_model_configs['pre_prompt'],
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agent_mode=json.dumps(conversation_override_model_configs['agent_mode']),
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)
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if is_model_config_override:
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# build new app model config
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if 'model' not in args['model_config']:
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raise ValueError('model_config.model is required')
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if 'completion_params' not in args['model_config']['model']:
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raise ValueError('model_config.model.completion_params is required')
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completion_params = AppModelConfigService.validate_model_completion_params(
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cp=args['model_config']['model']['completion_params'],
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model_name=app_model_config.model_dict["name"]
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)
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app_model_config_model = app_model_config.model_dict
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app_model_config_model['completion_params'] = completion_params
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app_model_config = AppModelConfig(
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id=app_model_config.id,
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app_id=app_model.id,
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provider="",
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model_id="",
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configs="",
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opening_statement=app_model_config.opening_statement,
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suggested_questions=app_model_config.suggested_questions,
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model=json.dumps(app_model_config_model),
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user_input_form=app_model_config.user_input_form,
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pre_prompt=app_model_config.pre_prompt,
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agent_mode=app_model_config.agent_mode,
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)
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else:
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if app_model.app_model_config_id is None:
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raise AppModelConfigBrokenError()
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app_model_config = app_model.app_model_config
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if not app_model_config:
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raise AppModelConfigBrokenError()
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if is_model_config_override:
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if not isinstance(user, Account):
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raise Exception("Only account can override model config")
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# validate config
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model_config = AppModelConfigService.validate_configuration(
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account=user,
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config=args['model_config'],
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mode=app_model.mode
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)
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app_model_config = AppModelConfig(
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id=app_model_config.id,
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app_id=app_model.id,
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provider="",
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model_id="",
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configs="",
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opening_statement=model_config['opening_statement'],
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suggested_questions=json.dumps(model_config['suggested_questions']),
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suggested_questions_after_answer=json.dumps(model_config['suggested_questions_after_answer']),
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more_like_this=json.dumps(model_config['more_like_this']),
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model=json.dumps(model_config['model']),
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user_input_form=json.dumps(model_config['user_input_form']),
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pre_prompt=model_config['pre_prompt'],
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agent_mode=json.dumps(model_config['agent_mode']),
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)
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# clean input by app_model_config form rules
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inputs = cls.get_cleaned_inputs(inputs, app_model_config)
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generate_task_id = str(uuid.uuid4())
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pubsub = redis_client.pubsub()
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pubsub.subscribe(PubHandler.generate_channel_name(user, generate_task_id))
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user = cls.get_real_user_instead_of_proxy_obj(user)
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generate_worker_thread = threading.Thread(target=cls.generate_worker, kwargs={
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'flask_app': current_app._get_current_object(),
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'generate_task_id': generate_task_id,
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'app_model': app_model,
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'app_model_config': app_model_config,
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'query': query,
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'inputs': inputs,
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'user': user,
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'conversation': conversation,
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'streaming': streaming,
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'is_model_config_override': is_model_config_override
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})
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generate_worker_thread.start()
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# wait for 5 minutes to close the thread
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cls.countdown_and_close(generate_worker_thread, pubsub, user, generate_task_id)
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return cls.compact_response(pubsub, streaming)
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@classmethod
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def get_real_user_instead_of_proxy_obj(cls, user: Union[Account, EndUser]):
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if isinstance(user, Account):
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user = db.session.query(Account).get(user.id)
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elif isinstance(user, EndUser):
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user = db.session.query(EndUser).get(user.id)
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else:
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raise Exception("Unknown user type")
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return user
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@classmethod
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def generate_worker(cls, flask_app: Flask, generate_task_id: str, app_model: App, app_model_config: AppModelConfig,
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query: str, inputs: dict, user: Union[Account, EndUser],
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conversation: Conversation, streaming: bool, is_model_config_override: bool):
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with flask_app.app_context():
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try:
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if conversation:
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# fixed the state of the conversation object when it detached from the original session
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conversation = db.session.query(Conversation).filter_by(id=conversation.id).first()
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# run
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Completion.generate(
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task_id=generate_task_id,
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app=app_model,
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app_model_config=app_model_config,
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query=query,
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inputs=inputs,
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user=user,
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conversation=conversation,
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streaming=streaming,
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is_override=is_model_config_override,
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)
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except ConversationTaskStoppedException:
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pass
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except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
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LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError,
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ModelCurrentlyNotSupportError) as e:
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db.session.rollback()
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PubHandler.pub_error(user, generate_task_id, e)
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except LLMAuthorizationError:
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db.session.rollback()
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PubHandler.pub_error(user, generate_task_id, LLMAuthorizationError('Incorrect API key provided'))
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except Exception as e:
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db.session.rollback()
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logging.exception("Unknown Error in completion")
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PubHandler.pub_error(user, generate_task_id, e)
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@classmethod
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def countdown_and_close(cls, worker_thread, pubsub, user, generate_task_id) -> threading.Thread:
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# wait for 5 minutes to close the thread
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timeout = 300
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def close_pubsub():
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sleep_iterations = 0
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while sleep_iterations < timeout and worker_thread.is_alive():
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time.sleep(1)
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sleep_iterations += 1
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if worker_thread.is_alive():
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PubHandler.stop(user, generate_task_id)
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try:
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pubsub.close()
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except:
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pass
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countdown_thread = threading.Thread(target=close_pubsub)
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countdown_thread.start()
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return countdown_thread
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@classmethod
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def generate_more_like_this(cls, app_model: App, user: Union[Account | EndUser],
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message_id: str, streaming: bool = True) -> Union[dict | Generator]:
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if not user:
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raise ValueError('user cannot be None')
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message = db.session.query(Message).filter(
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Message.id == message_id,
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Message.app_id == app_model.id,
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Message.from_source == ('api' if isinstance(user, EndUser) else 'console'),
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Message.from_end_user_id == (user.id if isinstance(user, EndUser) else None),
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Message.from_account_id == (user.id if isinstance(user, Account) else None),
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).first()
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if not message:
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raise MessageNotExistsError()
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current_app_model_config = app_model.app_model_config
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more_like_this = current_app_model_config.more_like_this_dict
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if not current_app_model_config.more_like_this or more_like_this.get("enabled", False) is False:
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raise MoreLikeThisDisabledError()
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app_model_config = message.app_model_config
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if message.override_model_configs:
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override_model_configs = json.loads(message.override_model_configs)
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pre_prompt = override_model_configs.get("pre_prompt", '')
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elif app_model_config:
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pre_prompt = app_model_config.pre_prompt
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else:
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raise AppModelConfigBrokenError()
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generate_task_id = str(uuid.uuid4())
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pubsub = redis_client.pubsub()
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pubsub.subscribe(PubHandler.generate_channel_name(user, generate_task_id))
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user = cls.get_real_user_instead_of_proxy_obj(user)
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generate_worker_thread = threading.Thread(target=cls.generate_more_like_this_worker, kwargs={
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'flask_app': current_app._get_current_object(),
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'generate_task_id': generate_task_id,
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'app_model': app_model,
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'app_model_config': app_model_config,
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'message': message,
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'pre_prompt': pre_prompt,
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'user': user,
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'streaming': streaming
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})
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generate_worker_thread.start()
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cls.countdown_and_close(generate_worker_thread, pubsub, user, generate_task_id)
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return cls.compact_response(pubsub, streaming)
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@classmethod
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def generate_more_like_this_worker(cls, flask_app: Flask, generate_task_id: str, app_model: App,
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app_model_config: AppModelConfig, message: Message, pre_prompt: str,
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user: Union[Account, EndUser], streaming: bool):
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with flask_app.app_context():
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try:
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# run
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Completion.generate_more_like_this(
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task_id=generate_task_id,
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app=app_model,
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user=user,
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message=message,
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pre_prompt=pre_prompt,
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app_model_config=app_model_config,
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streaming=streaming
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)
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except ConversationTaskStoppedException:
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pass
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except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
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LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError,
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ModelCurrentlyNotSupportError) as e:
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db.session.rollback()
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PubHandler.pub_error(user, generate_task_id, e)
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except LLMAuthorizationError:
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db.session.rollback()
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PubHandler.pub_error(user, generate_task_id, LLMAuthorizationError('Incorrect API key provided'))
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except Exception as e:
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db.session.rollback()
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logging.exception("Unknown Error in completion")
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PubHandler.pub_error(user, generate_task_id, e)
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@classmethod
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def get_cleaned_inputs(cls, user_inputs: dict, app_model_config: AppModelConfig):
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if user_inputs is None:
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user_inputs = {}
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filtered_inputs = {}
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# Filter input variables from form configuration, handle required fields, default values, and option values
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input_form_config = app_model_config.user_input_form_list
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for config in input_form_config:
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input_config = list(config.values())[0]
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variable = input_config["variable"]
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input_type = list(config.keys())[0]
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if variable not in user_inputs or not user_inputs[variable]:
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if "required" in input_config and input_config["required"]:
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raise ValueError(f"{variable} is required in input form")
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else:
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filtered_inputs[variable] = input_config["default"] if "default" in input_config else ""
|
||
|
|
continue
|
||
|
|
|
||
|
|
value = user_inputs[variable]
|
||
|
|
|
||
|
|
if input_type == "select":
|
||
|
|
options = input_config["options"] if "options" in input_config else []
|
||
|
|
if value not in options:
|
||
|
|
raise ValueError(f"{variable} in input form must be one of the following: {options}")
|
||
|
|
else:
|
||
|
|
if 'max_length' in variable:
|
||
|
|
max_length = variable['max_length']
|
||
|
|
if len(value) > max_length:
|
||
|
|
raise ValueError(f'{variable} in input form must be less than {max_length} characters')
|
||
|
|
|
||
|
|
filtered_inputs[variable] = value
|
||
|
|
|
||
|
|
return filtered_inputs
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def compact_response(cls, pubsub: PubSub, streaming: bool = False) -> Union[dict | Generator]:
|
||
|
|
generate_channel = list(pubsub.channels.keys())[0].decode('utf-8')
|
||
|
|
if not streaming:
|
||
|
|
try:
|
||
|
|
for message in pubsub.listen():
|
||
|
|
if message["type"] == "message":
|
||
|
|
result = message["data"].decode('utf-8')
|
||
|
|
result = json.loads(result)
|
||
|
|
if result.get('error'):
|
||
|
|
cls.handle_error(result)
|
||
|
|
|
||
|
|
return cls.get_message_response_data(result.get('data'))
|
||
|
|
except ValueError as e:
|
||
|
|
if e.args[0] != "I/O operation on closed file.": # ignore this error
|
||
|
|
raise CompletionStoppedError()
|
||
|
|
else:
|
||
|
|
logging.exception(e)
|
||
|
|
raise
|
||
|
|
finally:
|
||
|
|
try:
|
||
|
|
pubsub.unsubscribe(generate_channel)
|
||
|
|
except ConnectionError:
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
def generate() -> Generator:
|
||
|
|
try:
|
||
|
|
for message in pubsub.listen():
|
||
|
|
if message["type"] == "message":
|
||
|
|
result = message["data"].decode('utf-8')
|
||
|
|
result = json.loads(result)
|
||
|
|
if result.get('error'):
|
||
|
|
cls.handle_error(result)
|
||
|
|
|
||
|
|
event = result.get('event')
|
||
|
|
if event == "end":
|
||
|
|
logging.debug("{} finished".format(generate_channel))
|
||
|
|
break
|
||
|
|
|
||
|
|
if event == 'message':
|
||
|
|
yield "data: " + json.dumps(cls.get_message_response_data(result.get('data'))) + "\n\n"
|
||
|
|
elif event == 'chain':
|
||
|
|
yield "data: " + json.dumps(cls.get_chain_response_data(result.get('data'))) + "\n\n"
|
||
|
|
elif event == 'agent_thought':
|
||
|
|
yield "data: " + json.dumps(cls.get_agent_thought_response_data(result.get('data'))) + "\n\n"
|
||
|
|
except ValueError as e:
|
||
|
|
if e.args[0] != "I/O operation on closed file.": # ignore this error
|
||
|
|
logging.exception(e)
|
||
|
|
raise
|
||
|
|
finally:
|
||
|
|
try:
|
||
|
|
pubsub.unsubscribe(generate_channel)
|
||
|
|
except ConnectionError:
|
||
|
|
pass
|
||
|
|
|
||
|
|
return generate()
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def get_message_response_data(cls, data: dict):
|
||
|
|
response_data = {
|
||
|
|
'event': 'message',
|
||
|
|
'task_id': data.get('task_id'),
|
||
|
|
'id': data.get('message_id'),
|
||
|
|
'answer': data.get('text'),
|
||
|
|
'created_at': int(time.time())
|
||
|
|
}
|
||
|
|
|
||
|
|
if data.get('mode') == 'chat':
|
||
|
|
response_data['conversation_id'] = data.get('conversation_id')
|
||
|
|
|
||
|
|
return response_data
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def get_chain_response_data(cls, data: dict):
|
||
|
|
response_data = {
|
||
|
|
'event': 'chain',
|
||
|
|
'id': data.get('chain_id'),
|
||
|
|
'task_id': data.get('task_id'),
|
||
|
|
'message_id': data.get('message_id'),
|
||
|
|
'type': data.get('type'),
|
||
|
|
'input': data.get('input'),
|
||
|
|
'output': data.get('output'),
|
||
|
|
'created_at': int(time.time())
|
||
|
|
}
|
||
|
|
|
||
|
|
if data.get('mode') == 'chat':
|
||
|
|
response_data['conversation_id'] = data.get('conversation_id')
|
||
|
|
|
||
|
|
return response_data
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def get_agent_thought_response_data(cls, data: dict):
|
||
|
|
response_data = {
|
||
|
|
'event': 'agent_thought',
|
||
|
|
'id': data.get('agent_thought_id'),
|
||
|
|
'chain_id': data.get('chain_id'),
|
||
|
|
'task_id': data.get('task_id'),
|
||
|
|
'message_id': data.get('message_id'),
|
||
|
|
'position': data.get('position'),
|
||
|
|
'thought': data.get('thought'),
|
||
|
|
'tool': data.get('tool'), # todo use real dataset obj replace it
|
||
|
|
'tool_input': data.get('tool_input'),
|
||
|
|
'observation': data.get('observation'),
|
||
|
|
'answer': data.get('answer') if not data.get('thought') else '',
|
||
|
|
'created_at': int(time.time())
|
||
|
|
}
|
||
|
|
|
||
|
|
if data.get('mode') == 'chat':
|
||
|
|
response_data['conversation_id'] = data.get('conversation_id')
|
||
|
|
|
||
|
|
return response_data
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def handle_error(cls, result: dict):
|
||
|
|
logging.debug("error: %s", result)
|
||
|
|
error = result.get('error')
|
||
|
|
description = result.get('description')
|
||
|
|
|
||
|
|
# handle errors
|
||
|
|
llm_errors = {
|
||
|
|
'LLMBadRequestError': LLMBadRequestError,
|
||
|
|
'LLMAPIConnectionError': LLMAPIConnectionError,
|
||
|
|
'LLMAPIUnavailableError': LLMAPIUnavailableError,
|
||
|
|
'LLMRateLimitError': LLMRateLimitError,
|
||
|
|
'ProviderTokenNotInitError': ProviderTokenNotInitError,
|
||
|
|
'QuotaExceededError': QuotaExceededError,
|
||
|
|
'ModelCurrentlyNotSupportError': ModelCurrentlyNotSupportError
|
||
|
|
}
|
||
|
|
|
||
|
|
if error in llm_errors:
|
||
|
|
raise llm_errors[error](description)
|
||
|
|
elif error == 'LLMAuthorizationError':
|
||
|
|
raise LLMAuthorizationError('Incorrect API key provided')
|
||
|
|
else:
|
||
|
|
raise Exception(description)
|