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1) Do some rename refactor ,prepare for LLMProcess refactor

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2) Fix merge bugs.
This commit is contained in:
Liu Zhicong
2023-12-06 13:31:05 -08:00
parent 35d204ac05
commit 8739bf6a76
44 changed files with 693 additions and 2043 deletions
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.gitignore
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@@ -11,3 +11,4 @@ math_school_env.db
workflows.db workflows.db
rootfs/test_doc/
doc/promps/Check TODO.md
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# Check (TODO)
目的是根据Todo Log, 结合自己的角色检查TODO是否争取完成（非客观性TODO给出是否有所改进的评价）
doc/promps/Do TODO.md
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# Do (TODO)
目标是结合 角色定义，手头的工具，已知知识 完成一个确定的任务。
完成任务时应使用ReAct的方法：应在给出执行动作前，先自言自语的输出一个计划，然后在动作（这个自言自语会变成TODO Logs）
src/[44 → doc/promps/Introspection.md
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doc/promps/Review Task.md
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# Review (Task/Todo)
目的是结合已知信息（重点是已经进行操作的记录），对失败的，完成的不好的任务进行思考，尝试给出更好的解决方案
1. 管理学方法：更换负责人
2. 管理学方法：拆分
3. 给出建议（该建议可以在下次一次DO-Check）循环中被使用
doc/promps/process message.md
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# Process Message
处理消息的首要是目的是分析消息的意图，并给予回复
doc/promps/self-learn.md
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doc/workflow.drawio
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rootfs/agents/Jarvis/agent.toml
-1
View File
@@ -5,7 +5,6 @@ max_token_size = 128000
enable_timestamp = "true" enable_timestamp = "true"
owner_prompt = "I am your master{name}" owner_prompt = "I am your master{name}"
contact_prompt = "I am your master's friend{name}" contact_prompt = "I am your master's friend{name}"
owner_env = "calender"
[[prompt]] [[prompt]]
role = "system" role = "system"
rootfs/agents/TextSummary/agent.py
+6 -6
View File
@@ -1,6 +1,6 @@
import copy import copy
from aios.agent.agent_base import CustomAIAgent, AgentPrompt from aios.agent.agent_base import CustomAIAgent, LLMPrompt
from aios.knowledge.data.writer import split_text from aios.knowledge.data.writer import split_text
from aios.proto.agent_msg import AgentMsg, AgentMsgType from aios.proto.agent_msg import AgentMsg, AgentMsgType
from aios.proto.compute_task import ComputeTaskResultCode from aios.proto.compute_task import ComputeTaskResultCode
@@ -19,10 +19,10 @@ class TextSummaryAgent(CustomAIAgent):
chunks = split_text(msg.body, separators=["\n\n", "\n"], chunk_size=4000, chunk_overlap=200, length_function=len) chunks = split_text(msg.body, separators=["\n\n", "\n"], chunk_size=4000, chunk_overlap=200, length_function=len)
prompt = AgentPrompt() prompt = LLMPrompt()
prompt.system_message = {"role":"system","content":"Your job is to generate a summary based on the input."} prompt.system_message = "Your job is to generate a summary based on the input."
if len(chunks) == 1: if len(chunks) == 1:
prompt.append(AgentPrompt(chunks[0])) prompt.append(LLMPrompt(chunks[0]))
resp = await self.do_llm_complection(prompt) resp = await self.do_llm_complection(prompt)
if resp.result_code != ComputeTaskResultCode.OK: if resp.result_code != ComputeTaskResultCode.OK:
return msg.create_error_resp(resp.error_str) return msg.create_error_resp(resp.error_str)
@@ -31,14 +31,14 @@ class TextSummaryAgent(CustomAIAgent):
segments = [] segments = []
for i, chunk in enumerate(chunks): for i, chunk in enumerate(chunks):
seg_prompt = copy.deepcopy(prompt) seg_prompt = copy.deepcopy(prompt)
seg_prompt.append(AgentPrompt(chunk)) seg_prompt.append(LLMPrompt(chunk))
resp = await self.do_llm_complection(seg_prompt) resp = await self.do_llm_complection(seg_prompt)
if resp.result_code != ComputeTaskResultCode.OK: if resp.result_code != ComputeTaskResultCode.OK:
return msg.create_error_resp(resp.error_str) return msg.create_error_resp(resp.error_str)
segments.append(resp.result_str) segments.append(resp.result_str)
segments_str = "\n".join(segments) segments_str = "\n".join(segments)
prompt.append(AgentPrompt(f"以下文本分段之后的各段摘要，请合并生成一个完整摘要：\n{segments_str}")) prompt.append(LLMPrompt(f"以下文本分段之后的各段摘要，请合并生成一个完整摘要：\n{segments_str}"))
resp = await self.do_llm_complection(prompt) resp = await self.do_llm_complection(prompt)
if resp.result_code != ComputeTaskResultCode.OK: if resp.result_code != ComputeTaskResultCode.OK:
return msg.create_error_resp(resp.error_str) return msg.create_error_resp(resp.error_str)
src/aios/__init__.py
+5 -4
View File
@@ -1,13 +1,14 @@
from .proto.agent_msg import * from .proto.agent_msg import *
from .proto.compute_task import * from .proto.compute_task import *
from .proto.ai_function import *
from .proto.agent_task import *
from .agent.agent_base import AgentPrompt,CustomAIAgent, AgentTodo from .agent.agent_base import *
from .agent.chatsession import AIChatSession from .agent.chatsession import AIChatSession
from .agent.agent import AIAgent,AIAgentTemplete, BaseAIAgent from .agent.agent import AIAgent,AIAgentTemplete, BaseAIAgent
from .agent.role import AIRole,AIRoleGroup from .agent.role import AIRole,AIRoleGroup
# from .agent.workflow import Workflow from .agent.workflow import Workflow
from .agent.ai_function import SimpleAIFunction, SimpleAIOperation
from .frame.compute_kernel import ComputeKernel,ComputeTask,ComputeTaskResult,ComputeTaskState,ComputeTaskType from .frame.compute_kernel import ComputeKernel,ComputeTask,ComputeTaskResult,ComputeTaskState,ComputeTaskType
from .frame.compute_node import ComputeNode,LocalComputeNode from .frame.compute_node import ComputeNode,LocalComputeNode
@@ -20,7 +21,7 @@ from .environment.environment import BaseEnvironment,SimpleEnvironment,Composite
# from .environment.workflow_env import WorkflowEnvironment,CalenderEnvironment,CalenderEvent,PaintEnvironment # from .environment.workflow_env import WorkflowEnvironment,CalenderEnvironment,CalenderEvent,PaintEnvironment
from .environment.text_to_speech_function import TextToSpeechFunction from .environment.text_to_speech_function import TextToSpeechFunction
from .environment.image_2_text_function import Image2TextFunction from .environment.image_2_text_function import Image2TextFunction
from .environment.workspace_env import WorkspaceEnvironment,TodoListEnvironment,TodoListType from .environment.workspace_env import WorkspaceEnvironment
from .storage.storage import ResourceLocation,AIStorage,UserConfig,UserConfigItem from .storage.storage import ResourceLocation,AIStorage,UserConfig,UserConfigItem
src/aios/agent/agent.py
+47 -45
View File
@@ -13,10 +13,12 @@ import copy
import sys import sys
from ..proto.agent_msg import AgentMsg from ..proto.agent_msg import AgentMsg
from ..proto.ai_function import *
from ..proto.agent_task import *
from ..proto.compute_task import *
from .agent_base import * from .agent_base import *
from .chatsession import * from .chatsession import *
from .ai_function import *
from ..environment.workspace_env import WorkspaceEnvironment, TodoListType from ..environment.workspace_env import WorkspaceEnvironment, TodoListType
from ..frame.contact_manager import ContactManager,Contact,FamilyMember from ..frame.contact_manager import ContactManager,Contact,FamilyMember
@@ -69,7 +71,7 @@ class AIAgentTemplete:
self.template_id:str = None self.template_id:str = None
self.introduce:str = None self.introduce:str = None
self.author:str = None self.author:str = None
self.prompt:AgentPrompt = None self.prompt:LLMPrompt = None
def load_from_config(self,config:dict) -> bool: def load_from_config(self,config:dict) -> bool:
if config.get("llm_model_name") is not None: if config.get("llm_model_name") is not None:
@@ -79,7 +81,7 @@ class AIAgentTemplete:
if config.get("template_id") is not None: if config.get("template_id") is not None:
self.template_id = config["template_id"] self.template_id = config["template_id"]
if config.get("prompt") is not None: if config.get("prompt") is not None:
self.prompt = AgentPrompt() self.prompt = LLMPrompt()
if self.prompt.load_from_config(config["prompt"]) is False: if self.prompt.load_from_config(config["prompt"]) is False:
logger.error("load prompt from config failed!") logger.error("load prompt from config failed!")
return False return False
@@ -90,9 +92,9 @@ class AIAgentTemplete:
class AIAgent(BaseAIAgent): class AIAgent(BaseAIAgent):
def __init__(self) -> None: def __init__(self) -> None:
self.role_prompt:AgentPrompt = None self.role_prompt:LLMPrompt = None
self.agent_prompt:AgentPrompt = None self.agent_prompt:LLMPrompt = None
self.agent_think_prompt:AgentPrompt = None self.agent_think_prompt:LLMPrompt = None
self.llm_model_name:str = None self.llm_model_name:str = None
self.max_token_size:int = 128000 self.max_token_size:int = 128000
self.agent_energy = 15 self.agent_energy = 15
@@ -149,26 +151,26 @@ class AIAgent(BaseAIAgent):
self.enable_thread = bool(config["enable_thread"]) self.enable_thread = bool(config["enable_thread"])
if config.get("prompt") is not None: if config.get("prompt") is not None:
self.agent_prompt = AgentPrompt() self.agent_prompt = LLMPrompt()
self.agent_prompt.load_from_config(config["prompt"]) self.agent_prompt.load_from_config(config["prompt"])
if config.get("think_prompt") is not None: if config.get("think_prompt") is not None:
self.agent_think_prompt = AgentPrompt() self.agent_think_prompt = LLMPrompt()
self.agent_think_prompt.load_from_config(config["think_prompt"]) self.agent_think_prompt.load_from_config(config["think_prompt"])
def load_todo_config(todo_type:str) -> bool: def load_todo_config(todo_type:str) -> bool:
todo_config = config.get(todo_type) todo_config = config.get(todo_type)
if todo_config is not None: if todo_config is not None:
if todo_config.get("do") is not None: if todo_config.get("do") is not None:
prompt = AgentPrompt() prompt = LLMPrompt()
prompt.load_from_config(todo_config["do"]) prompt.load_from_config(todo_config["do"])
self.todo_prompts[todo_type]["do"] = prompt self.todo_prompts[todo_type]["do"] = prompt
if todo_config.get("check") is not None: if todo_config.get("check") is not None:
prompt = AgentPrompt() prompt = LLMPrompt()
prompt.load_from_config(todo_config["check"]) prompt.load_from_config(todo_config["check"])
self.todo_prompts[todo_type]["check"] = prompt self.todo_prompts[todo_type]["check"] = prompt
if todo_config.get("review_prompt") is not None: if todo_config.get("review_prompt") is not None:
prompt = AgentPrompt() prompt = LLMPrompt()
prompt.load_from_config(todo_config["review_prompt"]) prompt.load_from_config(todo_config["review_prompt"])
self.todo_prompts[todo_type]["review"] = prompt self.todo_prompts[todo_type]["review"] = prompt
@@ -224,16 +226,16 @@ class AIAgent(BaseAIAgent):
def get_max_token_size(self) -> int: def get_max_token_size(self) -> int:
return self.max_token_size return self.max_token_size
def get_agent_role_prompt(self) -> AgentPrompt: def get_agent_role_prompt(self) -> LLMPrompt:
return self.role_prompt return self.role_prompt
def _get_remote_user_prompt(self,remote_user:str) -> AgentPrompt: def _get_remote_user_prompt(self,remote_user:str) -> LLMPrompt:
cm = ContactManager.get_instance() cm = ContactManager.get_instance()
contact = cm.find_contact_by_name(remote_user) contact = cm.find_contact_by_name(remote_user)
if contact is None: if contact is None:
#create guest prompt #create guest prompt
if self.guest_prompt_str is not None: if self.guest_prompt_str is not None:
prompt = AgentPrompt() prompt = LLMPrompt()
prompt.system_message = {"role":"system","content":self.guest_prompt_str} prompt.system_message = {"role":"system","content":self.guest_prompt_str}
return prompt return prompt
return None return None
@@ -241,25 +243,25 @@ class AIAgent(BaseAIAgent):
if contact.is_family_member: if contact.is_family_member:
if self.owner_promp_str is not None: if self.owner_promp_str is not None:
real_str = self.owner_promp_str.format_map(contact.to_dict()) real_str = self.owner_promp_str.format_map(contact.to_dict())
prompt = AgentPrompt() prompt = LLMPrompt()
prompt.system_message = {"role":"system","content":real_str} prompt.system_message = {"role":"system","content":real_str}
return prompt return prompt
else: else:
if self.contact_prompt_str is not None: if self.contact_prompt_str is not None:
real_str = self.contact_prompt_str.format_map(contact.to_dict()) real_str = self.contact_prompt_str.format_map(contact.to_dict())
prompt = AgentPrompt() prompt = LLMPrompt()
prompt.system_message = {"role":"system","content":real_str} prompt.system_message = {"role":"system","content":real_str}
return prompt return prompt
return None return None
def get_agent_prompt(self) -> AgentPrompt: def get_agent_prompt(self) -> LLMPrompt:
return self.agent_prompt return self.agent_prompt
async def _get_agent_think_prompt(self) -> AgentPrompt: async def _get_agent_think_prompt(self) -> LLMPrompt:
return self.agent_think_prompt return self.agent_think_prompt
def _format_msg_by_env_value(self,prompt:AgentPrompt): def _format_msg_by_env_value(self,prompt:LLMPrompt):
for msg in prompt.messages: for msg in prompt.messages:
old_content = msg.get("content") old_content = msg.get("content")
msg["content"] = old_content.format_map(self.agent_workspace) msg["content"] = old_content.format_map(self.agent_workspace)
@@ -284,7 +286,7 @@ class AIAgent(BaseAIAgent):
return image_path return image_path
async def _process_msg(self,msg:AgentMsg,workspace = None) -> AgentMsg: async def _process_msg(self,msg:AgentMsg,workspace = None) -> AgentMsg:
msg_prompt = AgentPrompt() msg_prompt = LLMPrompt()
if msg.msg_type == AgentMsgType.TYPE_GROUPMSG: if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
need_process = False need_process = False
if msg.is_image_msg(): if msg.is_image_msg():
@@ -378,7 +380,7 @@ class AIAgent(BaseAIAgent):
workspace = self.get_workspace_by_msg(msg) workspace = self.get_workspace_by_msg(msg)
prompt = AgentPrompt() prompt = LLMPrompt()
if workspace: if workspace:
prompt.append(workspace.get_prompt()) prompt.append(workspace.get_prompt())
prompt.append(workspace.get_role_prompt(self.agent_id)) prompt.append(workspace.get_role_prompt(self.agent_id))
@@ -390,7 +392,7 @@ class AIAgent(BaseAIAgent):
if self.need_session_summmary(msg,chatsession): if self.need_session_summmary(msg,chatsession):
# get relate session(todos) summary # get relate session(todos) summary
summary = self.llm_select_session_summary(msg,chatsession) summary = self.llm_select_session_summary(msg,chatsession)
prompt.append(AgentPrompt(summary)) prompt.append(LLMPrompt(summary))
known_info_str = "# Known information\n" known_info_str = "# Known information\n"
have_known_info = False have_known_info = False
@@ -399,7 +401,7 @@ class AIAgent(BaseAIAgent):
have_known_info = True have_known_info = True
known_info_str += f"## todo\n{todos_str}\n" known_info_str += f"## todo\n{todos_str}\n"
inner_functions,function_token_len = BaseAIAgent.get_inner_functions(self.agent_workspace) inner_functions,function_token_len = BaseAIAgent.get_inner_functions(self.agent_workspace)
system_prompt_len = self.token_len(prompt=prompt) system_prompt_len = ComputeKernel.llm_num_tokens(prompt)
input_len = len(msg.body) input_len = len(msg.body)
if msg.msg_type == AgentMsgType.TYPE_GROUPMSG: if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
history_str,history_token_len = await self._get_prompt_from_session_for_groupchat(chatsession,system_prompt_len + function_token_len,input_len) history_str,history_token_len = await self._get_prompt_from_session_for_groupchat(chatsession,system_prompt_len + function_token_len,input_len)
@@ -410,7 +412,7 @@ class AIAgent(BaseAIAgent):
known_info_str += history_str known_info_str += history_str
if have_known_info: if have_known_info:
known_info_prompt = AgentPrompt(known_info_str) known_info_prompt = LLMPrompt(known_info_str)
prompt.append(known_info_prompt) # chat context prompt.append(known_info_prompt) # chat context
prompt.append(msg_prompt) prompt.append(msg_prompt)
@@ -436,7 +438,7 @@ class AIAgent(BaseAIAgent):
final_result = llm_result.resp final_result = llm_result.resp
await workspace.exec_op_list(llm_result.op_list,self.agent_id) await workspace.exec_op_list(llm_result.action_list,self.agent_id)
is_ignore = False is_ignore = False
result_prompt_str = "" result_prompt_str = ""
@@ -471,12 +473,12 @@ class AIAgent(BaseAIAgent):
return None return None
async def _get_history_prompt_for_think(self,chatsession:AIChatSession,summary:str,system_token_len:int,pos:int)->(AgentPrompt,int): async def _get_history_prompt_for_think(self,chatsession:AIChatSession,summary:str,system_token_len:int,pos:int)->(LLMPrompt,int):
history_len = (self.max_token_size * 0.7) - system_token_len history_len = (self.max_token_size * 0.7) - system_token_len
messages = chatsession.read_history(self.history_len,pos,"natural") # read messages = chatsession.read_history(self.history_len,pos,"natural") # read
result_token_len = 0 result_token_len = 0
result_prompt = AgentPrompt() result_prompt = LLMPrompt()
have_summary = False have_summary = False
if summary is not None: if summary is not None:
if len(summary) > 1: if len(summary) > 1:
@@ -511,7 +513,7 @@ class AIAgent(BaseAIAgent):
history_len = (self.max_token_size * 0.7) - system_token_len - input_token_len history_len = (self.max_token_size * 0.7) - system_token_len - input_token_len
messages = chatsession.read_history(self.history_len) # read messages = chatsession.read_history(self.history_len) # read
result_token_len = 0 result_token_len = 0
result_prompt = AgentPrompt() result_prompt = LLMPrompt()
read_history_msg = 0 read_history_msg = 0
for msg in reversed(messages): for msg in reversed(messages):
read_history_msg += 1 read_history_msg += 1
@@ -569,13 +571,13 @@ class AIAgent(BaseAIAgent):
async def _llm_read_report(self,report:AgentReport,worksapce:WorkspaceEnvironment): async def _llm_read_report(self,report:AgentReport,worksapce:WorkspaceEnvironment):
work_summary = worksapce.get_work_summary(self.agent_id) work_summary = worksapce.get_work_summary(self.agent_id)
prompt : AgentPrompt = AgentPrompt() prompt : LLMPrompt = LLMPrompt()
prompt.append(self.agent_prompt) prompt.append(self.agent_prompt)
prompt.append(worksapce.get_role_prompt(self.agent_id)) prompt.append(worksapce.get_role_prompt(self.agent_id))
prompt.append(self.read_report_prompt) prompt.append(self.read_report_prompt)
# report is a message from other agent(human) about work # report is a message from other agent(human) about work
prompt.append(AgentPrompt(work_summary)) prompt.append(LLMPrompt(work_summary))
prompt.append(AgentPrompt(report.content)) prompt.append(LLMPrompt(report.content))
task_result:ComputeTaskResult = await self.do_llm_complection(prompt) task_result:ComputeTaskResult = await self.do_llm_complection(prompt)
@@ -606,7 +608,7 @@ class AIAgent(BaseAIAgent):
do_prompts = self._can_do_todo(todo_list_type, todo) do_prompts = self._can_do_todo(todo_list_type, todo)
if do_prompts: if do_prompts:
prompt : AgentPrompt = AgentPrompt() prompt : LLMPrompt = LLMPrompt()
prompt.append(self.agent_prompt) prompt.append(self.agent_prompt)
prompt.append(workspace.get_role_prompt(self.agent_id)) prompt.append(workspace.get_role_prompt(self.agent_id))
prompt.append(do_prompts) prompt.append(do_prompts)
@@ -635,13 +637,13 @@ class AIAgent(BaseAIAgent):
check_prompts = self._can_check_todo(todo_list_type, todo) check_prompts = self._can_check_todo(todo_list_type, todo)
if check_prompts: if check_prompts:
prompt : AgentPrompt = AgentPrompt() prompt : LLMPrompt = LLMPrompt()
prompt.append(self.agent_prompt) prompt.append(self.agent_prompt)
prompt.append(workspace.get_role_prompt(self.agent_id)) prompt.append(workspace.get_role_prompt(self.agent_id))
prompt.append(check_prompts) prompt.append(check_prompts)
if todo.last_check_result: if todo.last_check_result:
prompt.append(AgentPrompt(todo.last_check_result)) prompt.append(LLMPrompt(todo.last_check_result))
prompt.append(todo.detail) prompt.append(todo.detail)
prompt.append(todo.result) prompt.append(todo.result)
@@ -669,7 +671,7 @@ class AIAgent(BaseAIAgent):
prompt.append(review_prompts) prompt.append(review_prompts)
todo_tree = todo_list.get_todo_tree("/") todo_tree = todo_list.get_todo_tree("/")
prompt.append(AgentPrompt(todo_tree)) prompt.append(LLMPrompt(todo_tree))
do_result : AgentTodoResult = await self._llm_review_todo(todo, prompt, workspace) do_result : AgentTodoResult = await self._llm_review_todo(todo, prompt, workspace)
todo.last_review_time = datetime.datetime.now().timestamp() todo.last_review_time = datetime.datetime.now().timestamp()
@@ -690,7 +692,7 @@ class AIAgent(BaseAIAgent):
logger.info(f"agent {self.agent_id} ,check:{check_count} todo,do:{do_count} todo.") logger.info(f"agent {self.agent_id} ,check:{check_count} todo,do:{do_count} todo.")
def _can_review_todo(self, todo_list_type: TodoListType, todo:AgentTodo) -> AgentPrompt: def _can_review_todo(self, todo_list_type: TodoListType, todo:AgentTodo) -> LLMPrompt:
do_prompts = self.todo_prompts[todo_list_type].get("review") do_prompts = self.todo_prompts[todo_list_type].get("review")
if not do_prompts: if not do_prompts:
return None return None
@@ -701,7 +703,7 @@ class AIAgent(BaseAIAgent):
return do_prompts return do_prompts
def _can_check_todo(self, todo_list_type: TodoListType, todo:AgentTodo) -> AgentPrompt: def _can_check_todo(self, todo_list_type: TodoListType, todo:AgentTodo) -> LLMPrompt:
do_prompts = self.todo_prompts[todo_list_type].get("check") do_prompts = self.todo_prompts[todo_list_type].get("check")
if not do_prompts: if not do_prompts:
return None return None
@@ -720,7 +722,7 @@ class AIAgent(BaseAIAgent):
return do_prompts return do_prompts
def _can_do_todo(self, todo_list_type: TodoListType, todo:AgentTodo) -> AgentPrompt: def _can_do_todo(self, todo_list_type: TodoListType, todo:AgentTodo) -> LLMPrompt:
do_prompts = self.todo_prompts[todo_list_type].get("do") do_prompts = self.todo_prompts[todo_list_type].get("do")
if not do_prompts: if not do_prompts:
return None return None
@@ -739,7 +741,7 @@ class AIAgent(BaseAIAgent):
return do_prompts return do_prompts
async def _llm_do_todo(self, todo: AgentTodo, prompt: AgentPrompt, workspace: WorkspaceEnvironment) -> AgentTodoResult: async def _llm_do_todo(self, todo: AgentTodo, prompt: LLMPrompt, workspace: WorkspaceEnvironment) -> AgentTodoResult:
result = AgentTodoResult() result = AgentTodoResult()
task_result:ComputeTaskResult = await self.do_llm_complection(prompt, is_json_resp=True) task_result:ComputeTaskResult = await self.do_llm_complection(prompt, is_json_resp=True)
@@ -763,7 +765,7 @@ class AIAgent(BaseAIAgent):
resp = await AIBus.get_default_bus().post_message(msg) resp = await AIBus.get_default_bus().post_message(msg)
logging.info(f"agent {self.agent_id} send msg to {msg.target} result:{resp}") logging.info(f"agent {self.agent_id} send msg to {msg.target} result:{resp}")
result_str, have_error = await workspace.exec_op_list(llm_result.op_list, self.agent_id) result_str, have_error = await workspace.exec_op_list(llm_result.action_list, self.agent_id)
if have_error: if have_error:
result.result_code = AgentTodoResult.TODO_RESULT_CODE_EXEC_OP_ERROR result.result_code = AgentTodoResult.TODO_RESULT_CODE_EXEC_OP_ERROR
#result.error_str = error_str #result.error_str = error_str
@@ -771,7 +773,7 @@ class AIAgent(BaseAIAgent):
result.result_str = result_str result.result_str = result_str
return result return result
async def _llm_check_todo(self, todo: AgentTodo, prompt: AgentPrompt, workspace: WorkspaceEnvironment) -> AgentTodoResult: async def _llm_check_todo(self, todo: AgentTodo, prompt: LLMPrompt, workspace: WorkspaceEnvironment) -> AgentTodoResult:
result = AgentTodoResult() result = AgentTodoResult()
inner_functions,_ = BaseAIAgent.get_inner_functions(workspace) inner_functions,_ = BaseAIAgent.get_inner_functions(workspace)
@@ -786,7 +788,7 @@ class AIAgent(BaseAIAgent):
todo.last_check_result = task_result.result_str todo.last_check_result = task_result.result_str
return result return result
async def _llm_review_todo(self, todo:AgentTodo, prompt: AgentPrompt, workspace: WorkspaceEnvironment): async def _llm_review_todo(self, todo:AgentTodo, prompt: LLMPrompt, workspace: WorkspaceEnvironment):
inner_functions,_ = BaseAIAgent.get_inner_functions(workspace) inner_functions,_ = BaseAIAgent.get_inner_functions(workspace)
task_result:ComputeTaskResult = await self.do_llm_complection(prompt,inner_functions=inner_functions) task_result:ComputeTaskResult = await self.do_llm_complection(prompt,inner_functions=inner_functions)
@@ -842,10 +844,10 @@ class AIAgent(BaseAIAgent):
while True: while True:
cur_pos = chatsession.summarize_pos cur_pos = chatsession.summarize_pos
summary = chatsession.summary summary = chatsession.summary
prompt:AgentPrompt = AgentPrompt() prompt:LLMPrompt = LLMPrompt()
#prompt.append(self._get_agent_prompt()) #prompt.append(self._get_agent_prompt())
prompt.append(await self._get_agent_think_prompt()) prompt.append(await self._get_agent_think_prompt())
system_prompt_len = self.token_len(prompt=prompt) system_prompt_len = ComputeKernel.llm_num_tokens(prompt)
#think env? #think env?
history_prompt,next_pos = await self._get_history_prompt_for_think(chatsession,summary,system_prompt_len,cur_pos) history_prompt,next_pos = await self._get_history_prompt_for_think(chatsession,summary,system_prompt_len,cur_pos)
prompt.append(history_prompt) prompt.append(history_prompt)
@@ -864,7 +866,7 @@ class AIAgent(BaseAIAgent):
chatsession.update_think_progress(next_pos,new_summary) chatsession.update_think_progress(next_pos,new_summary)
return return
async def get_prompt_from_session(self,chatsession:AIChatSession,system_token_len,input_token_len) -> AgentPrompt: async def get_prompt_from_session(self,chatsession:AIChatSession,system_token_len,input_token_len) -> LLMPrompt:
# TODO: get prompt from group chat is different from single chat # TODO: get prompt from group chat is different from single chat
if self.enable_thread: if self.enable_thread:
return None return None
src/aios/agent/agent_base.py
+10 -405
View File
@@ -11,400 +11,15 @@ import shlex
import json import json
from typing import List, Tuple from typing import List, Tuple
from .ai_function import FunctionItem, AIFunction from ..proto.ai_function import *
from ..proto.agent_msg import AgentMsg, AgentMsgType from ..proto.agent_msg import *
from ..proto.compute_task import ComputeTaskResult,ComputeTaskResultCode from ..proto.compute_task import *
from ..environment.environment import BaseEnvironment from ..environment.environment import *
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
class AgentPrompt:
def __init__(self,prompt_str = None) -> None:
self.messages = []
if prompt_str:
self.messages.append({"role":"user","content":prompt_str})
self.system_message = None
def as_str(self)->str:
result_str = ""
if self.system_message:
result_str += self.system_message.get("role") + ":" + self.system_message.get("content") + "\n"
if self.messages:
for msg in self.messages:
result_str += msg.get("role") + ":" + msg.get("content") + "\n"
return result_str
def to_message_list(self):
result = []
if self.system_message:
result.append(self.system_message)
result.extend(self.messages)
return result
def append(self,prompt):
if prompt is None:
return
if prompt.system_message is not None:
if self.system_message is None:
self.system_message = copy.deepcopy(prompt.system_message)
else:
self.system_message["content"] += prompt.system_message.get("content")
self.messages.extend(prompt.messages)
def load_from_config(self,config:list) -> bool:
if isinstance(config,list) is not True:
logger.error("prompt is not list!")
return False
self.messages = []
for msg in config:
if msg.get("content"):
if msg.get("role") == "system":
self.system_message = msg
else:
self.messages.append(msg)
else:
logger.error("prompt message has no content!")
return True
class LLMResult:
def __init__(self) -> None:
self.state : str = "ignore"
self.resp : str = ""
self.raw_resp = None
self.paragraphs : dict[str,FunctionItem] = []
self.post_msgs : List[AgentMsg] = []
self.send_msgs : List[AgentMsg] = []
self.calls : List[FunctionItem] = []
self.post_calls : List[FunctionItem] = []
self.op_list : List[FunctionItem] = [] # op_list is a optimize design for saving token
@classmethod
def from_json_str(self,llm_json_str:str) -> 'LLMResult':
r = LLMResult()
if llm_json_str is None:
r.state = "ignore"
return r
if llm_json_str == "ignore":
r.state = "ignore"
return r
llm_json = json.loads(llm_json_str)
r.state = llm_json.get("state")
r.resp = llm_json.get("resp")
r.raw_resp = llm_json
post_msgs = llm_json.get("post_msg")
r.post_msgs = []
if post_msgs:
for msg in post_msgs:
new_msg = AgentMsg()
target_id = msg.get("target")
msg_content = msg.get("content")
new_msg.set("",target_id,msg_content)
r.post_msgs.append(new_msg)
#new_msg.msg_type = AgentMsgType.TYPE_MSG
r.calls = llm_json.get("calls")
r.post_calls = llm_json.get("post_calls")
r.op_list = llm_json.get("op_list")
return r
@classmethod
def from_str(self,llm_result_str:str,valid_func:List[str]=None) -> 'LLMResult':
r = LLMResult()
if llm_result_str is None:
r.state = "ignore"
return r
if llm_result_str == "ignore":
r.state = "ignore"
return r
if llm_result_str[0] == "{":
return LLMResult.from_json_str(llm_result_str)
# if llm_result_str.startswith("json"):
# return LLMResult.from_json_str(llm_result_str[4:])
lines = llm_result_str.splitlines()
is_need_wait = False
def check_args(func_item:FunctionItem):
match func_name:
case "send_msg":# /send_msg $target_id
if len(func_args) != 1:
return False
new_msg = AgentMsg()
target_id = func_item.args[0]
msg_content = func_item.body
new_msg.set("",target_id,msg_content)
r.send_msgs.append(new_msg)
is_need_wait = True
return True
case "post_msg":# /post_msg $target_id
if len(func_args) != 1:
return False
new_msg = AgentMsg()
target_id = func_item.args[0]
msg_content = func_item.body
new_msg.set("",target_id,msg_content)
r.post_msgs.append(new_msg)
return True
case "call":# /call $func_name $args_str
r.calls.append(func_item)
is_need_wait = True
return True
case "post_call": # /post_call $func_name,$args_str
r.post_calls.append(func_item)
return True
case _:
if valid_func is not None:
if func_name in valid_func:
r.paragraphs[func_name] = func_item
return True
return False
current_func : FunctionItem = None
for line in lines:
if line.startswith("##/"):
if current_func:
if check_args(current_func) is False:
r.resp += current_func.dumps()
func_name,func_args = AgentMsg.parse_function_call(line[3:])
current_func = FunctionItem(func_name,func_args)
else:
if current_func:
current_func.append_body(line + "\n")
else:
r.resp += line + "\n"
if current_func:
if check_args(current_func) is False:
r.resp += current_func.dumps()
if len(r.send_msgs) > 0 or len(r.calls) > 0:
r.state = "waiting"
else:
r.state = "reponsed"
return r
class AgentReport:
def __init__(self):
pass
class AgentTodoResult:
TODO_RESULT_CODE_OK = 0,
TODO_RESULT_CODE_LLM_ERROR = 1,
TODO_RESULT_CODE_EXEC_OP_ERROR = 2
def __init__(self) -> None:
self.result_code = AgentTodoResult.TODO_RESULT_CODE_OK
self.result_str = None
self.error_str = None
self.op_list = None
def to_dict(self) -> dict:
result = {}
result["result_code"] = self.result_code
result["result_str"] = self.result_str
result["error_str"] = self.error_str
result["op_list"] = self.op_list
return result
class AgentTodo:
TODO_STATE_WAIT_ASSIGN = "wait_assign"
TODO_STATE_INIT = "init"
TODO_STATE_PENDING = "pending"
TODO_STATE_WAITING_CHECK = "wait_check"
TODO_STATE_EXEC_FAILED = "exec_failed"
TDDO_STATE_CHECKFAILED = "check_failed"
TODO_STATE_CANCEL = "cancel"
TODO_STATE_DONE = "done"
TODO_STATE_REVIEWED = "reviewed"
TODO_STATE_EXPIRED = "expired"
def __init__(self):
self.todo_id = "todo#" + uuid.uuid4().hex
self.title = None
self.detail = None
self.todo_path = None # get parent todo,sub todo by path
#self.parent = None
self.create_time = time.time()
self.state = "wait_assign"
self.worker = None
self.checker = None
self.createor = None
self.need_check = True
self.due_date = time.time() + 3600 * 24 * 2
self.last_do_time = None
self.last_check_time = None
self.last_review_time = None
self.depend_todo_ids = []
self.sub_todos = {}
self.result : AgentTodoResult = None
self.last_check_result = None
self.retry_count = 0
self.raw_obj = None
@classmethod
def from_dict(cls,json_obj:dict) -> 'AgentTodo':
todo = AgentTodo()
if json_obj.get("id") is not None:
todo.todo_id = json_obj.get("id")
todo.title = json_obj.get("title")
todo.state = json_obj.get("state")
create_time = json_obj.get("create_time")
if create_time:
todo.create_time = datetime.fromisoformat(create_time).timestamp()
todo.detail = json_obj.get("detail")
due_date = json_obj.get("due_date")
if due_date:
todo.due_date = datetime.fromisoformat(due_date).timestamp()
last_do_time = json_obj.get("last_do_time")
if last_do_time:
todo.last_do_time = datetime.fromisoformat(last_do_time).timestamp()
last_check_time = json_obj.get("last_check_time")
if last_check_time:
todo.last_check_time = datetime.fromisoformat(last_check_time).timestamp()
last_review_time = json_obj.get("last_review_time")
if last_review_time:
todo.last_review_time = datetime.fromisoformat(last_review_time).timestamp()
todo.depend_todo_ids = json_obj.get("depend_todo_ids")
todo.need_check = json_obj.get("need_check")
#todo.result = json_obj.get("result")
#todo.last_check_result = json_obj.get("last_check_result")
todo.worker = json_obj.get("worker")
todo.checker = json_obj.get("checker")
todo.createor = json_obj.get("createor")
if json_obj.get("retry_count"):
todo.retry_count = json_obj.get("retry_count")
todo.raw_obj = json_obj
return todo
def to_dict(self) -> dict:
if self.raw_obj:
result = self.raw_obj
else:
result = {}
result["id"] = self.todo_id
#result["parent_id"] = self.parent_id
result["title"] = self.title
result["state"] = self.state
result["create_time"] = datetime.fromtimestamp(self.create_time).isoformat()
result["detail"] = self.detail
result["due_date"] = datetime.fromtimestamp(self.due_date).isoformat()
result["last_do_time"] = datetime.fromtimestamp(self.last_do_time).isoformat() if self.last_do_time else None
result["last_check_time"] = datetime.fromtimestamp(self.last_check_time).isoformat() if self.last_check_time else None
result["last_review_time"] = datetime.fromtimestamp(self.last_review_time).isoformat() if self.last_review_time else None
result["depend_todo_ids"] = self.depend_todo_ids
result["need_check"] = self.need_check
result["worker"] = self.worker
result["checker"] = self.checker
result["createor"] = self.createor
result["retry_count"] = self.retry_count
return result
def to_prompt(self) -> AgentPrompt:
json_str = json.dumps(self.raw_obj)
return AgentPrompt(json_str)
def can_review(self) -> bool:
if self.state != AgentTodo.TODO_STATE_DONE:
return False
now = datetime.now().timestamp()
if self.last_review_time:
time_diff = now - self.last_review_time
if time_diff < 60*15:
logger.info(f"todo {self.title} is already reviewed, ignore")
return False
return True
def can_check(self)->bool:
if self.state != AgentTodo.TODO_STATE_WAITING_CHECK:
return False
now = datetime.now().timestamp()
if self.last_check_time:
time_diff = now - self.last_check_time
if time_diff < 60*15:
logger.info(f"todo {self.title} is already checked, ignore")
return False
return True
def can_do(self) -> bool:
match self.state:
case AgentTodo.TODO_STATE_DONE:
logger.info(f"todo {self.title} is done, ignore")
return False
case AgentTodo.TODO_STATE_CANCEL:
logger.info(f"todo {self.title} is cancel, ignore")
return False
case AgentTodo.TODO_STATE_EXPIRED:
logger.info(f"todo {self.title} is expired, ignore")
return False
case AgentTodo.TODO_STATE_EXEC_FAILED:
if self.retry_count > 3:
logger.info(f"todo {self.title} retry count ({self.retry_count}) is too many, ignore")
return False
now = datetime.now().timestamp()
time_diff = self.due_date - now
if time_diff < 0:
logger.info(f"todo {self.title} is expired, ignore")
self.state = AgentTodo.TODO_STATE_EXPIRED
return False
if time_diff > 7*24*3600:
logger.info(f"todo {self.title} is far before due date, ignore")
return False
if self.last_do_time:
time_diff = now - self.last_do_time
if time_diff < 60*15:
logger.info(f"todo {self.title} is already do ignore")
return False
logger.info(f"todo {self.title} can do.")
return True
class AgentWorkLog:
def __init__(self) -> None:
pass
class BaseAIAgent(abc.ABC): class BaseAIAgent(abc.ABC):
@@ -420,19 +35,9 @@ class BaseAIAgent(abc.ABC):
def get_max_token_size(self) -> int: def get_max_token_size(self) -> int:
pass pass
def token_len(self, text:str=None, prompt:AgentPrompt=None) -> int: @abstractmethod
from ..frame.compute_kernel import ComputeKernel async def _process_msg(self,msg:AgentMsg,workspace = None) -> AgentMsg:
if text: pass
return ComputeKernel.llm_num_tokens_from_text(text,self.get_llm_model_name())
elif prompt:
result = 0
if prompt.system_message:
result += ComputeKernel.llm_num_tokens_from_text(prompt.system_message.get("content"),self.get_llm_model_name())
for msg in prompt.messages:
result += ComputeKernel.llm_num_tokens_from_text(msg.get("content"),self.get_llm_model_name())
return result
else:
return 0
@classmethod @classmethod
def get_inner_functions(cls, env:BaseEnvironment) -> (dict,int): def get_inner_functions(cls, env:BaseEnvironment) -> (dict,int):
@@ -458,7 +63,7 @@ class BaseAIAgent(abc.ABC):
async def do_llm_complection( async def do_llm_complection(
self, self,
prompt:AgentPrompt, prompt:LLMPrompt,
org_msg:AgentMsg=None, org_msg:AgentMsg=None,
env:BaseEnvironment=None, env:BaseEnvironment=None,
inner_functions=None, inner_functions=None,
@@ -503,7 +108,7 @@ class BaseAIAgent(abc.ABC):
inner_func_call_node = result_message.get("function_call") inner_func_call_node = result_message.get("function_call")
if inner_func_call_node: if inner_func_call_node:
call_prompt : AgentPrompt = copy.deepcopy(prompt) call_prompt : LLMPrompt = copy.deepcopy(prompt)
func_msg = copy.deepcopy(result_message) func_msg = copy.deepcopy(result_message)
del func_msg["tool_calls"] del func_msg["tool_calls"]
call_prompt.messages.append(func_msg) call_prompt.messages.append(func_msg)
@@ -515,7 +120,7 @@ class BaseAIAgent(abc.ABC):
self, self,
env: BaseEnvironment, env: BaseEnvironment,
inner_func_call_node: dict, inner_func_call_node: dict,
prompt: AgentPrompt, prompt: LLMPrompt,
inner_functions: dict, inner_functions: dict,
org_msg:AgentMsg, org_msg:AgentMsg,
stack_limit = 5 stack_limit = 5
src/aios/agent/behavior.py
-4
View File
@@ -1,4 +0,0 @@
# TODO: let agent develolp custmized behavior easily
class AgentBehavior:
def __init__(self) -> None:
pass
src/aios/agent/llm_process.py
+149
View File
@@ -0,0 +1,149 @@
# Old name is behavior, I belive new name "llm_process" is better
from abc import ABC,abstractmethod
import copy
import json
import shlex
from typing import Any, Callable, Optional,Dict,Awaitable,List
from enum import Enum
from ..proto.compute_task import *
from ..proto.ai_function import *
from ..frame.compute_kernel import *
import logging
logger = logging.getLogger(__name__)
MIN_PREDICT_TOKEN_LEN = 32
class BaseLLMProcess:
def __init__(self) -> None:
self.enable_json_resp = False
self.model_name = "gpt-4"
self.max_token = 2000 # include input prompt
self.timeout = 1800 # 30 min
@abstractmethod
async def prepare_prompt(self) -> LLMPrompt:
pass
@abstractmethod
async def get_inner_function(self,func_name:str) -> AIFunction:
pass
async def _execute_inner_func(self,inner_func_call_node,prompt: LLMPrompt,stack_limit = 5) -> ComputeTaskResult:
arguments = None
try:
func_name = inner_func_call_node.get("name")
arguments = json.loads(inner_func_call_node.get("arguments"))
logger.info(f"LLMProcess execute inner func:{func_name} :\n\t {json.dumps(arguments)}")
func_node : AIFunction = await self.get_inner_function(func_name)
if func_node is None:
result_str:str = f"execute {func_name} error,function not found"
else:
result_str:str = await func_node.execute(**arguments)
except Exception as e:
result_str = f"execute {func_name} error:{str(e)}"
logger.error(f"LLMProcess execute inner func:{func_name} error:\n\t{e}")
logger.info("LLMProcess execute inner func result:" + result_str)
prompt.messages.append({"role":"function","content":result_str,"name":func_name})
if self.enable_json_resp:
resp_mode = "json"
else:
resp_mode = "text"
max_result_token = self.max_token - ComputeKernel.llm_num_tokens(prompt)
if max_result_token < MIN_PREDICT_TOKEN_LEN:
task_result = ComputeTaskResult()
task_result.result_code = ComputeTaskResultCode.ERROR
task_result.error_str = f"prompt too long,can not predict"
return task_result
task_result: ComputeTaskResult = await (ComputeKernel.get_instance().do_llm_completion(
prompt,
resp_mode=resp_mode,
mode_name=self.model_name,
max_token=max_result_token,
inner_functions=prompt.inner_functions,
timeout=self.timeout))
if task_result.result_code != ComputeTaskResultCode.OK:
logger.error(f"llm compute error:{task_result.error_str}")
return task_result
inner_func_call_node = None
if stack_limit > 0:
result_message : dict = task_result.result.get("message")
if result_message:
inner_func_call_node = result_message.get("function_call")
if inner_func_call_node:
func_msg = copy.deepcopy(result_message)
del func_msg["tool_calls"]#TODO: support tool_calls?
prompt.messages.append(func_msg)
else:
logger.error(f"inner function call stack limit reached")
task_result.result_code = ComputeTaskResultCode.ERROR
task_result.error_str = "inner function call stack limit reached"
return task_result
if inner_func_call_node:
return await self._execute_inner_func(inner_func_call_node,prompt,stack_limit-1)
else:
return task_result
async def process(self) -> LLMResult:
if self.enable_json_resp:
resp_mode = "json"
else:
resp_mode = "text"
prompt = await self.prepare_prompt()
max_result_token = self.max_token - ComputeKernel.llm_num_tokens(prompt)
if max_result_token < MIN_PREDICT_TOKEN_LEN:
return LLMResult.from_error_str(f"prompt too long,can not predict")
task_result: ComputeTaskResult = await (ComputeKernel.get_instance().do_llm_completion(
prompt,
resp_mode=resp_mode,
mode_name=self.model_name,
max_token=max_result_token,
inner_functions=prompt.inner_functions,
timeout=self.timeout))
if task_result.result_code != ComputeTaskResultCode.OK:
err_str = f"do_llm_completion error:{task_result.error_str}"
logger.error(err_str)
return LLMResult.from_error_str(err_str)
result_message = task_result.result.get("message")
inner_func_call_node = None
if result_message:
inner_func_call_node = result_message.get("function_call")
if inner_func_call_node:
call_prompt : LLMPrompt = copy.deepcopy(prompt)
func_msg = copy.deepcopy(result_message)
del func_msg["tool_calls"]
call_prompt.messages.append(func_msg)
task_result = await self._execute_inner_func(inner_func_call_node,call_prompt)
# parse task_result to LLM Result
if self.enable_json_resp:
llm_result = LLMResult.from_json_str(task_result.result_str)
else:
llm_result = LLMResult.from_str(task_result.result_str)
# execute op_list in LLM Result?
return llm_result
#class LLMProcess
src/aios/agent/role.py
+4 -4
View File
@@ -1,6 +1,6 @@
import logging import logging
from .agent_base import AgentPrompt from .agent_base import LLMPrompt
class AIRole: class AIRole:
def __init__(self) -> None: def __init__(self) -> None:
@@ -9,7 +9,7 @@ class AIRole:
self.role_id :str = None # $workflow_id.$sub_workflow_id.$role_name self.role_id :str = None # $workflow_id.$sub_workflow_id.$role_name
self.fullname : str = None self.fullname : str = None
self.agent_name : str = None self.agent_name : str = None
self.prompt : AgentPrompt = None self.prompt : LLMPrompt = None
self.introduce : str = None self.introduce : str = None
self.agent = None self.agent = None
self.enable_function_list : list[str] = None self.enable_function_list : list[str] = None
@@ -31,7 +31,7 @@ class AIRole:
prompt_node = config.get("prompt") prompt_node = config.get("prompt")
if prompt_node: if prompt_node:
self.prompt = AgentPrompt() self.prompt = LLMPrompt()
if self.prompt.load_from_config(prompt_node) is False: if self.prompt.load_from_config(prompt_node) is False:
logging.error("load prompt failed!") logging.error("load prompt failed!")
return False return False
@@ -56,7 +56,7 @@ class AIRole:
def get_name(self) -> str: def get_name(self) -> str:
return self.role_name return self.role_name
def get_prompt(self) -> AgentPrompt: def get_prompt(self) -> LLMPrompt:
return self.prompt return self.prompt
class AIRoleGroup: class AIRoleGroup:
src/aios/agent/workflow.py
+15 -15
View File
@@ -9,11 +9,11 @@ from abc import ABC, abstractmethod
from ..proto.compute_task import * from ..proto.compute_task import *
from ..proto.agent_msg import * from ..proto.agent_msg import *
from ..proto.ai_function import *
from .agent_base import * from .agent_base import *
from .chatsession import AIChatSession from .chatsession import AIChatSession
from .role import AIRole,AIRoleGroup from .role import AIRole,AIRoleGroup
from .ai_function import AIFunction,FunctionItem
from ..frame.compute_kernel import ComputeKernel from ..frame.compute_kernel import ComputeKernel
from ..frame.bus import AIBus from ..frame.bus import AIBus
@@ -48,7 +48,7 @@ class Workflow:
def __init__(self) -> None: def __init__(self) -> None:
self.workflow_name : str = None self.workflow_name : str = None
self.workflow_id : str = None self.workflow_id : str = None
self.rule_prompt : AgentPrompt = None self.rule_prompt : LLMPrompt = None
self.workflow_config = None self.workflow_config = None
self.role_group : dict = None self.role_group : dict = None
self.input_filter : MessageFilter= None self.input_filter : MessageFilter= None
@@ -83,7 +83,7 @@ class Workflow:
self.db_file = self.owner_workflow.db_file self.db_file = self.owner_workflow.db_file
if config.get("prompt") is not None: if config.get("prompt") is not None:
self.rule_prompt = AgentPrompt() self.rule_prompt = LLMPrompt()
if self.rule_prompt.load_from_config(config.get("prompt")) is False: if self.rule_prompt.load_from_config(config.get("prompt")) is False:
logger.error("Workflow load prompt failed") logger.error("Workflow load prompt failed")
return False return False
@@ -279,7 +279,7 @@ class Workflow:
logger.info(f"{msg.sender} post message {msg.msg_id} to AIBus: {msg.target}") logger.info(f"{msg.sender} post message {msg.msg_id} to AIBus: {msg.target}")
return await self.get_bus().send_message(msg) return await self.get_bus().send_message(msg)
async def role_call(self,func_item:FunctionItem,the_role:AIRole): async def role_call(self,func_item:ActionItem,the_role:AIRole):
logger.info(f"{the_role.role_id} call {func_item.name} ") logger.info(f"{the_role.role_id} call {func_item.name} ")
arguments = func_item.args arguments = func_item.args
@@ -290,11 +290,11 @@ class Workflow:
result_str:str = await func_node.execute(**arguments) result_str:str = await func_node.execute(**arguments)
return result_str return result_str
async def role_post_call(self,func_item:FunctionItem,the_role:AIRole): async def role_post_call(self,func_item:ActionItem,the_role:AIRole):
logger.info(f"{the_role.role_id} post call {func_item.name} ") logger.info(f"{the_role.role_id} post call {func_item.name} ")
return await self.role_call(func_item,the_role) return await self.role_call(func_item,the_role)
def _format_msg_by_env_value(self,prompt:AgentPrompt): def _format_msg_by_env_value(self,prompt:LLMPrompt):
if self.workflow_env is None: if self.workflow_env is None:
return return
@@ -326,7 +326,7 @@ class Workflow:
return result_func return result_func
return None return None
async def _role_execute_func(self,the_role:AIRole,inenr_func_call_node:dict,prompt:AgentPrompt,org_msg:AgentMsg,stack_limit = 5) -> [str,int]: async def _role_execute_func(self,the_role:AIRole,inenr_func_call_node:dict,prompt:LLMPrompt,org_msg:AgentMsg,stack_limit = 5) -> [str,int]:
func_name = inenr_func_call_node.get("name") func_name = inenr_func_call_node.get("name")
arguments = json.loads(inenr_func_call_node.get("arguments")) arguments = json.loads(inenr_func_call_node.get("arguments"))
@@ -372,7 +372,7 @@ class Workflow:
async def role_process_msg(self,msg:AgentMsg,the_role:AIRole,workflow_chat_session:AIChatSession) -> AgentMsg: async def role_process_msg(self,msg:AgentMsg,the_role:AIRole,workflow_chat_session:AIChatSession) -> AgentMsg:
msg.target = the_role.get_role_id() msg.target = the_role.get_role_id()
prompt = AgentPrompt() prompt = LLMPrompt()
prompt.append(the_role.agent.agent_prompt) prompt.append(the_role.agent.agent_prompt)
prompt.append(self.get_workflow_rule_prompt()) prompt.append(self.get_workflow_rule_prompt())
prompt.append(the_role.get_prompt()) prompt.append(the_role.get_prompt())
@@ -382,7 +382,7 @@ class Workflow:
#support group chat, user content include sender name! #support group chat, user content include sender name!
prompt.append(await self._get_prompt_from_session(the_role,workflow_chat_session)) prompt.append(await self._get_prompt_from_session(the_role,workflow_chat_session))
msg_prompt = AgentPrompt() msg_prompt = LLMPrompt()
msg_prompt.messages = [{"role":"user","content":f"user name is {msg.sender}, his question is :{msg.body}"}] msg_prompt.messages = [{"role":"user","content":f"user name is {msg.sender}, his question is :{msg.body}"}]
prompt.append(msg_prompt) prompt.append(msg_prompt)
@@ -461,20 +461,20 @@ class Workflow:
# message will be saved in role.process_message # message will be saved in role.process_message
pass pass
this_llm_resp_prompt = AgentPrompt() this_llm_resp_prompt = LLMPrompt()
this_llm_resp_prompt.messages = [{"role":"assistant","content":result_str}] this_llm_resp_prompt.messages = [{"role":"assistant","content":result_str}]
prompt.append(this_llm_resp_prompt) prompt.append(this_llm_resp_prompt)
result_prompt = AgentPrompt() result_prompt = LLMPrompt()
result_prompt.messages = [{"role":"user","content":result_prompt_str}] result_prompt.messages = [{"role":"user","content":result_prompt_str}]
prompt.append(result_prompt) prompt.append(result_prompt)
return await _do_process_msg() return await _do_process_msg()
return await _do_process_msg() return await _do_process_msg()
async def _get_prompt_from_session(self,the_role:AIRole,chatsession:AIChatSession) -> AgentPrompt: async def _get_prompt_from_session(self,the_role:AIRole,chatsession:AIChatSession) -> LLMPrompt:
messages = chatsession.read_history(the_role.history_len) # read last 10 message messages = chatsession.read_history(the_role.history_len) # read last 10 message
result_prompt = AgentPrompt() result_prompt = LLMPrompt()
for msg in reversed(messages): for msg in reversed(messages):
if msg.sender == the_role.role_id: if msg.sender == the_role.role_id:
@@ -484,10 +484,10 @@ class Workflow:
return result_prompt return result_prompt
def _get_knowlege_prompt(self,role_name:str) -> AgentPrompt: def _get_knowlege_prompt(self,role_name:str) -> LLMPrompt:
pass pass
def get_workflow_rule_prompt(self) -> AgentPrompt: def get_workflow_rule_prompt(self) -> LLMPrompt:
return self.rule_prompt return self.rule_prompt
# def _env_event_to_msg(self,env_event:EnvironmentEvent) -> AgentMsg: # def _env_event_to_msg(self,env_event:EnvironmentEvent) -> AgentMsg:
src/aios/environment/asr_function.py
+1 -1
View File
@@ -2,7 +2,7 @@ import logging
from typing import Dict from typing import Dict
from ..frame.compute_kernel import ComputeKernel from ..frame.compute_kernel import ComputeKernel
from ..agent.ai_function import AIFunction from ..proto.ai_function import *
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
src/aios/environment/code_interpreter_function.py
+1 -1
View File
@@ -1,6 +1,6 @@
from typing import Dict from typing import Dict
from ..agent.ai_function import AIFunction from ..proto.ai_function import *
from .code_interpreter import execute_code from .code_interpreter import execute_code
src/aios/environment/duckduckgo_text_search_function.py
+1 -1
View File
@@ -1,7 +1,7 @@
import json import json
from typing import Dict from typing import Dict
from ..agent.ai_function import AIFunction from ..proto.ai_function import *
from duckduckgo_search import AsyncDDGS from duckduckgo_search import AsyncDDGS
src/aios/environment/environment.py
+1 -1
View File
@@ -4,7 +4,7 @@
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from typing import Any, Callable, Optional,Dict,Awaitable,List from typing import Any, Callable, Optional,Dict,Awaitable,List
import logging import logging
from ..agent.ai_function import AIFunction, AIOperation from ..proto.ai_function import *
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
src/aios/environment/image_2_text_function.py
+1 -1
View File
@@ -2,7 +2,7 @@ import logging
from typing import Dict from typing import Dict
from ..frame.compute_kernel import ComputeKernel from ..frame.compute_kernel import ComputeKernel
from ..agent.ai_function import AIFunction from ..proto.ai_function import *
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
src/aios/environment/script_to_speech_function.py
+1 -1
View File
@@ -5,7 +5,7 @@ import random
from pathlib import Path from pathlib import Path
from typing import Dict from typing import Dict
from ..agent.ai_function import AIFunction from ..proto.ai_function import *
from ..frame.compute_kernel import ComputeKernel from ..frame.compute_kernel import ComputeKernel
from ..storage.storage import AIStorage from ..storage.storage import AIStorage
src/aios/environment/sql_database_function.py
+1 -1
View File
@@ -3,7 +3,7 @@ from typing import Dict
from cachetools import TLRUCache, cached from cachetools import TLRUCache, cached
from ..agent.ai_function import AIFunction from ..proto.ai_function import *
from .sql_database import SQLDatabase, get_from_env from .sql_database import SQLDatabase, get_from_env
src/aios/environment/text_to_speech_function.py
+1 -1
View File
@@ -5,7 +5,7 @@ import random
from pathlib import Path from pathlib import Path
from typing import Dict from typing import Dict
from ..agent.ai_function import AIFunction from ..proto.ai_function import *
from ..frame.compute_kernel import ComputeKernel from ..frame.compute_kernel import ComputeKernel
from ..storage.storage import AIStorage from ..storage.storage import AIStorage
src/aios/environment/workflow_env.py
+2 -2
View File
@@ -10,7 +10,7 @@ from typing import Optional
import aiosqlite import aiosqlite
from ..proto.compute_task import * from ..proto.compute_task import *
from ..agent.ai_function import SimpleAIFunction from ..proto.ai_function import *
from ..frame.compute_kernel import ComputeKernel from ..frame.compute_kernel import ComputeKernel
from ..frame.contact_manager import ContactManager,Contact,FamilyMember from ..frame.contact_manager import ContactManager,Contact,FamilyMember
from ..storage.storage import AIStorage from ..storage.storage import AIStorage
@@ -302,7 +302,7 @@ class CalenderEnvironment(SimpleEnvironment):
return f'exec paint OK, saved as a local file, path is: {result.result["file"]}' return f'exec paint OK, saved as a local file, path is: {result.result["file"]}'
class PaintEnvironment(BaseEnvironment): class PaintEnvironment(SimpleEnvironment):
def __init__(self, env_id: str) -> None: def __init__(self, env_id: str) -> None:
super().__init__(env_id) super().__init__(env_id)
self.is_run = False self.is_run = False
src/aios/environment/workspace_env.py
+9 -5
View File
@@ -6,8 +6,12 @@ import sqlite3
import asyncio import asyncio
from typing import Any,List,Dict from typing import Any,List,Dict
import chardet import chardet
from ..agent.agent_base import AgentMsg,AgentTodo,AgentPrompt,AgentTodoResult
from ..agent.ai_function import AIFunction,SimpleAIFunction, SimpleAIOperation from ..proto.agent_task import *
from ..proto.ai_function import *
from ..proto.compute_task import *
from ..agent.agent_base import *
from ..storage.storage import AIStorage,ResourceLocation from ..storage.storage import AIStorage,ResourceLocation
from .environment import SimpleEnvironment, CompositeEnvironment from .environment import SimpleEnvironment, CompositeEnvironment
@@ -289,16 +293,16 @@ class WorkspaceEnvironment(CompositeEnvironment):
def get_prompt(self) -> AgentMsg: def get_prompt(self) -> AgentMsg:
return None return None
def get_role_prompt(self,role_id:str) -> AgentPrompt: def get_role_prompt(self,role_id:str) -> LLMPrompt:
return None return None
def get_do_prompt(self,todo:AgentTodo=None)->AgentPrompt: def get_do_prompt(self,todo:AgentTodo=None)->LLMPrompt:
return None return None
# result mean: list[op_error_str],have_error # result mean: list[op_error_str],have_error
async def exec_op_list(self,oplist:List,agent_id:str)->tuple[List[str],bool]: async def exec_op_list(self,oplist:List,agent_id:str)->tuple[List[str],bool]:
result_str = "op list is none" result_str = "op list is none"
if oplist is None: if oplist is None or len(oplist) == 0:
return None,False return None,False
result_str = [] result_str = []
src/aios/frame/compute_kernel.py
+9 -3
View File
@@ -8,7 +8,7 @@ from asyncio import Queue
from ..proto.compute_task import * from ..proto.compute_task import *
from ..knowledge import ObjectID from ..knowledge import ObjectID
from ..agent.agent_base import AgentPrompt
from .compute_node import ComputeNode from .compute_node import ComputeNode
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@@ -106,6 +106,9 @@ class ComputeKernel:
@staticmethod @staticmethod
def llm_num_tokens_from_text(text:str,model:str) -> int: def llm_num_tokens_from_text(text:str,model:str) -> int:
if model is None:
model = "gpt4"
try: try:
encoding = tiktoken.encoding_for_model(model) encoding = tiktoken.encoding_for_model(model)
except KeyError: except KeyError:
@@ -115,9 +118,12 @@ class ComputeKernel:
token_count = len(encoding.encode(text)) token_count = len(encoding.encode(text))
return token_count return token_count
@staticmethod
def llm_num_tokens(prompt: LLMPrompt, model_name: str = None) -> int:
return ComputeKernel.llm_num_tokens_from_text(prompt.as_str(), model_name)
# friendly interface for use: # friendly interface for use:
def llm_completion(self, prompt: AgentPrompt, resp_mode:str="text",mode_name: Optional[str] = None, max_token: int = 0,inner_functions = None): def llm_completion(self, prompt: LLMPrompt, resp_mode:str="text",mode_name: Optional[str] = None, max_token: int = 0,inner_functions = None):
# craete a llm_work_task ,push on queue's end # craete a llm_work_task ,push on queue's end
# then task_schedule would run this task.(might schedule some work_task to another host) # then task_schedule would run this task.(might schedule some work_task to another host)
task_req = ComputeTask() task_req = ComputeTask()
@@ -153,7 +159,7 @@ class ComputeKernel:
return time_out_result return time_out_result
async def do_llm_completion(self, prompt: AgentPrompt,resp_mode:str="text", mode_name: Optional[str]=None, max_token:int=0, inner_functions=None, timeout=60) -> str: async def do_llm_completion(self, prompt: LLMPrompt,resp_mode:str="text", mode_name: Optional[str]=None, max_token:int=0, inner_functions=None, timeout=60) -> str:
task_req = self.llm_completion(prompt, resp_mode,mode_name, max_token,inner_functions) task_req = self.llm_completion(prompt, resp_mode,mode_name, max_token,inner_functions)
return await self._wait_task(task_req, timeout) return await self._wait_task(task_req, timeout)
src/aios/proto/agent_msg.py
+1 -6
View File
@@ -232,9 +232,4 @@ class AgentMsg:
def get_quote_msg_id(self) -> str: def get_quote_msg_id(self) -> str:
return self.quote_msg_id return self.quote_msg_id
@classmethod
def parse_function_call(cls,func_string:str):
str_list = shlex.split(func_string)
func_name = str_list[0]
params = str_list[1:]
return func_name, params
src/aios/proto/agent_task.py
+221
View File
@@ -0,0 +1,221 @@
import datetime
import time
from anyio import Path
class AgentTodoResult:
TODO_RESULT_CODE_OK = 0,
TODO_RESULT_CODE_LLM_ERROR = 1,
TODO_RESULT_CODE_EXEC_OP_ERROR = 2
def __init__(self) -> None:
self.result_code = AgentTodoResult.TODO_RESULT_CODE_OK
self.result_str = None
self.error_str = None
self.op_list = None
def to_dict(self) -> dict:
result = {}
result["result_code"] = self.result_code
result["result_str"] = self.result_str
result["error_str"] = self.error_str
result["op_list"] = self.op_list
return result
class AgentTodo:
TODO_STATE_WAIT_ASSIGN = "wait_assign"
TODO_STATE_INIT = "init"
TODO_STATE_PENDING = "pending"
TODO_STATE_WAITING_CHECK = "wait_check"
TODO_STATE_EXEC_FAILED = "exec_failed"
TDDO_STATE_CHECKFAILED = "check_failed"
TODO_STATE_CASNCEL = "cancel"
TODO_STATE_DONE = "done"
TODO_STATE_EXPIRED = "expired"
def __init__(self):
self.todo_id = "todo#" + uuid.uuid4().hex
self.title = None
self.detail = None
self.todo_path = None # get parent todo,sub todo by path
#self.parent = None
self.create_time = time.time()
self.state = "wait_assign"
self.worker = None
self.checker = None
self.createor = None
self.need_check = True
self.due_date = time.time() + 3600 * 24 * 2
self.last_do_time = None
self.last_check_time = None
self.last_review_time = None
self.depend_todo_ids = []
self.sub_todos = {}
self.result : AgentTodoResult = None
self.last_check_result = None
self.retry_count = 0
self.raw_obj = None
@classmethod
def from_dict(cls,json_obj:dict) -> 'AgentTodo':
todo = AgentTodo()
if json_obj.get("id") is not None:
todo.todo_id = json_obj.get("id")
todo.title = json_obj.get("title")
todo.state = json_obj.get("state")
create_time = json_obj.get("create_time")
if create_time:
todo.create_time = datetime.fromisoformat(create_time).timestamp()
todo.detail = json_obj.get("detail")
due_date = json_obj.get("due_date")
if due_date:
todo.due_date = datetime.fromisoformat(due_date).timestamp()
last_do_time = json_obj.get("last_do_time")
if last_do_time:
todo.last_do_time = datetime.fromisoformat(last_do_time).timestamp()
last_check_time = json_obj.get("last_check_time")
if last_check_time:
todo.last_check_time = datetime.fromisoformat(last_check_time).timestamp()
last_review_time = json_obj.get("last_review_time")
if last_review_time:
todo.last_review_time = datetime.fromisoformat(last_review_time).timestamp()
todo.depend_todo_ids = json_obj.get("depend_todo_ids")
todo.need_check = json_obj.get("need_check")
#todo.result = json_obj.get("result")
#todo.last_check_result = json_obj.get("last_check_result")
todo.worker = json_obj.get("worker")
todo.checker = json_obj.get("checker")
todo.createor = json_obj.get("createor")
if json_obj.get("retry_count"):
todo.retry_count = json_obj.get("retry_count")
todo.raw_obj = json_obj
return todo
def to_dict(self) -> dict:
if self.raw_obj:
result = self.raw_obj
else:
result = {}
result["id"] = self.todo_id
#result["parent_id"] = self.parent_id
result["title"] = self.title
result["state"] = self.state
result["create_time"] = datetime.fromtimestamp(self.create_time).isoformat()
result["detail"] = self.detail
result["due_date"] = datetime.fromtimestamp(self.due_date).isoformat()
result["last_do_time"] = datetime.fromtimestamp(self.last_do_time).isoformat() if self.last_do_time else None
result["last_check_time"] = datetime.fromtimestamp(self.last_check_time).isoformat() if self.last_check_time else None
result["last_review_time"] = datetime.fromtimestamp(self.last_review_time).isoformat() if self.last_review_time else None
result["depend_todo_ids"] = self.depend_todo_ids
result["need_check"] = self.need_check
result["worker"] = self.worker
result["checker"] = self.checker
result["createor"] = self.createor
result["retry_count"] = self.retry_count
return result
def can_check(self)->bool:
if self.state != AgentTodo.TODO_STATE_WAITING_CHECK:
return False
now = datetime.now().timestamp()
if self.last_check_time:
time_diff = now - self.last_check_time
if time_diff < 60*15:
logger.info(f"todo {self.title} is already checked, ignore")
return False
return True
def can_do(self) -> bool:
match self.state:
case AgentTodo.TODO_STATE_DONE:
logger.info(f"todo {self.title} is done, ignore")
return False
case AgentTodo.TODO_STATE_CASNCEL:
logger.info(f"todo {self.title} is cancel, ignore")
return False
case AgentTodo.TODO_STATE_EXPIRED:
logger.info(f"todo {self.title} is expired, ignore")
return False
case AgentTodo.TODO_STATE_EXEC_FAILED:
if self.retry_count > 3:
logger.info(f"todo {self.title} retry count ({self.retry_count}) is too many, ignore")
return False
now = datetime.now().timestamp()
time_diff = self.due_date - now
if time_diff < 0:
logger.info(f"todo {self.title} is expired, ignore")
self.state = AgentTodo.TODO_STATE_EXPIRED
return False
if time_diff > 7*24*3600:
logger.info(f"todo {self.title} is far before due date, ignore")
return False
if self.last_do_time:
time_diff = now - self.last_do_time
if time_diff < 60*15:
logger.info(f"todo {self.title} is already do ignore")
return False
logger.info(f"todo {self.title} can do.")
return True
class AgentTask:
def __init__(self) -> None:
self.task_id : str = "task#" + uuid.uuid4().hex
self.task_path : Path = None # get parent todo,sub todo by path
self.title = None
self.detail = None
self.create_time = time.time()
self.state = "wait_assign"
self.worker = None
self.createor = None
self.due_date = time.time() + 3600 * 24 * 2
self.depend_task_ids = []
self.step_todos = {}
self.last_plan_time = None
self.last_check_time = None
#self.last_review_time = None
self.result : LLMResult = None
self.last_check_result = None
self.retry_count = 0
self.raw_obj = None
class AgentWorkLog:
def __init__(self) -> None:
pass
class AgentReport:
def __init__(self) -> None:
pass
src/aios/agent/ai_function.py → src/aios/proto/ai_function.py
+1 -1
View File
@@ -73,7 +73,7 @@ class AIFunction:
#def load_from_config(self,config:dict) -> bool: #def load_from_config(self,config:dict) -> bool:
# pass # pass
class FunctionItem: class ActionItem:
def __init__(self,name,args) -> None: def __init__(self,name,args) -> None:
self.name = name self.name = name
self.args = args self.args = args
src/aios/proto/compute_task.py
+170 -1
View File
@@ -1,11 +1,20 @@
import copy
from enum import Enum from enum import Enum
import json
import shlex
import uuid import uuid
import time import time
from typing import Union from typing import List, Union
from ..proto.ai_function import *
from ..knowledge import ObjectID from ..knowledge import ObjectID
from ..storage.storage import AIStorage from ..storage.storage import AIStorage
import logging
logger = logging.getLogger(__name__)
class ComputeTaskResultCode(Enum): class ComputeTaskResultCode(Enum):
OK = 0 OK = 0
TIMEOUT = 1 TIMEOUT = 1
@@ -31,6 +40,164 @@ class ComputeTaskType(Enum):
TEXT_EMBEDDING ="text_embedding" TEXT_EMBEDDING ="text_embedding"
IMAGE_EMBEDDING ="image_embedding" IMAGE_EMBEDDING ="image_embedding"
class LLMPrompt:
def __init__(self,prompt_str = None) -> None:
self.messages = []
if prompt_str:
self.messages.append({"role":"user","content":prompt_str})
self.system_message = None
def as_str(self)->str:
result_str = ""
if self.system_message:
result_str += self.system_message.get("role") + ":" + self.system_message.get("content") + "\n"
if self.messages:
for msg in self.messages:
result_str += msg.get("role") + ":" + msg.get("content") + "\n"
return result_str
def to_message_list(self):
result = []
if self.system_message:
result.append(self.system_message)
result.extend(self.messages)
return result
def append(self,prompt:'LLMPrompt'):
if prompt is None:
return
if prompt.system_message is not None:
if self.system_message is None:
self.system_message = copy.deepcopy(prompt.system_message)
else:
self.system_message["content"] += prompt.system_message.get("content")
self.messages.extend(prompt.messages)
def load_from_config(self,config:list) -> bool:
if isinstance(config,list) is not True:
logger.error("prompt is not list!")
return False
self.messages = []
for msg in config:
if msg.get("content"):
if msg.get("role") == "system":
self.system_message = msg
else:
self.messages.append(msg)
else:
logger.error("prompt message has no content!")
return True
class LLMResultStates(Enum):
IGNORE = "ignore"
OK = "ok" # process done
ERROR = "error"
class LLMResult:
def __init__(self) -> None:
self.state : str = LLMResultStates.IGNORE
self.compute_error_str = None
self.resp : str = "" # llm say:
self.raw_result = None # raw result from compute kernel
self.inner_functions : List[AIFunction] = []
self.action_list : List[ActionItem] = [] # op_list is a optimize design for saving token
#self.post_msgs : List[AgentMsg] = [] # move to op_list
# self.send_msgs : List[AgentMsg] = [] # move to op_list
@classmethod
def from_error_str(self,error_str:str) -> 'LLMResult':
r = LLMResult()
r.state = "error"
r.compute_error_str = error_str
return r
@classmethod
def from_json_str(self,llm_json_str:str) -> 'LLMResult':
r = LLMResult()
if llm_json_str is None:
r.state = LLMResultStates.IGNORE
return r
if llm_json_str == "**IGNORE**":
r.state = LLMResultStates.IGNORE
return r
llm_json = json.loads(llm_json_str)
r.resp = llm_json.get("resp")
r.raw_result = llm_json
r.action_list = llm_json.get("actions")
return r
@classmethod
def parse_action(cls,func_string:str):
str_list = shlex.split(func_string)
func_name = str_list[0]
params = str_list[1:]
return func_name, params
@classmethod
def from_str(self,llm_result_str:str,valid_func:List[str]=None) -> 'LLMResult':
r = LLMResult()
if llm_result_str is None:
r.state = LLMResultStates.IGNORE
return r
if llm_result_str == "**IGNORE**":
r.state = LLMResultStates.IGNORE
return r
if llm_result_str[0] == "{":
return LLMResult.from_json_str(llm_result_str)
lines = llm_result_str.splitlines()
is_need_wait = False
def check_args(action_item:ActionItem):
match action_item.name:
case "post_msg":# /post_msg $target_id
if len(action_item.args) != 1:
return False
new_msg = AgentMsg()
target_id = action_item.args[0]
msg_content = action_item.body
new_msg.set("",target_id,msg_content)
return True
return False
current_action : ActionItem = None
for line in lines:
if line.startswith("##/"):
if current_action:
if check_args(current_action) is False:
r.resp += current_action.dumps()
else:
r.action_list.append(current_action)
action_name,action_args = LLMResult.parse_action(line[3:])
current_action = ActionItem(action_name,action_args)
else:
if current_action:
current_action.append_body(line + "\n")
else:
r.resp += line + "\n"
if current_action:
if check_args(current_action) is False:
r.resp += current_action.dumps()
else:
r.action_list.append(current_action)
return r
class ComputeTask: class ComputeTask:
def __init__(self) -> None: def __init__(self) -> None:
@@ -140,3 +307,5 @@ class ComputeTaskResult:
self.task_id = task.task_id self.task_id = task.task_id
self.callchain_id = task.callchain_id self.callchain_id = task.callchain_id
task.result = self task.result = self
src/aios_kernel/code_interpreter.py
-426
View File
@@ -1,426 +0,0 @@
import logging
import os
import pathlib
import shutil
import subprocess
import sys
import re
import time
import ast
from concurrent.futures import ThreadPoolExecutor
from hashlib import md5
from typing import Optional, Union, List, Tuple
from generic_escape import GenericEscape
from aios_kernel import AIStorage
try:
import docker
except ImportError:
docker = None
CODE_BLOCK_PATTERN = r"```[ \t]*(\w+)?[ \t]*\r?\n(.*?)\r?\n[ \t]*```"
UNKNOWN = "unknown"
TIMEOUT_MSG = "Timeout"
DEFAULT_TIMEOUT = 600
WIN32 = sys.platform == "win32"
PATH_SEPARATOR = WIN32 and "\\" or "/"
logger = logging.getLogger(__name__)
BUILT_IN_MODULES = set(
[
"sys",
"os",
"math",
"random",
"datetime",
"json",
"re",
"subprocess",
"time",
"threading",
"logging",
"collections",
"itertools",
"functools",
"operator",
"pathlib",
"shutil",
"tempfile",
"pickle",
"io",
"argparse",
"typing",
"unittest",
"contextlib",
"abc",
"heapq",
"bisect",
"copy",
"decimal",
"fractions",
"hashlib",
"secrets",
"statistics",
"difflib",
"doctest",
"enum",
"inspect",
"traceback",
"weakref",
"gc",
"mmap",
"msvcrt",
"winreg",
"array",
"audioop",
"binascii",
"cProfile",
"concurrent.futures",
"configparser",
"csv",
"ctypes",
"dateutil",
"dis",
"fnmatch",
"getopt",
"glob",
"gzip",
"pdb",
"pprint",
"profile",
"pstats",
"queue",
"socket",
"sqlite3",
"ssl",
"struct",
"tarfile",
"telnetlib",
"timeit",
"tokenize",
"uuid",
"xml",
"zipfile",
"zlib",
]
)
def get_imports(code: str) -> List[str]:
root = ast.parse(code)
imports = []
for node in ast.iter_child_nodes(root):
if isinstance(node, ast.Import):
module_names = [alias.name for alias in node.names]
elif isinstance(node, ast.ImportFrom):
module_names = [node.module]
else:
continue
for name in module_names:
# Exclude built-in modules
if name not in BUILT_IN_MODULES:
imports.append(name)
return imports
def write_requirements(code: str, requirements_filepath: str):
imports = get_imports(code)
with open(requirements_filepath, "w") as file:
for module in imports:
file.write(module + "\n")
def _cmd(lang):
if lang.startswith("python") or lang in ["bash", "sh", "powershell"]:
return lang
if lang in ["shell"]:
return "sh"
if lang in ["ps1"]:
return "powershell"
raise NotImplementedError(f"{lang} not recognized in code execution")
def create_runner(code: str, timeout: int = 30) -> str:
"""
Create a Python script that runs the code and prints the output
"""
code = GenericEscape().escape(code)
# Create a runner script
runner = f"""
import os
import subprocess
my_env = os.environ.copy()
my_env["PYTHONIOENCODING"] = "utf-8"
process = subprocess.Popen(
f"python -i -q -u".split(),
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True,
bufsize=0,
universal_newlines=True,
env=my_env
)
process.stdin.write("{code}" + "\\n")
process.stdin.write("exit()\\n")
process.stdin.flush()
try:
process.wait({timeout})
except Exception as e:
process.terminate()
for line in iter(process.stdout.readline, ""):
print(line)
for line in iter(process.stderr.readline, ""):
if line.startswith(">>>"):
continue
print(line)
"""
return runner
def _run_cmd(cmd: [str], work_dir: str, timeout: int) -> str:
if WIN32:
logger.warning("SIGALRM is not supported on Windows. No timeout will be enforced.")
result = subprocess.run(
cmd,
cwd=work_dir,
capture_output=True,
text=True,
)
else:
with ThreadPoolExecutor(max_workers=1) as executor:
future = executor.submit(
subprocess.run,
cmd,
cwd=work_dir,
capture_output=True,
text=True,
)
result = future.result(timeout=timeout)
return result
def execute_code(
code: Optional[str] = None,
timeout: Optional[int] = None,
filename: Optional[str] = None,
work_dir: Optional[str] = None,
use_docker: Optional[Union[List[str], str, bool]] = None,
lang: Optional[str] = "python",
) -> Tuple[int, str]:
"""Execute code in a docker container.
This function is not tested on MacOS.
Args:
code (Optional, str): The code to execute.
If None, the code from the file specified by filename will be executed.
Either code or filename must be provided.
timeout (Optional, int): The maximum execution time in seconds.
If None, a default timeout will be used. The default timeout is 600 seconds. On Windows, the timeout is not enforced when use_docker=False.
filename (Optional, str): The file name to save the code or where the code is stored when `code` is None.
If None, a file with a randomly generated name will be created.
The randomly generated file will be deleted after execution.
The file name must be a relative path. Relative paths are relative to the working directory.
work_dir (Optional, str): The working directory for the code execution.
If None, a default working directory will be used.
The default working directory is the "extensions" directory under
"path_to_autogen".
use_docker (Optional, list, str or bool): The docker image to use for code execution.
If a list or a str of image name(s) is provided, the code will be executed in a docker container
with the first image successfully pulled.
If None, False or empty, the code will be executed in the current environment.
Default is None, which will be converted into an empty list when docker package is available.
Expected behaviour:
- If `use_docker` is explicitly set to True and the docker package is available, the code will run in a Docker container.
- If `use_docker` is explicitly set to True but the Docker package is missing, an error will be raised.
- If `use_docker` is not set (i.e., left default to None) and the Docker package is not available, a warning will be displayed, but the code will run natively.
If the code is executed in the current environment,
the code must be trusted.
lang (Optional, str): The language of the code. Default is "python".
Returns:
int: 0 if the code executes successfully.
str: The error message if the code fails to execute; the stdout otherwise.
"""
if all((code is None, filename is None)):
error_msg = f"Either {code=} or {filename=} must be provided."
logger.error(error_msg)
raise AssertionError(error_msg)
# Warn if use_docker was unspecified (or None), and cannot be provided (the default).
# In this case the current behavior is to fall back to run natively, but this behavior
# is subject to change.
if use_docker is None:
if docker is None:
use_docker = False
logger.warning(
"execute_code was called without specifying a value for use_docker. Since the python docker package is not available, code will be run natively. Note: this fallback behavior is subject to change"
)
else:
# Default to true
use_docker = True
timeout = timeout or DEFAULT_TIMEOUT
original_filename = filename
if WIN32 and lang in ["sh", "shell"] and (not use_docker):
lang = "ps1"
if filename is None:
code_hash = md5(code.encode()).hexdigest()
# create a file with a automatically generated name
filename = f"tmp_code_{code_hash}.{'py' if lang.startswith('python') else lang}"
if work_dir is None:
WORKING_DIR = os.path.join(AIStorage.get_instance().get_myai_dir(), "tmp_code")
pathlib.Path(WORKING_DIR).mkdir(exist_ok=True)
work_dir = os.path.join(WORKING_DIR, code_hash)
pathlib.Path(work_dir).mkdir(exist_ok=True)
filepath = os.path.join(work_dir, filename)
file_dir = os.path.dirname(filepath)
os.makedirs(file_dir, exist_ok=True)
if code is not None:
write_requirements(code, os.path.join(file_dir, "requirements.txt"))
code = create_runner(code, 30)
with open(filepath, "w", encoding="utf-8") as fout:
fout.write(code)
# check if already running in a docker container
in_docker_container = os.path.exists("/.dockerenv")
if not use_docker or in_docker_container:
try:
env_cmd = ["python", "-m", "venv", os.path.join(file_dir, "venv")]
_run_cmd(env_cmd, file_dir, timeout)
if WIN32:
venv_path = os.path.join(file_dir, "venv", "Scripts")
else:
venv_path = os.path.join(file_dir, "venv", "bin")
pip_cmd = [os.path.join(venv_path, "python"), "-m", "pip", "install", "-r", "requirements.txt"]
_run_cmd(pip_cmd, file_dir, timeout)
# already running in a docker container
cmd = [
os.path.join(venv_path, "python"),
f".\\{filename}" if WIN32 else filename,
]
result = _run_cmd(cmd, file_dir, timeout)
except TimeoutError:
if original_filename is None:
shutil.rmtree(os.path.join(file_dir, "venv"))
os.remove(filepath)
os.remove(os.path.join(file_dir, "requirements.txt"))
try:
os.removedirs(file_dir)
except Exception:
pass
return 1, TIMEOUT_MSG
if original_filename is None:
shutil.rmtree(os.path.join(file_dir, "venv"))
os.remove(filepath)
os.remove(os.path.join(file_dir, "requirements.txt"))
try:
os.removedirs(file_dir)
except Exception:
pass
if result.returncode:
logs = result.stderr
if original_filename is None:
abs_path = str(pathlib.Path(filepath).absolute())
logs = logs.replace(str(abs_path), "").replace(filename, "")
else:
abs_path = str(pathlib.Path(work_dir).absolute()) + PATH_SEPARATOR
logs = logs.replace(str(abs_path), "")
else:
logs = result.stdout
return result.returncode, logs
# create a docker client
client = docker.from_env()
image_list = (
["python:3-alpine", "python:3", "python:3-windowsservercore"]
if use_docker is True
else [use_docker]
if isinstance(use_docker, str)
else use_docker
)
for image in image_list:
# check if the image exists
try:
client.images.get(image)
break
except docker.errors.ImageNotFound:
# pull the image
logger.info("Pulling image", image)
try:
client.images.pull(image, stream=True, decode=True)
break
except docker.errors.DockerException as e:
logger.error("Failed to pull image", image)
logger.exception(e)
# get a randomized str based on current time to wrap the exit code
exit_code_str = f"exitcode{time.time()}"
start_str = f'start{time.time()}'
abs_path = pathlib.Path(work_dir).absolute()
cmd = [
"sh",
"-c",
f"pip install --quiet -r requirements.txt; echo -n {start_str}; {_cmd(lang)} {filename}; exit_code=$?; echo -n {exit_code_str}; echo -n $exit_code; echo {exit_code_str};",
]
# create a docker container
container = client.containers.run(
image,
command=cmd,
working_dir="/workspace",
detach=True,
# get absolute path to the working directory
volumes={abs_path: {"bind": "/workspace", "mode": "rw"}},
)
start_time = time.time()
while container.status != "exited" and time.time() - start_time < timeout:
# Reload the container object
container.reload()
if container.status != "exited":
container.stop()
container.remove()
if original_filename is None:
os.remove(filepath)
return 1, TIMEOUT_MSG, image
# get the container logs
logs: str = container.logs().decode("utf-8").rstrip()
start_pos = logs.find(start_str)
if start_pos != -1:
logs = logs[start_pos + len(start_str):]
# # commit the image
# tag = filename.replace("/", "")
# container.commit(repository="python", tag=tag)
# remove the container
container.remove()
# check if the code executed successfully
exit_code = container.attrs["State"]["ExitCode"]
if exit_code == 0:
# extract the exit code from the logs
pattern = re.compile(f"{exit_code_str}(\\d+){exit_code_str}")
match = pattern.search(logs)
exit_code = 1 if match is None else int(match.group(1))
# remove the exit code from the logs
logs = logs if match is None else pattern.sub("", logs)
if original_filename is None:
os.remove(filepath)
os.remove(os.path.join(file_dir, "requirements.txt"))
os.removedirs(file_dir)
if exit_code:
logs = logs.replace(f"/workspace/{filename if original_filename is None else ''}", "")
# return the exit code, logs and image
return exit_code, logs
src/aios_kernel/code_interpreter_function.py
-41
View File
@@ -1,41 +0,0 @@
from typing import Dict
from aios_kernel.ai_function import AIFunction
from aios_kernel.code_interpreter import execute_code
class CodeInterpreterFunction(AIFunction):
def __init__(self):
self.func_id = "code_interpreter"
self.description = "execute python code"
def get_name(self) -> str:
return self.func_id
def get_description(self) -> str:
return self.description
def get_parameters(self) -> Dict:
return {
"type": "object",
"properties": {
"code": {"type": "string", "description": "python code"}
}
}
async def execute(self, **kwargs) -> str:
code = kwargs.get("code")
ret_code, result = execute_code(code=code)
if ret_code == 0:
return result.strip()
else:
return result.strip()
def is_local(self) -> bool:
return True
def is_in_zone(self) -> bool:
return True
def is_ready_only(self) -> bool:
return False
src/aios_kernel/duckduckgo_text_search_function.py
-52
View File
@@ -1,52 +0,0 @@
import json
from typing import Dict
from aios_kernel.ai_function import AIFunction
from duckduckgo_search import AsyncDDGS
class DuckDuckGoTextSearchFunction(AIFunction):
def __init__(self):
self.name = "duckduckgo_text_search"
self.description = "Search text from duckduckgo.com"
self.region = "wt-wt"
self.safesearch = "moderate"
self.time = "y"
self.max_results = 5
def get_name(self) -> str:
return self.name
def get_description(self) -> str:
return self.description
def get_parameters(self) -> Dict:
return {"type": "object",
"properties": {
"query": {"type": "string", "description": "The query to search for."}
}
}
async def execute(self, **kwargs) -> str:
query = kwargs.get("query")
async with AsyncDDGS() as ddgs:
results = [r async for r in ddgs.text(
query,
region=self.region,
safesearch=self.safesearch,
timelimit=self.time,
backend="api",
max_results=self.max_results
)]
return json.dumps(results)
def is_local(self) -> bool:
return True
def is_in_zone(self) -> bool:
return True
def is_ready_only(self) -> bool:
return False
src/aios_kernel/sql_database.py
-493
View File
@@ -1,493 +0,0 @@
"""
Taken from: langchain
SQLAlchemy wrapper around a database.
"""
from __future__ import annotations
import os
import warnings
from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence, Union
import sqlalchemy
from sqlalchemy import MetaData, Table, create_engine, inspect, select, text
from sqlalchemy.engine import Engine
from sqlalchemy.exc import ProgrammingError, SQLAlchemyError
from sqlalchemy.schema import CreateTable
from sqlalchemy.types import NullType
def get_from_env(key: str, env_key: str, default: Optional[str] = None) -> str:
"""Get a value from a dictionary or an environment variable."""
if env_key in os.environ and os.environ[env_key]:
return os.environ[env_key]
elif default is not None:
return default
else:
raise ValueError(
f"Did not find {key}, please add an environment variable"
f" `{env_key}` which contains it, or pass"
f" `{key}` as a named parameter."
)
def _format_index(index: sqlalchemy.engine.interfaces.ReflectedIndex) -> str:
return (
f'Name: {index["name"]}, Unique: {index["unique"]},'
f' Columns: {str(index["column_names"])}'
)
def truncate_word(content: Any, *, length: int, suffix: str = "...") -> str:
"""
Truncate a string to a certain number of words, based on the max string
length.
"""
if not isinstance(content, str) or length <= 0:
return content
if len(content) <= length:
return content
return content[: length - len(suffix)].rsplit(" ", 1)[0] + suffix
class SQLDatabase:
"""SQLAlchemy wrapper around a database."""
def __init__(
self,
engine: Engine,
schema: Optional[str] = None,
metadata: Optional[MetaData] = None,
ignore_tables: Optional[List[str]] = None,
include_tables: Optional[List[str]] = None,
sample_rows_in_table_info: int = 3,
indexes_in_table_info: bool = False,
custom_table_info: Optional[dict] = None,
view_support: bool = False,
max_string_length: int = 300,
):
"""Create engine from database URI."""
self._engine = engine
self._schema = schema
if include_tables and ignore_tables:
raise ValueError("Cannot specify both include_tables and ignore_tables")
self._inspector = inspect(self._engine)
# including view support by adding the views as well as tables to the all
# tables list if view_support is True
self._all_tables = set(
self._inspector.get_table_names(schema=schema)
+ (self._inspector.get_view_names(schema=schema) if view_support else [])
)
self._include_tables = set(include_tables) if include_tables else set()
if self._include_tables:
missing_tables = self._include_tables - self._all_tables
if missing_tables:
raise ValueError(
f"include_tables {missing_tables} not found in database"
)
self._ignore_tables = set(ignore_tables) if ignore_tables else set()
if self._ignore_tables:
missing_tables = self._ignore_tables - self._all_tables
if missing_tables:
raise ValueError(
f"ignore_tables {missing_tables} not found in database"
)
usable_tables = self.get_usable_table_names()
self._usable_tables = set(usable_tables) if usable_tables else self._all_tables
if not isinstance(sample_rows_in_table_info, int):
raise TypeError("sample_rows_in_table_info must be an integer")
self._sample_rows_in_table_info = sample_rows_in_table_info
self._indexes_in_table_info = indexes_in_table_info
self._custom_table_info = custom_table_info
if self._custom_table_info:
if not isinstance(self._custom_table_info, dict):
raise TypeError(
"table_info must be a dictionary with table names as keys and the "
"desired table info as values"
)
# only keep the tables that are also present in the database
intersection = set(self._custom_table_info).intersection(self._all_tables)
self._custom_table_info = dict(
(table, self._custom_table_info[table])
for table in self._custom_table_info
if table in intersection
)
self._max_string_length = max_string_length
self._metadata = metadata or MetaData()
# including view support if view_support = true
self._metadata.reflect(
views=view_support,
bind=self._engine,
only=list(self._usable_tables),
schema=self._schema,
)
@classmethod
def from_uri(
cls, database_uri: str, engine_args: Optional[dict] = None, **kwargs: Any
) -> SQLDatabase:
"""Construct a SQLAlchemy engine from URI."""
_engine_args = engine_args or {}
return cls(create_engine(database_uri, **_engine_args), **kwargs)
@classmethod
def from_databricks(
cls,
catalog: str,
schema: str,
host: Optional[str] = None,
api_token: Optional[str] = None,
warehouse_id: Optional[str] = None,
cluster_id: Optional[str] = None,
engine_args: Optional[dict] = None,
**kwargs: Any,
) -> SQLDatabase:
"""
Class method to create an SQLDatabase instance from a Databricks connection.
This method requires the 'databricks-sql-connector' package. If not installed,
it can be added using `pip install databricks-sql-connector`.
Args:
catalog (str): The catalog name in the Databricks database.
schema (str): The schema name in the catalog.
host (Optional[str]): The Databricks workspace hostname, excluding
'https://' part. If not provided, it attempts to fetch from the
environment variable 'DATABRICKS_HOST'. If still unavailable and if
running in a Databricks notebook, it defaults to the current workspace
hostname. Defaults to None.
api_token (Optional[str]): The Databricks personal access token for
accessing the Databricks SQL warehouse or the cluster. If not provided,
it attempts to fetch from 'DATABRICKS_TOKEN'. If still unavailable
and running in a Databricks notebook, a temporary token for the current
user is generated. Defaults to None.
warehouse_id (Optional[str]): The warehouse ID in the Databricks SQL. If
provided, the method configures the connection to use this warehouse.
Cannot be used with 'cluster_id'. Defaults to None.
cluster_id (Optional[str]): The cluster ID in the Databricks Runtime. If
provided, the method configures the connection to use this cluster.
Cannot be used with 'warehouse_id'. If running in a Databricks notebook
and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the
cluster the notebook is attached to. Defaults to None.
engine_args (Optional[dict]): The arguments to be used when connecting
Databricks. Defaults to None.
**kwargs (Any): Additional keyword arguments for the `from_uri` method.
Returns:
SQLDatabase: An instance of SQLDatabase configured with the provided
Databricks connection details.
Raises:
ValueError: If 'databricks-sql-connector' is not found, or if both
'warehouse_id' and 'cluster_id' are provided, or if neither
'warehouse_id' nor 'cluster_id' are provided and it's not executing
inside a Databricks notebook.
"""
try:
from databricks import sql # noqa: F401
except ImportError:
raise ValueError(
"databricks-sql-connector package not found, please install with"
" `pip install databricks-sql-connector`"
)
context = None
try:
from dbruntime.databricks_repl_context import get_context
context = get_context()
except ImportError:
pass
default_host = context.browserHostName if context else None
if host is None:
host = get_from_env("host", "DATABRICKS_HOST", default_host)
default_api_token = context.apiToken if context else None
if api_token is None:
api_token = get_from_env("api_token", "DATABRICKS_TOKEN", default_api_token)
if warehouse_id is None and cluster_id is None:
if context:
cluster_id = context.clusterId
else:
raise ValueError(
"Need to provide either 'warehouse_id' or 'cluster_id'."
)
if warehouse_id and cluster_id:
raise ValueError("Can't have both 'warehouse_id' or 'cluster_id'.")
if warehouse_id:
http_path = f"/sql/1.0/warehouses/{warehouse_id}"
else:
http_path = f"/sql/protocolv1/o/0/{cluster_id}"
uri = (
f"databricks://token:{api_token}@{host}?"
f"http_path={http_path}&catalog={catalog}&schema={schema}"
)
return cls.from_uri(database_uri=uri, engine_args=engine_args, **kwargs)
@classmethod
def from_cnosdb(
cls,
url: str = "127.0.0.1:8902",
user: str = "root",
password: str = "",
tenant: str = "cnosdb",
database: str = "public",
) -> SQLDatabase:
"""
Class method to create an SQLDatabase instance from a CnosDB connection.
This method requires the 'cnos-connector' package. If not installed, it
can be added using `pip install cnos-connector`.
Args:
url (str): The HTTP connection host name and port number of the CnosDB
service, excluding "http://" or "https://", with a default value
of "127.0.0.1:8902".
user (str): The username used to connect to the CnosDB service, with a
default value of "root".
password (str): The password of the user connecting to the CnosDB service,
with a default value of "".
tenant (str): The name of the tenant used to connect to the CnosDB service,
with a default value of "cnosdb".
database (str): The name of the database in the CnosDB tenant.
Returns:
SQLDatabase: An instance of SQLDatabase configured with the provided
CnosDB connection details.
"""
try:
from cnosdb_connector import make_cnosdb_langchain_uri
uri = make_cnosdb_langchain_uri(url, user, password, tenant, database)
return cls.from_uri(database_uri=uri)
except ImportError:
raise ValueError(
"cnos-connector package not found, please install with"
" `pip install cnos-connector`"
)
@property
def dialect(self) -> str:
"""Return string representation of dialect to use."""
return self._engine.dialect.name
def get_usable_table_names(self) -> Iterable[str]:
"""Get names of tables available."""
if self._include_tables:
return sorted(self._include_tables)
return sorted(self._all_tables - self._ignore_tables)
def get_table_names(self) -> Iterable[str]:
"""Get names of tables available."""
warnings.warn(
"This method is deprecated - please use `get_usable_table_names`."
)
return self.get_usable_table_names()
@property
def table_info(self) -> str:
"""Information about all tables in the database."""
return self.get_table_info()
def get_table_info(self, table_names: Optional[List[str]] = None) -> str:
"""Get information about specified tables.
Follows best practices as specified in: Rajkumar et al, 2022
(https://arxiv.org/abs/2204.00498)
If `sample_rows_in_table_info`, the specified number of sample rows will be
appended to each table description. This can increase performance as
demonstrated in the paper.
"""
all_table_names = self.get_usable_table_names()
if table_names is not None:
missing_tables = set(table_names).difference(all_table_names)
if missing_tables:
raise ValueError(f"table_names {missing_tables} not found in database")
all_table_names = table_names
meta_tables = [
tbl
for tbl in self._metadata.sorted_tables
if tbl.name in set(all_table_names)
and not (self.dialect == "sqlite" and tbl.name.startswith("sqlite_"))
]
tables = []
for table in meta_tables:
if self._custom_table_info and table.name in self._custom_table_info:
tables.append(self._custom_table_info[table.name])
continue
# Ignore JSON datatyped columns
for k, v in table.columns.items():
if type(v.type) is NullType:
table._columns.remove(v)
# add create table command
create_table = str(CreateTable(table).compile(self._engine))
table_info = f"{create_table.rstrip()}"
has_extra_info = (
self._indexes_in_table_info or self._sample_rows_in_table_info
)
if has_extra_info:
table_info += "\n\n/*"
if self._indexes_in_table_info:
table_info += f"\n{self._get_table_indexes(table)}\n"
if self._sample_rows_in_table_info:
table_info += f"\n{self._get_sample_rows(table)}\n"
if has_extra_info:
table_info += "*/"
tables.append(table_info)
tables.sort()
final_str = "\n\n".join(tables)
return final_str
def _get_table_indexes(self, table: Table) -> str:
indexes = self._inspector.get_indexes(table.name)
indexes_formatted = "\n".join(map(_format_index, indexes))
return f"Table Indexes:\n{indexes_formatted}"
def _get_sample_rows(self, table: Table) -> str:
# build the select command
command = select(table).limit(self._sample_rows_in_table_info)
# save the columns in string format
columns_str = "\t".join([col.name for col in table.columns])
try:
# get the sample rows
with self._engine.connect() as connection:
sample_rows_result = connection.execute(command) # type: ignore
# shorten values in the sample rows
sample_rows = list(
map(lambda ls: [str(i)[:100] for i in ls], sample_rows_result)
)
# save the sample rows in string format
sample_rows_str = "\n".join(["\t".join(row) for row in sample_rows])
# in some dialects when there are no rows in the table a
# 'ProgrammingError' is returned
except ProgrammingError:
sample_rows_str = ""
return (
f"{self._sample_rows_in_table_info} rows from {table.name} table:\n"
f"{columns_str}\n"
f"{sample_rows_str}"
)
def _execute(
self,
command: str,
fetch: Union[Literal["all"], Literal["one"]] = "all",
) -> Sequence[Dict[str, Any]]:
"""
Executes SQL command through underlying engine.
If the statement returns no rows, an empty list is returned.
"""
with self._engine.begin() as connection:
if self._schema is not None:
if self.dialect == "snowflake":
connection.exec_driver_sql(
"ALTER SESSION SET search_path = %s", (self._schema,)
)
elif self.dialect == "bigquery":
connection.exec_driver_sql("SET @@dataset_id=?", (self._schema,))
elif self.dialect == "mssql":
pass
elif self.dialect == "trino":
connection.exec_driver_sql("USE ?", (self._schema,))
elif self.dialect == "duckdb":
# Unclear which parameterized argument syntax duckdb supports.
# The docs for the duckdb client say they support multiple,
# but `duckdb_engine` seemed to struggle with all of them:
# https://github.com/Mause/duckdb_engine/issues/796
connection.exec_driver_sql(f"SET search_path TO {self._schema}")
elif self.dialect == "oracle":
connection.exec_driver_sql(
f"ALTER SESSION SET CURRENT_SCHEMA = {self._schema}"
)
else: # postgresql and other compatible dialects
connection.exec_driver_sql("SET search_path TO %s", (self._schema,))
cursor = connection.execute(text(command))
if cursor.returns_rows:
if fetch == "all":
result = [x._asdict() for x in cursor.fetchall()]
elif fetch == "one":
first_result = cursor.fetchone()
result = [] if first_result is None else [first_result._asdict()]
else:
raise ValueError("Fetch parameter must be either 'one' or 'all'")
return result
return []
def run(
self,
command: str,
fetch: Union[Literal["all"], Literal["one"]] = "all",
) -> str:
"""Execute a SQL command and return a string representing the results.
If the statement returns rows, a string of the results is returned.
If the statement returns no rows, an empty string is returned.
"""
result = self._execute(command, fetch)
# Convert columns values to string to avoid issues with sqlalchemy
# truncating text
res = [
tuple(truncate_word(c, length=self._max_string_length) for c in r.values())
for r in result
]
if not res:
return ""
else:
return str(res)
def get_table_info_no_throw(self, table_names: Optional[List[str]] = None) -> str:
"""Get information about specified tables.
Follows best practices as specified in: Rajkumar et al, 2022
(https://arxiv.org/abs/2204.00498)
If `sample_rows_in_table_info`, the specified number of sample rows will be
appended to each table description. This can increase performance as
demonstrated in the paper.
"""
try:
return self.get_table_info(table_names)
except ValueError as e:
"""Format the error message"""
return f"Error: {e}"
def run_no_throw(
self,
command: str,
fetch: Union[Literal["all"], Literal["one"]] = "all",
) -> str:
"""Execute a SQL command and return a string representing the results.
If the statement returns rows, a string of the results is returned.
If the statement returns no rows, an empty string is returned.
If the statement throws an error, the error message is returned.
"""
try:
return self.run(command, fetch)
except SQLAlchemyError as e:
"""Format the error message"""
return f"Error: {e}"
src/aios_kernel/sql_database_function.py
-112
View File
@@ -1,112 +0,0 @@
from datetime import timedelta, datetime
from typing import Dict
from cachetools import TLRUCache, cached
from aios_kernel.ai_function import AIFunction
from aios_kernel.sql_database import SQLDatabase, get_from_env
def _my_ttu(_key, _value, now):
return now + timedelta(seconds=600)
database_cache = TLRUCache(ttu=_my_ttu, maxsize=10000, timer=datetime.now)
@cached(cache=database_cache)
def get_database(uri: str) -> SQLDatabase:
return SQLDatabase.from_uri(uri)
class GetTableInfosFunction(AIFunction):
def __init__(self):
super().__init__()
self.name = "get_table_infos"
self.description = "Get table informations in the database"
def get_name(self) -> str:
return self.name
def get_description(self) -> str:
return self.description
def get_parameters(self) -> Dict:
return {
"type": "object",
"properties": {
"database_url": {"type": "string", "description": "Database URL,Can be set to None"},
}
}
async def execute(self, **kwargs) -> str:
database_url: str = kwargs.get("database_url")
if (database_url is None
or database_url.strip() == ""
or database_url.strip().lower() == "none"
or database_url.strip().lower() == "null"):
database_url = get_from_env(key="database url", env_key="DATABASE_URL")
if database_url is None:
return "error: database_url is None"
database = get_database(database_url)
tables = database.get_usable_table_names()
table_infos = database.get_table_info(tables)
return table_infos
def is_local(self) -> bool:
return True
def is_in_zone(self) -> bool:
return True
def is_ready_only(self) -> bool:
return False
class ExecuteSqlFunction(AIFunction):
def __init__(self):
super().__init__()
self.name = "execute_sql"
self.description = """
Input to this function is a detailed and correct SQL query, output is a result from the database.
If the query is not correct, an error message will be returned.
If an error is returned, rewrite the query, check the query, and try again.
"""
def get_name(self) -> str:
return self.name
def get_description(self) -> str:
return self.description
def get_parameters(self) -> Dict:
return {
"type": "object",
"properties": {
"database_url": {"type": "string", "description": "Database URL,Can be set to None"},
"sql": {"type": "string", "description": "SQL to execute"}
}
}
async def execute(self, **kwargs) -> str:
database_url = kwargs.get("database_url")
if (database_url is None
or database_url.strip() == ""
or database_url.strip().lower() == "none"
or database_url.strip().lower() == "null"):
database_url = get_from_env(key="database url", env_key="DATABASE_URL")
if database_url is None:
return "error: database_url is None"
sql = kwargs.get("sql")
database = get_database(database_url)
return database.run_no_throw(sql)
def is_local(self) -> bool:
return True
def is_in_zone(self) -> bool:
return True
def is_ready_only(self) -> bool:
return False
src/component/common_environment/shell.py
+1 -1
View File
@@ -1,6 +1,6 @@
import os import os
from typing import Any,List,Dict from typing import Any,List,Dict
from aios import AgentMsg,AgentTodo,AgentPrompt from aios import AgentMsg,AgentTodo,LLMPrompt
from aios import SimpleAIFunction, SimpleAIOperation from aios import SimpleAIFunction, SimpleAIOperation
from aios import SimpleEnvironment from aios import SimpleEnvironment
src/component/mail_environment/issue.py
+2 -2
View File
@@ -218,7 +218,7 @@ class IssueAgent(CustomAIAgent):
super().__init__(agent_id, llm_model_name, max_token_size) super().__init__(agent_id, llm_model_name, max_token_size)
class IssueParserEnvironment(Environment): class IssueParserEnvironment(SimpleEnvironment):
def __init__(self, env_id: str, storage: IssueStorage) -> None: def __init__(self, env_id: str, storage: IssueStorage) -> None:
super().__init__(env_id) super().__init__(env_id)
self.storage = storage self.storage = storage
@@ -305,7 +305,7 @@ class IssueParser:
mail_desc = Mail.prompt_desc() mail_desc = Mail.prompt_desc()
issue_desc = Issue.prompt_desc() issue_desc = Issue.prompt_desc()
prompt = AgentPrompt() prompt = LLMPrompt()
prompt.system_message = {"role": "system", "content": f''' prompt.system_message = {"role": "system", "content": f'''
I'm a CEO of a company named 巴克云; You'ar my assistant, and you should help me to manage my issues. Issues is a concept in software development of this company, but I use it to manage my work. I'm a CEO of a company named 巴克云; You'ar my assistant, and you should help me to manage my issues. Issues is a concept in software development of this company, but I use it to manage my work.
I'll give you a mail in json format, {mail_desc}; I'll give you a mail in json format, {mail_desc};
src/component/openai_node/open_ai_node.py
+4 -1
View File
@@ -1,3 +1,4 @@
import asyncio
import openai import openai
from openai import AsyncOpenAI from openai import AsyncOpenAI
import os import os
@@ -237,7 +238,8 @@ class OpenAI_ComputeNode(ComputeNode):
return result return result
logger.info(f"openai response: {resp}") logger.info(f"openai response: {resp}")
if mode_name == "gpt-4-vision-preview": #TODO: gpt-4v api is image_2_text ?
if mode_name == "gpt-4-vision-preview":
status_code = resp.choices[0].finish_reason status_code = resp.choices[0].finish_reason
if status_code is None: if status_code is None:
status_code = resp.choices[0].finish_details['type'] status_code = resp.choices[0].finish_details['type']
@@ -265,6 +267,7 @@ class OpenAI_ComputeNode(ComputeNode):
if token_usage: if token_usage:
result.result_refers["token_usage"] = token_usage result.result_refers["token_usage"] = token_usage
logger.info(f"openai success response: {result.result_str}") logger.info(f"openai success response: {result.result_str}")
return result return result
case _: case _:
src/component/tg_tunnel.py
+6 -2
View File
@@ -87,7 +87,7 @@ class TelegramTunnel(AgentTunnel):
async def _run_app(): async def _run_app():
try: try:
update_id = (await self.bot.get_updates())[0].update_id update_id = (await self.bot.get_updates())[0].update_id
except IndexError: except Exception as e:
update_id = None update_id = None
except Exception as e: except Exception as e:
logger.error(f"tg_tunnel error:{e}") logger.error(f"tg_tunnel error:{e}")
@@ -97,7 +97,11 @@ class TelegramTunnel(AgentTunnel):
#logger.info("listening for new messages...") #logger.info("listening for new messages...")
while True: while True:
try: try:
update_id = await self._do_process_raw_message(self.bot, update_id) if update_id:
update_id = await self._do_process_raw_message(self.bot, update_id)
else:
update_id = (await self.bot.get_updates())[0].update_id
except NetworkError: except NetworkError:
await asyncio.sleep(1) await asyncio.sleep(1)
except Forbidden: except Forbidden:
src/component/workflow_manager/workflow_manager.py
+1 -1
View File
@@ -2,7 +2,7 @@ import logging
import toml import toml
import os import os
from aios import AIStorage,PackageEnv,PackageEnvManager,PackageMediaInfo,PackageInstallTask from aios import AIStorage,PackageEnv,PackageEnvManager,PackageMediaInfo,PackageInstallTask,Workflow
from agent_manager import AgentManager from agent_manager import AgentManager
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
src/requirements.txt
+5 -3
View File
@@ -47,7 +47,7 @@ mpmath>=1.3.0
multidict>=6.0.4 multidict>=6.0.4
numpy>=1.25.2 numpy>=1.25.2
onnxruntime>=1.15.1 onnxruntime>=1.15.1
openai>=1.0.0
overrides>=7.4.0 overrides>=7.4.0
packaging>=23.1 packaging>=23.1
pandas>=2.1.0 pandas>=2.1.0
@@ -139,8 +139,9 @@ sentence-transformers==2.2.2
tiktoken tiktoken
markdown markdown
PyPDF2 PyPDF2
srt==3.5.3 srt
webvtt-py==0.4.6 webvtt-py
openai
docker docker
generic_escape generic_escape
duckduckgo-search duckduckgo-search
@@ -152,3 +153,4 @@ oracledb
html2text html2text
docx2txt docx2txt
opencv-python opencv-python
src/service/aios_shell/aios_shell.py
+4 -4
View File
@@ -40,7 +40,7 @@ from sd_node import *
from st_node import * from st_node import *
from agent_manager import AgentManager from agent_manager import AgentManager
# from workflow_manager import WorkflowManager from workflow_manager import WorkflowManager
from knowledge_manager import KnowledgePipelineManager from knowledge_manager import KnowledgePipelineManager
from tg_tunnel import TelegramTunnel from tg_tunnel import TelegramTunnel
from email_tunnel import EmailTunnel from email_tunnel import EmailTunnel
@@ -148,9 +148,9 @@ class AIOS_Shell:
if await AgentManager.get_instance().initial() is not True: if await AgentManager.get_instance().initial() is not True:
logger.error("agent manager initial failed!") logger.error("agent manager initial failed!")
return False return False
# if await WorkflowManager.get_instance().initial() is not True: if await WorkflowManager.get_instance().initial() is not True:
# logger.error("workflow manager initial failed!") logger.error("workflow manager initial failed!")
# return False return False
open_ai_node = OpenAI_ComputeNode.get_instance() open_ai_node = OpenAI_ComputeNode.get_instance()
if await open_ai_node.initial() is not True: if await open_ai_node.initial() is not True: