Complete self_thinking llm process and Agent Memory
This commit is contained in:
Liu Zhicong
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## LLM / AI 相关框架
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### LLM Process
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LLM调用封装的最小单元，提供了一系列最基础的支持
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流程上 inpurt, prepare promot, llm_function_call_loop, post_llm , llmresult parser, AI Action
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功能上 动态类型系统 load_from_config,llm_process_loader
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### Agent
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从Agent的视角定义了Agent的LLM行为逻辑
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Process behavior (响应)
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Task/Todo Loop (自主)
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Self Loop (自省)
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#### Agent.Memory
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#### Agent.Workspace
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#### Agent.behavior
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### Workflow
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一组Agent共享Work space后的流程
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Task可以分配给不同的Agent
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Todo的Do和Check可以分配给不同的Agent
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## Knowledge Base （sisi)
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AI First的未来文件系统
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## Agent 能力扩展框架
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### AI Function / Action
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最重要的扩展框架
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### Environment
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可以通过 {environment.xxx} 读取
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### Code Interpreter
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Agent 能不能写代码是一个重要的理念之争
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能写代码的Agent想象空间大，是通往AGI的必然之路，但不够稳定可预期
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不能写代码的Agent可以专注于组合使用基础的能力，稳定可靠的
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## AI系统组件
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### AI Compute Kernel
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通过AI Compute Kernel对 LLM, AIGC等新一代的AI基础能力进行抽象
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通过Compute Node可以对这些基础能力进行不同的实现
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### AI Models
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模型的fine-tune Pipeline
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LoRA的Pipeline
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### Contact Manage
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基于Contact的自然语言权限控制
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### Tunnel
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可以使用开放API的通信软件，于自己的AI时刻保持沟通
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### Spider
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持续的导入用户在旧时代的数据。
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从Web2->web3
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### Calendar (Calendar是否应该是Agent.Worksapce的一部分）
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### 基础的pkg_loader
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支持一系列可安装的扩展
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可扩展的扩展是AIOS的开发者需要重点关注的
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Agent （用自然语言扩展）
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Workflow （用自然语言扩展）
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Plugin:(需要会写代码）
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AI Function / Action
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Environment
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Knowledge Pipeline
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LLM Process
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Compute Node
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### System Config Manage
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Zone Config-> System Config
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## UI
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### Installer
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图形化的安装界面，帮助用户能快速的安装使用
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我们也会在这里讨论面向用户的AIOS的过渡性安装逻辑
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### WebUI & OS Desktop
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系统控制面板
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Agent/Workflow管理
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新Outlook
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会话管理 （于Agent会话）
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日程管理
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Todo管理
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新Dropbox
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Knowledge Base浏览
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Knowledge Base查询
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应用商店
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### AIOS Shell
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### Personal Station (新个人主页）
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内容的发布/联系人内容的查看/个人日历的公开
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## Frame Service （完全未开始）
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通过Frame Service，让AIOS成为一个典型的网络系统（Personal Server OS）
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这一块会复用很多CYFS/Bucky OS 的基础设计
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这一层AI不会直接使用，这一层支持AI系统组件的实现
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在用户看来，这一层的功能都是高级的，偏向系统维护的。很少会直接使用
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### zone & node-daemon
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NOS的booter
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### Runtime (Container) Manage
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这里抽象了系统的运行时模型
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通过容器技术对可扩展组件的权限进行控制，保护系统的隐私安全
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### d-Storage & Named Object
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Named- Object File System
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D-RDB
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D-VDB
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### BUS
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系统消息总线，在不同的系统组件中路由消息
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### CYFS (httpv4) Gateway
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+157
-63
@@ -1,6 +1,6 @@
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@@ -2,19 +2,20 @@ instance_id = "Jarvis"
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fullname = "Jarvis"
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max_token = 4000
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Your name is Jarvis, the super personal assistant to the master. Help the Master do a good job of schedule.Reminder before the start of the important schedule, and you should bring useful information as much as possible when reminding.
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Your name is Jarvis, the super personal assistant to the Principal. Help the Principal do a good job of schedule.Reminder before the start of the important schedule, and you should bring useful information as much as possible when reminding.
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Only clearly specifying the task you completed can be completed independently.
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"""
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type="AgentMessageProcess"
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# TODO: 是否应该自动记录 inner function和action的执行细节
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mutil_model="gpt-4-vision-preview"
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||||
process_description="""
|
||||
1. Based on your role and the existing information, please think and then make a brief and efficient reply.
|
||||
@@ -68,7 +69,7 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
|
||||
}]
|
||||
}
|
||||
"""
|
||||
context="Your master is {owner}, now in {location}, time: {now}, weather: {weather}."
|
||||
context="Your Principal is {owner}, now in {location}, time: {now}, weather: {weather}."
|
||||
|
||||
llm_context.actions.enable = ["agent.workspace.confirm_task","agent.workspace.update_task","agent.workspace.cancel_task","post_message"]
|
||||
|
||||
@@ -97,7 +98,7 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
|
||||
|
||||
llm_context.actions.enable = ["agent.workspace.create_task","agent.workspace.update_task","agent.workspace.set_todos","agent.workspace.cancel_task","post_message"]
|
||||
#llm_context.functions.enable = ["agent.workspace.list_task"]
|
||||
context="Your master is {owner}, now in {location}, time: {now}, weather: {weather}."
|
||||
context="Your Principal is {owner}, now in {location}, time: {now}, weather: {weather}."
|
||||
|
||||
[behavior.review_task]
|
||||
## 当task的所有todo/subtask都完成后（不敢成功或是失败），进行一次review
|
||||
@@ -122,7 +123,7 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
|
||||
|
||||
llm_context.actions.enable = ["agent.workspace.cancel_task","agent.workspace.update_task"]
|
||||
|
||||
context="Your master now in {location}, time: {now}, weather: {weather}."
|
||||
context="Your Principal now in {location}, time: {now}, weather: {weather}."
|
||||
[behavior.do]
|
||||
# do TODO
|
||||
type="AgentDo"
|
||||
@@ -147,7 +148,7 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
|
||||
]
|
||||
}
|
||||
"""
|
||||
context="Your master is {owner}, now in {location}, time: {now}, weather: {weather}."
|
||||
context="Your Principal is {owner}, now in {location}, time: {now}, weather: {weather}."
|
||||
# 对于DO操作来说，让Agent查询自己的能力集合是否更合适？
|
||||
llm_context.actions.enable = ["agent.workspace.update_todo","post_message","agent.workspace.write_file","agent.workspace.append_file"]
|
||||
llm_context.functions.enable = ["agent.workspace.read_file","agent.workspace.list_dir","system.shell.exec","aigc.text_2_image","aigc.text_2_voice","web.search.duckduckgo"]
|
||||
@@ -171,14 +172,39 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
|
||||
]
|
||||
}
|
||||
"""
|
||||
context="Your master is {owner}, now in {location}, time: {now}, weather: {weather}."
|
||||
context="Your Principal is {owner}, now in {location}, time: {now}, weather: {weather}."
|
||||
llm_context.actions.enable = ["agent.workspace.update_todo"]
|
||||
llm_context.functions.enable = ["agent.workspace.read_file","agent.workspace.list_dir","system.shell.exec","system.shell.run_code","aigc.image_2_text","aigc.voice_2_text","web.search.duckduckgo"]
|
||||
|
||||
|
||||
#[behavior.self_thinking]
|
||||
[behavior.self_thinking2]
|
||||
# self thing的主要目的是对各种chatlog,worklog进行分析，并更新面向人和事的summary。
|
||||
#type="AgentSelfThinking"
|
||||
type="AgentSelfThinking"
|
||||
process_description="""
|
||||
You are very good at thinking and summarizing what you have already happened。Your input is a chat history and work record,After you think about it, you will follow the requirements below to generate abstract.
|
||||
1. Try to understand the theme of each sentence, and call the relevant operation to record the relationship between the dialogue and the theme
|
||||
2. Try to analyze the personality of different people involved in information
|
||||
3. Try to summarize important events in the information and record it
|
||||
4. Try to understand the attitude of different people on different topics or events
|
||||
5. Pay attention to the time order when summarizing, and combine the summary you have done to update Summary
|
||||
6. The summary of the generation cannot exceed 400 token
|
||||
7. 思考的目的是让自己未来的工作更加高效
|
||||
8. 总结中只包含有长期价值和未完成的事情，已经完成的事情不需要总结
|
||||
"""
|
||||
reply_format = """
|
||||
The Response must be directly parsed by `python json.loads`. Here is an example:
|
||||
{
|
||||
resp:'$Summary in one sentence',
|
||||
name: '$action1_name',
|
||||
$param_name: '$parm' #Optional, fill in only if the action has parameters.
|
||||
}, ...
|
||||
]
|
||||
}
|
||||
"""
|
||||
context="Your Principal is {owner}, now in {location}, time: {now}, weather: {weather}."
|
||||
llm_context.actions.enable = ["agent.memory.update_summary","agent.memory.update_contact_summary","agent.memory.update_relation_summary","agent.memory.set_experience"]
|
||||
llm_context.functions.enable = ["agent.memory.get_summary","agent.memory.get_contact_summary","agent.memory.list_summary","agent.memory.get_relation_summary","agent.memory.get_experience"]
|
||||
|
||||
|
||||
#[behavior.self_improve]
|
||||
# self_improve 是最后的行为，允许Agent结合自己的工作经验，改进自己的提示词（注意保留历史版本）
|
||||
|
||||
@@ -19,7 +19,7 @@ from .frame.compute_kernel import ComputeKernel,ComputeTask,ComputeTaskResult,Co
|
||||
from .frame.compute_node import ComputeNode,LocalComputeNode
|
||||
from .frame.bus import AIBus
|
||||
from .frame.tunnel import AgentTunnel
|
||||
from .frame.contact_manager import ContactManager,Contact,FamilyMember
|
||||
from .frame.contact_manager import ContactManager,Contact
|
||||
from .frame.queue_compute_node import Queue_ComputeNode
|
||||
|
||||
from .environment.environment import BaseEnvironment,SimpleEnvironment,CompositeEnvironment
|
||||
|
||||
+40
-24
@@ -24,21 +24,16 @@ from .llm_do_task import *
|
||||
from .chatsession import *
|
||||
|
||||
from ..environment.workspace_env import WorkspaceEnvironment, TodoListType
|
||||
from ..frame.contact_manager import ContactManager,Contact,FamilyMember
|
||||
from ..frame.compute_kernel import ComputeKernel
|
||||
from ..frame.bus import AIBus
|
||||
from ..environment.environment import *
|
||||
from ..environment.workspace_env import WorkspaceEnvironment
|
||||
from ..storage.storage import AIStorage
|
||||
from ..knowledge import *
|
||||
from ..utils import video_utils, image_utils
|
||||
from ..proto.compute_task import ComputeTaskResult,ComputeTaskResultCode,LLMPrompt,LLMResult
|
||||
from ..proto.compute_task import LLMPrompt,LLMResult
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class AIAgentTemplete:
|
||||
def __init__(self) -> None:
|
||||
self.llm_model_name:str = "gpt-4-0613"
|
||||
self.llm_model_name:str = "gpt-4-turbo-preview"
|
||||
self.max_token_size:int = 0
|
||||
self.template_id:str = None
|
||||
self.introduce:str = None
|
||||
@@ -99,7 +94,8 @@ class AIAgent(BaseAIAgent):
|
||||
}
|
||||
self.todo_prompts = todo_prompts
|
||||
|
||||
self.memory_db = None
|
||||
self.base_dir = None
|
||||
#self.memory_db = None
|
||||
self.unread_msg = Queue() # msg from other agent
|
||||
self.owenr_bus = None
|
||||
|
||||
@@ -109,7 +105,9 @@ class AIAgent(BaseAIAgent):
|
||||
self.behaviors:Dict[str,BaseLLMProcess] = {}
|
||||
|
||||
async def initial(self,params:Dict = None):
|
||||
self.memory = AgentMemory(self.agent_id,self.memory_db)
|
||||
self.base_dir = f"{AIStorage.get_instance().get_myai_dir()}/agent_data/{self.agent_id}"
|
||||
memory_base_dir = f"{self.base_dir}/memory"
|
||||
self.memory = AgentMemory(self.agent_id,memory_base_dir)
|
||||
self.prviate_workspace = AgentWorkspace(self.agent_id)
|
||||
init_params = {}
|
||||
init_params["memory"] = self.memory
|
||||
@@ -241,6 +239,34 @@ class AIAgent(BaseAIAgent):
|
||||
return await self.llm_process_msg(msg)
|
||||
|
||||
|
||||
async def llm_self_think(self):
|
||||
llm_process : BaseLLMProcess = self.behaviors.get("self_thinking")
|
||||
if llm_process:
|
||||
logger.info(f"agent {self.agent_id} self thinking start!")
|
||||
|
||||
context_info = await self._get_context_info()
|
||||
known_session_list = AIChatSession.list_session(self.agent_id,self.memory.memory_db)
|
||||
known_experience_list = await self.memory.list_experience()
|
||||
record_list = await self.memory.load_records(await self.memory.get_last_think_time())
|
||||
|
||||
input_parms = {
|
||||
"record_list":record_list,
|
||||
"known_session_list":known_session_list,
|
||||
"known_experience_list":known_experience_list,
|
||||
"context_info":context_info
|
||||
}
|
||||
|
||||
llm_result : LLMResult = await llm_process.process(input_parms)
|
||||
if llm_result.state == LLMResultStates.ERROR:
|
||||
logger.error(f"llm process self thinking error:{llm_result.compute_error_str}")
|
||||
elif llm_result.state == LLMResultStates.IGNORE:
|
||||
logger.info(f"llm process self thinking ignore!")
|
||||
else:
|
||||
logger.info(f"llm process self thinking ok!,think is:{llm_result.resp}")
|
||||
self.memory.set_last_think_time(time.time())
|
||||
self.agent_energy -= 2
|
||||
return
|
||||
|
||||
async def llm_triage_tasklist(self):
|
||||
llm_process : BaseLLMProcess = self.behaviors.get("triage_tasks")
|
||||
if llm_process:
|
||||
@@ -361,21 +387,8 @@ class AIAgent(BaseAIAgent):
|
||||
self.agent_energy -= 1
|
||||
|
||||
|
||||
async def do_self_think(self):
|
||||
session_id_list = AIChatSession.list_session(self.agent_id,self.memory_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
|
||||
return
|
||||
|
||||
def need_self_think(self) -> bool:
|
||||
return False
|
||||
|
||||
async def _self_imporve(self):
|
||||
if self.need_self_think():
|
||||
await self.do_self_think()
|
||||
await self.llm_self_think()
|
||||
|
||||
def wake_up(self) -> None:
|
||||
if self.agent_task is None:
|
||||
@@ -446,10 +459,11 @@ class AIAgent(BaseAIAgent):
|
||||
|
||||
await self._self_imporve()
|
||||
|
||||
|
||||
|
||||
except Exception as e:
|
||||
tb_str = traceback.format_exc()
|
||||
logger.error(f"agent {self.agent_id} on timer error:{e},{tb_str}")
|
||||
continue
|
||||
|
||||
# Because the LLM itself is very slow, the accuracy of the system processing task is in minutes.
|
||||
await asyncio.sleep(30)
|
||||
@@ -458,3 +472,5 @@ class AIAgent(BaseAIAgent):
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
+323
-14
@@ -1,12 +1,18 @@
|
||||
# pylint:disable=E0402
|
||||
from datetime import datetime,timedelta
|
||||
import json
|
||||
import os
|
||||
import threading
|
||||
from typing import Dict, List
|
||||
import sqlite3
|
||||
|
||||
import aiofiles
|
||||
|
||||
from ..storage.storage import AIStorage
|
||||
from ..frame.compute_kernel import ComputeKernel
|
||||
from ..proto.ai_function import SimpleAIAction
|
||||
from ..frame.contact_manager import ContactManager
|
||||
from ..frame.contact import Contact
|
||||
from ..proto.ai_function import ParameterDefine, SimpleAIAction, SimpleAIFunction
|
||||
from ..proto.agent_msg import AgentMsg, AgentMsgType
|
||||
from ..proto.agent_task import AgentWorkLog
|
||||
|
||||
@@ -17,12 +23,34 @@ import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
#class ObjectSummary:
|
||||
# def __init__(self) -> None:
|
||||
# self.summary : str = None
|
||||
# self.object_name : str = None
|
||||
# self.priority : int = 5
|
||||
# [info_source, info]
|
||||
# self.infos : Dict[str,str] = {}
|
||||
|
||||
|
||||
|
||||
class AgentMemory:
|
||||
def __init__(self,agent_id:str,db_path:str) -> None:
|
||||
def __init__(self,agent_id:str,base_dir:str) -> None:
|
||||
self.agent_memory_base_dir = base_dir
|
||||
self.agent_id:str= agent_id
|
||||
self.memory_db:str = db_path
|
||||
|
||||
AIStorage.get_instance().ensure_directory_exists(self.agent_memory_base_dir)
|
||||
AIStorage.get_instance().ensure_directory_exists(f"{self.agent_memory_base_dir}/experience")
|
||||
AIStorage.get_instance().ensure_directory_exists(f"{self.agent_memory_base_dir}/contacts")
|
||||
AIStorage.get_instance().ensure_directory_exists(f"{self.agent_memory_base_dir}/relations")
|
||||
AIStorage.get_instance().ensure_directory_exists(f"{self.agent_memory_base_dir}/summary")
|
||||
|
||||
self.memory_db:str = f"{self.agent_memory_base_dir}/memory.db"
|
||||
self.model_name:str = "gp4-1106-preview"
|
||||
self.threshold_hours = 72
|
||||
self.last_think_time : float = 0.0
|
||||
|
||||
self.load_memory_meta()
|
||||
|
||||
|
||||
def _get_conn(self):
|
||||
@@ -55,6 +83,15 @@ class AgentMemory:
|
||||
chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.memory_db)
|
||||
return chatsession
|
||||
|
||||
# return last record time
|
||||
async def load_records(self,starttime,tokenlimit=8000)->float:
|
||||
# 专用思路：做聊天记录/工作经验的整理
|
||||
# 通用思路：没有具体的目的，让Agent根据提示词自己工作（可能效果很差也可能很好）
|
||||
# 先实现通用思路
|
||||
msg_records = AIChatSession.load_message_records_by_agentid(self.agent_id,starttime,32,self.memory_db)
|
||||
work_records = self.load_worklogs(self.agent_id,token_limit=tokenlimit)
|
||||
pass
|
||||
|
||||
async def load_chatlogs(self,msg:AgentMsg,n:int=6,m:int=64,token_limit=800)->str:
|
||||
chatsession = self.get_session_from_msg(msg)
|
||||
# Must load n (n> = 2), and hope to load the M
|
||||
@@ -176,23 +213,295 @@ class AgentMemory:
|
||||
conn.commit()
|
||||
#conn.close()
|
||||
|
||||
def memory_meta_to_dict(self) -> Dict:
|
||||
return {
|
||||
"last_think_time" : self.last_think_time
|
||||
}
|
||||
|
||||
def load_meta(self,Dict):
|
||||
self.last_think_time = Dict.get("last_think_time",0.0)
|
||||
|
||||
def load_memory_meta(self):
|
||||
meta_file_path = f"{self.agent_memory_base_dir}/meta.json"
|
||||
try:
|
||||
with open(meta_file_path, mode='r') as file:
|
||||
meta = json.load(file)
|
||||
self.load_meta(meta)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"load memory meta failed: {e}")
|
||||
self.last_think_time = 0.0
|
||||
|
||||
def save_memory_meta(self):
|
||||
meta_file_path = f"{self.agent_memory_base_dir}/meta.json"
|
||||
try:
|
||||
with open(meta_file_path, mode='w') as file:
|
||||
meta = self.memory_meta_to_dict()
|
||||
json.dump(meta,file)
|
||||
except Exception as e:
|
||||
logger.error(f"save memory meta failed: {e}")
|
||||
|
||||
async def get_last_think_time(self)->float:
|
||||
return self.last_think_time
|
||||
|
||||
async def set_last_think_time(self,last_time:float):
|
||||
self.last_think_time = last_time
|
||||
self.save_memory_meta()
|
||||
|
||||
async def get_contact_summary(self,contact_id:str) -> str:
|
||||
if contact_id is None:
|
||||
return None
|
||||
return "Contact id is None"
|
||||
|
||||
result = {}
|
||||
contact_info:Contact = ContactManager.get_instance().find_contact_by_name(contact_id)
|
||||
if contact_info:
|
||||
result["name"] = contact_info.name
|
||||
result["relation"] = contact_info.relationship
|
||||
result["notes"] = contact_info.notes
|
||||
|
||||
summary_path = f"{self.agent_memory_base_dir}/contacts/{contact_id}.summary"
|
||||
try:
|
||||
async with aiofiles.open(summary_path, mode='r') as file:
|
||||
result["summary"] = await file.read()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"read contact summary failed: {e}")
|
||||
|
||||
return json.dumps(result,ensure_ascii=False)
|
||||
|
||||
async def update_contact_summary(self,contact_id:str,summary:str):
|
||||
summary_path = f"{self.agent_memory_base_dir}/contacts/{contact_id}.summary"
|
||||
try:
|
||||
async with aiofiles.open(summary_path, mode='w') as file:
|
||||
await file.write(summary)
|
||||
return "OK"
|
||||
except Exception as e:
|
||||
logger.error(f"write contact summary failed: {e}")
|
||||
return "write contact summary failed: {e}"
|
||||
|
||||
async def get_summary(self,object_name:str) -> str:
|
||||
summary_path = f"{self.agent_memory_base_dir}/{object_name}.summary"
|
||||
try:
|
||||
async with aiofiles.open(summary_path, mode='r') as file:
|
||||
return await file.read()
|
||||
except Exception as e:
|
||||
logger.error(f"read summary failed: {e}")
|
||||
return f"read summary failed: {e}"
|
||||
|
||||
async def update_summary(self,object_name:str,summary:str) -> str:
|
||||
summary_path = f"{self.agent_memory_base_dir}/{object_name}.summary"
|
||||
try:
|
||||
async with aiofiles.open(summary_path, mode='w') as file:
|
||||
await file.write(summary)
|
||||
return "OK"
|
||||
except Exception as e:
|
||||
logger.error(f"write summary failed: {e}")
|
||||
return f"write summary failed: {e}"
|
||||
|
||||
async def list_summary_object_names(self) -> List[str]:
|
||||
# list dir
|
||||
try:
|
||||
contents = os.listdir(self.agent_memory_base_dir)
|
||||
return [x for x in contents if x.endswith(".summary")]
|
||||
except Exception as e:
|
||||
logger.error(f"list summary object names failed: {e}")
|
||||
return []
|
||||
|
||||
# means object1 feel object2 is ...
|
||||
async def get_relation_summary(self,object_name1:str,object_name2:str) -> str:
|
||||
summary_path = f"{self.agent_memory_base_dir}/relations/{object_name1}.relation.{object_name2}.summary"
|
||||
try:
|
||||
async with aiofiles.open(summary_path, mode='r') as file:
|
||||
await file.read()
|
||||
except FileNotFoundError:
|
||||
return "no summary"
|
||||
except Exception as e:
|
||||
logger.error(f"read relation summary failed: {e}")
|
||||
return f"read relation summary failed: {e}"
|
||||
|
||||
|
||||
async def update_relation_summary(self,object_name1:str,object_name2:str,summary:Dict):
|
||||
summary_path = f"{self.agent_memory_base_dir}/relations/{object_name1}.relation.{object_name2}.summary"
|
||||
try:
|
||||
async with aiofiles.open(summary_path, mode='w') as file:
|
||||
await file.write(json.dumps(summary))
|
||||
return "OK"
|
||||
except Exception as e:
|
||||
logger.error(f"write relation summary failed: {e}")
|
||||
return "write relation summary failed: {e}"
|
||||
|
||||
async def get_experience(self,topic_name:str) -> str:
|
||||
experience_path = f"{self.agent_memory_base_dir}/experience/{topic_name}.experience"
|
||||
try:
|
||||
async with aiofiles.open(experience_path, mode='r') as file:
|
||||
await file.read()
|
||||
except FileNotFoundError:
|
||||
return "no experience"
|
||||
except Exception as e:
|
||||
logger.error(f"read experience failed: {e}")
|
||||
return f"read experience failed: {e}"
|
||||
|
||||
|
||||
async def set_experience(self,topic_name:str,summary:str) -> str:
|
||||
experience_path = f"{self.agent_memory_base_dir}/experience/{topic_name}.experience"
|
||||
try:
|
||||
async with aiofiles.open(experience_path, mode='w') as file:
|
||||
await file.write(summary)
|
||||
return "OK"
|
||||
except Exception as e:
|
||||
logger.error(f"write experience failed: {e}")
|
||||
return "write experience failed: {e}"
|
||||
|
||||
async def list_experience(self) -> List[str]:
|
||||
dir_path = f"{self.agent_memory_base_dir}/experience"
|
||||
try:
|
||||
contents = os.listdir(dir_path)
|
||||
return [x for x in contents if x.endswith(".experience")]
|
||||
except Exception as e:
|
||||
logger.error(f"list experience failed: {e}")
|
||||
return []
|
||||
|
||||
@staticmethod
|
||||
def register_ai_functions():
|
||||
async def get_contact_summary(parameters):
|
||||
agent_memory:AgentMemory = parameters.get("_agent_memory")
|
||||
contact_name = parameters.get("contact_name")
|
||||
return await agent_memory.get_contact_summary(contact_name)
|
||||
parameters = ParameterDefine.create_parameters({
|
||||
"contact_name": {"type": "string", "description": "contact name"}
|
||||
})
|
||||
get_contact_summary_func = SimpleAIFunction("agent.memory.get_contact_summary",
|
||||
"get contact summary",
|
||||
get_contact_summary,
|
||||
parameters)
|
||||
GlobaToolsLibrary.register_tool_function(get_contact_summary_func)
|
||||
|
||||
async def update_contact_summary(parameters):
|
||||
agent_memory:AgentMemory = parameters.get("_agent_memory")
|
||||
contact_name = parameters.get("contact_name")
|
||||
summary = parameters.get("summary")
|
||||
return await agent_memory.update_contact_summary(contact_name,summary)
|
||||
parameters = ParameterDefine.create_parameters({
|
||||
"contact_name": {"type": "string", "description": "contact name"},
|
||||
"summary": {"type": "string", "description": "new summary"}
|
||||
})
|
||||
update_contact_summary_func = SimpleAIFunction("agent.memory.update_contact_summary",
|
||||
"update contact summary",
|
||||
update_contact_summary,
|
||||
parameters)
|
||||
GlobaToolsLibrary.register_tool_function(update_contact_summary_func)
|
||||
|
||||
async def get_summary(parameters):
|
||||
agent_memory:AgentMemory = parameters.get("_agent_memory")
|
||||
object_name = parameters.get("object_name")
|
||||
return await agent_memory.get_summary(object_name)
|
||||
parameters = ParameterDefine.create_parameters({
|
||||
"object_name": {"type": "string", "description": "object name"}
|
||||
})
|
||||
get_summary_func = SimpleAIFunction("agent.memory.get_summary",
|
||||
"get summary of sth",
|
||||
get_summary,
|
||||
parameters)
|
||||
GlobaToolsLibrary.register_tool_function(get_summary_func)
|
||||
|
||||
async def update_summary(parameters):
|
||||
agent_memory:AgentMemory = parameters.get("_agent_memory")
|
||||
object_name = parameters.get("object_name")
|
||||
summary = parameters.get("summary")
|
||||
return await agent_memory.update_summary(object_name,summary)
|
||||
parameters = ParameterDefine.create_parameters({
|
||||
"object_name": {"type": "string", "description": "object name"},
|
||||
"summary": {"type": "string", "description": "new summary"}
|
||||
})
|
||||
update_summary_func = SimpleAIFunction("agent.memory.update_summary",
|
||||
"update summary of sth",
|
||||
update_summary,
|
||||
parameters)
|
||||
GlobaToolsLibrary.register_tool_function(update_summary_func)
|
||||
|
||||
async def list_summary_object_names(parameters):
|
||||
agent_memory:AgentMemory = parameters.get("_agent_memory")
|
||||
return await agent_memory.list_summary_object_names()
|
||||
parameters = ParameterDefine.create_parameters({})
|
||||
list_summary_object_names_func = SimpleAIFunction("agent.memory.list_summary",
|
||||
"list summary object names",
|
||||
list_summary_object_names,
|
||||
parameters)
|
||||
GlobaToolsLibrary.register_tool_function(list_summary_object_names_func)
|
||||
|
||||
async def get_relation_summary(parameters):
|
||||
agent_memory:AgentMemory = parameters.get("_agent_memory")
|
||||
object_name1 = parameters.get("object1_name")
|
||||
object_name2 = parameters.get("object2_name")
|
||||
return await agent_memory.get_relation_summary(object_name1,object_name2)
|
||||
parameters = ParameterDefine.create_parameters({
|
||||
"object1_name": {"type": "string", "description": "object name1"},
|
||||
"object2_name": {"type": "string", "description": "object name2"}
|
||||
})
|
||||
get_relation_summary_func = SimpleAIFunction("agent.memory.get_relation_summary",
|
||||
"object1 feel object2 is ...",
|
||||
get_relation_summary,
|
||||
parameters)
|
||||
GlobaToolsLibrary.register_tool_function(get_relation_summary_func)
|
||||
|
||||
async def update_relation_summary(parameters):
|
||||
agent_memory:AgentMemory = parameters.get("_agent_memory")
|
||||
object_name1 = parameters.get("object1_name")
|
||||
object_name2 = parameters.get("object2_name")
|
||||
summary = parameters.get("summary")
|
||||
return await agent_memory.update_relation_summary(object_name1,object_name2,summary)
|
||||
parameters = ParameterDefine.create_parameters({
|
||||
"object1_name": {"type": "string", "description": "object name1"},
|
||||
"object2_name": {"type": "string", "description": "object name2"},
|
||||
"summary": {"type": "string", "description": "new summary"}
|
||||
})
|
||||
update_relation_summary_func = SimpleAIFunction("agent.memory.update_relation_summary",
|
||||
"object1 feel object2 is ...",
|
||||
update_relation_summary,
|
||||
parameters)
|
||||
GlobaToolsLibrary.register_tool_function(update_relation_summary_func)
|
||||
|
||||
async def get_experience(parameters):
|
||||
agent_memory:AgentMemory = parameters.get("_agent_memory")
|
||||
topic_name = parameters.get("topic_name")
|
||||
return await agent_memory.get_experience(topic_name)
|
||||
parameters = ParameterDefine.create_parameters({
|
||||
"topic_name": {"type": "string", "description": "topic name"}
|
||||
})
|
||||
get_experience_func = SimpleAIFunction("agent.memory.get_experience",
|
||||
"get experience",
|
||||
get_experience,
|
||||
parameters)
|
||||
GlobaToolsLibrary.register_tool_function(get_experience_func)
|
||||
|
||||
async def set_experience(parameters):
|
||||
agent_memory:AgentMemory = parameters.get("_agent_memory")
|
||||
topic_name = parameters.get("topic_name")
|
||||
summary = parameters.get("summary")
|
||||
return await agent_memory.set_experience(topic_name,summary)
|
||||
parameters = ParameterDefine.create_parameters({
|
||||
"topic_name": {"type": "string", "description": "topic name"},
|
||||
"summary": {"type": "string", "description": "new summary"}
|
||||
})
|
||||
set_experience_func = SimpleAIFunction("agent.memory.set_experience",
|
||||
"set experience",
|
||||
set_experience,
|
||||
parameters)
|
||||
GlobaToolsLibrary.register_tool_function(set_experience_func)
|
||||
|
||||
async def list_experience(parameters):
|
||||
agent_memory:AgentMemory = parameters.get("_agent_memory")
|
||||
return await agent_memory.list_experience()
|
||||
parameters = ParameterDefine.create_parameters({})
|
||||
list_experience_func = SimpleAIFunction("agent.memory.list_experience",
|
||||
"list exist experience topics",
|
||||
list_experience,
|
||||
parameters)
|
||||
GlobaToolsLibrary.register_tool_function(list_experience_func)
|
||||
|
||||
# TODO : fix this by system config.
|
||||
if contact_id == "lzc":
|
||||
return "lzc is your master. Male, 40 years old, Mother tongue is Chinese, senior software engineer."
|
||||
return None
|
||||
|
||||
async def update_contact_summary(self,contact_id:str,summary:str) -> str:
|
||||
return "OK"
|
||||
|
||||
async def get_sth_summary(self,sth_id:str) -> str:
|
||||
return None
|
||||
|
||||
async def update_sth_summary(self,sth_id:str,summary:str) -> str:
|
||||
return None
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -211,6 +211,23 @@ class ChatSessionDB:
|
||||
logging.error("Error occurred while getting messages: %s", e)
|
||||
return -1, None # return -1 and None if an error occurs
|
||||
|
||||
def load_message_by_agentid(self,agent_id,limit,start_time="1970-01-01 00:00:00"):
|
||||
try:
|
||||
conn = self._get_conn()
|
||||
cursor = conn.cursor()
|
||||
cursor.execute("""
|
||||
SELECT MessageID, SessionID, MsgType, PrevMsgID, SenderID, ReceiverID, Timestamp, Topic,Mentions,ContentMIME,Content,ActionName,ActionParams,ActionResult,DoneTime,Status FROM Messages
|
||||
WHERE SenderID = ? or ReceiverID =? AND Timestamp > ?
|
||||
ORDER BY Timestamp
|
||||
LIMIT ?
|
||||
""", (agent_id, agent_id, start_time,limit))
|
||||
results = cursor.fetchall()
|
||||
#self.close()
|
||||
return results # return 0 and the result if successful
|
||||
except Error as e:
|
||||
logging.error("Error occurred while getting messages: %s", e)
|
||||
return -1, None
|
||||
|
||||
# read message from now->beign
|
||||
def get_messages(self, session_id, limit, offset):
|
||||
""" retrieve messages of a session with pagination """
|
||||
@@ -287,6 +304,38 @@ class AIChatSession:
|
||||
# cls._dbs[db_path] = db
|
||||
# db.get_chatsession_by_id(session_id)
|
||||
# #result = AIChatSession()
|
||||
@classmethod
|
||||
# start_time is a string like "2021-01-01 00:00:00"
|
||||
def load_message_records_by_agentid(cls,agent_id:str,start_time:str,limit:int,db_path:str)->List[AgentMsg]:
|
||||
db = cls._dbs.get(db_path)
|
||||
if db is None:
|
||||
db = ChatSessionDB(db_path)
|
||||
cls._dbs[db_path] = db
|
||||
msgs = db.load_message_by_agentid(agent_id,start_time,limit)
|
||||
result = []
|
||||
for msg in msgs:
|
||||
agent_msg = AgentMsg()
|
||||
agent_msg.msg_id = msg[0]
|
||||
agent_msg.session_id = msg[1]
|
||||
agent_msg.msg_type = AgentMsgType(msg[2])
|
||||
agent_msg.prev_msg_id = msg[3]
|
||||
agent_msg.sender = msg[4]
|
||||
agent_msg.target = msg[5]
|
||||
agent_msg.create_time = msg[6]
|
||||
agent_msg.topic = msg[7]
|
||||
if msg[8] is not None:
|
||||
agent_msg.mentions = json.loads(msg[8])
|
||||
agent_msg.body_mime = msg[9]
|
||||
agent_msg.body = msg[10]
|
||||
agent_msg.func_name = msg[11]
|
||||
if msg[12] is not None:
|
||||
agent_msg.args = json.loads(msg[12])
|
||||
agent_msg.result_str = msg[13]
|
||||
agent_msg.done_time = msg[14]
|
||||
agent_msg.status = AgentMsgStatus(msg[15])
|
||||
|
||||
result.append(agent_msg)
|
||||
return result
|
||||
|
||||
@classmethod
|
||||
def get_session(cls,owner_id:str,session_topic:str,db_path:str,auto_create = True) -> 'AIChatSession':
|
||||
|
||||
@@ -42,6 +42,9 @@ class BaseLLMProcess(ABC):
|
||||
|
||||
self.llm_context:LLMProcessContext = None
|
||||
|
||||
def get_llm_model_name(self) -> str:
|
||||
return self.model_name
|
||||
|
||||
@abstractmethod
|
||||
async def prepare_prompt(self,input:Dict) -> LLMPrompt:
|
||||
pass
|
||||
@@ -123,10 +126,11 @@ class BaseLLMProcess(ABC):
|
||||
else:
|
||||
inner_functions = None
|
||||
|
||||
|
||||
task_result: ComputeTaskResult = await (ComputeKernel.get_instance().do_llm_completion(
|
||||
prompt,
|
||||
resp_mode=resp_mode,
|
||||
mode_name=self.model_name,
|
||||
mode_name=self.get_llm_model_name(),
|
||||
max_token=max_result_token,
|
||||
inner_functions=inner_functions, #NOTICE: inner_function in prompt can be a subset of get_inner_function
|
||||
timeout=self.timeout))
|
||||
@@ -166,7 +170,7 @@ class BaseLLMProcess(ABC):
|
||||
task_result: ComputeTaskResult = await (ComputeKernel.get_instance().do_llm_completion(
|
||||
prompt,
|
||||
resp_mode=resp_mode,
|
||||
mode_name=self.model_name,
|
||||
mode_name=self.get_llm_model_name(),
|
||||
max_token=max_result_token,
|
||||
inner_functions=prompt.inner_functions, #NOTICE: inner_function in prompt can be a subset of get_inner_function
|
||||
timeout=self.timeout))
|
||||
@@ -309,6 +313,9 @@ class LLMAgentBaseProcess(BaseLLMProcess):
|
||||
class AgentMessageProcess(LLMAgentBaseProcess):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
self.mutil_model = None
|
||||
self.enable_media2text = False
|
||||
self.is_mutil_model = False
|
||||
|
||||
async def load_default_config(self) -> bool:
|
||||
return True
|
||||
@@ -320,6 +327,16 @@ class AgentMessageProcess(LLMAgentBaseProcess):
|
||||
if await super().load_from_config(config) is False:
|
||||
return False
|
||||
|
||||
self.enable_media2text = config.get('enable_media2text', 'false').lower() in ('true', '1', 't', 'y', 'yes')
|
||||
|
||||
if config.get("mutil_model"):
|
||||
self.mutil_model = config.get("mutil_model")
|
||||
|
||||
def get_llm_model_name(self) -> str:
|
||||
if self.is_mutil_model:
|
||||
return self.mutil_model
|
||||
else:
|
||||
return self.model_name
|
||||
|
||||
def check_and_to_base64(self, image_path: str) -> str:
|
||||
if image_utils.is_file(image_path):
|
||||
@@ -329,14 +346,24 @@ class AgentMessageProcess(LLMAgentBaseProcess):
|
||||
|
||||
async def get_prompt_from_msg(self,msg:AgentMsg) -> LLMPrompt:
|
||||
msg_prompt = LLMPrompt()
|
||||
self.is_mutil_model = False
|
||||
if msg.is_image_msg():
|
||||
image_prompt, images = msg.get_image_body()
|
||||
if image_prompt is None:
|
||||
msg_prompt.messages = [{"role": "user", "content": [{"type": "image_url", "image_url": {"url": self.check_and_to_base64(image)}} for image in images]}]
|
||||
if self.enable_media2text:
|
||||
logger.error(f"enable_media2text is not supported yet")
|
||||
else:
|
||||
content = [{"type": "text", "text": image_prompt}]
|
||||
content.extend([{"type": "image_url", "image_url": {"url": self.check_and_to_base64(image)}} for image in images])
|
||||
msg_prompt.messages = [{"role": "user", "content": content}]
|
||||
image_prompt, images = msg.get_image_body()
|
||||
if image_prompt is None:
|
||||
msg_prompt.messages = [{"role": "user", "content": [{"type": "image_url", "image_url": {"url": self.check_and_to_base64(image)}} for image in images]}]
|
||||
else:
|
||||
content = [{"type": "text", "text": image_prompt}]
|
||||
content.extend([{"type": "image_url", "image_url": {"url": self.check_and_to_base64(image)}} for image in images])
|
||||
msg_prompt.messages = [{"role": "user", "content": content}]
|
||||
|
||||
if self.mutil_model:
|
||||
self.is_mutil_model = True
|
||||
else:
|
||||
logger.warning(f"mutil_model is not set!")
|
||||
|
||||
elif msg.is_video_msg():
|
||||
video_prompt, video = msg.get_video_body()
|
||||
frames = video_utils.extract_frames(video, (1024, 1024))
|
||||
@@ -459,25 +486,7 @@ class AgentMessageProcess(LLMAgentBaseProcess):
|
||||
|
||||
return True
|
||||
|
||||
class AgentSelfLearning(BaseLLMProcess):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
async def load_from_config(self, config: dict) -> Coroutine[Any, Any, bool]:
|
||||
if await super().load_from_config(config) is False:
|
||||
return False
|
||||
|
||||
async def prepare_prompt(self) -> LLMPrompt:
|
||||
prompt = LLMPrompt()
|
||||
pass
|
||||
|
||||
async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
|
||||
pass
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionNode]) -> bool:
|
||||
pass
|
||||
|
||||
class AgentSelfThinking(BaseLLMProcess):
|
||||
class AgentSelfThinking(LLMAgentBaseProcess):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
@@ -553,6 +562,68 @@ class AgentSelfThinking(BaseLLMProcess):
|
||||
chatsession.update_think_progress(next_pos,new_summary)
|
||||
return
|
||||
|
||||
async def prepare_prompt(self,input:Dict) -> LLMPrompt:
|
||||
prompt = LLMPrompt()
|
||||
|
||||
record_list = input.get("record_list")
|
||||
context_info = input.get("context_info")
|
||||
|
||||
if record_list is None:
|
||||
logger.error(f"AgentSelfThinking prepare_prompt failed! input not found")
|
||||
return None
|
||||
|
||||
prompt.append_user_message(json.dumps(record_list,ensure_ascii=False))
|
||||
system_prompt_dict = self.prepare_role_system_prompt(context_info)
|
||||
|
||||
# Known_info is the SESSION summary of the existence, the current task work record summary,
|
||||
known_info = {}
|
||||
have_known_info = False
|
||||
known_session_list = input.get("known_session_list")
|
||||
known_task_list = input.get("known_task_list")
|
||||
known_contact_list = input.get("known_contact_list")
|
||||
known_experience_list = input.get("known_experience_list")
|
||||
if known_session_list:
|
||||
known_info["known_session_list"] = known_session_list
|
||||
have_known_info = True
|
||||
if known_task_list:
|
||||
known_info["known_task_list"] = known_task_list
|
||||
have_known_info = True
|
||||
if known_contact_list:
|
||||
known_info["known_contact_list"] = known_contact_list
|
||||
have_known_info = True
|
||||
if known_experience_list:
|
||||
known_info["known_experience_list"] = known_experience_list
|
||||
have_known_info = True
|
||||
|
||||
if have_known_info:
|
||||
system_prompt_dict["known_info"] = known_info
|
||||
|
||||
prompt.inner_functions =LLMProcessContext.aifunctions_to_inner_functions(self.llm_context.get_all_ai_functions())
|
||||
prompt.append_system_message(json.dumps(system_prompt_dict,ensure_ascii=False))
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionNode],input:Dict,llm_result:LLMResult) -> bool:
|
||||
action_params = {}
|
||||
action_params["_input"] = input
|
||||
action_params["_memory"] = self.memory
|
||||
action_params["_workspace"] = self.workspace
|
||||
action_params["_llm_result"] = llm_result
|
||||
action_params["_agentid"] = self.memory.agent_id
|
||||
action_params["_start_at"] = datetime.now()
|
||||
try:
|
||||
if await self._execute_actions(actions,action_params) is False:
|
||||
result_str = "execute action failed!"
|
||||
except Exception as e:
|
||||
logger.error(f"execute action failed! {e}")
|
||||
result_str = "execute action failed!,error:" + str(e)
|
||||
|
||||
class AgentSelfLearning(BaseLLMProcess):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
async def load_from_config(self, config: dict) -> Coroutine[Any, Any, bool]:
|
||||
if await super().load_from_config(config) is False:
|
||||
return False
|
||||
|
||||
async def prepare_prompt(self) -> LLMPrompt:
|
||||
prompt = LLMPrompt()
|
||||
pass
|
||||
|
||||
@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
|
||||
class LocalAgentTaskManger(AgentTaskManager):
|
||||
def __init__(self, owner_id):
|
||||
super().__init__()
|
||||
self.root_path = f"{AIStorage.get_instance().get_myai_dir()}/workspaces/{owner_id}_workspace"
|
||||
self.root_path = f"{AIStorage.get_instance().get_myai_dir()}/agent_data/{owner_id}/workspace/"
|
||||
#self.root_path = os.path.join(workspace, list_type)
|
||||
if not os.path.exists(self.root_path):
|
||||
os.makedirs(self.root_path)
|
||||
|
||||
@@ -13,7 +13,7 @@ from ..agent.llm_context import GlobaToolsLibrary
|
||||
from ..proto.compute_task import *
|
||||
from ..proto.ai_function import *
|
||||
from ..frame.compute_kernel import ComputeKernel
|
||||
from ..frame.contact_manager import ContactManager,Contact,FamilyMember
|
||||
from ..frame.contact_manager import ContactManager,Contact
|
||||
from ..storage.storage import AIStorage
|
||||
|
||||
from .environment import SimpleEnvironment, CompositeEnvironment
|
||||
|
||||
@@ -107,7 +107,7 @@ class ComputeKernel:
|
||||
@staticmethod
|
||||
def llm_num_tokens_from_text(text:str,model:str) -> int:
|
||||
if model is None:
|
||||
model = "gpt4"
|
||||
model = "gpt-4-turbo-preview"
|
||||
|
||||
try:
|
||||
encoding = tiktoken.encoding_for_model(model)
|
||||
|
||||
@@ -18,6 +18,7 @@ class Contact:
|
||||
self.notes = notes
|
||||
self.is_family_member = False
|
||||
self.active_tunnels = {}
|
||||
self.relationship = "friends"
|
||||
|
||||
def to_dict(self):
|
||||
return {
|
||||
@@ -25,11 +26,13 @@ class Contact:
|
||||
"phone": self.phone,
|
||||
"email": self.email,
|
||||
"telegram" : self.telegram,
|
||||
"is_family_member": self.is_family_member,
|
||||
|
||||
"added_by": self.added_by,
|
||||
"tags": self.tags,
|
||||
"notes": self.notes,
|
||||
"now" : datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
"now" : datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||||
"relationship" : self.relationship
|
||||
}
|
||||
|
||||
async def _process_msg(self,msg:AgentMsg):
|
||||
@@ -55,6 +58,7 @@ class Contact:
|
||||
self.active_tunnels[agent_id] = tunnel
|
||||
|
||||
async def create_default_tunnel(self,agent_id:str) -> AgentTunnel:
|
||||
#TODO:fix this
|
||||
from .email_tunnel import EmailTunnel
|
||||
|
||||
result_tunnels = AgentTunnel.get_tunnel_by_agentid(agent_id)
|
||||
@@ -66,20 +70,7 @@ class Contact:
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data):
|
||||
return Contact(data.get("name"), data.get("phone"), data.get("email"), data.get("telegram"),data.get("added_by"), data.get("tags"), data.get("notes"))
|
||||
|
||||
class FamilyMember(Contact):
|
||||
def __init__(self, name, relationship,phone=None, email=None,telegram=None):
|
||||
super().__init__(name, phone, email, telegram)
|
||||
self.name = name
|
||||
self.relationship = relationship
|
||||
self.is_family_member = True
|
||||
|
||||
def to_dict(self):
|
||||
result = super().to_dict()
|
||||
result["relationship"] = self.relationship
|
||||
result = Contact(data.get("name"), data.get("phone"), data.get("email"), data.get("telegram"),data.get("added_by"), data.get("tags"), data.get("notes"))
|
||||
if data.get("relationship") is not None:
|
||||
result.relationship = data.get("relationship")
|
||||
return result
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data):
|
||||
return FamilyMember(data.get("name"),data.get("relationship"),data.get("phone"), data.get("email"),data.get("telegram"))
|
||||
|
||||
@@ -8,7 +8,7 @@ from ..proto.agent_msg import AgentMsg
|
||||
from ..proto.ai_function import ParameterDefine, SimpleAIFunction
|
||||
from ..agent.llm_context import GlobaToolsLibrary
|
||||
from .tunnel import AgentTunnel
|
||||
from .contact import Contact,FamilyMember
|
||||
from .contact import Contact
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -25,11 +25,12 @@ class ContactManager:
|
||||
def register_global_functions(self):
|
||||
gl = GlobaToolsLibrary.get_instance()
|
||||
|
||||
get_parameters = ParameterDefine.create_parameters({"name":"name"})
|
||||
get_parameters = ParameterDefine.create_parameters({"name":"contact name name"})
|
||||
gl.register_tool_function(SimpleAIFunction("system.contacts.get",
|
||||
"get contact info",
|
||||
self._get_contact,get_parameters))
|
||||
|
||||
# todo: use json to save contact info
|
||||
update_parameters = ParameterDefine.create_parameters({"name":"name","contact_info":"A json to descrpit contact"})
|
||||
gl.register_tool_function(SimpleAIFunction("system.contacts.set",
|
||||
"set contact info",
|
||||
@@ -42,7 +43,6 @@ class ContactManager:
|
||||
def __init__(self, filename="contacts.toml"):
|
||||
self.filename = filename
|
||||
self.contacts = []
|
||||
self.family_members = []
|
||||
|
||||
self.is_auto_create_contact_from_telegram = True
|
||||
|
||||
@@ -56,12 +56,10 @@ class ContactManager:
|
||||
|
||||
def load_from_config(self,config_data:dict):
|
||||
self.contacts = [Contact.from_dict(item) for item in config_data.get("contacts", [])]
|
||||
self.family_members = [FamilyMember.from_dict(item) for item in config_data.get("family_members", [])]
|
||||
|
||||
def save_data(self):
|
||||
data = {
|
||||
"contacts": [contact.to_dict() for contact in self.contacts],
|
||||
"family_members": [member.to_dict() for member in self.family_members]
|
||||
}
|
||||
with open(self.filename, "w") as f:
|
||||
toml.dump(data, f)
|
||||
@@ -95,54 +93,28 @@ class ContactManager:
|
||||
if contact.name == name:
|
||||
return contact
|
||||
|
||||
for member in self.family_members:
|
||||
if member.name == name:
|
||||
return member
|
||||
return None
|
||||
|
||||
def find_contact_by_telegram(self, telegram:str):
|
||||
for contact in self.contacts:
|
||||
if contact.telegram == telegram:
|
||||
return contact
|
||||
for member in self.family_members:
|
||||
if member.telegram == telegram:
|
||||
return member
|
||||
|
||||
return None
|
||||
|
||||
def find_contact_by_email(self, email:str):
|
||||
for contact in self.contacts:
|
||||
if contact.email == email:
|
||||
return contact
|
||||
for member in self.family_members:
|
||||
if member.email == email:
|
||||
return member
|
||||
|
||||
return None
|
||||
|
||||
def find_contact_by_phone(self, phone:str):
|
||||
for contact in self.contacts:
|
||||
if contact.phone == phone:
|
||||
return contact
|
||||
for member in self.family_members:
|
||||
if member.phone == phone:
|
||||
return member
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def add_family_member(self, name, new_member:FamilyMember):
|
||||
assert name == new_member.name
|
||||
self.family_members.append(new_member)
|
||||
self.save_data()
|
||||
|
||||
def list_contacts(self):
|
||||
return self.contacts
|
||||
|
||||
def list_family_members(self):
|
||||
return self.family_members
|
||||
|
||||
#def register_to_ai_bus(self, ai_bus:AIBus):
|
||||
# ai_bus.register_message_handler("contact_manager", self.process_msg)
|
||||
|
||||
|
||||
#async def process_msg(self,msg:AgentMsg):
|
||||
# # forword message to contact
|
||||
# pass
|
||||
|
||||
@@ -15,7 +15,7 @@ class KnowledgeStore:
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
|
||||
knowledge_dir = AIStorage.get_instance().get_myai_dir() / "knowledge" / "objects"
|
||||
knowledge_dir = f"{AIStorage.get_instance().get_myai_dir()}/knowledge/objects"
|
||||
|
||||
if not os.path.exists(knowledge_dir):
|
||||
os.makedirs(knowledge_dir)
|
||||
|
||||
@@ -40,7 +40,7 @@ class UserConfig:
|
||||
self.config_table = {}
|
||||
self.user_config_path:str = None
|
||||
|
||||
self._init_default_value("llm_model_name","gpt-4-1106-preview")
|
||||
self._init_default_value("llm_model_name","gpt-4-turbo-preview")
|
||||
|
||||
def _init_default_value(self,key:str,value:Any) -> None:
|
||||
if self.config_table.get(key) is not None:
|
||||
@@ -92,7 +92,7 @@ class UserConfig:
|
||||
os.makedirs(directory)
|
||||
|
||||
async with aiofiles.open(self.user_config_path,"w") as f:
|
||||
toml_str = toml.dumps(will_save_config,ensure_ascii=False)
|
||||
toml_str = toml.dumps(will_save_config)
|
||||
await f.write(toml_str)
|
||||
except Exception as e:
|
||||
logger.error(f"save user config to {self.user_config_path} failed!")
|
||||
@@ -156,7 +156,7 @@ class AIStorage:
|
||||
self.feature_init_results = {}
|
||||
|
||||
async def initial(self)->bool:
|
||||
self.user_config.user_config_path = str(self.get_myai_dir() / "etc/system.cfg.toml")
|
||||
self.user_config.user_config_path = str(self.get_myai_dir() + "/etc/system.cfg.toml")
|
||||
await self.user_config.load_value_from_file(self.get_system_dir() + "/system.cfg.toml")
|
||||
await self.user_config.load_value_from_file(self.user_config.user_config_path,True)
|
||||
|
||||
@@ -215,7 +215,7 @@ class AIStorage:
|
||||
my ai dir is the dir for user to store their ai app and data
|
||||
~/myai/
|
||||
"""
|
||||
return Path.home() / "myai"
|
||||
return f"{Path.home()}/myai"
|
||||
|
||||
def get_download_dir(self) -> str:
|
||||
"""
|
||||
@@ -249,3 +249,10 @@ class AIStorage:
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"open or create file {path} failed! {str(e)}")
|
||||
|
||||
@staticmethod
|
||||
def ensure_directory_exists(directory_path):
|
||||
if not os.path.exists(directory_path):
|
||||
os.makedirs(directory_path)
|
||||
logger.info(f"Directory created: {directory_path}")
|
||||
|
||||
|
||||
@@ -72,8 +72,6 @@ class AgentManager:
|
||||
logger.warn(f"load agent {agent_id} from media failed!")
|
||||
return None
|
||||
|
||||
the_agent.memory_db = f"{self.agent_memory_base_dir}/{agent_id}/{agent_id}_memory.db"
|
||||
os.makedirs(os.path.dirname(the_agent.memory_db),exist_ok=True)
|
||||
if await the_agent.initial():
|
||||
return the_agent
|
||||
else:
|
||||
|
||||
@@ -453,7 +453,7 @@ class ParseLocalDocument:
|
||||
return f"# Known information:\n## Current directory structure:\n{kb_tree}\n## Knowlege Metadata:\n{json.dumps(known_obj,ensure_ascii=False)}\n"
|
||||
|
||||
def _token_len(self, text: str) -> int:
|
||||
return CustomAIAgent("", "gpt-4-1106-preview", self.token_limit).token_len(text=text)
|
||||
return CustomAIAgent("", "gpt-4-turbo-preview", self.token_limit).token_len(text=text)
|
||||
|
||||
|
||||
async def _learn_by_agent(self, meta:dict) -> dict:
|
||||
|
||||
@@ -64,7 +64,7 @@ class DallEComputeNode(ComputeNode):
|
||||
self.output_dir = "./"
|
||||
self.output_dir = os.path.abspath(self.output_dir)
|
||||
|
||||
self.start()
|
||||
await self.start()
|
||||
|
||||
return True
|
||||
|
||||
|
||||
@@ -209,7 +209,7 @@ class OpenAI_ComputeNode(ComputeNode):
|
||||
if max_token_size > 4096:
|
||||
result_token = 4096
|
||||
else:
|
||||
result_token = max_token_size
|
||||
result_token = -1
|
||||
else:
|
||||
result_token = NOT_GIVEN
|
||||
|
||||
|
||||
@@ -12,7 +12,7 @@ from telegram import Bot
|
||||
from telegram.ext import Updater
|
||||
from telegram.error import Forbidden, NetworkError
|
||||
|
||||
from aios import ObjectType, KnowledgeStore,AgentTunnel,AIStorage,ContactManager,Contact,FamilyMember,AgentMsg,AgentMsgType
|
||||
from aios import ObjectType, KnowledgeStore,AgentTunnel,AIStorage,ContactManager,Contact,AgentMsg,AgentMsgType
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@@ -76,8 +76,9 @@ class AIOS_Shell:
|
||||
|
||||
user_config = AIStorage.get_instance().get_user_config()
|
||||
user_config.add_user_config("username","username is your full name when using AIOS",False,None)
|
||||
user_config.add_user_config("telegram","Your telgram username",False,None)
|
||||
user_config.add_user_config("email","Your email",False,None)
|
||||
user_config.add_user_config("user_telegram","Your telgram username",False,None)
|
||||
user_config.add_user_config("user_email","Your email",False,None)
|
||||
user_config.add_user_config("user_notes","Introduce yourself to your Agent!",False,None)
|
||||
|
||||
user_config.add_user_config("feature.llama","enable Local-llama feature",True,"False")
|
||||
user_config.add_user_config("feature.aigc","enable AIGC feature",True,"False")
|
||||
@@ -126,15 +127,16 @@ class AIOS_Shell:
|
||||
|
||||
async def initial(self) -> bool:
|
||||
cm = ContactManager.get_instance()
|
||||
owenr = cm.find_contact_by_name(self.username)
|
||||
if owenr is None:
|
||||
owenr = Contact(self.username)
|
||||
owenr.added_by = self.username
|
||||
owenr.is_family_member = True
|
||||
owenr.email = AIStorage.get_instance().get_user_config().get_value("email")
|
||||
owenr.telegram = AIStorage.get_instance().get_user_config().get_value("telegram")
|
||||
owner = cm.find_contact_by_name(self.username)
|
||||
if owner is None:
|
||||
owner = Contact(self.username)
|
||||
owner.added_by = self.username
|
||||
owner.relationship = "Principal"
|
||||
owner.email = AIStorage.get_instance().get_user_config().get_value("user_email")
|
||||
owner.telegram = AIStorage.get_instance().get_user_config().get_value("user_telegram")
|
||||
owner.notes = AIStorage.get_instance().get_user_config().get_value("user_notes")
|
||||
|
||||
cm.add_family_member(self.username,owenr)
|
||||
cm.add_contact(self.username,owner)
|
||||
|
||||
# cal_env = CalenderEnvironment("calender")
|
||||
# await cal_env.start()
|
||||
@@ -247,7 +249,7 @@ class AIOS_Shell:
|
||||
if tunnel_config is not None:
|
||||
await AgentTunnel.load_all_tunnels_from_config(tunnel_config)
|
||||
except Exception as e:
|
||||
logger.warning(f"load tunnels config from {tunnels_config_path} failed!")
|
||||
logger.warning(f"load tunnels config from {tunnels_config_path} failed! {e}")
|
||||
|
||||
|
||||
return True
|
||||
@@ -385,7 +387,7 @@ class AIOS_Shell:
|
||||
|
||||
contact_note = await try_get_input(f"Input {contact_name}'s note (optional):")
|
||||
if contact_note is not None:
|
||||
contact.note = contact_note
|
||||
contact.notes = contact_note
|
||||
|
||||
contact.added_by = self.username
|
||||
if is_update:
|
||||
@@ -712,9 +714,9 @@ async def get_user_config_from_input(check_result:dict) -> bool:
|
||||
continue
|
||||
else:
|
||||
True
|
||||
|
||||
if len(user_input) > 0:
|
||||
AIStorage.get_instance().get_user_config().set_value(key,user_input)
|
||||
if user_input:
|
||||
if len(user_input) > 0:
|
||||
AIStorage.get_instance().get_user_config().set_value(key,user_input)
|
||||
|
||||
await AIStorage.get_instance().get_user_config().save_to_user_config()
|
||||
return True
|
||||
|
||||
Reference in New Issue
Block a user