Files
zen-agent/qwen_agent/agents/virtual_memory_agent.py

98 lines
4.4 KiB
Python

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