chore: lack of the user message
Novice
10 months ago
parent
dd02a9ac9d
commit
48be8fb6cc
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from abc import ABC, abstractmethod
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from collections.abc import Sequence
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from core.model_runtime.entities.message_entities import PromptMessage
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class BaseMemory(ABC):
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@abstractmethod
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def get_history_prompt_messages(self) -> Sequence[PromptMessage]:
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"""
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Get the history prompt messages
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"""
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@abstractmethod
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def get_history_prompt_text(self) -> str:
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"""
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Get the history prompt text
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"""
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import json
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from collections.abc import Sequence
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from typing import Optional
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from core.model_manager import ModelInstance
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from core.model_runtime.entities import (
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ImagePromptMessageContent,
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PromptMessageRole,
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TextPromptMessageContent,
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)
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from core.model_runtime.entities.message_entities import (
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AssistantPromptMessage,
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PromptMessage,
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UserPromptMessage,
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)
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from core.prompt.entities.advanced_prompt_entities import LLMMemoryType
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from core.prompt.utils.extract_thread_messages import extract_thread_messages
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from extensions.ext_database import db
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from models.model import Conversation, Message
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from models.workflow import WorkflowNodeExecution, WorkflowNodeExecutionStatus
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class ModelContextMemory:
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def __init__(self, conversation: Conversation, node_id: str, model_instance: ModelInstance) -> None:
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self.conversation = conversation
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self.node_id = node_id
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self.model_instance = model_instance
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def get_history_prompt_messages(
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self, max_token_limit: int = 2000, message_limit: Optional[int] = None
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) -> Sequence[PromptMessage]:
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"""
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Get history prompt messages.
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:param max_token_limit: max token limit
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:param message_limit: message limit
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"""
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# fetch limited messages, and return reversed
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query = (
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db.session.query(
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Message.id,
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Message.query,
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Message.answer,
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Message.created_at,
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Message.workflow_run_id,
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Message.parent_message_id,
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Message.answer_tokens,
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)
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.filter(
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Message.conversation_id == self.conversation.id,
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)
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.order_by(Message.created_at.desc())
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)
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if message_limit and message_limit > 0:
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message_limit = min(message_limit, 500)
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else:
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message_limit = 500
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messages = query.limit(message_limit).all()
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# instead of all messages from the conversation, we only need to extract messages
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# that belong to the thread of last message
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thread_messages = extract_thread_messages(messages)
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# for newly created message, its answer is temporarily empty, we don't need to add it to memory
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if thread_messages and not thread_messages[0].answer and thread_messages[0].answer_tokens == 0:
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thread_messages.pop(0)
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if len(thread_messages) == 0:
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return []
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last_thread_message = list(reversed(thread_messages))[0]
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last_node_execution = (
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db.session.query(WorkflowNodeExecution)
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.filter(
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WorkflowNodeExecution.workflow_run_id == last_thread_message.workflow_run_id,
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WorkflowNodeExecution.node_id == self.node_id,
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WorkflowNodeExecution.status.in_(
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[WorkflowNodeExecutionStatus.SUCCEEDED, WorkflowNodeExecutionStatus.EXCEPTION]
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),
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)
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.order_by(WorkflowNodeExecution.created_at.desc())
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.first()
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)
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prompt_messages: list[PromptMessage] = []
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# files = db.session.query(MessageFile).filter(MessageFile.message_id == message.id).all()
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# if files:
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# file_extra_config = None
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# if self.conversation.mode not in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
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# file_extra_config = FileUploadConfigManager.convert(self.conversation.model_config)
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# else:
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# if message.workflow_run_id:
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# workflow_run = (
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# db.session.query(WorkflowRun).filter(WorkflowRun.id == message.workflow_run_id).first()
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# )
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# if workflow_run and workflow_run.workflow:
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# file_extra_config = FileUploadConfigManager.convert(
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# workflow_run.workflow.features_dict, is_vision=False
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# )
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# detail = ImagePromptMessageContent.DETAIL.LOW
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# if file_extra_config and app_record:
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# file_objs = file_factory.build_from_message_files(
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# message_files=files, tenant_id=app_record.tenant_id, config=file_extra_config
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# )
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# if file_extra_config.image_config and file_extra_config.image_config.detail:
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# detail = file_extra_config.image_config.detail
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# else:
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# file_objs = []
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# if not file_objs:
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# prompt_messages.append(UserPromptMessage(content=message.query))
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# else:
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# prompt_message_contents: list[PromptMessageContentUnionTypes] = []
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# prompt_message_contents.append(TextPromptMessageContent(data=message.query))
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# for file in file_objs:
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# prompt_message = file_manager.to_prompt_message_content(
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# file,
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# image_detail_config=detail,
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# )
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# prompt_message_contents.append(prompt_message)
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# prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
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# else:
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# prompt_messages.append(UserPromptMessage(content=message.query))
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if last_node_execution and last_node_execution.process_data:
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try:
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process_data = json.loads(last_node_execution.process_data)
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if process_data.get("memory_type", "") == LLMMemoryType.INDEPENDENT:
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for prompt in process_data.get("prompts", []):
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if prompt.get("role") == "user":
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prompt_messages.append(
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UserPromptMessage(
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content=prompt.get("content"),
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)
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)
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elif prompt.get("role") == "assistant":
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prompt_messages.append(
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AssistantPromptMessage(
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content=prompt.get("content"),
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)
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)
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output = (
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json.loads(last_node_execution.outputs).get("text", "") if last_node_execution.outputs else ""
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)
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prompt_messages.append(AssistantPromptMessage(content=output))
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except json.JSONDecodeError:
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pass
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if not prompt_messages:
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return []
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# prune the chat message if it exceeds the max token limit
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curr_message_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
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if curr_message_tokens > max_token_limit:
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pruned_memory = []
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while curr_message_tokens > max_token_limit and len(prompt_messages) > 1:
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pruned_memory.append(prompt_messages.pop(0))
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curr_message_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
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return prompt_messages
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def get_history_prompt_text(
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self,
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human_prefix: str = "Human",
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ai_prefix: str = "Assistant",
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max_token_limit: int = 2000,
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message_limit: Optional[int] = None,
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) -> str:
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"""
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Get history prompt text.
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:param human_prefix: human prefix
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:param ai_prefix: ai prefix
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:param max_token_limit: max token limit
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:param message_limit: message limit
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:return:
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"""
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prompt_messages = self.get_history_prompt_messages(max_token_limit=max_token_limit, message_limit=message_limit)
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string_messages = []
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for m in prompt_messages:
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if m.role == PromptMessageRole.USER:
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role = human_prefix
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elif m.role == PromptMessageRole.ASSISTANT:
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role = ai_prefix
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else:
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continue
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if isinstance(m.content, list):
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inner_msg = ""
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for content in m.content:
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if isinstance(content, TextPromptMessageContent):
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inner_msg += f"{content.data}\n"
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elif isinstance(content, ImagePromptMessageContent):
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inner_msg += "[image]\n"
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string_messages.append(f"{role}: {inner_msg.strip()}")
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else:
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message = f"{role}: {m.content}"
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string_messages.append(message)
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return "\n".join(string_messages)
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