mirror of
https://github.com/zenlm/zen-agent.git
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98 lines
4.4 KiB
Python
98 lines
4.4 KiB
Python
# Copyright 2023 The Qwen team, Alibaba Group. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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from typing import Dict, Iterator, List, Optional, Union
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from qwen_agent.agents.assistant import Assistant
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from qwen_agent.llm import BaseChatModel
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from qwen_agent.llm.schema import DEFAULT_SYSTEM_MESSAGE, FUNCTION, USER, ContentItem, Message
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from qwen_agent.settings import MAX_LLM_CALL_PER_RUN
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from qwen_agent.tools import BaseTool
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DEFAULT_NAME = 'Virtual Memory Agent'
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DEFAULT_DESC = 'This agent can utilize tools to retrieve useful information from external resources or long conversation histories to aid in responding.'
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class VirtualMemoryAgent(Assistant):
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def __init__(self,
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function_list: Optional[List[Union[str, Dict, BaseTool]]] = None,
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llm: Optional[Union[Dict, BaseChatModel]] = None,
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system_message: Optional[str] = DEFAULT_SYSTEM_MESSAGE,
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name: Optional[str] = DEFAULT_NAME,
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description: Optional[str] = DEFAULT_DESC,
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files: Optional[List[str]] = None,
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rag_cfg: Optional[Dict] = None):
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# Add one default retrieval tool
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self.retrieval_tool_name = 'retrieval'
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super().__init__(function_list=[self.retrieval_tool_name] + (function_list or []),
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llm=llm,
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system_message=system_message,
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name=name,
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description=description,
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files=files,
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rag_cfg=rag_cfg)
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def _run(self, messages: List[Message], lang: str = 'en', **kwargs) -> Iterator[List[Message]]:
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ori_messages = messages
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messages = copy.deepcopy(messages)
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num_llm_calls_available = MAX_LLM_CALL_PER_RUN
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response = []
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while True and num_llm_calls_available > 0:
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num_llm_calls_available -= 1
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output_stream = self._call_llm(messages=self._format_file(messages) + response,
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functions=[func.function for func in self.function_map.values()])
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output: List[Message] = []
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for output in output_stream:
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if output:
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yield response + output
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if output:
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response.extend(output)
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use_tool, action, action_input, _ = self._detect_tool(response[-1])
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if use_tool:
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observation = self._call_tool(action, action_input, messages=messages)
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if action == self.retrieval_tool_name:
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# Filling the knowledge
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messages = self._prepend_knowledge_prompt(messages=ori_messages, lang=lang, knowledge=observation)
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observation = 'The relevant content has already been retrieved and updated in the previous system message.'
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fn_msg = Message(
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role=FUNCTION,
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name=action,
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content=observation,
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)
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response.append(fn_msg)
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yield response
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else:
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break
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def _format_file(self, messages: List[Message], lang: str = 'en') -> List[Message]:
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if lang == 'en':
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file_prefix = '[file]({f_name})'
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else:
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file_prefix = '[文件]({f_name})'
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new_messages = []
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for msg in messages:
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if msg.role == USER and isinstance(msg.content, list):
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new_content = []
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for x in msg.content:
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if x.file:
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new_content.append(ContentItem(text=file_prefix.format(f_name=x.file)))
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else:
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new_content.append(x)
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new_messages.append(Message(role=msg.role, content=new_content, name=msg.name))
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else:
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new_messages.append(msg)
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return new_messages
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