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read mail with issue tree pipeline works

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tsukasa
2023-11-20 22:01:18 +08:00
parent 9c00187041
commit a63e9b6745
12 changed files with 215 additions and 140 deletions
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src/aios_kernel/agent.py
+12 -8
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@@ -14,6 +14,7 @@ import sys
from .agent_base import AgentMsg, AgentMsgStatus, AgentMsgType, FunctionItem, LLMResult, AgentPrompt, AgentReport, \
AgentTodo, AgentTodoResult, AgentWorkLog, BaseAIAgent
from .chatsession import AIChatSession
from .compute_task import ComputeTaskResult,ComputeTaskResultCode
from .ai_function import AIFunction
@@ -287,6 +288,7 @@ class AIAgent(BaseAIAgent):
return None
def _get_inner_functions(self) -> dict:
if self.owner_env is None:
return None,0
@@ -357,6 +359,7 @@ class AIAgent(BaseAIAgent):
else:
return task_result
def get_agent_prompt(self) -> AgentPrompt:
return self.agent_prompt
@@ -520,7 +523,7 @@ class AIAgent(BaseAIAgent):
if todo_count > 0:
have_known_info = True
known_info_str += f"## todo\n{todos_str}\n"
inner_functions,function_token_len = self._get_inner_functions()
inner_functions,function_token_len = BaseAIAgent.get_inner_functions(self.owner_env)
system_prompt_len = prompt.get_prompt_token_len()
input_len = len(msg.body)
if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
@@ -540,7 +543,7 @@ class AIAgent(BaseAIAgent):
logger.debug(f"Agent {self.agent_id} do llm token static system:{system_prompt_len},function:{function_token_len},history:{history_token_len},input:{input_len}, totoal prompt:{system_prompt_len + function_token_len + history_token_len} ")
#task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,self.llm_model_name,self.max_token_size,inner_functions)
task_result = await self._do_llm_complection(prompt,inner_functions,msg)
task_result = await self._do_llm_complection(prompt,msg,inner_functions=inner_functions)
if task_result.result_code != ComputeTaskResultCode.OK:
error_resp = msg.create_error_resp(task_result.error_str)
return error_resp
@@ -778,9 +781,9 @@ class AIAgent(BaseAIAgent):
todo_tree = workspace.get_todo_tree("/")
prompt.append(AgentPrompt(todo_tree))
inner_functions,function_token_len = self._get_inner_functions()
inner_functions,_ = BaseAIAgent.get_inner_functions(self.owner_env)
task_result:ComputeTaskResult = await self._do_llm_complection(prompt,inner_functions)
task_result:ComputeTaskResult = await self._do_llm_complection(prompt,inner_functions=inner_functions)
if task_result.result_code != ComputeTaskResultCode.OK:
logger.error(f"_llm_review_todos compute error:{task_result.error_str}")
return
@@ -897,7 +900,8 @@ class AIAgent(BaseAIAgent):
prompt.append(todo.detail)
prompt.append(todo.result)
task_result:ComputeTaskResult = await self._do_llm_complection(prompt,workspace.get_inner_functions(),None,True)
inner_functions,_ = BaseAIAgent.get_inner_functions(workspace)
task_result:ComputeTaskResult = await self._do_llm_complection(prompt,inner_functions=inner_functions,is_json_resp=True)
if task_result.result_code != ComputeTaskResultCode.OK:
logger.error(f"_llm_check_todo compute error:{task_result.error_str}")
@@ -1058,7 +1062,7 @@ class AIAgent(BaseAIAgent):
prompt.append(content_prompt)
env_functions = None
#env_functions,function_len = workspace.get_knowledge_base_ai_functions()
task_result:ComputeTaskResult = await self._do_llm_complection(prompt,env_functions,None,True)
task_result:ComputeTaskResult = await self._do_llm_complection(prompt,is_json_resp=True)
if task_result.result_code != ComputeTaskResultCode.OK:
result_obj = {}
result_obj["error_str"] = task_result.error_str
@@ -1091,9 +1095,8 @@ class AIAgent(BaseAIAgent):
prompt.append(known_info_prompt)
content_prompt = AgentPrompt(part_content)
prompt.append(content_prompt)
env_functions = None
#env_functions,function_len = workspace.get_knowledge_base_ai_functions()
task_result:ComputeTaskResult = await self._do_llm_complection(prompt,env_functions,None,True)
task_result:ComputeTaskResult = await self._do_llm_complection(prompt,is_json_resp=True)
if task_result.result_code != ComputeTaskResultCode.OK:
result_obj = {}
result_obj["error_str"] = task_result.error_str
@@ -1222,6 +1225,7 @@ class AIAgent(BaseAIAgent):
return task_result
def need_work(self) -> bool:
if self.do_prompt is not None:
return True