73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
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import logging
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from typing import Any, List, Dict
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from langchain.memory.chat_memory import BaseChatMemory
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from langchain.schema import get_buffer_string, BaseMessage, HumanMessage, AIMessage
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from langchain.schema.language_model import BaseLanguageModel
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from server.db.repository.message_repository import filter_message
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from server.db.models.message_model import MessageModel
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class ConversationBufferDBMemory(BaseChatMemory):
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conversation_id: str
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human_prefix: str = "Human"
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ai_prefix: str = "Assistant"
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llm: BaseLanguageModel
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memory_key: str = "history"
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max_token_limit: int = 2000
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message_limit: int = 10
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@property
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def buffer(self) -> List[BaseMessage]:
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"""String buffer of memory."""
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# fetch limited messages desc, and return reversed
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messages = filter_message(conversation_id=self.conversation_id, limit=self.message_limit)
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# 返回的记录按时间倒序，转为正序
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messages = list(reversed(messages))
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chat_messages: List[BaseMessage] = []
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for message in messages:
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chat_messages.append(HumanMessage(content=message["query"]))
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chat_messages.append(AIMessage(content=message["response"]))
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if not chat_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_buffer_length = self.llm.get_num_tokens(get_buffer_string(chat_messages))
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if curr_buffer_length > self.max_token_limit:
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pruned_memory = []
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while curr_buffer_length > self.max_token_limit and chat_messages:
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pruned_memory.append(chat_messages.pop(0))
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curr_buffer_length = self.llm.get_num_tokens(get_buffer_string(chat_messages))
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return chat_messages
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@property
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def memory_variables(self) -> List[str]:
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"""Will always return list of memory variables.
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:meta private:
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"""
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return [self.memory_key]
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def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
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"""Return history buffer."""
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buffer: Any = self.buffer
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if self.return_messages:
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final_buffer: Any = buffer
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else:
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final_buffer = get_buffer_string(
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buffer,
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human_prefix=self.human_prefix,
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ai_prefix=self.ai_prefix,
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)
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return {self.memory_key: final_buffer}
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def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
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"""Nothing should be saved or changed"""
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pass
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def clear(self) -> None:
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"""Nothing to clear, got a memory like a vault."""
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pass
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