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agent learn with pipeline input ok

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tsukasa
2023-12-05 18:50:32 +08:00
parent 1031d527c1
commit f08d709604
28 changed files with 495 additions and 671 deletions
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package-lock.json
Generated
+3
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@@ -0,0 +1,3 @@
{
"lockfileVersion": 1
}
rootfs/agents/JarvisPlus/agent.toml
+47 -3
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@@ -1,12 +1,12 @@
instance_id = "JarvisPlus"
fullname = "JarvisPlus"
llm_model_name = "gpt-4-1106-preview"
max_token_size = 4000
#enable_kb = "true"
enable_timestamp = "true"
owner_prompt = "I am your master {name} , now is {now}"
contact_prompt = "I am your master's friend {name}"
[work.do]
owner_env = ["knowledge"]
[[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.
@@ -58,6 +58,50 @@ The result of my planned execution must be directly parsed by `python json.loads
"""
[[learn.do]]
role = "system"
content = """
我是一名软件工程师，拥有非常优秀的资料学习能力。下面是我学习和整理资料的方法
1. 由于LLM的Token限制，我学习的可能只是资料的部分内容，此时我应能产生合适的学习中间结果，中间结果保存在metadata中。我要么产生中间结果，要么产生最终结果。
2. 当存在已知信息时，需参考已知信息的内容来思考结果。
3. 当我收到最后一部分内容时，我能结合已知的中间结果产生最终结果。
4. 现有资料库以文件系统的形式组织，我未来借助资料的摘要来浏览知识库
5. 我将学习过的资料另存在资料库的合适位置（以/开始的完整路径）。保存位置的目录深度不超过5层，文件夹名称长度不超过16个字符。
6. 总是以json格式返回思考结果，json格式如下
{
"op_list":[
{
"op":"learn",
"original_path":"$original_path",
"think":"$think_result",
"metadata":{...},
"tags":["tag1","tag2"...],
"path":["/graphic/opengl","/database/mysql"], # list of directories to save to.
"title":"$article_title",
"summary":"$summary",
"catalogs": [
{
# optional,catalogs is a tree
"title":"$catalog_name1",
"pos":"$pos:$length"
"children":[
{
"title":"$catalog_name 1.1",
"pos":"$pos:$length"
},
{
"title":"$catalog_name2",
"pos":"$pos:$length"
}
]
},
]
}
]
}
"""
[[prompt]]
role = "system"
content = """
rootfs/knowledge_pipelines/JarvisPlus/pipeline.toml
+8
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@@ -0,0 +1,8 @@
name = "JarvisPlus"
input.module = "scan_local"
input.params.workspace = "${myai_dir}/workspace/JarvisPlus"
input.params.path = "${myai_dir}/data"
parser.module = "parse_local"
parser.params.workspace = "${myai_dir}/workspace/JarvisPlus"
parser.params.assign_to = "JarvisPlus"
rootfs/knowledge_pipelines/Mia/input.py
+1 -2
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@@ -3,8 +3,7 @@ import aiofiles
import chardet
import logging
import string
from knowledge import ImageObjectBuilder, DocumentObjectBuilder, KnowledgePipelineEnvironment, KnowledgePipelineJournal
from aios_kernel.storage import AIStorage
from aios import AIStorage,ImageObjectBuilder, DocumentObjectBuilder, KnowledgePipelineEnvironment, KnowledgePipelineJournal
class KnowledgeDirSource:
def __init__(self, env: KnowledgePipelineEnvironment, config):
rootfs/knowledge_pipelines/Mia/parser.py
+1 -2
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@@ -1,8 +1,7 @@
# define a knowledge base class
import json
import string
from aios_kernel import ComputeKernel, AIStorage
from knowledge import *
from aios import *
class EmbeddingParser:
rootfs/knowledge_pipelines/Mia/query.py
+6 -7
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@@ -1,12 +1,11 @@
import os
import logging
import json
from aios_kernel import *
from knowledge import *
from aios import *
class KnowledgeEnvironment(Environment):
def __init__(self, env_id: str) -> None:
super().__init__(env_id)
class EmbeddingEnvironment(SimpleEnvironment):
def __init__(self, workspace: str) -> None:
super().__init__(workspace)
self.path = os.path.join(AIStorage.get_instance().get_myai_dir(), "knowledge/indices/embedding")
self._default_text_model = "all-MiniLM-L6-v2"
self._default_image_model = "clip-ViT-B-32"
@@ -93,5 +92,5 @@ class KnowledgeEnvironment(Environment):
content = "*** I have provided the following known information for your reference with json format:\n"
return content + self.tokens_from_objects(object_ids[index:index+1])
def init() -> KnowledgeEnvironment:
return KnowledgeEnvironment("embedding")
def init(workspace: str) -> EmbeddingEnvironment:
return EmbeddingEnvironment(workspace)
rootfs/knowledge_pipelines/pipelines.toml
+1 -1
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@@ -1,3 +1,3 @@
pipelines = [
"Mail/Sync"
"JarvisPlus"
]
src/aios/__init__.py
+4 -4
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@@ -6,8 +6,8 @@ from .agent.agent_base import AgentPrompt,CustomAIAgent, AgentTodo
from .agent.chatsession import AIChatSession
from .agent.agent import AIAgent,AIAgentTemplete, BaseAIAgent
from .agent.role import AIRole,AIRoleGroup
from .agent.workflow import Workflow
from .agent.ai_function import SimpleAIFunction
# 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_node import ComputeNode,LocalComputeNode
@@ -17,10 +17,10 @@ from .frame.contact_manager import ContactManager,Contact,FamilyMember
from .frame.queue_compute_node import Queue_ComputeNode
from .environment.environment import BaseEnvironment,SimpleEnvironment,CompositeEnvironment
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.image_2_text_function import Image2TextFunction
from .environment.workspace_env import ShellEnvironment,WorkspaceEnvironment,TodoListEnvironment,TodoListType
from .environment.workspace_env import WorkspaceEnvironment,TodoListEnvironment,TodoListType
from .storage.storage import ResourceLocation,AIStorage,UserConfig,UserConfigItem
src/aios/agent/agent.py
+45 -257
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@@ -33,67 +33,35 @@ from ..utils import video_utils, image_utils
logger = logging.getLogger(__name__)
DEFAULT_AGENT_READ_REPORT_PROMPT = """
"""
# DEFAULT_AGENT_READ_REPORT_PROMPT = """
# """
DEFAULT_AGENT_DO_PROMPT = """
You are a helpful AI assistant.
Solve tasks using your coding and language skills.
In the following cases, suggest python code (in a python coding block) for the user to execute.
1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time, check the operating system. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself.
2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.
Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.
When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.
If you want the user to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.
If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
When you find an answer, verify the answer carefully. Include verifiable evidence in your response if possible.
Reply "TERMINATE" in the end when everything is done.
"""
# DEFAULT_AGENT_DO_PROMPT = """
# You are a helpful AI assistant.
# Solve tasks using your coding and language skills.
# In the following cases, suggest python code (in a python coding block) for the user to execute.
# 1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time, check the operating system. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself.
# 2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.
# Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.
# When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.
# If you want the user to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.
# If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
# When you find an answer, verify the answer carefully. Include verifiable evidence in your response if possible.
# Reply "TERMINATE" in the end when everything is done.
# """
DEFAULT_AGENT_SELF_CHECK_PROMPT = """
# DEFAULT_AGENT_SELF_CHECK_PROMPT = """
"""
# """
DEFAULT_AGENT_GOAL_TO_TODO_PROMPT = """
我会给你一个目标，你需要结合自己的角色思考如何将其拆解成多个TODO。请直接返回json来表达这些TODO
"""
# DEFAULT_AGENT_GOAL_TO_TODO_PROMPT = """
# 我会给你一个目标，你需要结合自己的角色思考如何将其拆解成多个TODO。请直接返回json来表达这些TODO
# """
DEFAULT_AGENT_LEARN_PROMPT = """
我是一名软件工程师，拥有非常优秀的资料学习能力。下面是我学习和整理资料的方法
1. 由于LLM的Token限制，我学习的可能只是资料的部分内容，此时我应能产生合适的学习中间结果，中间结果保存在metadata中。我要么产生中间结果，要么产生最终结果。
2. 当存在已知信息时，需参考已知信息的内容来思考结果。
3. 当我收到最后一部分内容时，我能结合已知的中间结果产生最终结果。
4. 现有资料库以文件系统的形式组织，我未来借助资料的摘要来浏览知识库
5. 我将学习过的资料另存在资料库的合适位置（以/开始的完整路径）。保存位置的目录深度不超过5层，文件夹名称长度不超过16个字符。
6. 总是以json格式返回思考结果，json格式如下
{
think:"$think_result",
metadata:{...} , # temp result for long content
tags:["tag1","tag2"...],
path:["/graphic/opengl","/database/mysql"], # list of directories to save to.
title:"$article_title",
summary:"$summary",
catalogs: [{ # optional,catalogs is a tree
title:"$catalog_name1",
pos:"$pos:$length"
children:[
{
title:"$catalog_name 1.1",
pos:"$pos:$length"
}
]},
{
title:"$catalog_name2",
pos:"$pos:$length"
}
]
}
"""
DEFAULT_AGENT_LEARN_LONG_CONENT_PROMPT = """
我给你一段内容，尝试为期建立目录。目录的标题不能超过16个字，
目录要指向正文的位置（用字符偏移即可），整个目录的文本长度不能超过256个字节。并用json表达这个目录
"""
# DEFAULT_AGENT_LEARN_LONG_CONENT_PROMPT = """
# 我给你一段内容，尝试为期建立目录。目录的标题不能超过16个字，
# 目录要指向正文的位置（用字符偏移即可），整个目录的文本长度不能超过256个字节。并用json表达这个目录
# """
class AIAgentTemplete:
def __init__(self) -> None:
self.llm_model_name:str = "gpt-4-0613"
@@ -149,12 +117,12 @@ class AIAgent(BaseAIAgent):
todo_prompts = {}
todo_prompts[TodoListType.TO_WORK] = {
"do": DEFAULT_AGENT_DO_PROMPT,
"check": DEFAULT_AGENT_SELF_CHECK_PROMPT,
"do": None,
"check": None,
"review": None,
}
todo_prompts[TodoListType.TO_LEARN] = {
"do": DEFAULT_AGENT_LEARN_PROMPT,
"do": None,
"check": None,
"review": None,
}
@@ -313,9 +281,6 @@ class AIAgent(BaseAIAgent):
if event.type == "AgentThink":
return await self.do_self_think()
# async def _process_group_chat_msg(self,msg:AgentMsg) -> AgentMsg:
# session_topic = msg.target + "#" + msg.topic
# chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
@@ -500,7 +465,7 @@ class AIAgent(BaseAIAgent):
known_info_str = "# Known information\n"
have_known_info = False
todos_str,todo_count = await workspace.get_todo_tree()
todos_str,todo_count = await workspace.todo_list[TodoListType.TO_WORK].get_todo_tree()
if todo_count > 0:
have_known_info = True
known_info_str += f"## todo\n{todos_str}\n"
@@ -523,7 +488,7 @@ class AIAgent(BaseAIAgent):
logger.debug(f"Agent {self.agent_id} do llm token static system:{system_prompt_len},function:{function_token_len},history:{history_token_len},input:{input_len}, totoal prompt:{system_prompt_len + function_token_len + history_token_len} ")
task_result = await self.do_llm_complection(prompt,msg, env=self.agent_workspace,inner_functions=inner_functions)
task_result = await self.do_llm_complection(prompt,msg, inner_functions=inner_functions)
if task_result.result_code != ComputeTaskResultCode.OK:
error_resp = msg.create_error_resp(task_result.error_str)
return error_resp
@@ -700,7 +665,7 @@ class AIAgent(BaseAIAgent):
# await self._llm_review_todolist(workspace)
todo_list = workspace.todo_list[todo_list_type]
need_todo = todo_list.get_todo_list(self.agent_id)
need_todo = await todo_list.get_todo_list(self.agent_id)
check_count = 0
do_count = 0
@@ -826,7 +791,7 @@ class AIAgent(BaseAIAgent):
return do_prompts
async def _can_do_todo(self, todo_list_type: TodoListType, todo:AgentTodo) -> AgentPrompt:
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
@@ -848,7 +813,7 @@ class AIAgent(BaseAIAgent):
async def _llm_do_todo(self, todo: AgentTodo, prompt: AgentPrompt, workspace: WorkspaceEnvironment) -> AgentTodoResult:
result = AgentTodoResult()
task_result:ComputeTaskResult = await self.do_llm_complection(prompt)
task_result:ComputeTaskResult = await self.do_llm_complection(prompt, is_json_resp=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
@@ -902,204 +867,27 @@ class AIAgent(BaseAIAgent):
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
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}")
# 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 = {}
self.agent_energy -= 1
# llm_blance_knowledge_base(current_path,current_list,self_assessment_with_goal,learn_goal,learn_power)
# 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
# # 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:
src/aios/agent/agent_base.py
+9 -6
View File
@@ -14,7 +14,7 @@ from typing import List, Tuple
from .ai_function import FunctionItem, AIFunction
from ..proto.agent_msg import AgentMsg, AgentMsgType
from ..proto.compute_task import ComputeTaskResult,ComputeTaskResultCode
from ..environment.environment import Environment
from ..environment.environment import BaseEnvironment
logger = logging.getLogger(__name__)
@@ -129,6 +129,8 @@ class LLMResult:
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
@@ -368,7 +370,7 @@ class AgentTodo:
case AgentTodo.TODO_STATE_DONE:
logger.info(f"todo {self.title} is done, ignore")
return False
case AgentTodo.TODO_STATE_CASNCEL:
case AgentTodo.TODO_STATE_CANCEL:
logger.info(f"todo {self.title} is cancel, ignore")
return False
case AgentTodo.TODO_STATE_EXPIRED:
@@ -419,7 +421,7 @@ class BaseAIAgent(abc.ABC):
pass
def token_len(self, text:str=None, prompt:AgentPrompt=None) -> int:
from .compute_kernel import ComputeKernel
from ..frame.compute_kernel import ComputeKernel
if text:
return ComputeKernel.llm_num_tokens_from_text(text,self.get_llm_model_name())
elif prompt:
@@ -428,11 +430,12 @@ class BaseAIAgent(abc.ABC):
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
def get_inner_functions(cls, env:Environment) -> (dict,int):
def get_inner_functions(cls, env:BaseEnvironment) -> (dict,int):
if env is None:
return None,0
@@ -457,7 +460,7 @@ class BaseAIAgent(abc.ABC):
self,
prompt:AgentPrompt,
org_msg:AgentMsg=None,
env:Environment=None,
env:BaseEnvironment=None,
inner_functions=None,
is_json_resp=False,
) -> ComputeTaskResult:
@@ -510,7 +513,7 @@ class BaseAIAgent(abc.ABC):
async def _execute_func(
self,
env: Environment,
env: BaseEnvironment,
inner_func_call_node: dict,
prompt: AgentPrompt,
inner_functions: dict,
src/aios/agent/ai_function.py
+1 -1
View File
@@ -182,7 +182,7 @@ class SimpleAIOperation(AIOperation):
if self.func_handler is None:
return "error: function not implemented"
return await self.func_handler(**params)
return await self.func_handler(params)
class AIFunctionOperation(AIOperation):
src/aios/agent/workflow.py
+7 -7
View File
@@ -18,7 +18,7 @@ from .ai_function import AIFunction,FunctionItem
from ..frame.compute_kernel import ComputeKernel
from ..frame.bus import AIBus
from ..environment.environment import Environment,EnvironmentEvent
from ..environment.environment import BaseEnvironment
from ..environment.workflow_env import WorkflowEnvironment
@@ -490,15 +490,15 @@ class Workflow:
def get_workflow_rule_prompt(self) -> AgentPrompt:
return self.rule_prompt
def _env_event_to_msg(self,env_event:EnvironmentEvent) -> AgentMsg:
# def _env_event_to_msg(self,env_event:EnvironmentEvent) -> AgentMsg:
# pass
def get_inner_environment(self,env_id:str) -> BaseEnvironment:
pass
def get_inner_environment(self,env_id:str) -> Environment:
pass
def connect_to_environment(self,the_env:Environment,conn_info:dict) -> None:
def connect_to_environment(self,the_env:BaseEnvironment,conn_info:dict) -> None:
if the_env is not None:
self.workflow_env.add_owner_env(the_env)
self.workflow_env.add_env(the_env)
#for event2msg in conn_info:
# for k,v in event2msg:
src/aios/environment/environment.py
+8 -27
View File
@@ -36,7 +36,12 @@ class BaseEnvironment:
def get_all_ai_operations(self) -> List[AIOperation]:
pass
def __getitem__(self, key):
return self.get_value(key)
@abstractmethod
def get_value(self,key:str) -> Optional[str]:
pass
# _all_env = {}
# @classmethod
@@ -84,40 +89,16 @@ class SimpleEnvironment(BaseEnvironment):
class CompositeEnvironment(BaseEnvironment):
class CompositeEnvironment(SimpleEnvironment):
def __init__(self, workspace: str) -> None:
super().__init__(workspace)
self.envs:List[BaseEnvironment] = {}
self.functions: Dict[str,AIFunction] = {}
self.operations: Dict[str,AIOperation] = {}
self.envs: List[BaseEnvironment] = []
def add_env(self, env: BaseEnvironment) -> None:
self.envs.append[env]
self.envs.append(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/workflow_env.py
+7 -7
View File
@@ -15,13 +15,13 @@ from ..frame.compute_kernel import ComputeKernel
from ..frame.contact_manager import ContactManager,Contact,FamilyMember
from ..storage.storage import AIStorage
from .environment import Environment,EnvironmentEvent
from .environment import SimpleEnvironment, CompositeEnvironment
from .script_to_speech_function import ScriptToSpeechFunction
from .image_2_text_function import Image2TextFunction
logger = logging.getLogger(__name__)
class CalenderEvent(EnvironmentEvent):
class CalenderEvent(SimpleEnvironment):
def __init__(self,data) -> None:
super().__init__()
self.event_name = "timer"
@@ -31,7 +31,7 @@ class CalenderEvent(EnvironmentEvent):
return f"#event timer:{self.data}"
# AI Calender GOAL: Let user use "create notify after 2 days" to create a timer event
class CalenderEnvironment(Environment):
class CalenderEnvironment(SimpleEnvironment):
def __init__(self, env_id: str) -> None:
super().__init__(env_id)
self.db_file = AIStorage.get_instance().get_myai_dir() / "calender.db"
@@ -302,7 +302,7 @@ class CalenderEnvironment(Environment):
return f'exec paint OK, saved as a local file, path is: {result.result["file"]}'
class PaintEnvironment(Environment):
class PaintEnvironment(BaseEnvironment):
def __init__(self, env_id: str) -> None:
super().__init__(env_id)
self.is_run = False
@@ -327,14 +327,14 @@ class PaintEnvironment(Environment):
# Default Workflow Environment(Context)
class WorkflowEnvironment(Environment):
class WorkflowEnvironment(CompositeEnvironment):
def __init__(self, env_id: str,db_file:str) -> None:
super().__init__(env_id)
self.db_file = db_file
self.local = threading.local()
self.table_name = "WorkflowEnv_" + env_id
self.add_ai_function(ScriptToSpeechFunction())
self.add_ai_function(Image2TextFunction())
# self.add_ai_function(ScriptToSpeechFunction())
# self.add_ai_function(Image2TextFunction())
def _get_conn(self):
src/aios/environment/workspace_env.py
+27 -183
View File
@@ -1,5 +1,3 @@
# this env is designed for workflow owner filesystem, support file/directory operations
import json
import logging
import os
@@ -166,10 +164,8 @@ class TodoListEnvironment(SimpleEnvironment):
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)
with open(detail_path, mode='r', encoding="utf-8") as f:
todo_dict = json.load(f)
result_todo = AgentTodo.from_dict(todo_dict)
if result_todo:
relative_path = os.path.relpath(path, self.root_path)
@@ -189,9 +185,9 @@ class TodoListEnvironment(SimpleEnvironment):
try:
if parent_id:
parent_path = self._get_todo_path(parent_id)
todo_path = f"{parent_path}/{todo.title}"
todo_path = f"{parent_path}/{todo.todo_id}-{todo.title}"
else:
todo_path = todo.title
todo_path = f"{todo.todo_id}-{todo.title}"
dir_path = f"{self.root_path}/{todo_path}"
@@ -212,10 +208,11 @@ class TodoListEnvironment(SimpleEnvironment):
async def update_todo(self,todo_id:str,new_stat:str)->str:
try:
todo_path = self._get_todo_path(todo_id)
todo : AgentTodo = self.get_todo_by_fullpath(todo_path)
full_path = f"{self.root_path}/{todo_path}"
todo : AgentTodo = await self.get_todo_by_fullpath(full_path)
if todo:
todo.state = new_stat
detail_path = f"{self.root_path}/{todo.todo_path}/detail"
detail_path = f"{full_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
@@ -224,18 +221,32 @@ class TodoListEnvironment(SimpleEnvironment):
except Exception as e:
return str(e)
async def wait_todo_done(self,todo_id:str) -> AgentTodo:
async def wait_todo_done(self,todo_id:str,state=AgentTodo.TODO_STATE_WAITING_CHECK) -> AgentTodo:
todo_path = self._get_todo_path(todo_id)
full_path = f"{self.root_path}/{todo_path}"
async def check_done():
while True:
todo : AgentTodo = self.get_todo_by_fullpath(todo_path)
if todo:
if todo.state == AgentTodo.TODO_STATE_DONE:
todo : AgentTodo = await self.get_todo_by_fullpath(full_path)
if todo is None:
continue
if todo.state == AgentTodo.TODO_STATE_CANCEL:
break
elif todo.state == AgentTodo.TODO_STATE_EXPIRED:
break
elif todo.state == AgentTodo.TODO_STATE_WAITING_CHECK:
if state == AgentTodo.TODO_STATE_WAITING_CHECK:
break
elif todo.state == AgentTodo.TODO_STATE_DONE:
if state == AgentTodo.TODO_STATE_WAITING_CHECK:
break
elif todo.state == AgentTodo.TODO_STATE_DONE:
break
elif todo.state == AgentTodo.TODO_STATE_REVIEWED:
break
await asyncio.sleep(1)
asyncio.create_task(check_done())
return self.get_todo_by_fullpath(todo_path)
await check_done()
return await self.get_todo_by_fullpath(full_path)
async def append_worklog(self, todo:AgentTodo, result:AgentTodoResult):
@@ -254,171 +265,6 @@ class TodoListEnvironment(SimpleEnvironment):
json_obj["logs"] = logs
await f.write(json.dumps(json_obj))
class FilesystemEnvironment(SimpleEnvironment):
def __init__(self, workspace: str) -> None:
super().__init__(workspace)
self.root_path = workspace
# 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
return await self.write(op["path"],op["content"],is_append)
self.add_ai_operation(SimpleAIOperation(
op="write",
description="write file",
func_handler=write,
))
async def delete(op):
return await self.delete(op["path"])
self.add_ai_operation(SimpleAIOperation(
op="delete",
description="delete path",
func_handler=delete,
))
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
async def list(self,path:str,only_dir:bool=False) -> str:
directory_path = self.root_path + path
items = []
with await aiofiles.os.scandir(directory_path) as entries:
async for entry in entries:
is_dir = entry.is_dir()
if only_dir and not is_dir:
continue
item_type = "directory" if is_dir else "file"
items.append({"name": entry.name, "type": item_type})
return json.dumps(items)
# inner_function
async def read(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
# operation or inner_function (MOST IMPORTANT FUNCTION)
async def write(self,path:str,content:str,is_append:bool=False) -> str:
file_path = self.root_path + path
try:
if is_append:
async with aiofiles.open(file_path, mode='a', encoding="utf-8") as f:
await f.write(content)
else:
if content is None:
# create dir
dir_path = self.root_path + path
os.makedirs(dir_path)
return True
else:
file_path = self.root_path + path
os.makedirs(os.path.dirname(file_path),exist_ok=True)
async with aiofiles.open(file_path, mode='w', encoding="utf-8") as f:
await f.write(content)
return True
except Exception as e:
return str(e)
return None
# operation or inner_function
async def delete(self,path:str) -> str:
try:
file_path = self.root_path + path
os.remove(file_path)
except Exception as e:
return str(e)
return None
# operation or inner_function
async def move(self,path:str,new_path:str) -> str:
try:
file_path = self.root_path + path
new_path = self.root_path + new_path
os.rename(file_path,new_path)
except Exception as e:
return str(e)
return None
# inner_function
async def stat(self,path:str) -> str:
try:
file_path = self.root_path + path
stat = os.stat(file_path)
return json.dumps(stat)
except Exception as e:
return str(e)
# operation or inner_function
async def symlink(self,path:str,target:str) -> str:
try:
#file_path = self.root_path + path
target_path = self.root_path + target
dir_path = os.path.dirname(target_path)
os.makedirs(dir_path,exist_ok=True)
os.symlink(path,target_path)
except Exception as e:
logger.error("symlink failed:%s",e)
return str(e)
return None
class ShellEnvironment(SimpleEnvironment):
def __init__(self, workspace: str) -> None:
super().__init__(workspace)
operator_param = {
"command": "command will execute",
}
self.add_ai_function(SimpleAIFunction("shell_exec",
"execute shell command in linux bash",
self.shell_exec,operator_param))
#run_code_param = {
# "pycode": "python code will execute",
#}
#self.add_ai_function(SimpleAIFunction("run_code",
# "execute python code",
# self.run_code,run_code_param))
async def shell_exec(self,command:str) -> str:
import asyncio.subprocess
process = await asyncio.create_subprocess_shell(
command,
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE
)
stdout, stderr = await process.communicate()
returncode = process.returncode
if returncode == 0:
return f"Execute success! stdout is:\n{stdout}\n"
else:
return f"Execute failed! stderr is:\n{stderr}\n"
class WorkspaceEnvironment(CompositeEnvironment):
def __init__(self, env_id: str) -> None:
@@ -436,8 +282,6 @@ class WorkspaceEnvironment(CompositeEnvironment):
# default environments in workspace
self.add_env(self.todo_list[TodoListType.TO_WORK])
self.add_env(ShellEnvironment(self.root_path))
self.add_env(FilesystemEnvironment(self.root_path))
def set_root_path(self,path:str):
self.root_path = path
src/aios/knowledge/object/object.py
+3 -3
View File
@@ -73,6 +73,6 @@ class KnowledgeObject(ABC):
def encode(self) -> bytes:
return pickle.dumps(self)
# @staticmethod
# def decode(data: bytes) -> "ImageObject":
# return pickle.loads(data)
@staticmethod
def decode(data: bytes) -> "KnowledgeObject":
return pickle.loads(data)
src/aios/knowledge/pipeline.py
+1 -1
View File
@@ -47,7 +47,7 @@ class KnowledgePipelineJournalClient:
timestamp = datetime.datetime.now() if timestamp is None else timestamp
conn = sqlite3.connect(self.journal_path)
conn.execute(
"INSERT INTO journal (time, input, parser) VALUES (?, ?, ?, ?)",
"INSERT INTO journal (time, input, parser) VALUES (?, ?, ?)",
(timestamp, input, parser),
)
conn.commit()
src/component/agent_manager/agent_manager.py
+5 -5
View File
@@ -50,15 +50,15 @@ class AgentManager:
async def scan_all_agent(self)->None:
pass
async def register_environment(self, env_id: str, init_env) -> None:
def register_environment(self, env_id: str, init_env) -> None:
self.environments[env_id] = init_env
async def init_environment(self, env_id: str, workspace: str):
def init_environment(self, env_id: str, workspace: str):
if env_id not in self.environments:
logger.error(f"env {env_id} not found!")
return
return self.environments[env_id]
return self.environments[env_id](workspace)
async def is_exist(self,agent_id:str) -> bool:
the_aget = await self.get(agent_id)
@@ -127,9 +127,9 @@ class AgentManager:
owner_env = config["owner_env"]
def init_env(env_config: str):
_, ext = os.path.splitext(owner_env)
_, ext = os.path.splitext(env_config)
if ext == ".py":
env_path = os.path.join(agent_media.full_path, owner_env)
env_path = os.path.join(agent_media.full_path, env_config)
env = runpy.run_path(env_path)["init"](None, workspace.root_path)
else:
env = self.init_environment(env_config, workspace.root_path)
src/component/common_environment/__init__.py
+3
View File
@@ -0,0 +1,3 @@
from .local_document import LocalKnowledgeBase, ScanLocalDocument, ParseLocalDocument
from .local_file_system import FilesystemEnvironment
from .shell import ShellEnvironment
src/component/common_environment/local_document.py
+99 -132
View File
@@ -4,13 +4,18 @@ import chardet
import string
import sqlite3
import json
import re
import threading
import logging
from datetime import datetime
import hashlib
from markdown import Markdown
import PyPDF2
import datetime
from typing import Optional, List
from aios import KnowledgePipelineEnvironment, AIStorage, SimpleEnvironment, TodoListEnvironment, TodoListType, AgentTodo, CustomAIAgent
from aios import *
from .local_file_system import FilesystemEnvironment
logger = logging.getLogger(__name__)
class MetaDatabase:
def __init__(self,db_path:str):
@@ -79,7 +84,7 @@ class MetaDatabase:
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")
create_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cursor.execute('''
INSERT INTO documents (doc_path, length, last_modify, doc_hash,create_time)
VALUES (?, ?, ?, ?,?)
@@ -125,9 +130,9 @@ class MetaDatabase:
conn = self._get_conn()
cursor = conn.cursor()
create_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
create_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
summary = metadata.get("summary", "")
catalogs = metadata.get("catalogs","")
catalogs = json.dumps(metadata.get("catalogs", {}))
title = metadata.get("title","")
tags = ','.join(metadata.get("tags", []))
@@ -140,14 +145,14 @@ class MetaDatabase:
#llm_result["summary"]
#llm_result["tags"]
#llm_result["catalog"]
def set_knowledge_llm_result(self, doc_hash: str, llm_result: dict):
def set_knowledge_llm_result(self, doc_hash: str, meta: 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", []))
title = meta.get("title", "")
summary = meta.get("summary", "")
catalogs = json.dumps(meta.get("catalogs", {}))
tags = ','.join(meta.get("tags", []))
cursor.execute('''
UPDATE knowledge
@@ -156,6 +161,7 @@ class MetaDatabase:
''', (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()
@@ -227,12 +233,60 @@ class MetaDatabase:
''', (tag))
return [row[0] for row in cursor.fetchall()]
# singleton
class LearningCache:
_instance_lock = threading.Lock()
_instance = None
class LocalKnowledgeBase(SimpleEnvironment):
def __instance_init__(self):
self.cache = {}
self.cache_lock = threading.Lock()
def __new__(cls, *args, **kwargs):
if cls._instance is None:
with LearningCache._instance_lock:
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance.__instance_init__()
return cls._instance
def add(self, key, value):
with self.cache_lock:
self.cache[key] = value
def get(self, key):
with self.cache_lock:
return self.cache.get(key)
def remove(self, key):
with self.cache_lock:
return self.cache.pop(key, None)
class LocalKnowledgeBase(CompositeEnvironment):
def __init__(self, workspace: str) -> None:
super().__init__(workspace)
self.root_path = f"{self.root_path}/knowledge"
self.root_path = f"{workspace}/knowledge"
if os.path.exists(self.root_path) is False:
os.makedirs(self.root_path)
self.meta_db = MetaDatabase(f"{self.root_path}/kb.db")
self.learning_cache = LearningCache()
async def learn(op:dict):
full_path = op.get("original_path")
if not full_path:
return
meta = self.learning_cache.get(full_path)
meta.update(op)
self.add_ai_operation(SimpleAIOperation(
op="learn",
description="update knowledge llm summary",
func_handler=learn,
))
self.fs = FilesystemEnvironment(self.root_path)
self.add_env(self.fs)
async def get_knowledege_catalog(self,path:str=None,only_dir =True,max_depth:int=5)->str:
if path:
@@ -344,22 +398,32 @@ class ScanLocalDocument:
class ParseLocalDocument:
def __init__(self, env: KnowledgePipelineEnvironment, config):
def __init__(self, env: KnowledgePipelineEnvironment, config: dict):
self.env = env
workspace = string.Template(config["workspace"]).substitute(myai_dir=AIStorage.get_instance().get_myai_dir())
self.todo_list = TodoListEnvironment(workspace, TodoListType.TO_LEARN)
self.knowledge_base = LocalKnowledgeBase(workspace)
self.token_limit = config["token_limit"]
self.token_limit = config.get("token_limit", 4000)
self.assign_to = config.get("assign_to")
async def parse(self, full_path: str) -> str:
file_stat = os.stat(full_path)
if file_stat.st_size < 1:
return full_path
hash, meta_data = self._parse_document(full_path)
await self._learn(meta_data, full_path)
hash, parse_meta = self._parse_document(full_path)
parse_meta["original_path"] = full_path
llm_meta = await self._learn_by_agent(parse_meta)
self.knowledge_base.meta_db.add_doc(full_path,file_stat.st_size,file_stat.st_mtime,hash)
self.knowledge_base.meta_db.add_knowledge(hash,meta_data)
self.knowledge_base.meta_db.add_knowledge(hash,parse_meta)
self.knowledge_base.meta_db.set_knowledge_llm_result(hash,llm_meta)
path_list = llm_meta.get("path")
new_title = llm_meta.get("title")
if path_list:
for new_path in path_list:
new_path = f"{new_path}/{new_title}"
await self.knowledge_base.fs.symlink(full_path, new_path)
logger.info(f"create soft link {full_path} -> {new_path}")
return full_path
async def _get_meta_prompt(self,meta: dict,temp_meta = None,need_catalogs = False) -> str:
@@ -384,15 +448,15 @@ class ParseLocalDocument:
for key in temp_meta.keys():
known_obj[key] = temp_meta[key]
org_path = meta.get("full_path")
known_obj["orginal_path"] = org_path
org_path = meta.get("original_path")
known_obj["original_path"] = org_path
return f"# Known information:\n## Current directory structure:\n{kb_tree}\n## Knowlege Metadata:\n{json.dumps(known_obj)}\n"
async def _token_len(self, text: str) -> int:
def _token_len(self, text: str) -> int:
return CustomAIAgent("", "gpt-4-1106-preview", self.token_limit).token_len(text=text)
async def _learn(self, meta:dict, full_path:str):
async def _learn_by_agent(self, meta:dict) -> dict:
# 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)
@@ -405,24 +469,26 @@ class ParseLocalDocument:
# 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 = self.knowledge_base.load_knowledge_content(full_path)
full_content = await self.knowledge_base.load_knowledge_content(meta["original_path"])
full_content_len = self._token_len(full_content)
full_path = meta["original_path"]
self.knowledge_base.learning_cache.add(full_path, meta)
if full_content_len < self.token_limit():
if full_content_len < self.token_limit:
# 短文章不用总结catalog
todo = AgentTodo()
todo.worker = self.assign_to
todo.title = meta["title"]
meta_prompt = await self._get_meta_prompt(meta,None)
todo.detail = meta_prompt + full_content
self.todo_list.create_todo(None, todo)
await self.todo_list.create_todo(None, todo)
await self.todo_list.wait_todo_done(todo.todo_id)
else:
logger.warning(f"llm_read_article: article {full_path} use LLM loop learn!")
pos = 0
read_len = int(self.token_limit() * 1.2)
read_len = int(self.token_limit * 1.2)
temp_meta = {}
is_final = False
while pos < full_content_len:
_content = full_content[pos:pos+read_len]
@@ -435,16 +501,17 @@ class ParseLocalDocument:
part_content = f"<<Part:start at {pos}>>\n{_content}"
pos = pos + read_len
temp_meta = self.knowledge_base.learning_cache.get(full_path)
todo = AgentTodo()
todo.worker = self.assign_to
todo.title = meta["title"]
meta_prompt = await self._get_meta_prompt(meta,temp_meta)
todo.detail = meta_prompt + part_content
self.todo_list.create_todo(None, todo)
todo = await self.todo_list.wait_todo_done(todo.todo_id)
result_obj = json.loads(todo.result.result_str)
temp_meta = result_obj
if is_final:
break
return self.knowledge_base.learning_cache.remove(full_path)
def _parse_pdf_bookmarks(self,bookmarks, parent:list):
for item in bookmarks:
@@ -543,109 +610,9 @@ class ParseLocalDocument:
logger.error("parse document %s failed:%s",doc_path,e)
# traceback.print_exc()
if not "title" in meta_data:
meta_data["title"] = title
logger.info("parse document %s!",doc_path)
return hash_result, meta_data
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
src/component/common_environment/local_file_system.py
+139
View File
@@ -0,0 +1,139 @@
import json
import os
import aiofiles
from typing import Any,List,Dict
import chardet
from aios import SimpleAIOperation
from aios import SimpleEnvironment
class FilesystemEnvironment(SimpleEnvironment):
def __init__(self, workspace: str) -> None:
super().__init__(workspace)
self.root_path = workspace
# 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
return await self.write(op["path"],op["content"],is_append)
self.add_ai_operation(SimpleAIOperation(
op="write",
description="write file",
func_handler=write,
))
async def delete(op):
return await self.delete(op["path"])
self.add_ai_operation(SimpleAIOperation(
op="delete",
description="delete path",
func_handler=delete,
))
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
async def list(self,path:str,only_dir:bool=False) -> str:
directory_path = self.root_path + path
items = []
with await aiofiles.os.scandir(directory_path) as entries:
async for entry in entries:
is_dir = entry.is_dir()
if only_dir and not is_dir:
continue
item_type = "directory" if is_dir else "file"
items.append({"name": entry.name, "type": item_type})
return json.dumps(items)
# inner_function
async def read(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
# operation or inner_function (MOST IMPORTANT FUNCTION)
async def write(self,path:str,content:str,is_append:bool=False) -> str:
file_path = self.root_path + path
try:
if is_append:
async with aiofiles.open(file_path, mode='a', encoding="utf-8") as f:
await f.write(content)
else:
if content is None:
# create dir
dir_path = self.root_path + path
os.makedirs(dir_path)
return True
else:
file_path = self.root_path + path
os.makedirs(os.path.dirname(file_path),exist_ok=True)
async with aiofiles.open(file_path, mode='w', encoding="utf-8") as f:
await f.write(content)
return True
except Exception as e:
return str(e)
return None
# operation or inner_function
async def delete(self,path:str) -> str:
try:
file_path = self.root_path + path
os.remove(file_path)
except Exception as e:
return str(e)
return None
# operation or inner_function
async def move(self,path:str,new_path:str) -> str:
try:
file_path = self.root_path + path
new_path = self.root_path + new_path
os.rename(file_path,new_path)
except Exception as e:
return str(e)
return None
# inner_function
async def stat(self,path:str) -> str:
try:
file_path = self.root_path + path
stat = os.stat(file_path)
return json.dumps(stat)
except Exception as e:
return str(e)
# operation or inner_function
async def symlink(self,path:str,target:str) -> str:
try:
#file_path = self.root_path + path
target_path = self.root_path + target
dir_path = os.path.dirname(target_path)
os.makedirs(dir_path,exist_ok=True)
os.symlink(path,target_path)
except Exception as e:
logger.error("symlink failed:%s",e)
return str(e)
return None
src/component/common_environment/shell.py
+38
View File
@@ -0,0 +1,38 @@
import os
from typing import Any,List,Dict
from aios import AgentMsg,AgentTodo,AgentPrompt
from aios import SimpleAIFunction, SimpleAIOperation
from aios import SimpleEnvironment
class ShellEnvironment(SimpleEnvironment):
def __init__(self, workspace: str) -> None:
super().__init__(workspace)
operator_param = {
"command": "command will execute",
}
self.add_ai_function(SimpleAIFunction("shell_exec",
"execute shell command in linux bash",
self.shell_exec,operator_param))
#run_code_param = {
# "pycode": "python code will execute",
#}
#self.add_ai_function(SimpleAIFunction("run_code",
# "execute python code",
# self.run_code,run_code_param))
async def shell_exec(self,command:str) -> str:
import asyncio.subprocess
process = await asyncio.create_subprocess_shell(
command,
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE
)
stdout, stderr = await process.communicate()
returncode = process.returncode
if returncode == 0:
return f"Execute success! stdout is:\n{stdout}\n"
else:
return f"Execute failed! stderr is:\n{stderr}\n"
src/component/openai_node/open_ai_node.py
+1 -1
View File
@@ -214,7 +214,7 @@ class OpenAI_ComputeNode(ComputeNode):
client = AsyncOpenAI(api_key=self.openai_api_key)
try:
if llm_inner_functions is None:
if llm_inner_functions is None or len(llm_inner_functions) == 0:
logger.info(f"call openai {mode_name} prompts: {prompts}")
resp = await client.chat.completions.create(model=mode_name,
messages=prompts,
src/component/st_node/local_st_compute_node.py
+1 -1
View File
@@ -116,7 +116,7 @@ class LocalSentenceTransformer_Image_ComputeNode(Queue_ComputeNode):
def _load_image(self, source: Union[ObjectID, bytes]) -> Optional[Image]:
image_data = None
if isinstance(source, ObjectID):
from knowledge import KnowledgeStore, ImageObject
from aios import KnowledgeStore, ImageObject
buf = KnowledgeStore().get_object_store().get_object(source)
if buf is None:
src/component/workflow_manager/workflow_manager.py
+1 -1
View File
@@ -2,7 +2,7 @@ import logging
import toml
import os
from aios import Workflow,AIStorage,PackageEnv,PackageEnvManager,PackageMediaInfo,PackageInstallTask
from aios import AIStorage,PackageEnv,PackageEnvManager,PackageMediaInfo,PackageInstallTask
from agent_manager import AgentManager
logger = logging.getLogger(__name__)
src/package-lock.json
Generated
+3
View File
@@ -0,0 +1,3 @@
{
"lockfileVersion": 1
}
src/requirements.txt
+1 -2
View File
@@ -47,7 +47,7 @@ mpmath>=1.3.0
multidict>=6.0.4
numpy>=1.25.2
onnxruntime>=1.15.1
openai>=0.28.0
openai>=1.0.0
overrides>=7.4.0
packaging>=23.1
pandas>=2.1.0
@@ -97,7 +97,6 @@ mpmath==1.3.0
multidict==6.0.4
numpy==1.25.2
onnxruntime==1.15.1
openai==0.28.0
overrides==7.4.0
packaging==23.1
pandas==2.1.0
src/service/aios_shell/aios_shell.py
+22 -15
View File
@@ -40,10 +40,11 @@ from sd_node import *
from st_node import *
from agent_manager import AgentManager
from workflow_manager import WorkflowManager
# from workflow_manager import WorkflowManager
from knowledge_manager import KnowledgePipelineManager
from tg_tunnel import TelegramTunnel
from email_tunnel import EmailTunnel
from common_environment import LocalKnowledgeBase, FilesystemEnvironment, ShellEnvironment, ScanLocalDocument, ParseLocalDocument
from compute_node_config import *
@@ -130,22 +131,26 @@ class AIOS_Shell:
cm.add_family_member(self.username,owenr)
cal_env = CalenderEnvironment("calender")
await cal_env.start()
Environment.set_env_by_id("calender",cal_env)
# cal_env = CalenderEnvironment("calender")
# await cal_env.start()
# Environment.set_env_by_id("calender",cal_env)
workspace_env = ShellEnvironment("bash")
Environment.set_env_by_id("bash",workspace_env)
# workspace_env = ShellEnvironment("bash")
# Environment.set_env_by_id("bash",workspace_env)
paint_env = PaintEnvironment("paint")
Environment.set_env_by_id("paint",paint_env)
# paint_env = PaintEnvironment("paint")
# Environment.set_env_by_id("paint",paint_env)
AgentManager.get_instance().register_environment("bash", ShellEnvironment)
AgentManager.get_instance().register_environment("fs", FilesystemEnvironment)
AgentManager.get_instance().register_environment("knowledge", LocalKnowledgeBase)
if await AgentManager.get_instance().initial() is not True:
logger.error("agent manager initial failed!")
return False
if await WorkflowManager.get_instance().initial() is not True:
logger.error("workflow manager initial failed!")
return False
# if await WorkflowManager.get_instance().initial() is not True:
# logger.error("workflow manager initial failed!")
# return False
open_ai_node = OpenAI_ComputeNode.get_instance()
if await open_ai_node.initial() is not True:
@@ -217,6 +222,8 @@ class AIOS_Shell:
pipelines = KnowledgePipelineManager.initial(os.path.join(AIStorage().get_instance().get_myai_dir(), "knowledge/pipelines"))
pipelines.register_input("scan_local", ScanLocalDocument)
pipelines.register_parser("parse_local", ParseLocalDocument)
pipelines.load_dir(os.path.join(AIStorage().get_instance().get_system_app_dir(), "knowledge_pipelines"))
pipelines.load_dir(os.path.join(AIStorage().get_instance().get_myai_dir(), "knowledge_pipelines"))
asyncio.create_task(pipelines.run())
@@ -568,8 +575,8 @@ class AIOS_Shell:
target_exist = False
if await AgentManager.get_instance().is_exist(target_id):
target_exist = True
if await WorkflowManager.get_instance().is_exist(target_id):
target_exist = True
# if await WorkflowManager.get_instance().is_exist(target_id):
# target_exist = True
if target_exist is False:
show_text = FormattedText([("class:error", f"Target {target_id} not exist!")])
@@ -627,8 +634,8 @@ class AIOS_Shell:
db_path = ""
if await self.is_agent(self.current_target):
db_path = AgentManager.get_instance().db_path
else:
db_path = WorkflowManager.get_instance().db_file
# else:
# db_path = WorkflowManager.get_instance().db_file
chatsession:AIChatSession = AIChatSession.get_session(self.current_target,f"{self.username}#{self.current_topic}",db_path,False)
if chatsession is not None:
msgs = chatsession.read_history(num,offset)