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define bas environment

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This commit is contained in:
tsukasa
2023-12-01 14:29:10 +08:00
parent 66e407add5
commit b7b968d5f7
18 changed files with 1700 additions and 1023 deletions
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rootfs/agents/JarvisPlus/agent.toml
+1 -1
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@@ -6,7 +6,7 @@ enable_timestamp = "true"
owner_prompt = "I am your master {name} , now is {now}"
contact_prompt = "I am your master's friend {name}"
[[do_prompt]]
[work.do]
role = "system"
content = """
My name is JarvisPlus, I am the master's super personal assistant. I think hard and try my best to complete TODOs.
rootfs/knowledge_pipelines/Mail/Spider/pipeline.toml
-4
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@@ -1,4 +0,0 @@
name = "Mail.Issue"
input.module = "input.py"
input.params.path = "${myai_dir}/data"
rootfs/knowledge_pipelines/Mail/Spider/input.py → rootfs/knowledge_pipelines/Mail/Sync/input.py
View File
rootfs/knowledge_pipelines/Mail/Sync/pipeline.toml
+14
View File
@@ -0,0 +1,14 @@
name = "Mail.Sync"
input.module = "input.py"
[input.params]
path = "${myai_dir}/mail"
imap_server = "imap.qq.com"
imap_port = 993
address = "115620204@qq.com"
password = "zbbjpbukeonqbjja"
[input.params.fields]
from = "from"
to = "to"
subject = "subject"
rootfs/knowledge_pipelines/Mia/parser.py
+1 -1
View File
@@ -96,7 +96,7 @@ class EmbeddingParser:
async def parse(self, object: ObjectID) -> str:
obj = self.env.get_knowledge_store().load_object(object)
await self.__do_embedding(obj)
return "insert into vector store"
return str(object)
def init(env: KnowledgePipelineEnvironment, params: dict) -> EmbeddingParser:
return EmbeddingParser(env, params)
rootfs/knowledge_pipelines/pipelines.toml
+1 -1
View File
@@ -1,3 +1,3 @@
pipelines = [
"Mail/Issue"
"Mail/Sync"
]
src/aios/agent/agent.py
+174 -154
View File
@@ -18,6 +18,7 @@ from ..proto.compute_task import ComputeTaskResult,ComputeTaskResultCode
from .agent_base import *
from .chatsession import *
from .ai_function import *
from ..environment.workspace_env import WorkspaceEnvironment, TodoListType
from ..frame.contact_manager import ContactManager,Contact,FamilyMember
from ..frame.compute_kernel import ComputeKernel
@@ -145,17 +146,22 @@ class AIAgent(BaseAIAgent):
self.contact_prompt_str = None
self.history_len = 10
self.review_todo_prompt = None
self.read_report_prompt = None
self.do_prompt = None
self.check_prompt = None
self.goal_to_todo_prompt = None
todo_prompts = {}
todo_prompts[TodoListType.TO_WORK] = {
"do": DEFAULT_AGENT_DO_PROMPT,
"check": DEFAULT_AGENT_SELF_CHECK_PROMPT,
"review": None,
}
todo_prompts[TodoListType.TO_LEARN] = {
"do": DEFAULT_AGENT_LEARN_PROMPT,
"check": None,
"review": None,
}
self.todo_prompts = todo_prompts
self.learn_token_limit = 4000
self.learn_prompt = AgentPrompt(DEFAULT_AGENT_LEARN_PROMPT)
self.chat_db = None
self.unread_msg = Queue() # msg from other agent
@@ -163,19 +169,18 @@ class AIAgent(BaseAIAgent):
self.owenr_bus = None
self.enable_function_list = None
@classmethod
def create_from_templete(cls,templete:AIAgentTemplete, fullname:str):
# Agent just inherit from templete on craete,if template changed,agent will not change
result_agent = AIAgent()
result_agent.llm_model_name = templete.llm_model_name
result_agent.max_token_size = templete.max_token_size
result_agent.template_id = templete.template_id
result_agent.agent_id = "agent#" + uuid.uuid4().hex
result_agent.fullname = fullname
result_agent.powerby = templete.author
result_agent.agent_prompt = templete.prompt
return result_agent
# @classmethod
# def create_from_templete(cls,templete:AIAgentTemplete, fullname:str):
# # Agent just inherit from templete on craete,if template changed,agent will not change
# result_agent = AIAgent()
# result_agent.llm_model_name = templete.llm_model_name
# result_agent.max_token_size = templete.max_token_size
# result_agent.template_id = templete.template_id
# result_agent.agent_id = "agent#" + uuid.uuid4().hex
# result_agent.fullname = fullname
# result_agent.powerby = templete.author
# result_agent.agent_prompt = templete.prompt
# return result_agent
def load_from_config(self,config:dict) -> bool:
if config.get("instance_id") is None:
@@ -200,10 +205,24 @@ class AIAgent(BaseAIAgent):
self.agent_think_prompt = AgentPrompt()
self.agent_think_prompt.load_from_config(config["think_prompt"])
if config.get("do_prompt") is not None:
self.do_prompt = AgentPrompt()
self.do_prompt.load_from_config(config["do_prompt"])
self.wake_up()
def load_todo_config(todo_type:str) -> bool:
todo_config = config.get(todo_type)
if todo_config is not None:
if todo_config.get("do") is not None:
prompt = AgentPrompt()
prompt.load_from_config(todo_config["do"])
self.todo_prompts[todo_type]["do"] = prompt
if todo_config.get("check") is not None:
prompt = AgentPrompt()
prompt.load_from_config(todo_config["check"])
self.todo_prompts[todo_type]["check"] = prompt
if todo_config.get("review_prompt") is not None:
prompt = AgentPrompt()
prompt.load_from_config(todo_config["review_prompt"])
self.todo_prompts[todo_type]["review"] = prompt
load_todo_config(TodoListType.TO_WORK)
load_todo_config(TodoListType.TO_LEARN)
if config.get("guest_prompt") is not None:
self.guest_prompt_str = config["guest_prompt"]
@@ -234,6 +253,9 @@ class AIAgent(BaseAIAgent):
self.enable_timestamp = bool(config["enable_timestamp"])
if config.get("history_len"):
self.history_len = int(config.get("history_len"))
self.wake_up()
return True
def get_id(self) -> str:
@@ -372,7 +394,7 @@ class AIAgent(BaseAIAgent):
# self._format_msg_by_env_value(prompt)
# inner_functions,function_token_len = self._get_inner_functions()
# system_prompt_len = prompt.get_prompt_token_len()
# system_prompt_len = self.token_len(prompt=prompt)
# input_len = len(msg.body)
# history_prmpt,history_token_len = await self._get_prompt_from_session_for_groupchat(chatsession,system_prompt_len + function_token_len,input_len)
@@ -411,10 +433,10 @@ class AIAgent(BaseAIAgent):
# resp_msg = msg.create_group_resp_msg(self.agent_id,final_result)
# chatsession.append(msg)
# chatsession.append(resp_msg)
# return resp_msg
# return None
def get_workspace_by_msg(self,msg:AgentMsg) -> WorkspaceEnvironment:
return self.agent_workspace
@@ -528,7 +550,7 @@ class AIAgent(BaseAIAgent):
have_known_info = True
known_info_str += f"## todo\n{todos_str}\n"
inner_functions,function_token_len = BaseAIAgent.get_inner_functions(self.owner_env)
system_prompt_len = prompt.get_prompt_token_len()
system_prompt_len = self.token_len(prompt=prompt)
input_len = len(msg.body)
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)
@@ -600,9 +622,7 @@ class AIAgent(BaseAIAgent):
return None
async def _get_history_prompt_for_think(self,chatsession:AIChatSession,summary:str,system_token_len:int,pos:int)->(AgentPrompt,int):
history_len = (self.max_token_size * 0.7) - system_token_len
messages = chatsession.read_history(self.history_len,pos,"natural") # read
@@ -716,9 +736,7 @@ class AIAgent(BaseAIAgent):
worksapce.set_work_summary(self.agent_id,task_result.result_str)
# 尝试完成自己的TOOD （不依赖任何其他Agnet）
async def do_my_work(self) -> None:
async def _llm_run_todo_list(self, todo_list_type: TodoListType):
workspace : WorkspaceEnvironment = self.get_workspace_by_msg(None)
logger.info(f"agent {self.agent_id} do my work start!")
@@ -726,35 +744,26 @@ class AIAgent(BaseAIAgent):
#if await self.need_review_todolist():
# await self._llm_review_todolist(workspace)
todo_list = await workspace.get_todo_list(self.agent_id)
todo_list = workspace.todo_list[todo_list_type]
need_todo = todo_list.get_todo_list(self.agent_id)
check_count = 0
do_count = 0
review_count = 0
for todo in todo_list:
for todo in need_todo:
if self.agent_energy <= 0:
break
if await self.need_review_todo(todo,workspace):
review_result = await self._llm_review_todo(todo,workspace)
todo.last_review_time = datetime.datetime.now().timestamp()
do_prompts = self._can_do_todo(todo_list_type, todo)
if do_prompts:
prompt : AgentPrompt = AgentPrompt()
prompt.append(self.agent_prompt)
prompt.append(workspace.get_role_prompt(self.agent_id))
prompt.append(do_prompts)
prompt.append(todo.to_prompt())
elif await self.can_check(todo,workspace):
check_result : AgentTodoResult = await self._llm_check_todo(todo,workspace)
todo.last_check_time = datetime.datetime.now().timestamp()
match check_result.result_code:
case AgentTodoResult.TODO_RESULT_CODE_LLM_ERROR:
continue
case AgentTodoResult.TODO_RESULT_CODE_OK:
await workspace.update_todo(todo.todo_id,AgentTodo.TODO_STATE_DONE)
case AgentTodoResult.TODO_RESULT_CODE_EXEC_OP_ERROR:
await workspace.update_todo(todo.todo_id,AgentTodo.TDDO_STATE_CHECKFAILED)
await workspace.append_worklog(todo,check_result)
self.agent_energy -= 1
check_count += 1
elif await self.can_do(todo,workspace):
do_result : AgentTodoResult = await self._llm_do(todo,workspace)
do_result : AgentTodoResult = await self._llm_do_todo(todo, prompt, workspace)
todo.last_do_time = datetime.datetime.now().timestamp()
todo.retry_count += 1
@@ -762,97 +771,126 @@ class AIAgent(BaseAIAgent):
case AgentTodoResult.TODO_RESULT_CODE_LLM_ERROR:
continue
case AgentTodoResult.TODO_RESULT_CODE_OK:
await workspace.update_todo(todo.todo_id,AgentTodo.TODO_STATE_WAITING_CHECK)
await todo_list.update_todo(todo.todo_id,AgentTodo.TODO_STATE_WAITING_CHECK)
case AgentTodoResult.TODO_RESULT_CODE_EXEC_OP_ERROR:
await workspace.update_todo(todo.todo_id,AgentTodo.TODO_STATE_EXEC_FAILED)
await todo_list.update_todo(todo.todo_id,AgentTodo.TODO_STATE_EXEC_FAILED)
await workspace.append_worklog(todo,do_result)
await todo_list.append_worklog(todo,do_result)
self.agent_energy -= 2
do_count += 1
logger.info(f"agent {self.agent_id} ,check:{check_count} todo,do:{do_count} todo.")
# review_result = await self._llm_review_todo(todo,workspace)
# todo.last_review_time = datetime.datetime.now().timestamp()
continue
def get_review_todo_prompt(self,todo:AgentTodo) -> AgentPrompt:
return self.review_todo_prompt
check_prompts = self._can_check_todo(todo_list_type, todo)
if check_prompts:
prompt : AgentPrompt = AgentPrompt()
prompt.append(self.agent_prompt)
prompt.append(workspace.get_role_prompt(self.agent_id))
prompt.append(check_prompts)
async def _llm_review_todo(self,todo:AgentTodo,workspace:WorkspaceEnvironment):
prompt = AgentPrompt()
if todo.last_check_result:
prompt.append(AgentPrompt(todo.last_check_result))
prompt.append(todo.detail)
prompt.append(todo.result)
check_result: AgentTodoResult = await self._llm_check_todo(todo, prompt, workspace)
todo.last_check_time = datetime.datetime.now().timestamp()
match check_result.result_code:
case AgentTodoResult.TODO_RESULT_CODE_LLM_ERROR:
continue
case AgentTodoResult.TODO_RESULT_CODE_OK:
await todo_list.update_todo(todo.todo_id,AgentTodo.TODO_STATE_DONE)
case AgentTodoResult.TODO_RESULT_CODE_EXEC_OP_ERROR:
await todo_list.update_todo(todo.todo_id,AgentTodo.TDDO_STATE_CHECKFAILED)
await todo_list.append_worklog(todo, check_result)
self.agent_energy -= 1
check_count += 1
continue
review_prompts = self._can_review_todo(todo_list_type, todo)
if review_prompts:
prompt.append(workspace.get_prompt())
prompt.append(workspace.get_role_prompt(self.agent_id))
prompt.append(self.get_review_todo_prompt(todo))
prompt.append(review_prompts)
todo_tree = workspace.get_todo_tree("/")
todo_tree = todo_list.get_todo_tree("/")
prompt.append(AgentPrompt(todo_tree))
inner_functions,_ = BaseAIAgent.get_inner_functions(self.owner_env)
task_result:ComputeTaskResult = await self.do_llm_complection(prompt,inner_functions=inner_functions)
if task_result.result_code != ComputeTaskResultCode.OK:
logger.error(f"_llm_review_todos compute error:{task_result.error_str}")
return
do_result : AgentTodoResult = await self._llm_review_todo(todo, prompt, workspace)
todo.last_review_time = datetime.datetime.now().timestamp()
return
match do_result.result_code:
case AgentTodoResult.TODO_RESULT_CODE_LLM_ERROR:
continue
case AgentTodoResult.TODO_RESULT_CODE_EXEC_OP_ERROR:
continue
case AgentTodoResult.TODO_RESULT_CODE_OK:
await todo_list.update_todo(todo.todo_id,AgentTodo.TODO_STATE_REVIEWED)
def get_do_prompt(self,todo:AgentTodo) -> AgentPrompt:
return self.do_prompt
await todo_list.append_worklog(todo,do_result)
self.agent_energy -= 1
review_count += 1
continue
def get_prompt_from_todo(self,todo:AgentTodo) -> AgentPrompt:
json_str = json.dumps(todo.raw_obj)
return AgentPrompt(json_str)
logger.info(f"agent {self.agent_id} ,check:{check_count} todo,do:{do_count} todo.")
async def need_review_todo(self,todo:AgentTodo,workspace:WorkspaceEnvironment) -> bool:
return False
async def can_check(self,todo:AgentTodo,workspace:WorkspaceEnvironment) -> bool:
if self.get_check_prompt(todo) is None:
return False
def _can_review_todo(self, todo_list_type: TodoListType, todo:AgentTodo) -> AgentPrompt:
do_prompts = self.todo_prompts[todo_list_type].get("review")
if not do_prompts:
return None
if todo.can_review() is False:
return None
return do_prompts
def _can_check_todo(self, todo_list_type: TodoListType, todo:AgentTodo) -> AgentPrompt:
do_prompts = self.todo_prompts[todo_list_type].get("check")
if not do_prompts:
return None
if todo.can_check() is False:
return False
return None
if todo.checker is not None:
if todo.checker != self.agent_id:
return False
return None
else:
if self.can_do_unassigned_task is False:
return False
return None
else:
todo.checker = self.agent_id
return True
return do_prompts
async def _can_do_todo(self, todo_list_type: TodoListType, todo:AgentTodo) -> AgentPrompt:
do_prompts = self.todo_prompts[todo_list_type].get("do")
if not do_prompts:
return None
async def can_do(self,todo:AgentTodo,workspace:WorkspaceEnvironment) -> bool:
if todo.can_do() is False:
return False
return None
if todo.worker is not None:
if todo.worker != self.agent_id:
return False
return None
else:
if self.can_do_unassigned_task is False:
return False
return None
else:
todo.worker = self.agent_id
return True
return do_prompts
async def _llm_do(self,todo:AgentTodo,workspace:WorkspaceEnvironment) -> AgentTodoResult:
async def _llm_do_todo(self, todo: AgentTodo, prompt: AgentPrompt, workspace: WorkspaceEnvironment) -> AgentTodoResult:
result = AgentTodoResult()
prompt : AgentPrompt = AgentPrompt()
#prompt.append(self.agent_prompt)
prompt.append(workspace.get_role_prompt(self.agent_id))
do_prompt = workspace.get_do_prompt(todo)
if do_prompt is None:
do_prompt = self.get_do_prompt(todo)
prompt.append(do_prompt)
# There are general methods for executing todos, as well as customized ones that are more efficient for specific types of TODOS.
# Based on experience, an Agent can autonomously master/organize execution methods for a greater variety of TODO types.
#prompt.append(work_log_prompt)
prompt.append(self.get_prompt_from_todo(todo))
task_result:ComputeTaskResult = await self.do_llm_complection(prompt)
if task_result.error_str is not None:
@@ -883,37 +921,30 @@ class AIAgent(BaseAIAgent):
return result
async def append_toddo_result(self,todo,worksapce,llm_result,result_str):
pass
def get_check_prompt(self,todo:AgentTodo) -> AgentPrompt:
return self.check_prompt
async def _llm_check_todo(self, todo:AgentTodo,workspace:WorkspaceEnvironment) :
if self.get_check_prompt(todo) is None:
return None
prompt : AgentPrompt = AgentPrompt()
prompt.append(self.agent_prompt)
prompt.append(workspace.get_role_prompt(self.agent_id))
prompt.append(self.get_check_prompt(todo))
if todo.last_check_result:
prompt.append(AgentPrompt(todo.last_check_result))
prompt.append(todo.detail)
prompt.append(todo.result)
async def _llm_check_todo(self, todo: AgentTodo, prompt: AgentPrompt, workspace: WorkspaceEnvironment) -> AgentTodoResult:
result = AgentTodoResult()
inner_functions,_ = BaseAIAgent.get_inner_functions(workspace)
task_result:ComputeTaskResult = await self.do_llm_complection(prompt,inner_functions=inner_functions,is_json_resp=True)
if task_result.result_code != ComputeTaskResultCode.OK:
logger.error(f"_llm_check_todo compute error:{task_result.error_str}")
return False
if task_result.result_str == "OK":
return True
if task_result.error_str is not None:
logger.error(f"_llm_do compute error:{task_result.error_str}")
result.result_code = AgentTodoResult.TODO_RESULT_CODE_LLM_ERROR
result.error_str = task_result.error_str
return result
result.result_str = task_result.result_str
todo.last_check_result = task_result.result_str
return False
return result
async def _llm_review_todo(self, todo:AgentTodo, prompt: AgentPrompt, workspace: WorkspaceEnvironment):
inner_functions,_ = BaseAIAgent.get_inner_functions(self.owner_env)
task_result:ComputeTaskResult = await self.do_llm_complection(prompt,inner_functions=inner_functions)
if task_result.result_code != ComputeTaskResultCode.OK:
logger.error(f"_llm_review_todos compute error:{task_result.error_str}")
return
return
# 尝试自我学习，会主动获取、读取资料并进行整理
# LLM的本质能力是处理海量知识，应该让LLM能基于知识把自己的工作处理的更好
@@ -1121,16 +1152,15 @@ class AIAgent(BaseAIAgent):
used_energy = await self.think_chatsession(session_id)
self.agent_energy -= used_energy
todo_logs = await self.get_todo_logs()
for todo_log in todo_logs:
if self.agent_energy <= 0:
break
used_energy = await self.think_todo_log(todo_log)
self.agent_energy -= used_energy
# todo_logs = await self.get_todo_logs()
# for todo_log in todo_logs:
# if self.agent_energy <= 0:
# break
# used_energy = await self.think_todo_log(todo_log)
# self.agent_energy -= used_energy
return
async def think_todo_log(self,todo_log:AgentWorkLog):
pass
@@ -1146,7 +1176,7 @@ class AIAgent(BaseAIAgent):
prompt:AgentPrompt = AgentPrompt()
#prompt.append(self._get_agent_prompt())
prompt.append(await self._get_agent_think_prompt())
system_prompt_len = prompt.get_prompt_token_len()
system_prompt_len = self.token_len(prompt=prompt)
#think env?
history_prompt,next_pos = await self._get_history_prompt_for_think(chatsession,summary,system_prompt_len,cur_pos)
prompt.append(history_prompt)
@@ -1220,10 +1250,6 @@ class AIAgent(BaseAIAgent):
def need_self_think(self) -> bool:
return False
def need_self_learn(self) -> bool:
if self.learn_prompt is not None:
return True
return False
def wake_up(self) -> None:
if self.agent_task is None:
@@ -1248,26 +1274,20 @@ class AIAgent(BaseAIAgent):
continue
# complete & check todo
if self.need_work():
await self.do_my_work()
await self._llm_run_todo_list(TodoListType.TO_WORK)
# review other's todo
# self.review_other_works()
await self._llm_run_todo_list(TodoListType.TO_LEARN)
if self.need_self_think():
await self.do_self_think()
if self.need_self_learn():
await self.do_self_learn()
# review other's todo
# self.review_other_works()
except Exception as e:
tb_str = traceback.format_exc()
logger.error(f"agent {self.agent_id} on timer error:{e},{tb_str}")
continue
def token_len(self,text:str) -> int:
return ComputeKernel.llm_num_tokens_from_text(text,self.get_llm_model_name())
src/aios/agent/agent_base.py
+31 -14
View File
@@ -56,16 +56,6 @@ class AgentPrompt:
self.messages.extend(prompt.messages)
def get_prompt_token_len(self):
result = 0
if self.system_message:
result += len(self.system_message.get("content"))
for msg in self.messages:
result += len(msg.get("content"))
return result
def load_from_config(self,config:list) -> bool:
if isinstance(config,list) is not True:
logger.error("prompt is not list!")
@@ -245,8 +235,9 @@ class AgentTodo:
TODO_STATE_EXEC_FAILED = "exec_failed"
TDDO_STATE_CHECKFAILED = "check_failed"
TODO_STATE_CASNCEL = "cancel"
TODO_STATE_CANCEL = "cancel"
TODO_STATE_DONE = "done"
TODO_STATE_REVIEWED = "reviewed"
TODO_STATE_EXPIRED = "expired"
def __init__(self):
@@ -342,6 +333,23 @@ class AgentTodo:
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
@@ -410,9 +418,18 @@ class BaseAIAgent(abc.ABC):
def get_max_token_size(self) -> int:
pass
@abstractmethod
async def _process_msg(self,msg:AgentMsg,workspace = None) -> AgentMsg:
pass
def token_len(self, text:str=None, prompt:AgentPrompt=None) -> int:
from .compute_kernel import ComputeKernel
if text:
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())
else:
return 0
@classmethod
def get_inner_functions(cls, env:Environment) -> (dict,int):
src/aios/agent/ai_function.py
+63 -4
View File
@@ -9,9 +9,6 @@ class ParameterDefine:
class AIFunction:
def __init__(self) -> None:
self.description : str = None
@abstractmethod
def get_name(self) -> str:
"""
@@ -24,7 +21,7 @@ class AIFunction:
"""
return a detailed description of what the function does
"""
return self.description
pass
@abstractmethod
def get_parameters(self) -> Dict:
@@ -112,6 +109,9 @@ class SimpleAIFunction(AIFunction):
def get_name(self) -> str:
return self.func_id
def get_description(self) -> str:
return self.description
def get_parameters(self) -> Dict:
if self.parameters is not None:
result = {}
@@ -142,3 +142,62 @@ class SimpleAIFunction(AIFunction):
def is_ready_only(self) -> bool:
return False
class AIOperation:
@abstractmethod
def get_name(self) -> str:
"""
return the name of the operation (should be snake case)
"""
pass
@abstractmethod
def get_description(self) -> str:
"""
return a detailed description of what the operation does
"""
pass
@abstractmethod
async def execute(self, params: dict) -> str:
"""
Execute the function and return a JSON serializable dict.
The parameters are passed in the form of kwargs
"""
pass
class SimpleAIOperation(AIOperation):
def __init__(self,op:str,description:str,func_handler:Coroutine) -> None:
self.op = op
self.description = description
self.func_handler = func_handler
def get_name(self) -> str:
return self.op
def get_description(self) -> str:
return self.description
async def execute(self, params: Dict) -> str:
if self.func_handler is None:
return "error: function not implemented"
return await self.func_handler(**params)
class AIFunctionOperation(AIOperation):
def __init__(self, func: AIFunction) -> None:
self.func = func
super().__init__()
@abstractmethod
def get_name(self) -> str:
return self.func.get_name()
@abstractmethod
def get_description(self) -> str:
return self.func.get_description()
@abstractmethod
async def execute(self, params: dict) -> str:
self.func.execute(**params)
src/aios/environment/environment.py
+89 -107
View File
@@ -4,145 +4,127 @@
from abc import ABC, abstractmethod
from typing import Any, Callable, Optional,Dict,Awaitable,List
import logging
from ..agent.ai_function import AIFunction, AIOperation
from ..agent.ai_function import AIFunction
logger = logging.getLogger(__name__)
class EnvironmentEvent(ABC):
class BaseEnvironment:
@abstractmethod
def display(self) -> str:
def get_id(self) -> str:
pass
EnvironmentEventHandler = Callable[[str,EnvironmentEvent],Awaitable[Any]]
# @abstractmethod
# #TODO: how to use env? different env has different prompt
# def get_env_prompt(self) -> str:
# pass
class Environment:
_all_env = {}
@classmethod
def get_env_by_id(cls,env_id:str):
return cls._all_env.get(env_id)
@abstractmethod
def get_ai_function(self,func_name:str) -> AIFunction:
pass
@classmethod
def set_env_by_id(cls,id,env):
assert id == env.get_id()
cls._all_env[env.get_id()] = env
@abstractmethod
def get_all_ai_functions(self) -> List[AIFunction]:
pass
@abstractmethod
def get_ai_operation(self,op_name:str) -> AIOperation:
pass
@abstractmethod
def get_all_ai_operations(self) -> List[AIOperation]:
pass
# _all_env = {}
# @classmethod
# def get_env_by_id(cls,env_id:str):
# return cls._all_env.get(env_id)
# @classmethod
# def set_env_by_id(cls,id,env):
# assert id == env.get_id()
# cls._all_env[env.get_id()] = env
class SimpleEnvironment(BaseEnvironment):
def __init__(self, env_id: str) -> None:
self.env_id = env_id
self.values:Dict[str,str] = {}
self.get_handlers:Dict[str,Callable] = {}
self.owner_env:Dict[str,Environment] = {}
# self.valid_keys:Dict[str,bool] = None
self.event_handlers:Dict[str,List[EnvironmentEventHandler]]= {}
self.functions: Dict[str,AIFunction] = {}
self.operations: Dict[str,AIOperation] = {}
def get_id(self) -> str:
return self.env_id
def add_owner_env(self,env) -> None:
self.owner_env[env.get_id()] = env
#@abstractmethod
#TODO: how to use env? different env has different prompt
def get_env_prompt(self) -> str:
pass
def add_ai_function(self,func:AIFunction) -> None:
if self.functions.get(func.get_name()) is not None:
logger.warn(f"add ai_function {func.get_name()} in env {self.env_id}:function already exist")
self.functions[func.get_name()] = func
def get_ai_function(self,func_name:str) -> AIFunction:
func = self.functions.get(func_name)
if func is not None:
return func
for owner_env in self.owner_env.values():
func = owner_env.get_ai_function(func_name)
if func is not None:
return func
return None
#def enable_ai_function(self,func_name:str) -> None:
# pass
#def disable_ai_function(self,func_name:str) -> None:
# pass
def get_all_ai_functions(self) -> List[AIFunction]:
func_list = []
func_list.extend(self.functions.values())
for owner_env in self.owner_env.values():
func_list.extend(owner_env.get_all_ai_functions())
return func_list
@abstractmethod
def _do_get_value(self,key:str) -> Optional[str]:
pass
def add_ai_operation(self,op:AIOperation) -> None:
self.operations[op.get_name()] = op
def register_get_handler(self,key:str,handler:Callable) -> None:
h = self.get_handlers.get(key)
if h is not None:
logger.warn(f"register get_handler {key} in env {self.env_id}:handler already exist")
self.get_handlers[key] = handler
def attach_event_handler(self,event_id:str,handler:Callable) -> None:
handler_list = self.event_handlers.get(event_id)
if handler_list is None:
handler_list = []
self.event_handlers[event_id] = handler_list
handler_list.append(handler)
def remove_event_handler(self,event_id:str,handler:Callable) -> None:
handler_list = self.event_handlers.get(event_id)
if handler is not None:
handler_list.remove(handler)
return
logger.warn(f"remove event_handler {event_id} in env {self.env_id}:handler not found")
async def fire_event(self,event_id:str,event:EnvironmentEvent) -> None:
handler_list = self.event_handlers.get(event_id)
if handler_list is not None:
for handler in handler_list:
await handler(self.env_id,event)
else:
logger.debug(f"fire event {event_id} in env {self.env_id}:handler not found")
return
def __getitem__(self, key):
return self.get_value(key)
def get_value(self,key:str) -> Optional[str]:
handler = self.get_handlers.get(key)
if handler is not None:
return handler()
s = self.values.get(key)
if isinstance(s,str):
return s
else:
logger.warn(f"get value {key} in env {self.env_id} failed!,type is not str")
s = self._do_get_value(key)
if s is not None:
return s
if self.owner_env is not None:
for env in self.owner_env.values():
s = env.get_value(key)
if s is not None:
return s
logger.warn(f"get value {key} in env {self.env_id} failed!,not found")
def get_ai_operation(self,op_name:str) -> AIOperation:
op = self.operations.get(op_name)
if op is not None:
return op
return None
def set_value(self, key: str, str_value: str,is_storage:bool = True):
logger.info(f"set value {key} in env {self.env_id} to {str_value}")
self.values[key] = str_value
def get_all_ai_operations(self) -> List[AIOperation]:
op_list = []
op_list.extend(self.operations.values())
return op_list
class CompositeEnvironment(BaseEnvironment):
def __init__(self, env_id: str) -> None:
self.env_id = env_id
self.envs:Dict[str,BaseEnvironment] = {}
self.functions: Dict[str,AIFunction] = {}
self.operations: Dict[str,AIOperation] = {}
def get_id(self) -> str:
return self.env_id
def add_env(self, env: BaseEnvironment) -> None:
self.envs[env.get_id()] = env
functions = env.get_all_ai_functions()
for func in functions:
self.functions[func.get_name()] = func
operations = env.get_all_ai_operations()
for op in operations:
self.operations[op.get_name()] = op
def get_ai_function(self,func_name:str) -> AIFunction:
func = self.functions.get(func_name)
if func is not None:
return func
return None
def get_all_ai_functions(self) -> List[AIFunction]:
func_list = []
func_list.extend(self.functions.values())
return func_list
def get_ai_operation(self,op_name:str) -> AIOperation:
op = self.operations.get(op_name)
if op is not None:
return op
return None
def get_all_ai_operations(self) -> List[AIOperation]:
op_list = []
op_list.extend(self.operations.values())
return op_list
src/aios/environment/workspace_env.py
+275 -630
View File
@@ -1,109 +1,252 @@
# this env is designed for workflow owner filesystem, support file/directory operations
import hashlib
import json
import subprocess
import logging
import tempfile
import threading
import traceback
import time
import ast
import sys
import os
import re
import asyncio
import aiofiles
from typing import Any,List
import os
import chardet
from markdown import Markdown
import PyPDF2
from ..proto.agent_msg import *
from ..agent.agent_base import AgentTodo,AgentPrompt,AgentTodoResult
from ..agent.ai_function import AIFunction,SimpleAIFunction
from ..agent.agent_base import AgentMsg,AgentTodo,AgentPrompt,AgentTodoResult
from ..agent.ai_function import AIFunction,SimpleAIFunction, SimpleAIOperation
from ..storage.storage import AIStorage,ResourceLocation
from .simple_kb_db import SimpleKnowledgeDB
from .environment import Environment,EnvironmentEvent
from .environment import SimpleEnvironment, CompositeEnvironment
logger = logging.getLogger(__name__)
class WorkspaceEnvironment(Environment):
def __init__(self, env_id: str) -> None:
super().__init__(env_id)
myai_path = AIStorage.get_instance().get_myai_dir()
self.root_path = f"{myai_path}/workspace/{env_id}"
class TodoListType:
TO_WORK = "work"
TO_LEARN = "learn"
class TodoListEnvironment(SimpleEnvironment):
def __init__(self, root_path, list_type) -> None:
super.__init__(list_type)
self.root_path = os.path.join(root_path, list_type)
if not os.path.exists(self.root_path):
os.makedirs(self.root_path+"/todos")
os.makedirs(self.root_path)
self.known_todo = {}
self.kb_db = SimpleKnowledgeDB(f"{self.root_path}/kb.db")
self.doc_dirs = {}
self._scan_thread = None
self._scan_dirthread = None
async def create_todo(params):
todoObj = AgentTodo.from_dict(params["todo"])
parent_id = params.get("parent")
return await self.create_todo(parent_id,todoObj)
self.add_ai_operation(SimpleAIOperation(
op="create_todo",
description="create todo",
func_handler=create_todo,
))
def set_root_path(self,path:str):
self.root_path = path
async def update_todo(params):
todo_id = params["id"]
new_stat = params["state"]
return await self.update_todo(todo_id,new_stat)
self.add_ai_operation(SimpleAIOperation(
op="update_todo",
description="update todo",
func_handler=update_todo,
))
def get_prompt(self) -> AgentMsg:
# Task/todo system , create,update,delete,query
async def get_todo_tree(self,path:str = None,deep:int = 4):
if path:
directory_path = os.path.join(self.root_path, path)
else:
directory_path = self.root_path
str_result:str = "/todos\n"
todo_count:int = 0
async def scan_dir(directory_path:str,deep:int):
nonlocal str_result
nonlocal todo_count
if deep <= 0:
return
if os.path.exists(directory_path) is False:
return
for entry in os.scandir(directory_path):
is_dir = entry.is_dir()
if not is_dir:
continue
if entry.name.startswith("."):
continue
todo_count = todo_count + 1
str_result = str_result + f"{' '*(4-deep)}{entry.name}\n"
await scan_dir(entry.path,deep-1)
await scan_dir(directory_path,deep)
return str_result,todo_count
async def get_todo_list(self,agent_id:str,path:str = None)->List[AgentTodo]:
logger.info("get_todo_list:%s,%s",agent_id,path)
if path:
directory_path = os.path.join(self.root_path, path)
else:
directory_path = self.root_path
result_list:List[AgentTodo] = []
async def scan_dir(directory_path:str,deep:int,parent:AgentTodo=None):
nonlocal result_list
if os.path.exists(directory_path) is False:
return
for entry in os.scandir(directory_path):
is_dir = entry.is_dir()
if not is_dir:
continue
if entry.name.startswith("."):
continue
todo = await self.get_todo_by_fullpath(entry.path)
if todo:
if todo.worker:
if todo.worker != agent_id:
continue
if parent:
parent.sub_todos[todo.todo_id] = todo
result_list.append(todo)
todo.rank = int(todo.create_time)>>deep
await scan_dir(entry.path,deep + 1,todo)
return
await scan_dir(directory_path,0)
#sort by rank
result_list.sort(key=lambda x:(x.rank,x.title))
logger.info("get_todo_list return,todolist.length() is %d",len(result_list))
return result_list
async def get_todo_by_fullpath(self,path:str) -> AgentTodo:
logger.info("get_todo_by_fullpath:%s",path)
detail_path = path + "/detail"
try:
async with aiofiles.open(detail_path, mode='r', encoding="utf-8") as f:
content = await f.read(4096)
logger.debug("get_todo_by_fullpath:%s,content:%s",path,content)
todo_dict = json.loads(content)
result_todo = AgentTodo.from_dict(todo_dict)
if result_todo:
relative_path = os.path.relpath(path, self.root_path)
if not relative_path.startswith('/'):
relative_path = '/' + relative_path
result_todo.todo_path = relative_path
self.known_todo[result_todo.todo_id] = result_todo
else:
logger.error("get_todo_by_path:%s,parse failed!",path)
return result_todo
except Exception as e:
logger.error("get_todo_by_path:%s,failed:%s",path,e)
return None
def get_role_prompt(self,role_id:str) -> AgentPrompt:
async def get_todo(self,id:str) -> AgentTodo:
return self.known_todo.get(id)
async def create_todo(self,parent_id:str,todo:AgentTodo) -> str:
try:
if parent_id:
if parent_id not in self.known_todo:
logger.error("create_todo failed: parent_id not found!")
return False
parent_path = self.known_todo.get(parent_id).todo_path
todo_path = f"{parent_path}/{todo.title}"
else:
todo_path = todo.title
dir_path = f"{self.root_path}/{todo_path}"
os.makedirs(dir_path)
detail_path = f"{dir_path}/detail"
if todo.todo_path is None:
todo.todo_path = todo_path
logger.info("create_todo %s",detail_path)
async with aiofiles.open(detail_path, mode='w', encoding="utf-8") as f:
await f.write(json.dumps(todo.to_dict()))
self.known_todo[todo.todo_id] = todo
except Exception as e:
logger.error("create_todo failed:%s",e)
return str(e)
return None
def get_knowledge_base(self,root_dir=None,indent=0) -> str:
pass
def get_do_prompt(self,todo:AgentTodo=None)->AgentPrompt:
async def update_todo(self,todo_id:str,new_stat:str)->str:
try:
todo : AgentTodo = self.known_todo.get(todo_id)
if todo:
todo.state = new_stat
detail_path = f"{self.root_path}/{todo.todo_path}/detail"
async with aiofiles.open(detail_path, mode='w', encoding="utf-8") as f:
await f.write(json.dumps(todo.to_dict()))
return None
else:
return "todo not found."
except Exception as e:
return str(e)
# result mean: list[op_error_str],have_error
async def exec_op_list(self,oplist:List,agent_id:str)->tuple[List[str],bool]:
result_str = "op list is none"
if oplist is None:
return None,False
async def append_worklog(self, todo:AgentTodo, result:AgentTodoResult):
worklog = f"{self.root_path}/{todo.todo_path}/.worklog"
result_str = []
have_error = False
for op in oplist:
if op["op"] == "create":
await self.create(op["path"],op["content"])
elif op["op"] == "write_file":
async with aiofiles.open(worklog, mode='w+', encoding="utf-8") as f:
content = await f.read()
if len(content) > 0:
json_obj = json.loads(content)
else:
json_obj = {}
logs = json_obj.get("logs")
if logs is None:
logs = []
logs.append(result.to_dict())
json_obj["logs"] = logs
await f.write(json.dumps(json_obj))
class FilesystemEnvironment(SimpleEnvironment):
def __init__(self, root_path: str, env_id: str) -> None:
super().__init__(env_id)
self.root_path = root_path
# if op["op"] == "create":
# await self.create(op["path"],op["content"])
async def write(op):
is_append = op.get("is_append")
if is_append is None:
is_append = False
error_str = await self.write(op["path"],op["content"],is_append)
elif op["op"] == "delete":
error_str = await self.delete(op["path"])
elif op["op"] == "rename":
error_str = await self.rename(op["path"],op["new_name"])
elif op["op"] == "mkdir":
error_str = await self.mkdir(op["path"])
elif op["op"] == "create_todo":
todoObj = AgentTodo.from_dict(op["todo"])
todoObj.worker = agent_id
todoObj.createor = agent_id
parent_id = op.get("parent")
error_str = await self.create_todo(parent_id,todoObj)
elif op["op"] == "update_todo":
todo_id = op["id"]
new_stat = op["state"]
error_str = await self.update_todo(todo_id,new_stat)
else:
logger.error(f"execute op list failed: unknown op:{op['op']}")
error_str = f"execute op list failed: unknown op:{op['op']}"
return await self.write(op["path"],op["content"],is_append)
self.add_ai_operation(SimpleAIOperation(
op="write",
description="write file",
func_handler=write,
))
if error_str:
have_error = True
result_str.append(error_str)
else:
result_str.append(f"execute success!")
async def delete(op):
return await self.delete(op["path"])
self.add_ai_operation(SimpleAIOperation(
op="delete",
description="delete path",
func_handler=delete,
))
return result_str,have_error
async def rename(op):
return await self.move(op["path"],op["new_name"])
self.add_ai_operation(SimpleAIOperation(
op="rename",
description="rename path",
func_handler=rename,
))
# file system operation: list,read,write,delete,move,stat
# inner_function
@@ -202,559 +345,7 @@ class WorkspaceEnvironment(Environment):
return None
# TODO use diff to update large file content
async def update_by_diff(self,path:str,diff):
pass
# doc system （read_only,agent cann't modify doc）
# inner_function
async def list_db(self) -> str:
pass
# inner_function
async def get_db_desc(self,db_name:str) -> str:
pass
# inner_function
async def query(self,db_name:str,sql:str) -> str:
pass
# search (web)
# inner_function
async def google_search(self,keyword:str,opt=None) -> str:
pass
# inner_function
async def local_search(self,keyword:str,root_path=None ,opt=None) -> str:
pass
# inner_function, might be return a image is better
async def web_get(self,url:str) -> str:
pass
# inner_function
async def blockchain_get(self,chainid:str,query:dict) -> str:
pass
# code interpreter
# inner_function or operation
async def eval_code(self,pycode:str) -> str:
pass
# operation or inner_function
async def improve_code(self,path:str):
pass
# operation or inner_function
async def run(self,file_path:str)->str:
pass
# operation or inner_function
async def pub_service(self,project_path:str):
pass
# operation or inner_function
async def exec_tx(self,chain_id:str,tx:dict) -> str:
pass
# social ability
# operation or inner_function
async def post_message(self,target:str,msg:AgentMsg,wait_time) -> AgentMsg:
pass
# operation or inner_function
async def add_contact(self,name:str,contact_info) -> str:
pass
# inner_function , include contact realtime info
async def get_contact(self,name_list:List[str],opt:dict) -> List:
pass
# Task/todo system , create,update,delete,query
async def get_todo_tree(self,path:str = None,deep:int = 4):
if path:
directory_path = self.root_path + "/todos/" + path
else:
directory_path = self.root_path + "/todos"
str_result:str = "/todos\n"
todo_count:int = 0
async def scan_dir(directory_path:str,deep:int):
nonlocal str_result
nonlocal todo_count
if deep <= 0:
return
if os.path.exists(directory_path) is False:
return
for entry in os.scandir(directory_path):
is_dir = entry.is_dir()
if not is_dir:
continue
if entry.name.startswith("."):
continue
todo_count = todo_count + 1
str_result = str_result + f"{' '*(4-deep)}{entry.name}\n"
await scan_dir(entry.path,deep-1)
await scan_dir(directory_path,deep)
return str_result,todo_count
async def get_todo_list(self,agent_id:str,path:str = None)->List[AgentTodo]:
logger.info("get_todo_list:%s,%s",agent_id,path)
if path:
directory_path = self.root_path + "/todos/" + path
else:
directory_path = self.root_path + "/todos"
result_list:List[AgentTodo] = []
async def scan_dir(directory_path:str,deep:int,parent:AgentTodo=None):
nonlocal result_list
if os.path.exists(directory_path) is False:
return
for entry in os.scandir(directory_path):
is_dir = entry.is_dir()
if not is_dir:
continue
if entry.name.startswith("."):
continue
todo = await self.get_todo_by_fullpath(entry.path)
if todo:
if todo.worker:
if todo.worker != agent_id:
continue
if parent:
parent.sub_todos[todo.todo_id] = todo
result_list.append(todo)
todo.rank = int(todo.create_time)>>deep
await scan_dir(entry.path,deep + 1,todo)
return
await scan_dir(directory_path,0)
#sort by rank
result_list.sort(key=lambda x:(x.rank,x.title))
logger.info("get_todo_list return,todolist.length() is %d",len(result_list))
return result_list
async def get_todo_by_fullpath(self,path:str) -> AgentTodo:
logger.info("get_todo_by_fullpath:%s",path)
detail_path = path + "/detail"
try:
async with aiofiles.open(detail_path, mode='r', encoding="utf-8") as f:
content = await f.read(4096)
logger.debug("get_todo_by_fullpath:%s,content:%s",path,content)
todo_dict = json.loads(content)
result_todo = AgentTodo.from_dict(todo_dict)
if result_todo:
relative_path = os.path.relpath(path, self.root_path + "/todos/")
if not relative_path.startswith('/'):
relative_path = '/' + relative_path
result_todo.todo_path = relative_path
self.known_todo[result_todo.todo_id] = result_todo
else:
logger.error("get_todo_by_path:%s,parse failed!",path)
return result_todo
except Exception as e:
logger.error("get_todo_by_path:%s,failed:%s",path,e)
return None
async def get_todo(self,id:str) -> AgentTodo:
return self.known_todo.get(id)
async def create_todo(self,parent_id:str,todo:AgentTodo) -> str:
try:
if parent_id:
if parent_id not in self.known_todo:
logger.error("create_todo failed: parent_id not found!")
return False
parent_path = self.known_todo.get(parent_id).todo_path
todo_path = f"{parent_path}/{todo.title}"
else:
todo_path = todo.title
dir_path = f"{self.root_path}/todos/{todo_path}"
os.makedirs(dir_path)
detail_path = f"{dir_path}/detail"
if todo.todo_path is None:
todo.todo_path = todo_path
logger.info("create_todo %s",detail_path)
async with aiofiles.open(detail_path, mode='w', encoding="utf-8") as f:
await f.write(json.dumps(todo.to_dict()))
self.known_todo[todo.todo_id] = todo
except Exception as e:
logger.error("create_todo failed:%s",e)
return str(e)
return None
async def update_todo(self,todo_id:str,new_stat:str)->str:
try:
todo : AgentTodo = self.known_todo.get(todo_id)
if todo:
todo.state = new_stat
detail_path = f"{self.root_path}/todos/{todo.todo_path}/detail"
async with aiofiles.open(detail_path, mode='w', encoding="utf-8") as f:
await f.write(json.dumps(todo.to_dict()))
return None
else:
return "todo not found."
except Exception as e:
return str(e)
async def append_worklog(self,todo:AgentTodo,result:AgentTodoResult):
worklog = f"{self.root_path}/todos/{todo.todo_path}/.worklog"
async with aiofiles.open(worklog, mode='w+', encoding="utf-8") as f:
content = await f.read()
if len(content) > 0:
json_obj = json.loads(content)
else:
json_obj = {}
logs = json_obj.get("logs")
if logs is None:
logs = []
logs.append(result.to_dict())
json_obj["logs"] = logs
await f.write(json.dumps(json_obj))
async def set_wakeup_timer(self,todo_id:str,timestamp:int) -> str:
pass
# knowledge base system
def get_knowledge_base_ai_functions(self):
all_inner_function = []
all_inner_function.append(SimpleAIFunction("get_knowledge_catalog","get knowledge catalog in tree format",
self.get_knowledege_catalog,
{"path":f"catalog path,none is /","depth":"max depth of catalog tree,default is 4"}))
all_inner_function.append(SimpleAIFunction("get_knowledge","get knowledge metadata",
self.get_knowledge,
{"path":f"knowledge path"}))
all_inner_function.append(SimpleAIFunction("load_knowledge_content","load knowledge content",
self.load_knowledge_content,
{"path":f"knowledge path","pos":"start position of content","length":"length of content"}))
result_func = []
result_len = 0
for inner_func in all_inner_function:
func_name = inner_func.get_name()
this_func = {}
this_func["name"] = func_name
this_func["description"] = inner_func.get_description()
this_func["parameters"] = inner_func.get_parameters()
result_len += len(json.dumps(this_func)) / 4
result_func.append(this_func)
return result_func,result_len
async def get_knowledege_catalog(self,path:str=None,only_dir =True,max_depth:int=5)->str:
if path:
full_path = f"{self.root_path}/knowledge/{path}"
else:
full_path = f"{self.root_path}/knowledge"
catlogs,file_count = await self.get_directory_structure(full_path,max_depth,only_dir)
return catlogs
async def get_directory_structure(self,root_dir, max_depth:int=4, only_dir=True, indent=1):
file_count = 0
structure_str = ''
if os.path.isdir(root_dir):
sub_files = []
with os.scandir(root_dir) as it:
for entry in it:
if entry.is_dir():
sub_structure, sub_count = await self.get_directory_structure(entry.path, max_depth, only_dir, indent + 1)
if sub_structure:
structure_str += sub_structure
file_count += sub_count
else:
file_count += 1
sub_files.append(entry.name)
if only_dir is False:
for file_name in sub_files:
structure_str = structure_str + ' ' * (indent+1) + file_name + '\n'
dir_name = os.path.basename(root_dir)
dir_info = f"{dir_name} <count: {file_count}>"
structure_str = ' ' * indent + dir_info + '\n' + structure_str
if indent - 1 >= max_depth:
return None, file_count
else:
return structure_str, file_count
# inner_function
async def get_knowledge(self,path:str) -> str:
full_path = f"{self.root_path}/knowledge/{path}"
if os.islink(full_path):
org_path = os.readlink(full_path)
hash = self.kb_db.get_hash_by_doc_path(org_path)
if hash:
return self.kb_db.get_knowledge(org_path)
return "not found"
async def load_knowledge_content(self,path:str,pos:int=0,length:int=None) -> str:
if path.endswith("pdf"):
logger.info("load_knowledge_content:pdf")
dir_path = os.path.dirname(path)
base_name = os.path.basename(path)
text_content_path = f"{dir_path}/.{base_name}.txt"
if os.path.exists(text_content_path) is False:
return None
async with aiofiles.open(path, mode='r', encoding=cur_encode) as f:
await f.seek(pos)
content = await f.read(length)
return content
else:
async with aiofiles.open(path,'rb') as f:
cur_encode = chardet.detect(await f.read())['encoding']
async with aiofiles.open(path, mode='r', encoding=cur_encode) as f:
await f.seek(pos)
content = await f.read(length)
return content
return "load content failed."
def _add_document_dir(self,path:str):
self.doc_dirs[path] = 0
def _start_scan_document(self):
if self._scan_thread is None:
self._scan_thread = threading.Thread(target=self._scan_document)
self._scan_thread.start()
if self._scan_dirthread is None:
self._scan_dirthread = threading.Thread(target=self._scan_dir)
self._scan_dirthread.start()
def _parse_pdf_bookmarks(self,bookmarks, parent:list):
for item in bookmarks:
if isinstance(item,list):
self._parse_pdf_bookmarks(item,parent)
else:
if item.title:
new_item = {}
new_item["page"] = item.page.idnum
new_item["title"] = item.title
my_childs = []
if item.childs:
if len(item.childs) > 0:
self._parse_pdf_bookmarks(item.childs, my_childs)
new_item["childs"] = my_childs
parent.append(new_item)
else:
logger.warning("parse pdf bookmarks failed: item.title is None!")
return
def _parse_pdf(self,doc_path:str):
metadata = {}
with open(doc_path, 'rb') as file:
reader = PyPDF2.PdfReader(file)
try:
doc_info = reader.metadata
if doc_info:
if doc_info.title:
metadata["title"] = doc_info.title
if doc_info.author:
metadata["authors"] = doc_info.author
except Exception as e:
logger.warn("parse pdf metadata failed:%s",e)
dir_path = os.path.dirname(doc_path)
base_name = os.path.basename(doc_path)
text_content_path = f"{dir_path}/.{base_name}.txt"
full_text = ""
for page in reader.pages:
text = page.extract_text()
full_text += text
with open(text_content_path, 'w', encoding='utf-8') as f:
f.write(full_text)
try:
bookmarks = reader.outline
if bookmarks:
catalogs = []
self._parse_pdf_bookmarks(bookmarks,catalogs)
metadata["catalogs"] = json.dumps(catalogs)
except Exception as e:
logger.warn("parse pdf bookmarks failed:%s",e)
return metadata
def _parse_txt(self,doc_path:str):
return {}
def _parse_md(self,doc_path:str):
metadata = {}
cur_encode = "utf-8"
with open(doc_path,'rb') as f:
cur_encode = chardet.detect(f.read(1024))['encoding']
with open(doc_path, mode='r', encoding=cur_encode) as f:
content = f.read()
match = re.search(r'^# (.*)', content, re.MULTILINE)
if match:
metadata['title'] = match.group(1).strip()
md = Markdown(extensions=['toc'])
html_str = md.convert(content)
toc = md.toc
if toc:
metadata['catalogs'] = toc
return metadata
def _parse_document(self,doc_path:str):
hash_result = None
title = os.path.basename(doc_path)
meta_data = {}
with open(doc_path, "rb") as f:
hash_md5 = hashlib.md5()
for chunk in iter(lambda: f.read(1024*1024), b""):
hash_md5.update(chunk)
hash_result = hash_md5.hexdigest()
try:
if doc_path.endswith(".md"):
meta_data = self._parse_md(doc_path)
elif doc_path.endswith(".pdf"):
meta_data = self._parse_pdf(doc_path)
except Exception as e:
logger.error("parse document %s failed:%s",doc_path,e)
traceback.print_exc()
if meta_data.get("title"):
title = meta_data["title"]
logger.info("parse document %s!",doc_path)
return hash_result,title,meta_data
def _support_file(self,file_name:str) -> bool:
if file_name.startswith("."):
return False
if file_name.endswith(".pdf"):
return True
if file_name.endswith(".md"):
return True
if file_name.endswith(".txt"):
return True
return False
def _scan_dir(self):
while True:
time.sleep(10)
for directory in self.doc_dirs.keys():
now = time.time()
if now - self.doc_dirs[directory] > 60*15:
self.doc_dirs[directory] = time.time()
else:
continue
for root, dirs, files in os.walk(directory):
for file in files:
if self._support_file(file):
full_path = os.path.join(root, file)
full_path = os.path.normpath(full_path)
if self.kb_db.is_doc_exist(full_path):
continue
file_stat = os.stat(full_path)
if file_stat.st_size < 1:
continue
if file_stat.st_size < 1024*1024*8:
#parse and insert
hash,title,meta_data = self._parse_document(full_path)
self.kb_db.add_doc(full_path,file_stat.st_size,file_stat.st_mtime,hash)
self.kb_db.add_knowledge(hash,title,meta_data)
else:
self.kb_db.add_doc(full_path,file_stat.st_size,file_stat.st_mtime)
def _scan_document(self):
while True:
time.sleep(10)
parse_queue = self.kb_db.get_docs_without_hash()
for doc_path in parse_queue:
hash,title,meta_data = self._parse_document(doc_path)
self.kb_db.set_doc_hash(doc_path,hash)
self.kb_db.add_knowledge(hash,title,meta_data)
# merge to standard workspace env, **ABANDON this!**
class KnowledgeBaseFileSystemEnvironment(Environment):
def __init__(self, env_id: str) -> None:
super().__init__(env_id)
self.root_path = "."
operator_param = {
"path": "full path of target directory",
}
self.add_ai_function(SimpleAIFunction("list",
"list the files and sub directory in target directory,result is a json array",
self.list,operator_param))
operator_param = {
"path": "full path of target file",
}
self.add_ai_function(SimpleAIFunction("cat",
"cat the file content in target path,result is a string",
self.cat,operator_param))
def set_root_path(self,path:str):
self.root_path = path
async def list(self,path:str) -> str:
directory_path = self.root_path + path
items = []
with await aiofiles.os.scandir(directory_path) as entries:
async for entry in entries:
item_type = "directory" if entry.is_dir() else "file"
items.append({"name": entry.name, "type": item_type})
return json.dumps(items)
async def cat(self,path:str) -> str:
file_path = self.root_path + path
cur_encode = "utf-8"
async with aiofiles.open(file_path,'rb') as f:
cur_encode = chardet.detect(await f.read())['encoding']
async with aiofiles.open(file_path, mode='r', encoding=cur_encode) as f:
content = await f.read(2048)
return content
class ShellEnvironment(Environment):
class ShellEnvironment(SimpleEnvironment):
def __init__(self, env_id: str) -> None:
super().__init__(env_id)
@@ -787,3 +378,57 @@ class ShellEnvironment(Environment):
else:
return f"Execute failed! stderr is:\n{stderr}\n"
class WorkspaceEnvironment(CompositeEnvironment):
def __init__(self, env_id: str) -> None:
super().__init__(env_id)
myai_path = AIStorage.get_instance().get_myai_dir()
self.root_path = f"{myai_path}/workspace/{env_id}"
if not os.path.exists(self.root_path):
os.makedirs()
self.todo_list = {}
self.todo_list[TodoListType.TO_WORK] = TodoListEnvironment(self.root_path,TodoListType.TO_WORK)
self.todo_list[TodoListType.TO_LEARN] = TodoListEnvironment(self.root_path,TodoListType.TO_LEARN)
# default environments in workspace
self.add_env(self.todo_list[TodoListType.TO_WORK])
self.add_env(ShellEnvironment("shell"))
self.add_env(FilesystemEnvironment(self.root_path, "fs"))
def set_root_path(self,path:str):
self.root_path = path
def get_prompt(self) -> AgentMsg:
return None
def get_role_prompt(self,role_id:str) -> AgentPrompt:
return None
def get_do_prompt(self,todo:AgentTodo=None)->AgentPrompt:
return None
# result mean: list[op_error_str],have_error
async def exec_op_list(self,oplist:List,agent_id:str)->tuple[List[str],bool]:
result_str = "op list is none"
if oplist is None:
return None,False
result_str = []
have_error = False
for op in oplist:
operation = self.get_ai_operation(op["op"])
if operation:
error_str = await operation.execute(op)
else:
logger.error(f"execute op list failed: unknown op:{op['op']}")
error_str = f"execute op list failed: unknown op:{op['op']}"
if error_str:
have_error = True
result_str.append(error_str)
else:
result_str.append(f"execute success!")
return result_str,have_error
src/aios/knowledge/pipeline.py
+17 -15
View File
@@ -6,17 +6,13 @@ from . import ObjectID, KnowledgeStore
from enum import Enum
class KnowledgePipelineJournal:
def __init__(self, time: datetime.datetime, object_id: str, input: str, parser: str):
def __init__(self, time: datetime.datetime, input: str, parser: str):
self.time = time
self.object_id = None if object_id is None else ObjectID.from_base58(object_id)
self.input = input
self.parser = parser
def is_finish(self) -> bool:
return self.object_id is None
def get_object_id(self) -> ObjectID:
return self.object_id
return self.input is None
def get_input(self) -> str:
return self.input
@@ -28,7 +24,7 @@ class KnowledgePipelineJournal:
if self.is_finish():
return f"{self.time}: finished)"
else:
return f"{self.time}: object:{self.object_id} input:{self.input}, parser:{self.parser})"
return f"{self.time}: input:{self.input}, parser:{self.parser})"
# init sqlite3 client
class KnowledgePipelineJournalClient:
@@ -42,18 +38,17 @@ class KnowledgePipelineJournalClient:
'''CREATE TABLE IF NOT EXISTS journal (
id INTEGER PRIMARY KEY AUTOINCREMENT,
time DATETIME DEFAULT CURRENT_TIMESTAMP,
object_id TEXT,
input TEXT,
parser TEXT)'''
)
conn.commit()
def insert(self, object_id: ObjectID, input: str, parser: str, timestamp: datetime.datetime = None):
def insert(self, input: str, parser: str, timestamp: datetime.datetime = None):
timestamp = datetime.datetime.now() if timestamp is None else timestamp
conn = sqlite3.connect(self.journal_path)
conn.execute(
"INSERT INTO journal (time, object_id, input, parser) VALUES (?, ?, ?, ?)",
(timestamp, str(object_id), input, parser),
"INSERT INTO journal (time, input, parser) VALUES (?, ?, ?, ?)",
(timestamp, input, parser),
)
conn.commit()
@@ -61,7 +56,7 @@ class KnowledgePipelineJournalClient:
conn = sqlite3.connect(self.journal_path)
cursor = conn.cursor()
cursor.execute("SELECT * FROM journal ORDER BY id DESC LIMIT ?", (topn,))
return [KnowledgePipelineJournal(time, object_id, input, parser) for (_, time, object_id, input, parser) in cursor.fetchall()]
return [KnowledgePipelineJournal(time, input, parser) for (_, time, input, parser) in cursor.fetchall()]
class KnowledgePipelineEnvironment:
def __init__(self, pipeline_path: str):
@@ -87,8 +82,12 @@ class KnowledgePipelineState(Enum):
STOPPED = 2
FINISHED = 3
class NullParser:
async def parse(self, object_id):
return ""
class KnowledgePipeline:
def __init__(self, name: str, env: KnowledgePipelineEnvironment, input_init, input_params, parser_init, parser_params):
def __init__(self, name: str, env: KnowledgePipelineEnvironment, input_init, input_params=None, parser_init=None, parser_params=None):
self.name = name
self.state = KnowledgePipelineState.INIT
self.input_init = input_init
@@ -108,18 +107,21 @@ class KnowledgePipeline:
async def run(self):
if self.state == KnowledgePipelineState.INIT:
self.input = self.input_init(self.env, self.input_params)
if self.parser_init is None:
self.parser = NullParser()
else:
self.parser = self.parser_init(self.env, self.parser_params)
self.state = KnowledgePipelineState.RUNNING
if self.state == KnowledgePipelineState.RUNNING:
async for input in self.input.next():
if input is None:
self.state = KnowledgePipelineState.FINISHED
self.env.journal.insert(None, "finished", "finished")
self.env.journal.insert(None, None)
return
(object_id, input_journal) = input
if object_id is not None:
parser_journal = await self.parser.parse(object_id)
self.env.journal.insert(object_id, input_journal, parser_journal)
self.env.journal.insert(input_journal, parser_journal)
else:
return
if self.state == KnowledgePipelineState.STOPPED:
src/component/common_environment/local_document.py
+671
View File
@@ -0,0 +1,671 @@
# import os
# import aiofiles
# import chardet
# import logging
# import string
# from knowledge import ImageObjectBuilder, DocumentObjectBuilder, KnowledgePipelineEnvironment, KnowledgePipelineJournal
# from aios_kernel.storage import AIStorage
import os
import aiofiles
import chardet
import logging
import string
import sqlite3
import json
import threading
import logging
from datetime import datetime
from typing import Optional, List
from knowledge import ImageObjectBuilder, DocumentObjectBuilder, KnowledgePipelineEnvironment, KnowledgePipelineJournal
from aios_kernel import AIStorage, SimpleEnvironment
class ScanLocalDocument:
def __init__(self, env: KnowledgePipelineEnvironment, config):
self.env = env
path = string.Template(config["path"]).substitute(myai_dir=AIStorage.get_instance().get_myai_dir())
config["path"] = path
self.config = config
def path(self):
return self.config["path"]
async def next(self):
while True:
journals = self.env.journal.latest_journals(1)
from_time = 0
if len(journals) == 1:
latest_journal = journals[0]
if latest_journal.is_finish():
yield None
continue
from_time = os.path.getctime(latest_journal.get_input())
if os.path.getmtime(self.path()) <= from_time:
yield (None, None)
continue
file_pathes = sorted(os.listdir(self.path()), key=lambda x: os.path.getctime(os.path.join(self.path(), x)))
for rel_path in file_pathes:
file_path = os.path.join(self.path(), rel_path)
timestamp = os.path.getctime(file_path)
if timestamp <= from_time:
continue
ext = os.path.splitext(file_path)[1].lower()
if ext in ['.pdf', '.md', '.txt']:
logging.info(f"knowledge dir source found document file {file_path}")
yield (file_path, file_path)
yield (None, None)
class MetaDatabase:
def __init__(self,db_path:str):
self.db_path = db_path
self._get_conn()
def _get_conn(self):
""" get db connection """
local = threading.local()
if not hasattr(local, 'conn'):
local.conn = self._create_connection(self.db_path)
return local.conn
def _create_connection(self, db_file):
""" create a database connection to a SQLite database """
conn = None
try:
conn = sqlite3.connect(db_file)
except Exception as e:
logger.error("Error occurred while connecting to database: %s", e)
return None
if conn:
self._create_tables(conn)
return conn
def _create_tables(self,conn):
cursor = conn.cursor()
cursor.execute('''
CREATE TABLE IF NOT EXISTS documents (
doc_path TEXT PRIMARY KEY,
length INTEGER,
last_modify TEXT,
doc_hash TEXT,
create_time TEXT
)
''')
cursor.execute('''
CREATE TABLE IF NOT EXISTS knowledge (
doc_hash TEXT PRIMARY KEY,
title TEXT,
summary TEXT,
content TEXT,
catalogs TEXT,
tags TEXT,
llm_title TEXT,
llm_summary TEXT,
create_time TEXT
)
''')
cursor.execute('''
CREATE INDEX IF NOT EXISTS idx_documents_doc_hash
ON documents (doc_hash)
''')
cursor.execute('''
CREATE INDEX IF NOT EXISTS idx_knowledge_tags
ON knowledge (tags)
''')
conn.commit()
def add_doc(self, doc_path: str, length: int, last_modify: str, doc_hash: Optional[str] = None):
conn = self._get_conn()
cursor = conn.cursor()
create_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cursor.execute('''
INSERT INTO documents (doc_path, length, last_modify, doc_hash,create_time)
VALUES (?, ?, ?, ?,?)
''', (doc_path, length, last_modify, doc_hash,create_time))
conn.commit()
def is_doc_exist(self, doc_path: str) -> bool:
conn = self._get_conn()
cursor = conn.cursor()
cursor.execute('''
SELECT doc_path
FROM documents
WHERE doc_path = ?
''', (doc_path,))
return len(cursor.fetchall()) > 0
def set_doc_hash(self, doc_path: str, doc_hash: str):
conn = self._get_conn()
cursor = conn.cursor()
cursor.execute('''
UPDATE documents
SET doc_hash = ?
WHERE doc_path = ?
''', (doc_hash, doc_path))
conn.commit()
def get_docs_without_hash(self,limit:int=1024) -> List[str]:
conn = self._get_conn()
cursor = conn.cursor()
cursor.execute('''
SELECT doc_path
FROM documents
WHERE doc_hash IS NULL OR doc_hash = ''
ORDER BY create_time DESC
LIMIT ?
''',(limit,))
return [row[0] for row in cursor.fetchall()]
#metadata["summary"]
#metadata["catelogs"]
#metadata["tags"]
def add_knowledge(self, doc_hash: str, title: str, metadata: dict,content:str = None,):
conn = self._get_conn()
cursor = conn.cursor()
create_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
summary = metadata.get("summary", "")
catalogs = metadata.get("catalogs","")
tags = ','.join(metadata.get("tags", []))
cursor.execute('''
INSERT INTO knowledge (doc_hash, title , summary , catalogs , tags,create_time)
VALUES (?, ?, ?, ?, ?,?)
''', (doc_hash, title, summary, catalogs, tags,create_time))
conn.commit()
#llm_result["summary"]
#llm_result["tags"]
#llm_result["catelog"]
def set_knowledge_llm_result(self, doc_hash: str, llm_result: dict):
conn = self._get_conn()
cursor = conn.cursor()
title = llm_result.get("title", "")
summary = llm_result.get("summary", "")
catalogs = json.dumps(llm_result.get("catalogs", {}))
tags = ','.join(llm_result.get("tags", []))
cursor.execute('''
UPDATE knowledge
SET llm_title = ?,llm_summary = ?, catalogs = ?, tags = ?
WHERE doc_hash = ?
''', (title,summary, catalogs, tags, doc_hash))
conn.commit()
def get_hash_by_doc_path(self, doc_path: str) -> Optional[str]:
conn = self._get_conn()
cursor = conn.cursor()
cursor.execute('''
SELECT doc_hash
FROM documents
WHERE doc_path = ?
''', (doc_path,))
row = cursor.fetchone()
if row is None:
return None
return row[0]
def get_knowledge(self, doc_hash: str) -> Optional[dict]:
conn = self._get_conn()
cursor = conn.cursor()
cursor.execute('''
SELECT title, summary, catalogs, tags, llm_title, llm_summary
FROM knowledge
WHERE doc_hash = ?
''', (doc_hash,))
row = cursor.fetchone()
if row is None:
return None
# get doc path
cursor.execute('''
SELECT doc_path
FROM documents
WHERE doc_hash = ?
''', (doc_hash,))
row2 = cursor.fetchone()
if row2 is None:
return None
doc_path = row2[0]
return {
"full_path": doc_path,
"title": row[0],
"summary": row[1],
"catalogs": row[2],
"tags": row[3],
"llm_title" : row[4],
"llm_summary" : row[5],
}
def get_knowledge_without_llm_title(self,limit:int=16) -> List[str]:
conn = self._get_conn()
cursor = conn.cursor()
cursor.execute('''
SELECT doc_hash
FROM knowledge
WHERE llm_title IS NULL OR llm_title = ''
ORDER BY create_time DESC
LIMIT ?
''',(limit,))
return [row[0] for row in cursor.fetchall()]
def query_docs_by_tag(self, tag: str) -> List[str]:
conn = self._get_conn()
cursor = conn.cursor()
tag_json = json.dumps(tag) # 将标签转换为 JSON 字符串
cursor.execute('''
SELECT documents.doc_path
FROM documents
JOIN knowledge ON documents.doc_hash = knowledge.doc_hash
WHERE json_extract(knowledge.tags, '$') LIKE ?
''', (tag))
return [row[0] for row in cursor.fetchall()]
class DocumentKnowledgeBase(SimpleEnvironment):
async def get_knowledege_catalog(self,path:str=None,only_dir =True,max_depth:int=5)->str:
if path:
full_path = f"{self.root_path}/knowledge/{path}"
else:
full_path = f"{self.root_path}/knowledge"
catlogs,file_count = await self.get_directory_structure(full_path,max_depth,only_dir)
return catlogs
async def get_directory_structure(self,root_dir, max_depth:int=4, only_dir=True, indent=1):
file_count = 0
structure_str = ''
if os.path.isdir(root_dir):
sub_files = []
with os.scandir(root_dir) as it:
for entry in it:
if entry.is_dir():
sub_structure, sub_count = await self.get_directory_structure(entry.path, max_depth, only_dir, indent + 1)
if sub_structure:
structure_str += sub_structure
file_count += sub_count
else:
file_count += 1
sub_files.append(entry.name)
if only_dir is False:
for file_name in sub_files:
structure_str = structure_str + ' ' * (indent+1) + file_name + '\n'
dir_name = os.path.basename(root_dir)
dir_info = f"{dir_name} <count: {file_count}>"
structure_str = ' ' * indent + dir_info + '\n' + structure_str
if indent - 1 >= max_depth:
return None, file_count
else:
return structure_str, file_count
# inner_function
async def get_knowledge(self,path:str) -> str:
full_path = f"{self.root_path}/knowledge/{path}"
if os.islink(full_path):
org_path = os.readlink(full_path)
hash = self.kb_db.get_hash_by_doc_path(org_path)
if hash:
return self.kb_db.get_knowledge(org_path)
return "not found"
class ParseLocalDocument:
def _parse_pdf_bookmarks(self,bookmarks, parent:list):
for item in bookmarks:
if isinstance(item,list):
self._parse_pdf_bookmarks(item,parent)
else:
if item.title:
new_item = {}
new_item["page"] = item.page.idnum
new_item["title"] = item.title
my_childs = []
if item.childs:
if len(item.childs) > 0:
self._parse_pdf_bookmarks(item.childs, my_childs)
new_item["childs"] = my_childs
parent.append(new_item)
else:
logger.warning("parse pdf bookmarks failed: item.title is None!")
return
def _parse_pdf(self,doc_path:str):
metadata = {}
with open(doc_path, 'rb') as file:
reader = PyPDF2.PdfReader(file)
try:
doc_info = reader.metadata
if doc_info:
if doc_info.title:
metadata["title"] = doc_info.title
if doc_info.author:
metadata["authors"] = doc_info.author
except Exception as e:
logger.warn("parse pdf metadata failed:%s",e)
dir_path = os.path.dirname(doc_path)
base_name = os.path.basename(doc_path)
text_content_path = f"{dir_path}/.{base_name}.txt"
full_text = ""
for page in reader.pages:
text = page.extract_text()
full_text += text
with open(text_content_path, 'w', encoding='utf-8') as f:
f.write(full_text)
try:
bookmarks = reader.outline
if bookmarks:
catalogs = []
self._parse_pdf_bookmarks(bookmarks,catalogs)
metadata["catalogs"] = json.dumps(catalogs)
except Exception as e:
logger.warn("parse pdf bookmarks failed:%s",e)
return metadata
def _parse_txt(self,doc_path:str):
return {}
def _parse_md(self,doc_path:str):
metadata = {}
cur_encode = "utf-8"
with open(doc_path,'rb') as f:
cur_encode = chardet.detect(f.read(1024))['encoding']
with open(doc_path, mode='r', encoding=cur_encode) as f:
content = f.read()
match = re.search(r'^# (.*)', content, re.MULTILINE)
if match:
metadata['title'] = match.group(1).strip()
md = Markdown(extensions=['toc'])
html_str = md.convert(content)
toc = md.toc
if toc:
metadata['catalogs'] = toc
return metadata
def _parse_document(self,doc_path:str):
hash_result = None
title = os.path.basename(doc_path)
meta_data = {}
with open(doc_path, "rb") as f:
hash_md5 = hashlib.md5()
for chunk in iter(lambda: f.read(1024*1024), b""):
hash_md5.update(chunk)
hash_result = hash_md5.hexdigest()
try:
if doc_path.endswith(".md"):
meta_data = self._parse_md(doc_path)
elif doc_path.endswith(".pdf"):
meta_data = self._parse_pdf(doc_path)
except Exception as e:
logger.error("parse document %s failed:%s",doc_path,e)
traceback.print_exc()
if meta_data.get("title"):
title = meta_data["title"]
logger.info("parse document %s!",doc_path)
return hash_result,title,meta_data
async def parse(self, file_path: str) -> str:
# async def get_knowledege_catalog(self,path:str=None,only_dir =True,max_depth:int=5)->str:
# if path:
# full_path = f"{self.root_path}/knowledge/{path}"
# else:
# full_path = f"{self.root_path}/knowledge"
# catlogs,file_count = await self.get_directory_structure(full_path,max_depth,only_dir)
# return catlogs
# async def get_directory_structure(self,root_dir, max_depth:int=4, only_dir=True, indent=1):
# file_count = 0
# structure_str = ''
# if os.path.isdir(root_dir):
# sub_files = []
# with os.scandir(root_dir) as it:
# for entry in it:
# if entry.is_dir():
# sub_structure, sub_count = await self.get_directory_structure(entry.path, max_depth, only_dir, indent + 1)
# if sub_structure:
# structure_str += sub_structure
# file_count += sub_count
# else:
# file_count += 1
# sub_files.append(entry.name)
# if only_dir is False:
# for file_name in sub_files:
# structure_str = structure_str + ' ' * (indent+1) + file_name + '\n'
# dir_name = os.path.basename(root_dir)
# dir_info = f"{dir_name} <count: {file_count}>"
# structure_str = ' ' * indent + dir_info + '\n' + structure_str
# if indent - 1 >= max_depth:
# return None, file_count
# else:
# return structure_str, file_count
# # inner_function
# async def get_knowledge(self,path:str) -> str:
# full_path = f"{self.root_path}/knowledge/{path}"
# if os.islink(full_path):
# org_path = os.readlink(full_path)
# hash = self.kb_db.get_hash_by_doc_path(org_path)
# if hash:
# return self.kb_db.get_knowledge(org_path)
# return "not found"
async def load_knowledge_content(self,path:str,pos:int=0,length:int=None) -> str:
if path.endswith("pdf"):
logger.info("load_knowledge_content:pdf")
dir_path = os.path.dirname(path)
base_name = os.path.basename(path)
text_content_path = f"{dir_path}/.{base_name}.txt"
if os.path.exists(text_content_path) is False:
return None
async with aiofiles.open(path, mode='r', encoding=cur_encode) as f:
await f.seek(pos)
content = await f.read(length)
return content
else:
async with aiofiles.open(path,'rb') as f:
cur_encode = chardet.detect(await f.read())['encoding']
async with aiofiles.open(path, mode='r', encoding=cur_encode) as f:
await f.seek(pos)
content = await f.read(length)
return content
return "load content failed."
def _add_document_dir(self,path:str):
self.doc_dirs[path] = 0
def _parse_pdf_bookmarks(self,bookmarks, parent:list):
for item in bookmarks:
if isinstance(item,list):
self._parse_pdf_bookmarks(item,parent)
else:
if item.title:
new_item = {}
new_item["page"] = item.page.idnum
new_item["title"] = item.title
my_childs = []
if item.childs:
if len(item.childs) > 0:
self._parse_pdf_bookmarks(item.childs, my_childs)
new_item["childs"] = my_childs
parent.append(new_item)
else:
logger.warning("parse pdf bookmarks failed: item.title is None!")
return
def _parse_pdf(self,doc_path:str):
metadata = {}
with open(doc_path, 'rb') as file:
reader = PyPDF2.PdfReader(file)
try:
doc_info = reader.metadata
if doc_info:
if doc_info.title:
metadata["title"] = doc_info.title
if doc_info.author:
metadata["authors"] = doc_info.author
except Exception as e:
logger.warn("parse pdf metadata failed:%s",e)
dir_path = os.path.dirname(doc_path)
base_name = os.path.basename(doc_path)
text_content_path = f"{dir_path}/.{base_name}.txt"
full_text = ""
for page in reader.pages:
text = page.extract_text()
full_text += text
with open(text_content_path, 'w', encoding='utf-8') as f:
f.write(full_text)
try:
bookmarks = reader.outline
if bookmarks:
catalogs = []
self._parse_pdf_bookmarks(bookmarks,catalogs)
metadata["catalogs"] = json.dumps(catalogs)
except Exception as e:
logger.warn("parse pdf bookmarks failed:%s",e)
return metadata
def _parse_txt(self,doc_path:str):
return {}
def _parse_md(self,doc_path:str):
metadata = {}
cur_encode = "utf-8"
with open(doc_path,'rb') as f:
cur_encode = chardet.detect(f.read(1024))['encoding']
with open(doc_path, mode='r', encoding=cur_encode) as f:
content = f.read()
match = re.search(r'^# (.*)', content, re.MULTILINE)
if match:
metadata['title'] = match.group(1).strip()
md = Markdown(extensions=['toc'])
html_str = md.convert(content)
toc = md.toc
if toc:
metadata['catalogs'] = toc
return metadata
def _parse_document(self,doc_path:str):
hash_result = None
title = os.path.basename(doc_path)
meta_data = {}
with open(doc_path, "rb") as f:
hash_md5 = hashlib.md5()
for chunk in iter(lambda: f.read(1024*1024), b""):
hash_md5.update(chunk)
hash_result = hash_md5.hexdigest()
try:
if doc_path.endswith(".md"):
meta_data = self._parse_md(doc_path)
elif doc_path.endswith(".pdf"):
meta_data = self._parse_pdf(doc_path)
except Exception as e:
logger.error("parse document %s failed:%s",doc_path,e)
traceback.print_exc()
if meta_data.get("title"):
title = meta_data["title"]
logger.info("parse document %s!",doc_path)
return hash_result,title,meta_data
def _support_file(self,file_name:str) -> bool:
if file_name.startswith("."):
return False
if file_name.endswith(".pdf"):
return True
if file_name.endswith(".md"):
return True
if file_name.endswith(".txt"):
return True
return False
def _scan_dir(self):
while True:
time.sleep(10)
for directory in self.doc_dirs.keys():
now = time.time()
if now - self.doc_dirs[directory] > 60*15:
self.doc_dirs[directory] = time.time()
else:
continue
for root, dirs, files in os.walk(directory):
for file in files:
if self._support_file(file):
full_path = os.path.join(root, file)
full_path = os.path.normpath(full_path)
if self.kb_db.is_doc_exist(full_path):
continue
file_stat = os.stat(full_path)
if file_stat.st_size < 1:
continue
if file_stat.st_size < 1024*1024*8:
#parse and insert
hash,title,meta_data = self._parse_document(full_path)
self.kb_db.add_doc(full_path,file_stat.st_size,file_stat.st_mtime,hash)
self.kb_db.add_knowledge(hash,title,meta_data)
else:
self.kb_db.add_doc(full_path,file_stat.st_size,file_stat.st_mtime)
def _scan_document(self):
while True:
time.sleep(10)
parse_queue = self.kb_db.get_docs_without_hash()
for doc_path in parse_queue:
hash,title,meta_data = self._parse_document(doc_path)
self.kb_db.set_doc_hash(doc_path,hash)
self.kb_db.add_knowledge(hash,title,meta_data)
src/component/common_environment/to_learn_parser.py
+214
View File
@@ -0,0 +1,214 @@
# 尝试自我学习，会主动获取、读取资料并进行整理
# LLM的本质能力是处理海量知识，应该让LLM能基于知识把自己的工作处理的更好
async def do_self_learn(self) -> None:
# 不同的workspace是否应该有不同的学习方法？
workspace = self.get_workspace_by_msg(None)
hash_list = workspace.kb_db.get_knowledge_without_llm_title()
for hash in hash_list:
if self.agent_energy <= 0:
break
knowledge = workspace.kb_db.get_knowledge(hash)
if knowledge is None:
continue
full_path = knowledge.get("full_path")
if full_path is None:
continue
if os.path.exists(full_path) is False:
logger.warning(f"do_self_learn: knowledge {full_path} is not exists!")
continue
#TODO 可以用v-db 对不同目录的名字进行选择后，先进行一次快速的插入。有时间再慢慢用LLM整理
result_obj = await self._llm_read_article(knowledge,full_path)
#根据结果更新knowledge
if result_obj is not None:
workspace.kb_db.set_knowledge_llm_result(hash,result_obj)
# 在知识库中创建软链接
path_list = result_obj.get("path")
new_title = result_obj.get("title")
if path_list:
for new_path in path_list:
full_new_path = f"/knowledge{new_path}/{new_title}"
await workspace.symlink(full_path,full_new_path)
logger.info(f"create soft link {full_path} -> {full_new_path}")
self.agent_energy -= 1
# match item.type():
# case "book":
# self.llm_read_book(kb,item)
# learn_power -= 1
# case "article":
#
# self.llm_read_article(kb,item)
# learn_power -= 1
# case "video":
# self.llm_watch_video(kb,item)
# learn_power -= 1
# case "audio":
# self.llm_listen_audio(kb,item)
# learn_power -= 1
# case "code_project":
# self.llm_read_code_project(kb,item)
# learn_power -= 1
# case "image":
# self.llm_view_image(kb,item)
# learn_power -= 1
# case "other":
# self.llm_read_other(kb,item)
# learn_power -= 1
# case _:
# self.llm_learn_any(kb,item)
# pass
async def do_blance_knowledge_base(selft):
# 整理自己的知识库(让分类更平衡，更由于自己以后的工作)，并尝试更新学习目标
current_path = "/"
current_list = kb.get_list(current_path)
self_assessment_with_goal = self.get_self_assessment_with_goal()
learn_goal = {}
llm_blance_knowledge_base(current_path,current_list,self_assessment_with_goal,learn_goal,learn_power)
# 主动学习
# 方法目前只有使用搜索引擎一种？
for goal in learn_goal.items():
self.llm_learn_with_search_engine(kb,goal,learn_power)
if learn_power <= 0:
break
def parser_learn_llm_result(self,llm_result:LLMResult):
pass
async def gen_known_info_for_knowledge_prompt(self,knowledge_item:dict,temp_meta = None,need_catalogs = False) -> AgentPrompt:
workspace =self.get_workspace_by_msg(None)
kb_tree = await workspace.get_knowledege_catalog()
known_obj = {}
title = knowledge_item.get("title")
if title:
known_obj["title"] = title
summary = knowledge_item.get("summary")
if summary:
known_obj["summary"] = summary
tags = knowledge_item.get("tags")
if tags:
known_obj["tags"] = tags
if need_catalogs:
catalogs = knowledge_item.get("catalogs")
if catalogs:
known_obj["catalogs"] = catalogs
if temp_meta:
for key in temp_meta.keys():
known_obj[key] = temp_meta[key]
org_path = knowledge_item.get("full_path")
known_obj["orginal_path"] = org_path
know_info_str = f"# Known information:\n## Current directory structure:\n{kb_tree}\n## Knowlege Metadata:\n{json.dumps(known_obj)}\n"
return AgentPrompt(know_info_str)
async def _llm_read_article(self,knowledge_item:dict,full_path:str) -> ComputeTaskResult:
# Objectives:
# Obtain better titles, abstracts, table of contents (if necessary), tags
# Determine the appropriate place to put it (in line with the organization's goals)
# Known information:
# The reason why the target service's learn_prompt is being sorted
# Summary of the organization's work (if any)
# The current structure of the knowledge base (note the size control) gen_kb_tree_prompt (when empty, LLM should generate an appropriate initial directory structure)
# Original path, current title, abstract, table of contents
# Sorting long files (general tricks)
# Indicate that the input is part of the content, let LLM generate intermediate results for the task
# Enter the content in sequence, when the last content block is input, LLM gets the result
#full_content = item.get_article_full_content()
workspace = self.get_workspace_by_msg(None)
full_content_len = self.token_len(full_content)
if full_content_len < self.get_llm_learn_token_limit():
# 短文章不用总结catelog
#path_list,summary = llm_get_summary(summary,full_content)
#prompt = self.get_agent_role_prompt()
prompt = AgentPrompt()
prompt.append(self.get_learn_prompt())
known_info_prompt = await self.gen_known_info_for_knowledge_prompt(knowledge_item)
prompt.append(known_info_prompt)
content_prompt = AgentPrompt(full_content)
prompt.append(content_prompt)
env_functions = None
#env_functions,function_len = workspace.get_knowledge_base_ai_functions()
task_result:ComputeTaskResult = await self.do_llm_complection(prompt,is_json_resp=True)
if task_result.result_code != ComputeTaskResultCode.OK:
result_obj = {}
result_obj["error_str"] = task_result.error_str
return result_obj
result_obj = json.loads(task_result.result_str)
return result_obj
else:
logger.warning(f"llm_read_article: article {full_path} use LLM loop learn!")
pos = 0
read_len = int(self.get_llm_learn_token_limit() * 1.2)
temp_meta_data = {}
is_final = False
while pos < str_len:
_content = full_content[pos:pos+read_len]
part_cotent_len = len(_content)
if part_cotent_len < read_len:
# last chunk
is_final = True
part_content = f"<<Final Part:start at {pos}>>\n{_content}"
else:
part_content = f"<<Part:start at {pos}>>\n{_content}"
pos = pos + read_len
prompt = AgentPrompt()
prompt.append(self.get_learn_prompt())
known_info_prompt = await self.gen_known_info_for_knowledge_prompt(knowledge_item,temp_meta_data)
prompt.append(known_info_prompt)
content_prompt = AgentPrompt(part_content)
prompt.append(content_prompt)
#env_functions,function_len = workspace.get_knowledge_base_ai_functions()
task_result:ComputeTaskResult = await self.do_llm_complection(prompt,is_json_resp=True)
if task_result.result_code != ComputeTaskResultCode.OK:
result_obj = {}
result_obj["error_str"] = task_result.error_str
return result_obj
result_obj = json.loads(task_result.result_str)
temp_meta_data = result_obj
if is_final:
return result_obj
return None
async def do_self_think(self):
session_id_list = AIChatSession.list_session(self.agent_id,self.chat_db)
for session_id in session_id_list:
if self.agent_energy <= 0:
break
used_energy = await self.think_chatsession(session_id)
self.agent_energy -= used_energy
todo_logs = await self.get_todo_logs()
for todo_log in todo_logs:
if self.agent_energy <= 0:
break
used_energy = await self.think_todo_log(todo_log)
self.agent_energy -= used_energy
return
src/component/knowledge_manager/pipeline.py
+7 -2
View File
@@ -44,14 +44,19 @@ class KnowledgePipelineManager:
input_init = self.input_modules.get(input_module)
input_params = config["input"].get("params")
parser_module = config["parser"]["module"]
parser_config = config.get("parser")
if parser_config is None:
parser_init = None
parser_params = None
else:
parser_module = parser_config["module"]
_, ext = os.path.splitext(parser_module)
if ext == ".py":
parser_module = os.path.join(path, parser_module)
parser_init = runpy.run_path(parser_module)["init"]
else:
parser_init = self.parser_modules.get(parser_module)
parser_params = config["parser"].get("params")
parser_params = parser_config.get("params")
data_path = os.path.join(self.root_dir, name)
src/component/mail_environment/local.py
+1 -1
View File
@@ -22,7 +22,7 @@ class LocalEmail:
if latest_journal.is_finish():
yield None
continue
parsed = str(latest_journal.get_object_id())
parsed = latest_journal.get_input()
mail_id = self.mail_storage.next_mail_id(parsed)
if mail_id is None:
src/component/mail_environment/mail.py
+52 -19
View File
@@ -7,17 +7,20 @@ import datetime
from bs4 import BeautifulSoup
import sqlite3
import html2text
from urllib.parse import urlparse
from aios import *
class Mail:
def __init__(self, **kwargs) -> None:
self.from_addr = kwargs.get("From")
self.to_addr = kwargs.get("To")
self.subject = kwargs.get("Subject")
self.date = kwargs.get("Date")
self.bcc = kwargs.get("BCC")
self.cc = kwargs.get("CC")
self.reply_to = None
self.from_addr = kwargs.get("from")
self.to_addr = kwargs.get("to")
self.subject = kwargs.get("subject")
self.date = kwargs.get("date")
self.bcc = kwargs.get("bcc")
self.cc = kwargs.get("cc")
self.reply_to = kwargs.get("reply_to")
self.id: str = None
self.content: str = None
@@ -192,20 +195,36 @@ class MailStorage:
self.conn.commit()
await asyncio.sleep(10)
def download(self, uid, mail: mailparser.MailParser):
def download(self, uid, parser: mailparser.MailParser,
save_image=True,
from_field="From",
to_field="To",
subject_field="Subject",
date_field="Date",
reply_to_field="In-Reply-To",
cc_field="CC",
bcc_field="BCC"):
mail_dir = self.mail_dir(uid)
os.makedirs(dir)
if not os.path.exists(mail_dir):
os.makedirs(mail_dir)
meta = json.loads(mail.mail_json)
mail = Mail(**meta)
reply_to = meta.get("In-Reply-To")
src_meta = json.loads(parser.mail_json)
meta = {}
meta["from"] = src_meta.get(from_field)
meta["to"] = src_meta.get(to_field)
meta["subject"] = src_meta.get(subject_field)
meta["date"] = src_meta.get(date_field)
meta["bcc"] = src_meta.get(bcc_field)
meta["cc"] = src_meta.get(cc_field)
reply_to = src_meta.get(reply_to_field)
if reply_to:
mail.reply_to = self.uid_to_object_id(reply_to)
meta["reply_to"] = self.uid_to_object_id(reply_to)
mail = Mail(**meta)
h = html2text.HTML2Text()
h.ignore_links = True
h.ignore_images = True
mail_content = h.handle(mail.body)
mail_content = h.handle(parser.body)
mail.content = mail_content
mail.calculate_id()
@@ -216,8 +235,9 @@ class MailStorage:
with open(f"{mail_dir}/mail.txt", "w", encoding='utf-8') as f:
f.write(mail_content)
for attachment in mail.attachments:
if attachment['mail_content_type'] in ['image/png', 'image/jpeg', 'image/gif']:
if save_image:
for attachment in parser.attachments:
if attachment['mail_content_type'] in ['image/png', 'image/jpg', 'image/jpeg', 'image/gif', 'image/svg']:
filename = attachment['filename']
filefullname = f"{mail_dir}/{filename}"
image_data = attachment['payload']
@@ -230,7 +250,7 @@ class MailStorage:
logging.info(f"save email image {filename} success")
# get all image urls
soup = BeautifulSoup(mail.body, 'html.parser')
soup = BeautifulSoup(parser.body, 'html.parser')
img_tags = soup.find_all('img')
img_urls = [img['src'] for img in img_tags if 'src' in img.attrs]
logging.info(f'Found {len(img_urls)} images in email body')
@@ -239,18 +259,28 @@ class MailStorage:
for img_url in img_urls:
# keep the original image filename(last of url)
ext = img_url.split('/')[-1].split('.')[-1]
url_result = urlparse(img_url)
if url_result.scheme not in ['http', 'https']:
continue
ext = url_result.path.split('/')[-1].split('.')[-1]
if ext in ['png', 'jpg', 'jpeg', 'gif', 'svg']:
img_filename = os.path.join(mail_dir, f"{name_count}.{ext}")
else :
img_filename = os.path.join(mail_dir, f"{name_count}")
name_count += 1
# download image
try:
response = requests.get(img_url, stream=True)
except requests.exceptions.RequestException as e:
logging.error(f'Failed to download {img_url}: {e}')
continue
if response.status_code == 200:
with open(img_filename, 'wb') as img_file:
for chunk in response.iter_content(1024):
img_file.write(chunk)
logging.info(f'Downloaded {img_url} to {img_filename}')
else:
logging.info(f'Failed to download {img_url}')
logging.error(f'Failed to download {img_url}')
cursor = self.conn.cursor()
cursor.execute(
@@ -260,5 +290,8 @@ class MailStorage:
""",
(uid, mail.id, mail.date, mail.from_addr),
)
self.conn.commit()
return mail.id
src/component/mail_environment/spider.py
+24 -5
View File
@@ -1,9 +1,13 @@
import os
import logging
import json
import string
import imaplib
import mailparser
from aios import *
from knowledge import *
from aios_kernel.storage import AIStorage
from .mail import Mail, MailStorage
class EmailSpider:
@@ -16,14 +20,22 @@ class EmailSpider:
port=self.config.get('imap_port')
)
self.client.login(self.config.get('address'), self.config.get('password'))
self.mail_local_root = os.path.join(self.env.pipeline_path, self.config.get("address"))
os.makedirs(self.mail_local_root)
self.client.select("INBOX")
local_path = string.Template(config["path"]).substitute(myai_dir=AIStorage.get_instance().get_myai_dir())
local_path = os.path.join(local_path, self.config.get('address'))
self.mail_storage = MailStorage(local_path)
async def next(self):
while True:
try:
_, data = self.client.uid('search', None, "ALL")
except Exception as e:
self.env.get_logger().error(f"email spider error: {e}")
yield (None, None)
continue
uid_list = data[0].split()
if uid_list.len() == 0:
if len(uid_list) == 0:
yield (None, None)
continue
@@ -43,9 +55,16 @@ class EmailSpider:
_uid = int.from_bytes(uid)
if _uid > from_uid:
message_parts = "(BODY.PEEK[])"
try:
_, email_data = self.client.uid('fetch', uid, message_parts)
mail = mailparser.parse_from_bytes(email_data[0][1])
self.save_email(_uid, mail)
id = self.mail_storage.download(_uid, mail)
except Exception as e:
self.env.get_logger().error(f"email spider error: {e}")
yield (None, None)
break
yield (ObjectID.from_base58(id), str(_uid))
yield (None, None)