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156 | class LabChatAgent:
r"""
This is the Chat agent following the ReAct framework, with access to multiple tools
ranging papers, instruments and experiments.
"""
def __init__(
self,
chat_engine: InstructReActAgent = None,
):
self._chat_engine = chat_engine
self._short_memory_manager = ShortMemoryManager()
self._account_manager = AccountManager()
self._chatting_status = {}
self.reset_chatting_status()
def reset_chatting_status(self):
users = self._account_manager.get_users()
self._chatting_status = {user: False for user in users}
def update_chatting_status(self):
users = self._account_manager.get_users()
new_chatting_status = {user: False for user in users if user not in self._chatting_status.keys()}
self._chatting_status.update(new_chatting_status)
@property
def chat_engine(self) -> InstructReActAgent:
if self._chat_engine is None:
self._chat_engine = self.get_chat_engine()
return self._chat_engine
def is_chatting(self, user_id: str) -> bool:
return self._chatting_status[user_id]
def set_chatting(self, user_id: str, chatting: bool):
self._chatting_status[user_id] = chatting
@property
def short_memory_manager(self):
return self._short_memory_manager
async def chat(self, packed_msgs: PackedUserMessage) -> AgentResponse:
r""" Chat with agent. """
user_id = packed_msgs.user_id
self.set_chatting(user_id=user_id, chatting=True)
packed_json = packed_msgs.dumps()
chat_history = self.short_memory_manager.load_memory(user_id=user_id)
response = await self.chat_engine.achat(
message=packed_json,
chat_history=chat_history,
)
chat_history = self.chat_engine.memory.get()
self.short_memory_manager.save_memory(user_id=user_id, chat_history=chat_history)
self.chat_engine.reset()
ref_paths = response.metadata["references"]
if len(ref_paths) < 1:
ref_paths = None
agent_response = AgentResponse(
response=response.response,
references=ref_paths,
)
return agent_response
def test_chat(self, packed_msgs: PackedUserMessage) -> AgentResponse:
r""" Debug. """
user_id = packed_msgs.user_id
self.set_chatting(user_id=user_id, chatting=True)
packed_json = packed_msgs.dumps()
chat_history = self.short_memory_manager.load_memory(user_id=user_id)
response = self.chat_engine.chat(
message=packed_json,
chat_history=chat_history,
)
chat_history = self.chat_engine.memory.get()
self.short_memory_manager.save_memory(user_id=user_id, chat_history=chat_history)
ref_paths = response.metadata["references"]
if len(ref_paths) < 1:
ref_paths = None
agent_response = AgentResponse(
response=response.response,
references=ref_paths,
)
return agent_response
def get_tools(self) -> List[AsyncBaseTool]:
r""" Available tools. """
return [
ChatMemoryRetrieverTool(),
ExperimentLogRetrieveTool(),
CreateNewExperimentLogTool(),
SetCurrentExperimentTool(),
RecordExperimentLogTool(),
SharedPaperRetrieverTool(),
ArXivSearchDownloadTool(),
AddNewRecentPaperTool(),
RecentPaperRetrieveTool(),
RecentPaperSummarizeTool(),
InstrumentRetrieverTool(),
XYPlatformMoveTool(),
]
def get_chat_engine(self) -> InstructReActAgent:
tools = self.get_tools()
react_chat_formatter = ReActChatFormatter.from_defaults(system_header=LABRIDGE_CHAT_SYSTEM_HEADER)
chat_engine = InstructReActAgent.from_tools(
tools=tools,
react_chat_formatter=react_chat_formatter,
verbose=True,
llm=Settings.llm,
memory=ChatMemoryBuffer.from_defaults(token_limit=3000),
enable_instruct=False,
enable_comment=False,
max_iterations=20,
)
return chat_engine
|