mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-09-04 18:40:10 +00:00
feat(agent): Add trace for agent (#1407)
This commit is contained in:
Fangyin Cheng
GitHub
@@ -8,6 +8,7 @@ from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast
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from dbgpt._private.pydantic import Field
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from dbgpt.core import LLMClient, ModelMessageRoleType
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from dbgpt.util.error_types import LLMChatError
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from dbgpt.util.tracer import SpanType, root_tracer
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from dbgpt.util.utils import colored
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from ..actions.action import Action, ActionOutput
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@@ -199,13 +200,25 @@ class ConversableAgent(Role, Agent):
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is_recovery: Optional[bool] = False,
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) -> None:
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"""Send a message to recipient agent."""
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await recipient.receive(
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message=message,
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sender=self,
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reviewer=reviewer,
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request_reply=request_reply,
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is_recovery=is_recovery,
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)
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with root_tracer.start_span(
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"agent.send",
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metadata={
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"sender": self.get_name(),
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"recipient": recipient.get_name(),
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"reviewer": reviewer.get_name() if reviewer else None,
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"agent_message": message.to_dict(),
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"request_reply": request_reply,
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"is_recovery": is_recovery,
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"conv_uid": self.not_null_agent_context.conv_id,
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},
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):
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await recipient.receive(
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message=message,
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sender=self,
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reviewer=reviewer,
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request_reply=request_reply,
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is_recovery=is_recovery,
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)
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async def receive(
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self,
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@@ -217,16 +230,30 @@ class ConversableAgent(Role, Agent):
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is_recovery: Optional[bool] = False,
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) -> None:
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"""Receive a message from another agent."""
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await self._a_process_received_message(message, sender)
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if request_reply is False or request_reply is None:
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return
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with root_tracer.start_span(
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"agent.receive",
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metadata={
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"sender": sender.get_name(),
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"recipient": self.get_name(),
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"reviewer": reviewer.get_name() if reviewer else None,
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"agent_message": message.to_dict(),
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"request_reply": request_reply,
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"silent": silent,
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"is_recovery": is_recovery,
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"conv_uid": self.not_null_agent_context.conv_id,
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"is_human": self.is_human,
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},
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):
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await self._a_process_received_message(message, sender)
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if request_reply is False or request_reply is None:
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return
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if not self.is_human:
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reply = await self.generate_reply(
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received_message=message, sender=sender, reviewer=reviewer
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)
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if reply is not None:
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await self.send(reply, sender)
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if not self.is_human:
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reply = await self.generate_reply(
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received_message=message, sender=sender, reviewer=reviewer
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)
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if reply is not None:
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await self.send(reply, sender)
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def prepare_act_param(self) -> Dict[str, Any]:
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"""Prepare the parameters for the act method."""
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@@ -244,13 +271,44 @@ class ConversableAgent(Role, Agent):
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logger.info(
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f"generate agent reply!sender={sender}, rely_messages_len={rely_messages}"
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)
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root_span = root_tracer.start_span(
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"agent.generate_reply",
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metadata={
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"sender": sender.get_name(),
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"recipient": self.get_name(),
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"reviewer": reviewer.get_name() if reviewer else None,
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"received_message": received_message.to_dict(),
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"conv_uid": self.not_null_agent_context.conv_id,
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"rely_messages": [msg.to_dict() for msg in rely_messages]
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if rely_messages
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else None,
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},
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)
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try:
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reply_message: AgentMessage = self._init_reply_message(
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received_message=received_message
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)
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await self._system_message_assembly(
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received_message.content, reply_message.context
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)
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with root_tracer.start_span(
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"agent.generate_reply._init_reply_message",
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metadata={
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"received_message": received_message.to_dict(),
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},
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) as span:
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# initialize reply message
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reply_message: AgentMessage = self._init_reply_message(
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received_message=received_message
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)
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span.metadata["reply_message"] = reply_message.to_dict()
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with root_tracer.start_span(
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"agent.generate_reply._system_message_assembly",
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metadata={
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"reply_message": reply_message.to_dict(),
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},
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) as span:
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# assemble system message
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await self._system_message_assembly(
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received_message.content, reply_message.context
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)
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span.metadata["assembled_system_messages"] = self.oai_system_message
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fail_reason = None
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current_retry_counter = 0
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@@ -270,36 +328,73 @@ class ConversableAgent(Role, Agent):
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retry_message, self, reviewer, request_reply=False
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)
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# 1.Think about how to do things
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llm_reply, model_name = await self.thinking(
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self._load_thinking_messages(
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received_message, sender, rely_messages
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thinking_messages = self._load_thinking_messages(
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received_message, sender, rely_messages
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)
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with root_tracer.start_span(
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"agent.generate_reply.thinking",
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metadata={
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"thinking_messages": [
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msg.to_dict() for msg in thinking_messages
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],
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},
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) as span:
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# 1.Think about how to do things
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llm_reply, model_name = await self.thinking(thinking_messages)
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reply_message.model_name = model_name
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reply_message.content = llm_reply
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span.metadata["llm_reply"] = llm_reply
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span.metadata["model_name"] = model_name
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with root_tracer.start_span(
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"agent.generate_reply.review",
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metadata={"llm_reply": llm_reply, "censored": self.get_name()},
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) as span:
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# 2.Review whether what is being done is legal
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approve, comments = await self.review(llm_reply, self)
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reply_message.review_info = AgentReviewInfo(
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approve=approve,
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comments=comments,
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)
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)
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reply_message.model_name = model_name
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reply_message.content = llm_reply
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span.metadata["approve"] = approve
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span.metadata["comments"] = comments
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# 2.Review whether what is being done is legal
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approve, comments = await self.review(llm_reply, self)
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reply_message.review_info = AgentReviewInfo(
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approve=approve,
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comments=comments,
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)
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# 3.Act based on the results of your thinking
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act_extent_param = self.prepare_act_param()
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act_out: Optional[ActionOutput] = await self.act(
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message=llm_reply,
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sender=sender,
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reviewer=reviewer,
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**act_extent_param,
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)
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if act_out:
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reply_message.action_report = act_out.dict()
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with root_tracer.start_span(
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"agent.generate_reply.act",
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metadata={
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"llm_reply": llm_reply,
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"sender": sender.get_name(),
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"reviewer": reviewer.get_name() if reviewer else None,
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"act_extent_param": act_extent_param,
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},
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) as span:
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# 3.Act based on the results of your thinking
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act_out: Optional[ActionOutput] = await self.act(
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message=llm_reply,
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sender=sender,
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reviewer=reviewer,
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**act_extent_param,
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)
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if act_out:
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reply_message.action_report = act_out.dict()
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span.metadata["action_report"] = act_out.dict() if act_out else None
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# 4.Reply information verification
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check_pass, reason = await self.verify(reply_message, sender, reviewer)
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is_success = check_pass
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with root_tracer.start_span(
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"agent.generate_reply.verify",
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metadata={
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"llm_reply": llm_reply,
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"sender": sender.get_name(),
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"reviewer": reviewer.get_name() if reviewer else None,
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},
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) as span:
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# 4.Reply information verification
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check_pass, reason = await self.verify(
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reply_message, sender, reviewer
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)
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is_success = check_pass
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span.metadata["check_pass"] = check_pass
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span.metadata["reason"] = reason
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# 5.Optimize wrong answers myself
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if not check_pass:
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current_retry_counter += 1
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@@ -319,6 +414,9 @@ class ConversableAgent(Role, Agent):
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err_message = AgentMessage(content=str(e))
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err_message.success = False
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return err_message
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finally:
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root_span.metadata["reply_message"] = reply_message.to_dict()
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root_span.end()
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async def thinking(
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self, messages: List[AgentMessage], prompt: Optional[str] = None
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@@ -378,7 +476,7 @@ class ConversableAgent(Role, Agent):
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) -> Optional[ActionOutput]:
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"""Perform actions."""
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last_out: Optional[ActionOutput] = None
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for action in self.actions:
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for i, action in enumerate(self.actions):
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# Select the resources required by acton
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need_resource = None
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if self.resources and len(self.resources) > 0:
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@@ -390,12 +488,27 @@ class ConversableAgent(Role, Agent):
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if not message:
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raise ValueError("The message content is empty!")
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last_out = await action.run(
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ai_message=message,
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resource=need_resource,
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rely_action_out=last_out,
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**kwargs,
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)
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with root_tracer.start_span(
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"agent.act.run",
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metadata={
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"message": message,
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"sender": sender.get_name() if sender else None,
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"recipient": self.get_name(),
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"reviewer": reviewer.get_name() if reviewer else None,
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"need_resource": need_resource.to_dict() if need_resource else None,
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"rely_action_out": last_out.dict() if last_out else None,
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"conv_uid": self.not_null_agent_context.conv_id,
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"action_index": i,
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"total_action": len(self.actions),
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},
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) as span:
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last_out = await action.run(
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ai_message=message,
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resource=need_resource,
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rely_action_out=last_out,
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**kwargs,
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)
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span.metadata["action_out"] = last_out.dict() if last_out else None
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return last_out
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async def correctness_check(
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@@ -446,12 +559,24 @@ class ConversableAgent(Role, Agent):
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reviewer (Agent): The reviewer agent.
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message (str): The message to send.
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"""
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await self.send(
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AgentMessage(content=message, current_goal=message),
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recipient,
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reviewer,
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request_reply=True,
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)
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agent_message = AgentMessage(content=message, current_goal=message)
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with root_tracer.start_span(
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"agent.initiate_chat",
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span_type=SpanType.AGENT,
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metadata={
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"sender": self.get_name(),
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"recipient": recipient.get_name(),
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"reviewer": reviewer.get_name() if reviewer else None,
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"agent_message": agent_message.to_dict(),
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"conv_uid": self.not_null_agent_context.conv_id,
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},
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):
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await self.send(
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agent_message,
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recipient,
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reviewer,
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request_reply=True,
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)
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#######################################################################
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# Private Function Begin
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@@ -506,8 +631,15 @@ class ConversableAgent(Role, Agent):
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model_name=oai_message.get("model_name", None),
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)
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self.memory.message_memory.append(gpts_message)
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return True
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with root_tracer.start_span(
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"agent.save_message_to_memory",
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metadata={
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"gpts_message": gpts_message.to_dict(),
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"conv_uid": self.not_null_agent_context.conv_id,
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},
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):
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self.memory.message_memory.append(gpts_message)
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return True
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def _print_received_message(self, message: AgentMessage, sender: Agent):
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# print the message received
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@@ -711,14 +843,27 @@ class ConversableAgent(Role, Agent):
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# Convert and tailor the information in collective memory into contextual
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# memory available to the current Agent
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current_goal_messages = self._convert_to_ai_message(
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self.memory.message_memory.get_between_agents(
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with root_tracer.start_span(
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"agent._load_thinking_messages",
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metadata={
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"sender": sender.get_name(),
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"recipient": self.get_name(),
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"conv_uid": self.not_null_agent_context.conv_id,
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"current_goal": current_goal,
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},
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) as span:
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# Get historical information from the memory
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memory_messages = self.memory.message_memory.get_between_agents(
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self.not_null_agent_context.conv_id,
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self.profile,
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sender.get_profile(),
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current_goal,
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)
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)
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span.metadata["memory_messages"] = [
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message.to_dict() for message in memory_messages
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]
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current_goal_messages = self._convert_to_ai_message(memory_messages)
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# When there is no target and context, the current received message is used as
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# the target problem
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