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easy_ui_based_generate_task_pipeline.py
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easy_ui_based_generate_task_pipeline.py
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import json
import logging
import time
from collections.abc import Generator
from typing import Optional, Union, cast
from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
from core.app.entities.app_invoke_entities import (
AgentChatAppGenerateEntity,
ChatAppGenerateEntity,
CompletionAppGenerateEntity,
)
from core.app.entities.queue_entities import (
QueueAgentMessageEvent,
QueueAgentThoughtEvent,
QueueAnnotationReplyEvent,
QueueErrorEvent,
QueueLLMChunkEvent,
QueueMessageEndEvent,
QueueMessageFileEvent,
QueueMessageReplaceEvent,
QueuePingEvent,
QueueRetrieverResourcesEvent,
QueueStopEvent,
)
from core.app.entities.task_entities import (
AgentMessageStreamResponse,
AgentThoughtStreamResponse,
ChatbotAppBlockingResponse,
ChatbotAppStreamResponse,
CompletionAppBlockingResponse,
CompletionAppStreamResponse,
EasyUITaskState,
ErrorStreamResponse,
MessageEndStreamResponse,
StreamResponse,
)
from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
from core.app.task_pipeline.message_cycle_manage import MessageCycleManage
from core.model_manager import ModelInstance
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
)
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.model_runtime.utils.encoders import jsonable_encoder
from core.prompt.utils.prompt_message_util import PromptMessageUtil
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
from events.message_event import message_was_created
from extensions.ext_database import db
from models.account import Account
from models.model import AppMode, Conversation, EndUser, Message, MessageAgentThought
logger = logging.getLogger(__name__)
class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleManage):
"""
EasyUIBasedGenerateTaskPipeline is a class that generate stream output and state management for Application.
"""
_task_state: EasyUITaskState
_application_generate_entity: Union[
ChatAppGenerateEntity,
CompletionAppGenerateEntity,
AgentChatAppGenerateEntity
]
def __init__(self, application_generate_entity: Union[
ChatAppGenerateEntity,
CompletionAppGenerateEntity,
AgentChatAppGenerateEntity
],
queue_manager: AppQueueManager,
conversation: Conversation,
message: Message,
user: Union[Account, EndUser],
stream: bool) -> None:
"""
Initialize GenerateTaskPipeline.
:param application_generate_entity: application generate entity
:param queue_manager: queue manager
:param conversation: conversation
:param message: message
:param user: user
:param stream: stream
"""
super().__init__(application_generate_entity, queue_manager, user, stream)
self._model_config = application_generate_entity.model_config
self._conversation = conversation
self._message = message
self._task_state = EasyUITaskState(
llm_result=LLMResult(
model=self._model_config.model,
prompt_messages=[],
message=AssistantPromptMessage(content=""),
usage=LLMUsage.empty_usage()
)
)
self._conversation_name_generate_thread = None
def process(self) -> Union[
ChatbotAppBlockingResponse,
CompletionAppBlockingResponse,
Generator[Union[ChatbotAppStreamResponse, CompletionAppStreamResponse], None, None]
]:
"""
Process generate task pipeline.
:return:
"""
db.session.refresh(self._conversation)
db.session.refresh(self._message)
db.session.close()
if self._application_generate_entity.app_config.app_mode != AppMode.COMPLETION:
# start generate conversation name thread
self._conversation_name_generate_thread = self._generate_conversation_name(
self._conversation,
self._application_generate_entity.query
)
generator = self._process_stream_response()
if self._stream:
return self._to_stream_response(generator)
else:
return self._to_blocking_response(generator)
def _to_blocking_response(self, generator: Generator[StreamResponse, None, None]) -> Union[
ChatbotAppBlockingResponse,
CompletionAppBlockingResponse
]:
"""
Process blocking response.
:return:
"""
for stream_response in generator:
if isinstance(stream_response, ErrorStreamResponse):
raise stream_response.err
elif isinstance(stream_response, MessageEndStreamResponse):
extras = {
'usage': jsonable_encoder(self._task_state.llm_result.usage)
}
if self._task_state.metadata:
extras['metadata'] = self._task_state.metadata
if self._conversation.mode == AppMode.COMPLETION.value:
response = CompletionAppBlockingResponse(
task_id=self._application_generate_entity.task_id,
data=CompletionAppBlockingResponse.Data(
id=self._message.id,
mode=self._conversation.mode,
message_id=self._message.id,
answer=self._task_state.llm_result.message.content,
created_at=int(self._message.created_at.timestamp()),
**extras
)
)
else:
response = ChatbotAppBlockingResponse(
task_id=self._application_generate_entity.task_id,
data=ChatbotAppBlockingResponse.Data(
id=self._message.id,
mode=self._conversation.mode,
conversation_id=self._conversation.id,
message_id=self._message.id,
answer=self._task_state.llm_result.message.content,
created_at=int(self._message.created_at.timestamp()),
**extras
)
)
return response
else:
continue
raise Exception('Queue listening stopped unexpectedly.')
def _to_stream_response(self, generator: Generator[StreamResponse, None, None]) \
-> Generator[Union[ChatbotAppStreamResponse, CompletionAppStreamResponse], None, None]:
"""
To stream response.
:return:
"""
for stream_response in generator:
if isinstance(self._application_generate_entity, CompletionAppGenerateEntity):
yield CompletionAppStreamResponse(
message_id=self._message.id,
created_at=int(self._message.created_at.timestamp()),
stream_response=stream_response
)
else:
yield ChatbotAppStreamResponse(
conversation_id=self._conversation.id,
message_id=self._message.id,
created_at=int(self._message.created_at.timestamp()),
stream_response=stream_response
)
def _process_stream_response(self) -> Generator[StreamResponse, None, None]:
"""
Process stream response.
:return:
"""
for message in self._queue_manager.listen():
event = message.event
if isinstance(event, QueueErrorEvent):
err = self._handle_error(event, self._message)
yield self._error_to_stream_response(err)
break
elif isinstance(event, QueueStopEvent | QueueMessageEndEvent):
if isinstance(event, QueueMessageEndEvent):
self._task_state.llm_result = event.llm_result
else:
self._handle_stop(event)
# handle output moderation
output_moderation_answer = self._handle_output_moderation_when_task_finished(
self._task_state.llm_result.message.content
)
if output_moderation_answer:
self._task_state.llm_result.message.content = output_moderation_answer
yield self._message_replace_to_stream_response(answer=output_moderation_answer)
# Save message
self._save_message()
yield self._message_end_to_stream_response()
elif isinstance(event, QueueRetrieverResourcesEvent):
self._handle_retriever_resources(event)
elif isinstance(event, QueueAnnotationReplyEvent):
annotation = self._handle_annotation_reply(event)
if annotation:
self._task_state.llm_result.message.content = annotation.content
elif isinstance(event, QueueAgentThoughtEvent):
yield self._agent_thought_to_stream_response(event)
elif isinstance(event, QueueMessageFileEvent):
response = self._message_file_to_stream_response(event)
if response:
yield response
elif isinstance(event, QueueLLMChunkEvent | QueueAgentMessageEvent):
chunk = event.chunk
delta_text = chunk.delta.message.content
if delta_text is None:
continue
if not self._task_state.llm_result.prompt_messages:
self._task_state.llm_result.prompt_messages = chunk.prompt_messages
# handle output moderation chunk
should_direct_answer = self._handle_output_moderation_chunk(delta_text)
if should_direct_answer:
continue
self._task_state.llm_result.message.content += delta_text
if isinstance(event, QueueLLMChunkEvent):
yield self._message_to_stream_response(delta_text, self._message.id)
else:
yield self._agent_message_to_stream_response(delta_text, self._message.id)
elif isinstance(event, QueueMessageReplaceEvent):
yield self._message_replace_to_stream_response(answer=event.text)
elif isinstance(event, QueuePingEvent):
yield self._ping_stream_response()
else:
continue
if self._conversation_name_generate_thread:
self._conversation_name_generate_thread.join()
def _save_message(self) -> None:
"""
Save message.
:return:
"""
llm_result = self._task_state.llm_result
usage = llm_result.usage
self._message = db.session.query(Message).filter(Message.id == self._message.id).first()
self._conversation = db.session.query(Conversation).filter(Conversation.id == self._conversation.id).first()
self._message.message = PromptMessageUtil.prompt_messages_to_prompt_for_saving(
self._model_config.mode,
self._task_state.llm_result.prompt_messages
)
self._message.message_tokens = usage.prompt_tokens
self._message.message_unit_price = usage.prompt_unit_price
self._message.message_price_unit = usage.prompt_price_unit
self._message.answer = PromptTemplateParser.remove_template_variables(llm_result.message.content.strip()) \
if llm_result.message.content else ''
self._message.answer_tokens = usage.completion_tokens
self._message.answer_unit_price = usage.completion_unit_price
self._message.answer_price_unit = usage.completion_price_unit
self._message.provider_response_latency = time.perf_counter() - self._start_at
self._message.total_price = usage.total_price
self._message.currency = usage.currency
self._message.message_metadata = json.dumps(jsonable_encoder(self._task_state.metadata)) \
if self._task_state.metadata else None
db.session.commit()
message_was_created.send(
self._message,
application_generate_entity=self._application_generate_entity,
conversation=self._conversation,
is_first_message=self._application_generate_entity.app_config.app_mode in [
AppMode.AGENT_CHAT,
AppMode.CHAT
] and self._application_generate_entity.conversation_id is None,
extras=self._application_generate_entity.extras
)
def _handle_stop(self, event: QueueStopEvent) -> None:
"""
Handle stop.
:return:
"""
model_config = self._model_config
model = model_config.model
model_instance = ModelInstance(
provider_model_bundle=model_config.provider_model_bundle,
model=model_config.model
)
# calculate num tokens
prompt_tokens = 0
if event.stopped_by != QueueStopEvent.StopBy.ANNOTATION_REPLY:
prompt_tokens = model_instance.get_llm_num_tokens(
self._task_state.llm_result.prompt_messages
)
completion_tokens = 0
if event.stopped_by == QueueStopEvent.StopBy.USER_MANUAL:
completion_tokens = model_instance.get_llm_num_tokens(
[self._task_state.llm_result.message]
)
credentials = model_config.credentials
# transform usage
model_type_instance = model_config.provider_model_bundle.model_type_instance
model_type_instance = cast(LargeLanguageModel, model_type_instance)
self._task_state.llm_result.usage = model_type_instance._calc_response_usage(
model,
credentials,
prompt_tokens,
completion_tokens
)
def _message_end_to_stream_response(self) -> MessageEndStreamResponse:
"""
Message end to stream response.
:return:
"""
self._task_state.metadata['usage'] = jsonable_encoder(self._task_state.llm_result.usage)
extras = {}
if self._task_state.metadata:
extras['metadata'] = self._task_state.metadata
return MessageEndStreamResponse(
task_id=self._application_generate_entity.task_id,
id=self._message.id,
**extras
)
def _agent_message_to_stream_response(self, answer: str, message_id: str) -> AgentMessageStreamResponse:
"""
Agent message to stream response.
:param answer: answer
:param message_id: message id
:return:
"""
return AgentMessageStreamResponse(
task_id=self._application_generate_entity.task_id,
id=message_id,
answer=answer
)
def _agent_thought_to_stream_response(self, event: QueueAgentThoughtEvent) -> Optional[AgentThoughtStreamResponse]:
"""
Agent thought to stream response.
:param event: agent thought event
:return:
"""
agent_thought: MessageAgentThought = (
db.session.query(MessageAgentThought)
.filter(MessageAgentThought.id == event.agent_thought_id)
.first()
)
db.session.refresh(agent_thought)
db.session.close()
if agent_thought:
return AgentThoughtStreamResponse(
task_id=self._application_generate_entity.task_id,
id=agent_thought.id,
position=agent_thought.position,
thought=agent_thought.thought,
observation=agent_thought.observation,
tool=agent_thought.tool,
tool_labels=agent_thought.tool_labels,
tool_input=agent_thought.tool_input,
message_files=agent_thought.files
)
return None
def _handle_output_moderation_chunk(self, text: str) -> bool:
"""
Handle output moderation chunk.
:param text: text
:return: True if output moderation should direct output, otherwise False
"""
if self._output_moderation_handler:
if self._output_moderation_handler.should_direct_output():
# stop subscribe new token when output moderation should direct output
self._task_state.llm_result.message.content = self._output_moderation_handler.get_final_output()
self._queue_manager.publish(
QueueLLMChunkEvent(
chunk=LLMResultChunk(
model=self._task_state.llm_result.model,
prompt_messages=self._task_state.llm_result.prompt_messages,
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(content=self._task_state.llm_result.message.content)
)
)
), PublishFrom.TASK_PIPELINE
)
self._queue_manager.publish(
QueueStopEvent(stopped_by=QueueStopEvent.StopBy.OUTPUT_MODERATION),
PublishFrom.TASK_PIPELINE
)
return True
else:
self._output_moderation_handler.append_new_token(text)
return False