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397 lines
16 KiB
Python
397 lines
16 KiB
Python
import decimal
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import json
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from typing import Optional, Union
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from core.callback_handler.entity.agent_loop import AgentLoop
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from core.callback_handler.entity.dataset_query import DatasetQueryObj
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from core.callback_handler.entity.llm_message import LLMMessage
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from core.callback_handler.entity.chain_result import ChainResult
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from core.model_providers.model_factory import ModelFactory
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from core.model_providers.models.entity.message import to_prompt_messages, MessageType
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from core.model_providers.models.llm.base import BaseLLM
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from core.prompt.prompt_builder import PromptBuilder
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from core.prompt.prompt_template import JinjaPromptTemplate
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from events.message_event import message_was_created
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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.dataset import DatasetQuery
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from models.model import AppModelConfig, Conversation, Account, Message, EndUser, App, MessageAgentThought, MessageChain
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class ConversationMessageTask:
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def __init__(self, task_id: str, app: App, app_model_config: AppModelConfig, user: Account,
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inputs: dict, query: str, streaming: bool, model_instance: BaseLLM,
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conversation: Optional[Conversation] = None, is_override: bool = False):
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self.task_id = task_id
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self.app = app
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self.tenant_id = app.tenant_id
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self.app_model_config = app_model_config
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self.is_override = is_override
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self.user = user
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self.inputs = inputs
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self.query = query
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self.streaming = streaming
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self.conversation = conversation
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self.is_new_conversation = False
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self.model_instance = model_instance
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self.message = None
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self.model_dict = self.app_model_config.model_dict
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self.provider_name = self.model_dict.get('provider')
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self.model_name = self.model_dict.get('name')
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self.mode = app.mode
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self.init()
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self._pub_handler = PubHandler(
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user=self.user,
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task_id=self.task_id,
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message=self.message,
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conversation=self.conversation,
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chain_pub=False, # disabled currently
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agent_thought_pub=True
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)
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def init(self):
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override_model_configs = None
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if self.is_override:
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override_model_configs = {
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"model": self.app_model_config.model_dict,
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"pre_prompt": self.app_model_config.pre_prompt,
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"agent_mode": self.app_model_config.agent_mode_dict,
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"opening_statement": self.app_model_config.opening_statement,
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"suggested_questions": self.app_model_config.suggested_questions_list,
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"suggested_questions_after_answer": self.app_model_config.suggested_questions_after_answer_dict,
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"more_like_this": self.app_model_config.more_like_this_dict,
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"sensitive_word_avoidance": self.app_model_config.sensitive_word_avoidance_dict,
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"user_input_form": self.app_model_config.user_input_form_list,
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}
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introduction = ''
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system_instruction = ''
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system_instruction_tokens = 0
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if self.mode == 'chat':
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introduction = self.app_model_config.opening_statement
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if introduction:
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prompt_template = JinjaPromptTemplate.from_template(template=introduction)
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prompt_inputs = {k: self.inputs[k] for k in prompt_template.input_variables if k in self.inputs}
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try:
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introduction = prompt_template.format(**prompt_inputs)
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except KeyError:
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pass
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if self.app_model_config.pre_prompt:
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system_message = PromptBuilder.to_system_message(self.app_model_config.pre_prompt, self.inputs)
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system_instruction = system_message.content
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model_instance = ModelFactory.get_text_generation_model(
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tenant_id=self.tenant_id,
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model_provider_name=self.provider_name,
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model_name=self.model_name
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)
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system_instruction_tokens = model_instance.get_num_tokens(to_prompt_messages([system_message]))
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if not self.conversation:
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self.is_new_conversation = True
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self.conversation = Conversation(
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app_id=self.app_model_config.app_id,
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app_model_config_id=self.app_model_config.id,
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model_provider=self.provider_name,
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model_id=self.model_name,
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override_model_configs=json.dumps(override_model_configs) if override_model_configs else None,
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mode=self.mode,
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name='',
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inputs=self.inputs,
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introduction=introduction,
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system_instruction=system_instruction,
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system_instruction_tokens=system_instruction_tokens,
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status='normal',
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from_source=('console' if isinstance(self.user, Account) else 'api'),
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from_end_user_id=(self.user.id if isinstance(self.user, EndUser) else None),
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from_account_id=(self.user.id if isinstance(self.user, Account) else None),
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)
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db.session.add(self.conversation)
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db.session.flush()
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self.message = Message(
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app_id=self.app_model_config.app_id,
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model_provider=self.provider_name,
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model_id=self.model_name,
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override_model_configs=json.dumps(override_model_configs) if override_model_configs else None,
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conversation_id=self.conversation.id,
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inputs=self.inputs,
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query=self.query,
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message="",
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message_tokens=0,
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message_unit_price=0,
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answer="",
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answer_tokens=0,
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answer_unit_price=0,
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provider_response_latency=0,
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total_price=0,
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currency=self.model_instance.get_currency(),
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from_source=('console' if isinstance(self.user, Account) else 'api'),
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from_end_user_id=(self.user.id if isinstance(self.user, EndUser) else None),
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from_account_id=(self.user.id if isinstance(self.user, Account) else None),
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agent_based=self.app_model_config.agent_mode_dict.get('enabled'),
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)
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db.session.add(self.message)
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db.session.flush()
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def append_message_text(self, text: str):
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self._pub_handler.pub_text(text)
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def save_message(self, llm_message: LLMMessage, by_stopped: bool = False):
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message_tokens = llm_message.prompt_tokens
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answer_tokens = llm_message.completion_tokens
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message_unit_price = self.model_instance.get_token_price(1, MessageType.HUMAN)
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answer_unit_price = self.model_instance.get_token_price(1, MessageType.ASSISTANT)
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total_price = self.calc_total_price(message_tokens, message_unit_price, answer_tokens, answer_unit_price)
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self.message.message = llm_message.prompt
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self.message.message_tokens = message_tokens
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self.message.message_unit_price = message_unit_price
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self.message.answer = PromptBuilder.process_template(llm_message.completion.strip()) if llm_message.completion else ''
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self.message.answer_tokens = answer_tokens
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self.message.answer_unit_price = answer_unit_price
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self.message.provider_response_latency = llm_message.latency
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self.message.total_price = total_price
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db.session.commit()
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message_was_created.send(
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self.message,
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conversation=self.conversation,
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is_first_message=self.is_new_conversation
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)
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if not by_stopped:
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self.end()
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def init_chain(self, chain_result: ChainResult):
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message_chain = MessageChain(
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message_id=self.message.id,
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type=chain_result.type,
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input=json.dumps(chain_result.prompt),
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output=''
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)
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db.session.add(message_chain)
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db.session.flush()
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return message_chain
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def on_chain_end(self, message_chain: MessageChain, chain_result: ChainResult):
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message_chain.output = json.dumps(chain_result.completion)
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self._pub_handler.pub_chain(message_chain)
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def on_agent_start(self, message_chain: MessageChain, agent_loop: AgentLoop) -> MessageAgentThought:
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message_agent_thought = MessageAgentThought(
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message_id=self.message.id,
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message_chain_id=message_chain.id,
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position=agent_loop.position,
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thought=agent_loop.thought,
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tool=agent_loop.tool_name,
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tool_input=agent_loop.tool_input,
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message=agent_loop.prompt,
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answer=agent_loop.completion,
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created_by_role=('account' if isinstance(self.user, Account) else 'end_user'),
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created_by=self.user.id
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)
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db.session.add(message_agent_thought)
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db.session.flush()
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self._pub_handler.pub_agent_thought(message_agent_thought)
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return message_agent_thought
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def on_agent_end(self, message_agent_thought: MessageAgentThought, agent_model_instant: BaseLLM,
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agent_loop: AgentLoop):
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agent_message_unit_price = agent_model_instant.get_token_price(1, MessageType.HUMAN)
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agent_answer_unit_price = agent_model_instant.get_token_price(1, MessageType.ASSISTANT)
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loop_message_tokens = agent_loop.prompt_tokens
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loop_answer_tokens = agent_loop.completion_tokens
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loop_total_price = self.calc_total_price(
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loop_message_tokens,
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agent_message_unit_price,
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loop_answer_tokens,
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agent_answer_unit_price
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)
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message_agent_thought.observation = agent_loop.tool_output
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message_agent_thought.tool_process_data = '' # currently not support
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message_agent_thought.message_token = loop_message_tokens
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message_agent_thought.message_unit_price = agent_message_unit_price
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message_agent_thought.answer_token = loop_answer_tokens
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message_agent_thought.answer_unit_price = agent_answer_unit_price
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message_agent_thought.latency = agent_loop.latency
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message_agent_thought.tokens = agent_loop.prompt_tokens + agent_loop.completion_tokens
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message_agent_thought.total_price = loop_total_price
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message_agent_thought.currency = agent_model_instant.get_currency()
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db.session.flush()
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def on_dataset_query_end(self, dataset_query_obj: DatasetQueryObj):
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dataset_query = DatasetQuery(
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dataset_id=dataset_query_obj.dataset_id,
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content=dataset_query_obj.query,
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source='app',
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source_app_id=self.app.id,
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created_by_role=('account' if isinstance(self.user, Account) else 'end_user'),
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created_by=self.user.id
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)
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db.session.add(dataset_query)
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def calc_total_price(self, message_tokens, message_unit_price, answer_tokens, answer_unit_price):
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message_tokens_per_1k = (decimal.Decimal(message_tokens) / 1000).quantize(decimal.Decimal('0.001'),
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rounding=decimal.ROUND_HALF_UP)
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answer_tokens_per_1k = (decimal.Decimal(answer_tokens) / 1000).quantize(decimal.Decimal('0.001'),
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rounding=decimal.ROUND_HALF_UP)
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total_price = message_tokens_per_1k * message_unit_price + answer_tokens_per_1k * answer_unit_price
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return total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
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def end(self):
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self._pub_handler.pub_end()
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class PubHandler:
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def __init__(self, user: Union[Account | EndUser], task_id: str,
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message: Message, conversation: Conversation,
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chain_pub: bool = False, agent_thought_pub: bool = False):
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self._channel = PubHandler.generate_channel_name(user, task_id)
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self._stopped_cache_key = PubHandler.generate_stopped_cache_key(user, task_id)
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self._task_id = task_id
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self._message = message
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self._conversation = conversation
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self._chain_pub = chain_pub
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self._agent_thought_pub = agent_thought_pub
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@classmethod
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def generate_channel_name(cls, user: Union[Account | EndUser], task_id: str):
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if not user:
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raise ValueError("user is required")
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user_str = 'account-' + str(user.id) if isinstance(user, Account) else 'end-user-' + str(user.id)
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return "generate_result:{}-{}".format(user_str, task_id)
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@classmethod
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def generate_stopped_cache_key(cls, user: Union[Account | EndUser], task_id: str):
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user_str = 'account-' + str(user.id) if isinstance(user, Account) else 'end-user-' + str(user.id)
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return "generate_result_stopped:{}-{}".format(user_str, task_id)
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def pub_text(self, text: str):
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content = {
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'event': 'message',
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'data': {
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'task_id': self._task_id,
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'message_id': str(self._message.id),
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'text': text,
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'mode': self._conversation.mode,
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'conversation_id': str(self._conversation.id)
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}
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}
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redis_client.publish(self._channel, json.dumps(content))
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if self._is_stopped():
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self.pub_end()
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raise ConversationTaskStoppedException()
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def pub_chain(self, message_chain: MessageChain):
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if self._chain_pub:
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content = {
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'event': 'chain',
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'data': {
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'task_id': self._task_id,
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'message_id': self._message.id,
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'chain_id': message_chain.id,
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'type': message_chain.type,
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'input': json.loads(message_chain.input),
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'output': json.loads(message_chain.output),
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'mode': self._conversation.mode,
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'conversation_id': self._conversation.id
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}
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}
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redis_client.publish(self._channel, json.dumps(content))
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if self._is_stopped():
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self.pub_end()
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raise ConversationTaskStoppedException()
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def pub_agent_thought(self, message_agent_thought: MessageAgentThought):
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if self._agent_thought_pub:
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content = {
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'event': 'agent_thought',
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'data': {
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'id': message_agent_thought.id,
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'task_id': self._task_id,
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'message_id': self._message.id,
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'chain_id': message_agent_thought.message_chain_id,
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'position': message_agent_thought.position,
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'thought': message_agent_thought.thought,
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'tool': message_agent_thought.tool,
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'tool_input': message_agent_thought.tool_input,
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'mode': self._conversation.mode,
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'conversation_id': self._conversation.id
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}
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}
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redis_client.publish(self._channel, json.dumps(content))
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if self._is_stopped():
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self.pub_end()
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raise ConversationTaskStoppedException()
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def pub_end(self):
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content = {
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'event': 'end',
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}
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redis_client.publish(self._channel, json.dumps(content))
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@classmethod
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def pub_error(cls, user: Union[Account | EndUser], task_id: str, e):
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content = {
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'error': type(e).__name__,
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'description': e.description if getattr(e, 'description', None) is not None else str(e)
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}
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channel = cls.generate_channel_name(user, task_id)
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redis_client.publish(channel, json.dumps(content))
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def _is_stopped(self):
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return redis_client.get(self._stopped_cache_key) is not None
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@classmethod
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def ping(cls, user: Union[Account | EndUser], task_id: str):
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content = {
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'event': 'ping'
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}
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channel = cls.generate_channel_name(user, task_id)
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redis_client.publish(channel, json.dumps(content))
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@classmethod
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def stop(cls, user: Union[Account | EndUser], task_id: str):
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stopped_cache_key = cls.generate_stopped_cache_key(user, task_id)
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redis_client.setex(stopped_cache_key, 600, 1)
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class ConversationTaskStoppedException(Exception):
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pass
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