diff --git a/dbgpt/agent/__init__.py b/dbgpt/agent/__init__.py index 49e8a6bb8..098a74e2a 100644 --- a/dbgpt/agent/__init__.py +++ b/dbgpt/agent/__init__.py @@ -1,6 +1,6 @@ """DB-GPT Multi-Agents Module.""" -from .actions.action import Action, ActionOutput # noqa: F401 +from .core.action import * # noqa: F401, F403 from .core.agent import ( # noqa: F401 Agent, AgentContext, @@ -13,12 +13,15 @@ initialize_agent, ) from .core.base_agent import ConversableAgent # noqa: F401 -from .core.llm.llm import LLMConfig # noqa: F401 +from .core.memory import * # noqa: F401, F403 +from .core.memory.gpts.gpts_memory import GptsMemory # noqa: F401 +from .core.plan import * # noqa: F401, F403 +from .core.profile import * # noqa: F401, F403 from .core.schema import PluginStorageType # noqa: F401 from .core.user_proxy_agent import UserProxyAgent # noqa: F401 -from .memory.gpts_memory import GptsMemory # noqa: F401 from .resource.resource_api import AgentResource, ResourceType # noqa: F401 from .resource.resource_loader import ResourceLoader # noqa: F401 +from .util.llm.llm import LLMConfig # noqa: F401 __ALL__ = [ "Agent", diff --git a/dbgpt/agent/actions/__init__.py b/dbgpt/agent/actions/__init__.py deleted file mode 100644 index c1000aaea..000000000 --- a/dbgpt/agent/actions/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Actions of Agent.""" diff --git a/dbgpt/agent/core/__init__.py b/dbgpt/agent/core/__init__.py index 67e379d50..7e37ca55b 100644 --- a/dbgpt/agent/core/__init__.py +++ b/dbgpt/agent/core/__init__.py @@ -1 +1,22 @@ -"""Core Module for the Agent.""" +"""Core Module for the Agent. + +There are four modules in DB-GPT agent core according the paper +`A survey on large language model based autonomous agents +` +by `Lei Wang, Chen Ma, Xueyang Feng, et al.`: + +1. Profiling Module: The profiling module aims to indicate the profiles of the agent +roles. + +2. Memory Module: It stores information perceived from the environment and leverages +the recorded memories to facilitate future actions. + +3. Planning Module: When faced with a complex task, humans tend to deconstruct it into +simpler subtasks and solve them individually. The planning module aims to empower the +agents with such human capability, which is expected to make the agent behave more +reasonably, powerfully, and reliably + +4. Action Module: The action module is responsible for translating the agent’s +decisions into specific outcomes. This module is located at the most downstream +position and directly interacts with the environment. +""" diff --git a/dbgpt/agent/core/action/__init__.py b/dbgpt/agent/core/action/__init__.py new file mode 100644 index 000000000..bea935a47 --- /dev/null +++ b/dbgpt/agent/core/action/__init__.py @@ -0,0 +1,20 @@ +"""Action Module. + +The action module is responsible for translating the agent’s decisions into specific +outcomes. This module is located at the most downstream position and directly interacts +with the environment. It is influenced by the profile, memory, and planning modules. + + +The Goal Of The Action Module: +-------- +1. Task Completion: Complete specific tasks, write a function in software development, +and make an iron pick in the game. + +2. Communication: Communicate with other agents. + +3. Environment exploration: Explore unfamiliar environments to expand its perception +and strike a balance between exploring and exploiting. +""" + +from .base import Action, ActionOutput # noqa: F401 +from .blank_action import BlankAction # noqa: F401 diff --git a/dbgpt/agent/actions/action.py b/dbgpt/agent/core/action/base.py similarity index 88% rename from dbgpt/agent/actions/action.py rename to dbgpt/agent/core/action/base.py index f2fd950b5..9d1195d80 100644 --- a/dbgpt/agent/actions/action.py +++ b/dbgpt/agent/core/action/base.py @@ -1,4 +1,5 @@ """Base Action class for defining agent actions.""" + import json from abc import ABC, abstractmethod from typing import ( @@ -21,12 +22,13 @@ field_description, model_fields, model_to_dict, + model_validator, ) from dbgpt.util.json_utils import find_json_objects +from dbgpt.vis.base import Vis -from ...vis.base import Vis -from ..resource.resource_api import AgentResource, ResourceType -from ..resource.resource_loader import ResourceLoader +from ...resource.resource_api import AgentResource, ResourceType +from ...resource.resource_loader import ResourceLoader T = TypeVar("T", bound=Union[BaseModel, List[BaseModel], None]) @@ -41,6 +43,20 @@ class ActionOutput(BaseModel): view: Optional[str] = None resource_type: Optional[str] = None resource_value: Optional[Any] = None + action: Optional[str] = None + thoughts: Optional[str] = None + observations: Optional[str] = None + + @model_validator(mode="before") + @classmethod + def pre_fill(cls, values: Any) -> Any: + """Pre-fill the values.""" + if not isinstance(values, dict): + return values + is_exe_success = values.get("is_exe_success", True) + if not is_exe_success and "observations" not in values: + values["observations"] = values.get("content") + return values @classmethod def from_dict( diff --git a/dbgpt/agent/actions/blank_action.py b/dbgpt/agent/core/action/blank_action.py similarity index 76% rename from dbgpt/agent/actions/blank_action.py rename to dbgpt/agent/core/action/blank_action.py index 04e5757dc..08d92863c 100644 --- a/dbgpt/agent/actions/blank_action.py +++ b/dbgpt/agent/core/action/blank_action.py @@ -3,8 +3,8 @@ import logging from typing import Optional -from ..resource.resource_api import AgentResource -from .action import Action, ActionOutput +from ...resource.resource_api import AgentResource +from .base import Action, ActionOutput logger = logging.getLogger(__name__) @@ -33,4 +33,8 @@ async def run( Just return the AI message. """ - return ActionOutput(is_exe_success=True, content=ai_message, view=ai_message) + return ActionOutput( + is_exe_success=True, + content=ai_message, + view=ai_message, + ) diff --git a/dbgpt/agent/core/agent.py b/dbgpt/agent/core/agent.py index 27bb70877..3964d29e4 100644 --- a/dbgpt/agent/core/agent.py +++ b/dbgpt/agent/core/agent.py @@ -9,9 +9,9 @@ from dbgpt.core import LLMClient from dbgpt.util.annotations import PublicAPI -from ..actions.action import ActionOutput -from ..memory.gpts_memory import GptsMemory from ..resource.resource_loader import ResourceLoader +from .action.base import ActionOutput +from .memory.agent_memory import AgentMemory class Agent(ABC): @@ -160,17 +160,20 @@ async def verify( verification result. """ + @property @abstractmethod - def get_name(self) -> str: - """Return name of the agent.""" + def name(self) -> str: + """Return the name of the agent.""" + @property @abstractmethod - def get_profile(self) -> str: - """Return profile of the agent.""" + def role(self) -> str: + """Return the role of the agent.""" + @property @abstractmethod - def get_describe(self) -> str: - """Return describe of the agent.""" + def desc(self) -> Optional[str]: + """Return the description of the agent.""" @dataclasses.dataclass @@ -204,7 +207,7 @@ class AgentGenerateContext: rely_messages: List[AgentMessage] = dataclasses.field(default_factory=list) final: Optional[bool] = True - memory: Optional[GptsMemory] = None + memory: Optional[AgentMemory] = None agent_context: Optional[AgentContext] = None resource_loader: Optional[ResourceLoader] = None llm_client: Optional[LLMClient] = None @@ -302,3 +305,9 @@ def copy(self) -> "AgentMessage": role=self.role, success=self.success, ) + + def get_dict_context(self) -> Dict[str, Any]: + """Return the context as a dictionary.""" + if isinstance(self.context, dict): + return self.context + return {} diff --git a/dbgpt/agent/core/agent_manage.py b/dbgpt/agent/core/agent_manage.py index 3cc85fa62..715c1c6ec 100644 --- a/dbgpt/agent/core/agent_manage.py +++ b/dbgpt/agent/core/agent_manage.py @@ -18,7 +18,7 @@ def participant_roles(agents: List[Agent]) -> str: # Default to all agents registered roles = [] for agent in agents: - roles.append(f"{agent.get_name()}: {agent.get_describe()}") + roles.append(f"{agent.name}: {agent.desc}") return "\n".join(roles) @@ -34,13 +34,13 @@ def mentioned_agents(message_content: str, agents: List[Agent]) -> Dict: mentions = dict() for agent in agents: regex = ( - r"(?<=\W)" + re.escape(agent.get_name()) + r"(?=\W)" + r"(?<=\W)" + re.escape(agent.name) + r"(?=\W)" ) # Finds agent mentions, taking word boundaries into account count = len( re.findall(regex, " " + message_content + " ") ) # Pad the message to help with matching if count > 0: - mentions[agent.get_name()] = count + mentions[agent.name] = count return mentions @@ -84,7 +84,7 @@ def register_agent( ) -> str: """Register an agent.""" inst = cls() - profile = inst.get_profile() + profile = inst.role if profile in self._agents and ( profile in self._core_agents or not ignore_duplicate ): @@ -110,13 +110,13 @@ def get_by_name(self, name: str) -> Type[ConversableAgent]: def get_describe_by_name(self, name: str) -> str: """Return the description of an agent by name.""" - return self._agents[name][1].desc + return self._agents[name][1].desc or "" def all_agents(self) -> Dict[str, str]: """Return a dictionary of all registered agents and their descriptions.""" result = {} for name, value in self._agents.items(): - result[name] = value[1].desc + result[name] = value[1].desc or "" return result def list_agents(self): @@ -125,7 +125,7 @@ def list_agents(self): for name, value in self._agents.items(): result.append( { - "name": value[1].profile, + "name": value[1].role, "desc": value[1].goal, } ) diff --git a/dbgpt/agent/core/base_agent.py b/dbgpt/agent/core/base_agent.py index 6dcaa1ee4..88cdb47cb 100644 --- a/dbgpt/agent/core/base_agent.py +++ b/dbgpt/agent/core/base_agent.py @@ -3,7 +3,7 @@ import asyncio import json import logging -from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast +from typing import Any, Dict, List, Optional, Tuple, Type, cast from dbgpt._private.pydantic import ConfigDict, Field from dbgpt.core import LLMClient, ModelMessageRoleType @@ -11,14 +11,15 @@ from dbgpt.util.tracer import SpanType, root_tracer from dbgpt.util.utils import colored -from ..actions.action import Action, ActionOutput -from ..memory.base import GptsMessage -from ..memory.gpts_memory import GptsMemory from ..resource.resource_api import AgentResource, ResourceClient from ..resource.resource_loader import ResourceLoader +from ..util.llm.llm import LLMConfig, LLMStrategyType +from ..util.llm.llm_client import AIWrapper +from .action.base import Action, ActionOutput from .agent import Agent, AgentContext, AgentMessage, AgentReviewInfo -from .llm.llm import LLMConfig, LLMStrategyType -from .llm.llm_client import AIWrapper +from .memory.agent_memory import AgentMemory +from .memory.gpts.base import GptsMessage +from .memory.gpts.gpts_memory import GptsMemory from .role import Role logger = logging.getLogger(__name__) @@ -33,26 +34,16 @@ class ConversableAgent(Role, Agent): actions: List[Action] = Field(default_factory=list) resources: List[AgentResource] = Field(default_factory=list) llm_config: Optional[LLMConfig] = None - memory: GptsMemory = Field(default_factory=GptsMemory) resource_loader: Optional[ResourceLoader] = None max_retry_count: int = 3 consecutive_auto_reply_counter: int = 0 llm_client: Optional[AIWrapper] = None - oai_system_message: List[Dict] = Field(default_factory=list) def __init__(self, **kwargs): """Create a new agent.""" Role.__init__(self, **kwargs) Agent.__init__(self) - def init_system_message(self) -> None: - """Initialize the system message.""" - content = self.prompt_template() - # TODO: Don't modify the original data, need to be optimized - self.oai_system_message = [ - {"content": content, "role": ModelMessageRoleType.SYSTEM} - ] - def check_available(self) -> None: """Check if the agent is available. @@ -63,7 +54,7 @@ def check_available(self) -> None: # check run context if self.agent_context is None: raise ValueError( - f"{self.name}[{self.profile}] Missing context in which agent is " + f"{self.name}[{self.role}] Missing context in which agent is " f"running!" ) @@ -90,20 +81,20 @@ def check_available(self) -> None: and action.resource_need not in have_resource_types ): raise ValueError( - f"{self.name}[{self.profile}] Missing resources required for " + f"{self.name}[{self.role}] Missing resources required for " "runtime！" ) else: if not self.is_human and not self.is_team: raise ValueError( - f"This agent {self.name}[{self.profile}] is missing action modules." + f"This agent {self.name}[{self.role}] is missing action modules." ) # llm check if not self.is_human and ( self.llm_config is None or self.llm_config.llm_client is None ): raise ValueError( - f"{self.name}[{self.profile}] Model configuration is missing or model " + f"{self.name}[{self.role}] Model configuration is missing or model " "service is unavailable！" ) @@ -161,14 +152,19 @@ async def build(self) -> "ConversableAgent": for action in self.actions: action.init_resource_loader(self.resource_loader) - # Initialize system messages - self.init_system_message() - # Initialize LLM Server if not self.is_human: if not self.llm_config or not self.llm_config.llm_client: raise ValueError("LLM client is not initialized！") self.llm_client = AIWrapper(llm_client=self.llm_config.llm_client) + self.memory.initialize( + self.name, + self.llm_config.llm_client, + importance_scorer=self.memory_importance_scorer, + insight_extractor=self.memory_insight_extractor, + ) + # Clone the memory structure + self.memory = self.memory.structure_clone() return self def bind(self, target: Any) -> "ConversableAgent": @@ -176,7 +172,7 @@ def bind(self, target: Any) -> "ConversableAgent": if isinstance(target, LLMConfig): self.llm_config = target elif isinstance(target, GptsMemory): - self.memory = target + raise ValueError("GptsMemory is not supported!") elif isinstance(target, AgentContext): self.agent_context = target elif isinstance(target, ResourceLoader): @@ -186,6 +182,8 @@ def bind(self, target: Any) -> "ConversableAgent": self.actions.extend(target) elif _is_list_of_type(target, AgentResource): self.resources = target + elif isinstance(target, AgentMemory): + self.memory = target return self async def send( @@ -200,9 +198,9 @@ async def send( with root_tracer.start_span( "agent.send", metadata={ - "sender": self.get_name(), - "recipient": recipient.get_name(), - "reviewer": reviewer.get_name() if reviewer else None, + "sender": self.name, + "recipient": recipient.name, + "reviewer": reviewer.name if reviewer else None, "agent_message": message.to_dict(), "request_reply": request_reply, "is_recovery": is_recovery, @@ -230,9 +228,9 @@ async def receive( with root_tracer.start_span( "agent.receive", metadata={ - "sender": sender.get_name(), - "recipient": self.get_name(), - "reviewer": reviewer.get_name() if reviewer else None, + "sender": sender.name, + "recipient": self.name, + "reviewer": reviewer.name if reviewer else None, "agent_message": message.to_dict(), "request_reply": request_reply, "silent": silent, @@ -271,14 +269,14 @@ async def generate_reply( root_span = root_tracer.start_span( "agent.generate_reply", metadata={ - "sender": sender.get_name(), - "recipient": self.get_name(), - "reviewer": reviewer.get_name() if reviewer else None, + "sender": sender.name, + "recipient": self.name, + "reviewer": reviewer.name if reviewer else None, "received_message": received_message.to_dict(), "conv_uid": self.not_null_agent_context.conv_id, - "rely_messages": [msg.to_dict() for msg in rely_messages] - if rely_messages - else None, + "rely_messages": ( + [msg.to_dict() for msg in rely_messages] if rely_messages else None + ), }, ) @@ -295,18 +293,6 @@ async def generate_reply( ) span.metadata["reply_message"] = reply_message.to_dict() - with root_tracer.start_span( - "agent.generate_reply._system_message_assembly", - metadata={ - "reply_message": reply_message.to_dict(), - }, - ) as span: - # assemble system message - await self._system_message_assembly( - received_message.content, reply_message.context - ) - span.metadata["assembled_system_messages"] = self.oai_system_message - fail_reason = None current_retry_counter = 0 is_success = True @@ -325,8 +311,11 @@ async def generate_reply( retry_message, self, reviewer, request_reply=False ) - thinking_messages = self._load_thinking_messages( - received_message, sender, rely_messages + thinking_messages = await self._load_thinking_messages( + received_message, + sender, + rely_messages, + context=reply_message.get_dict_context(), ) with root_tracer.start_span( "agent.generate_reply.thinking", @@ -345,7 +334,7 @@ async def generate_reply( with root_tracer.start_span( "agent.generate_reply.review", - metadata={"llm_reply": llm_reply, "censored": self.get_name()}, + metadata={"llm_reply": llm_reply, "censored": self.name}, ) as span: # 2.Review whether what is being done is legal approve, comments = await self.review(llm_reply, self) @@ -361,8 +350,8 @@ async def generate_reply( "agent.generate_reply.act", metadata={ "llm_reply": llm_reply, - "sender": sender.get_name(), - "reviewer": reviewer.get_name() if reviewer else None, + "sender": sender.name, + "reviewer": reviewer.name if reviewer else None, "act_extent_param": act_extent_param, }, ) as span: @@ -383,8 +372,8 @@ async def generate_reply( "agent.generate_reply.verify", metadata={ "llm_reply": llm_reply, - "sender": sender.get_name(), - "reviewer": reviewer.get_name() if reviewer else None, + "sender": sender.name, + "reviewer": reviewer.name if reviewer else None, }, ) as span: # 4.Reply information verification @@ -394,6 +383,9 @@ async def generate_reply( is_success = check_pass span.metadata["check_pass"] = check_pass span.metadata["reason"] = reason + + question: str = received_message.content or "" + ai_message: str = llm_reply or "" # 5.Optimize wrong answers myself if not check_pass: current_retry_counter += 1 @@ -403,7 +395,20 @@ async def generate_reply( reply_message, sender, reviewer, request_reply=False ) fail_reason = reason + await self.save_to_memory( + question=question, + ai_message=ai_message, + action_output=act_out, + check_pass=check_pass, + check_fail_reason=fail_reason, + ) else: + await self.save_to_memory( + question=question, + ai_message=ai_message, + action_output=act_out, + check_pass=check_pass, + ) break reply_message.success = is_success return reply_message @@ -437,8 +442,6 @@ async def thinking( try: if prompt: llm_messages = _new_system_message(prompt) + llm_messages - else: - llm_messages = self.oai_system_message + llm_messages if not self.llm_client: raise ValueError("LLM client is not initialized!") @@ -491,9 +494,9 @@ async def act( "agent.act.run", metadata={ "message": message, - "sender": sender.get_name() if sender else None, - "recipient": self.get_name(), - "reviewer": reviewer.get_name() if reviewer else None, + "sender": sender.name if sender else None, + "recipient": self.name, + "reviewer": reviewer.name if reviewer else None, "need_resource": need_resource.to_dict() if need_resource else None, "rely_action_out": last_out.to_dict() if last_out else None, "conv_uid": self.not_null_agent_context.conv_id, @@ -563,9 +566,9 @@ async def initiate_chat( "agent.initiate_chat", span_type=SpanType.AGENT, metadata={ - "sender": self.get_name(), - "recipient": recipient.get_name(), - "reviewer": reviewer.get_name() if reviewer else None, + "sender": self.name, + "recipient": recipient.name, + "reviewer": reviewer.name if reviewer else None, "agent_message": agent_message.to_dict(), "conv_uid": self.not_null_agent_context.conv_id, }, @@ -612,21 +615,27 @@ async def _a_append_message( gpts_message: GptsMessage = GptsMessage( conv_id=self.not_null_agent_context.conv_id, - sender=sender.get_profile(), - receiver=self.profile, + sender=sender.role, + receiver=self.role, role=role, rounds=self.consecutive_auto_reply_counter, current_goal=oai_message.get("current_goal", None), content=oai_message.get("content", None), - context=json.dumps(oai_message["context"], ensure_ascii=False) - if "context" in oai_message - else None, - review_info=json.dumps(oai_message["review_info"], ensure_ascii=False) - if "review_info" in oai_message - else None, - action_report=json.dumps(oai_message["action_report"], ensure_ascii=False) - if "action_report" in oai_message - else None, + context=( + json.dumps(oai_message["context"], ensure_ascii=False) + if "context" in oai_message + else None + ), + review_info=( + json.dumps(oai_message["review_info"], ensure_ascii=False) + if "review_info" in oai_message + else None + ), + action_report=( + json.dumps(oai_message["action_report"], ensure_ascii=False) + if "action_report" in oai_message + else None + ), model_name=oai_message.get("model_name", None), ) @@ -643,10 +652,10 @@ async def _a_append_message( def _print_received_message(self, message: AgentMessage, sender: Agent): # print the message received print("\n", "-" * 80, flush=True, sep="") - _print_name = self.name if self.name else self.profile + _print_name = self.name if self.name else self.role print( colored( - sender.get_name() if sender.get_name() else sender.get_profile(), + sender.name if sender.name else sender.role, "yellow", ), "(to", @@ -660,7 +669,7 @@ def _print_received_message(self, message: AgentMessage, sender: Agent): review_info = message.review_info if review_info: - name = sender.get_name() if sender.get_name() else sender.get_profile() + name = sender.name if sender.name else sender.role pass_msg = "Pass" if review_info.approve else "Reject" review_msg = f"{pass_msg}({review_info.comments})" approve_print = f">>>>>>>>{name} Review info: \n{review_msg}" @@ -668,7 +677,7 @@ def _print_received_message(self, message: AgentMessage, sender: Agent): action_report = message.action_report if action_report: - name = sender.get_name() if sender.get_name() else sender.get_profile() + name = sender.name if sender.name else sender.role action_msg = ( "execution succeeded" if action_report["is_exe_success"] @@ -690,42 +699,32 @@ async def _a_process_received_message(self, message: AgentMessage, sender: Agent self._print_received_message(message, sender) - async def _system_message_assembly( - self, question: Optional[str], context: Optional[Union[str, Dict]] = None - ): - # system message - self.init_system_message() - if len(self.oai_system_message) > 0: - resource_prompt_list = [] - for item in self.resources: - resource_client = self.not_null_resource_loader.get_resource_api( - item.type, ResourceClient - ) - if not resource_client: - raise ValueError( - f"Resource {item.type}:{item.value} missing resource loader" - f" implementation,unable to read resources!" - ) - resource_prompt_list.append( - await resource_client.get_resource_prompt(item, question) - ) - if context is None or not isinstance(context, dict): - context = {} - - resource_prompt = "" - if len(resource_prompt_list) > 0: - resource_prompt = "RESOURCES:" + "\n".join(resource_prompt_list) - - out_schema: Optional[str] = "" - if self.actions and len(self.actions) > 0: - out_schema = self.actions[0].ai_out_schema - for message in self.oai_system_message: - new_content = message["content"].format( - resource_prompt=resource_prompt, - out_schema=out_schema, - **context, + async def generate_resource_variables( + self, question: Optional[str] = None + ) -> Dict[str, Any]: + """Generate the resource variables.""" + resource_prompt_list = [] + for item in self.resources: + resource_client = self.not_null_resource_loader.get_resource_api( + item.type, ResourceClient + ) + if not resource_client: + raise ValueError( + f"Resource {item.type}:{item.value} missing resource loader" + f" implementation,unable to read resources!" ) - message["content"] = new_content + resource_prompt_list.append( + await resource_client.get_resource_prompt(item, question) + ) + + resource_prompt = "" + if len(resource_prompt_list) > 0: + resource_prompt = "RESOURCES:" + "\n".join(resource_prompt_list) + + out_schema: Optional[str] = "" + if self.actions and len(self.actions) > 0: + out_schema = self.actions[0].ai_out_schema + return {"resource_prompt": resource_prompt, "out_schema": out_schema} def _excluded_models( self, @@ -774,7 +773,7 @@ async def _a_select_llm_model( else: raise ValueError("No model service available!") except Exception as e: - logger.error(f"{self.profile} get next llm failed!{str(e)}") + logger.error(f"{self.role} get next llm failed!{str(e)}") raise ValueError(f"Failed to allocate model service,{str(e)}!") def _init_reply_message(self, received_message: AgentMessage) -> AgentMessage: @@ -803,9 +802,9 @@ def _convert_to_ai_message( if item.role: role = item.role else: - if item.receiver == self.profile: + if item.receiver == self.role: role = ModelMessageRoleType.HUMAN - elif item.sender == self.profile: + elif item.sender == self.role: role = ModelMessageRoleType.AI else: continue @@ -825,14 +824,80 @@ def _convert_to_ai_message( AgentMessage( content=content, role=role, - context=json.loads(item.context) - if item.context is not None - else None, + context=( + json.loads(item.context) if item.context is not None else None + ), ) ) return oai_messages - def _load_thinking_messages( + async def _load_thinking_messages( + self, + received_message: AgentMessage, + sender: Agent, + rely_messages: Optional[List[AgentMessage]] = None, + context: Optional[Dict[str, Any]] = None, + ) -> List[AgentMessage]: + observation = received_message.content + if not observation: + raise ValueError("The received message content is empty!") + memories = await self.read_memories(observation) + reply_message_str = "" + if context is None: + context = {} + if rely_messages: + copied_rely_messages = [m.copy() for m in rely_messages] + # When directly relying on historical messages, use the execution result + # content as a dependency + for message in copied_rely_messages: + action_report: Optional[ActionOutput] = ActionOutput.from_dict( + message.action_report + ) + if action_report: + # TODO: Modify in-place, need to be optimized + message.content = action_report.content + if message.name != self.role: + # TODO, use name + # Rely messages are not from the current agent + if message.role == ModelMessageRoleType.HUMAN: + reply_message_str += f"Question: {message.content}\n" + elif message.role == ModelMessageRoleType.AI: + reply_message_str += f"Observation: {message.content}\n" + if reply_message_str: + memories += "\n" + reply_message_str + + system_prompt = await self.build_prompt( + question=observation, + is_system=True, + most_recent_memories=memories, + **context, + ) + user_prompt = await self.build_prompt( + question=observation, + is_system=False, + most_recent_memories=memories, + **context, + ) + + agent_messages = [] + if system_prompt: + agent_messages.append( + AgentMessage( + content=system_prompt, + role=ModelMessageRoleType.SYSTEM, + ) + ) + if user_prompt: + agent_messages.append( + AgentMessage( + content=user_prompt, + role=ModelMessageRoleType.HUMAN, + ) + ) + + return agent_messages + + def _old_load_thinking_messages( self, received_message: AgentMessage, sender: Agent, @@ -846,8 +911,8 @@ def _load_thinking_messages( with root_tracer.start_span( "agent._load_thinking_messages", metadata={ - "sender": sender.get_name(), - "recipient": self.get_name(), + "sender": sender.name, + "recipient": self.name, "conv_uid": self.not_null_agent_context.conv_id, "current_goal": current_goal, }, @@ -855,8 +920,8 @@ def _load_thinking_messages( # Get historical information from the memory memory_messages = self.memory.message_memory.get_between_agents( self.not_null_agent_context.conv_id, - self.profile, - sender.get_profile(), + self.role, + sender.role, current_goal, ) span.metadata["memory_messages"] = [ diff --git a/dbgpt/agent/core/base_team.py b/dbgpt/agent/core/base_team.py index 2460086e3..b828ec1e0 100644 --- a/dbgpt/agent/core/base_team.py +++ b/dbgpt/agent/core/base_team.py @@ -1,13 +1,14 @@ """Base classes for managing a group of agents in a team chat.""" import logging -from typing import Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union from dbgpt._private.pydantic import BaseModel, ConfigDict, Field -from ..actions.action import ActionOutput +from .action.base import ActionOutput from .agent import Agent, AgentMessage from .base_agent import ConversableAgent +from .profile import ProfileConfig logger = logging.getLogger(__name__) @@ -86,7 +87,7 @@ def hire(self, agents: List[Agent]): @property def agent_names(self) -> List[str]: """Return the names of the agents in the group chat.""" - return [agent.get_profile() for agent in self.agents] + return [agent.role for agent in self.agents] def agent_by_name(self, name: str) -> Agent: """Return the agent with a given name.""" @@ -121,10 +122,14 @@ class ManagerAgent(ConversableAgent, Team): model_config = ConfigDict(arbitrary_types_allowed=True) - profile: str = "TeamManager" - goal: str = "manage all hired intelligent agents to complete mission objectives" - constraints: List[str] = [] - desc: str = goal + profile: ProfileConfig = ProfileConfig( + name="ManagerAgent", + profile="TeamManager", + goal="manage all hired intelligent agents to complete mission objectives", + constraints=[], + desc="manage all hired intelligent agents to complete mission objectives", + ) + is_team: bool = True # The management agent does not need to retry the exception. The actual execution @@ -149,6 +154,16 @@ async def thinking( self.messages.append(message.to_llm_message()) return message.content, None + async def _load_thinking_messages( + self, + received_message: AgentMessage, + sender: Agent, + rely_messages: Optional[List[AgentMessage]] = None, + context: Optional[Dict[str, Any]] = None, + ) -> List[AgentMessage]: + """Load messages for thinking.""" + return [AgentMessage(content=received_message.content)] + async def act( self, message: Optional[str], diff --git a/dbgpt/agent/core/memory/__init__.py b/dbgpt/agent/core/memory/__init__.py new file mode 100644 index 000000000..676c9857d --- /dev/null +++ b/dbgpt/agent/core/memory/__init__.py @@ -0,0 +1,16 @@ +"""Memory module for the agent.""" + +from .agent_memory import AgentMemory, AgentMemoryFragment # noqa: F401 +from .base import ( # noqa: F401 + ImportanceScorer, + InsightExtractor, + InsightMemoryFragment, + Memory, + MemoryFragment, + SensoryMemory, + ShortTermMemory, +) +from .hybrid import HybridMemory # noqa: F401 +from .llm import LLMImportanceScorer, LLMInsightExtractor # noqa: F401 +from .long_term import LongTermMemory, LongTermRetriever # noqa: F401 +from .short_term import EnhancedShortTermMemory # noqa: F401 diff --git a/dbgpt/agent/core/memory/agent_memory.py b/dbgpt/agent/core/memory/agent_memory.py new file mode 100644 index 000000000..6cb52879c --- /dev/null +++ b/dbgpt/agent/core/memory/agent_memory.py @@ -0,0 +1,282 @@ +"""Agent memory module.""" + +from datetime import datetime +from typing import Callable, List, Optional, Type, cast + +from dbgpt.core import LLMClient +from dbgpt.util.annotations import immutable, mutable +from dbgpt.util.id_generator import new_id + +from .base import ( + DiscardedMemoryFragments, + ImportanceScorer, + InsightExtractor, + Memory, + MemoryFragment, + ShortTermMemory, + WriteOperation, +) +from .gpts import GptsMemory, GptsMessageMemory, GptsPlansMemory + + +class AgentMemoryFragment(MemoryFragment): + """Default memory fragment for agent memory.""" + + def __init__( + self, + observation: str, + embeddings: Optional[List[float]] = None, + memory_id: Optional[int] = None, + importance: Optional[float] = None, + last_accessed_time: Optional[datetime] = None, + is_insight: bool = False, + ): + """Create a memory fragment.""" + if not memory_id: + # Generate a new memory id, we use snowflake id generator here. + memory_id = new_id() + self.observation = observation + self._embeddings = embeddings + self.memory_id: int = cast(int, memory_id) + self._importance: Optional[float] = importance + self._last_accessed_time: Optional[datetime] = last_accessed_time + self._is_insight = is_insight + + @property + def id(self) -> int: + """Return the memory id.""" + return self.memory_id + + @property + def raw_observation(self) -> str: + """Return the raw observation.""" + return self.observation + + @property + def embeddings(self) -> Optional[List[float]]: + """Return the embeddings of the memory fragment.""" + return self._embeddings + + def update_embeddings(self, embeddings: List[float]) -> None: + """Update the embeddings of the memory fragment. + + Args: + embeddings(List[float]): embeddings + """ + self._embeddings = embeddings + + def calculate_current_embeddings( + self, embedding_func: Callable[[List[str]], List[List[float]]] + ) -> List[float]: + """Calculate the embeddings of the memory fragment. + + Args: + embedding_func(Callable[[List[str]], List[List[float]]]): Function to + compute embeddings + + Returns: + List[float]: Embeddings of the memory fragment + """ + embeddings = embedding_func([self.observation]) + return embeddings[0] + + @property + def is_insight(self) -> bool: + """Return whether the memory fragment is an insight. + + Returns: + bool: Whether the memory fragment is an insight + """ + return self._is_insight + + @property + def importance(self) -> Optional[float]: + """Return the importance of the memory fragment. + + Returns: + Optional[float]: Importance of the memory fragment + """ + return self._importance + + def update_importance(self, importance: float) -> Optional[float]: + """Update the importance of the memory fragment. + + Args: + importance(float): Importance of the memory fragment + + Returns: + Optional[float]: Old importance + """ + old_importance = self._importance + self._importance = importance + return old_importance + + @property + def last_accessed_time(self) -> Optional[datetime]: + """Return the last accessed time of the memory fragment. + + Used to determine the least recently used memory fragment. + + Returns: + Optional[datetime]: Last accessed time + """ + return self._last_accessed_time + + def update_accessed_time(self, now: datetime) -> Optional[datetime]: + """Update the last accessed time of the memory fragment. + + Args: + now(datetime): Current time + + Returns: + Optional[datetime]: Old last accessed time + """ + old_time = self._last_accessed_time + self._last_accessed_time = now + return old_time + + @classmethod + def build_from( + cls: Type["AgentMemoryFragment"], + observation: str, + embeddings: Optional[List[float]] = None, + memory_id: Optional[int] = None, + importance: Optional[float] = None, + is_insight: bool = False, + last_accessed_time: Optional[datetime] = None, + **kwargs + ) -> "AgentMemoryFragment": + """Build a memory fragment from the given parameters.""" + return cls( + observation=observation, + embeddings=embeddings, + memory_id=memory_id, + importance=importance, + last_accessed_time=last_accessed_time, + is_insight=is_insight, + ) + + def copy(self: "AgentMemoryFragment") -> "AgentMemoryFragment": + """Return a copy of the memory fragment.""" + return AgentMemoryFragment.build_from( + observation=self.observation, + embeddings=self._embeddings, + memory_id=self.memory_id, + importance=self.importance, + last_accessed_time=self.last_accessed_time, + is_insight=self.is_insight, + ) + + +class AgentMemory(Memory[AgentMemoryFragment]): + """Agent memory.""" + + def __init__( + self, + memory: Optional[Memory[AgentMemoryFragment]] = None, + importance_scorer: Optional[ImportanceScorer[AgentMemoryFragment]] = None, + insight_extractor: Optional[InsightExtractor[AgentMemoryFragment]] = None, + gpts_memory: Optional[GptsMemory] = None, + ): + """Create an agent memory. + + Args: + memory(Memory[AgentMemoryFragment]): Memory to store fragments + importance_scorer(ImportanceScorer[AgentMemoryFragment]): Scorer to + calculate the importance of memory fragments + insight_extractor(InsightExtractor[AgentMemoryFragment]): Extractor to + extract insights from memory fragments + gpts_memory(GptsMemory): Memory to store GPTs related information + """ + if not memory: + memory = ShortTermMemory(buffer_size=5) + if not gpts_memory: + gpts_memory = GptsMemory() + self.memory: Memory[AgentMemoryFragment] = cast( + Memory[AgentMemoryFragment], memory + ) + self.importance_scorer = importance_scorer + self.insight_extractor = insight_extractor + self.gpts_memory = gpts_memory + + @immutable + def structure_clone( + self: "AgentMemory", now: Optional[datetime] = None + ) -> "AgentMemory": + """Return a structure clone of the memory. + + The gpst_memory is not cloned, it will be shared in whole agent memory. + """ + m = AgentMemory( + memory=self.memory.structure_clone(now), + importance_scorer=self.importance_scorer, + insight_extractor=self.insight_extractor, + gpts_memory=self.gpts_memory, + ) + m._copy_from(self) + return m + + @mutable + def initialize( + self, + name: Optional[str] = None, + llm_client: Optional[LLMClient] = None, + importance_scorer: Optional[ImportanceScorer[AgentMemoryFragment]] = None, + insight_extractor: Optional[InsightExtractor[AgentMemoryFragment]] = None, + real_memory_fragment_class: Optional[Type[AgentMemoryFragment]] = None, + ) -> None: + """Initialize the memory.""" + self.memory.initialize( + name=name, + llm_client=llm_client, + importance_scorer=importance_scorer or self.importance_scorer, + insight_extractor=insight_extractor or self.insight_extractor, + real_memory_fragment_class=real_memory_fragment_class + or AgentMemoryFragment, + ) + + @mutable + async def write( + self, + memory_fragment: AgentMemoryFragment, + now: Optional[datetime] = None, + op: WriteOperation = WriteOperation.ADD, + ) -> Optional[DiscardedMemoryFragments[AgentMemoryFragment]]: + """Write a memory fragment to the memory.""" + return await self.memory.write(memory_fragment, now) + + @immutable + async def read( + self, + observation: str, + alpha: Optional[float] = None, + beta: Optional[float] = None, + gamma: Optional[float] = None, + ) -> List[AgentMemoryFragment]: + """Read memory fragments related to the observation. + + Args: + observation(str): Observation + alpha(float): Importance weight + beta(float): Time weight + gamma(float): Randomness weight + + Returns: + List[AgentMemoryFragment]: List of memory fragments + """ + return await self.memory.read(observation, alpha, beta, gamma) + + @mutable + async def clear(self) -> List[AgentMemoryFragment]: + """Clear the memory.""" + return await self.memory.clear() + + @property + def plans_memory(self) -> GptsPlansMemory: + """Return the plan memory.""" + return self.gpts_memory.plans_memory + + @property + def message_memory(self) -> GptsMessageMemory: + """Return the message memory.""" + return self.gpts_memory.message_memory diff --git a/dbgpt/agent/core/memory/base.py b/dbgpt/agent/core/memory/base.py new file mode 100644 index 000000000..63ff428fd --- /dev/null +++ b/dbgpt/agent/core/memory/base.py @@ -0,0 +1,776 @@ +"""Memory for agent. + +Human memory follows a general progression from sensory memory that registers +perceptual inputs, to short-term memory that maintains information transiently, to +long-term memory that consolidates information over extended periods. +""" + +import asyncio +from abc import ABC, abstractmethod +from datetime import datetime +from enum import Enum +from typing import ( + Any, + Callable, + Dict, + Generic, + List, + Optional, + Tuple, + Type, + TypeVar, + Union, + cast, +) + +from dbgpt.core import LLMClient +from dbgpt.util.annotations import PublicAPI, immutable, mutable + +T = TypeVar("T", bound="MemoryFragment") +M = TypeVar("M", bound="Memory") + + +class WriteOperation(str, Enum): + """Write operation.""" + + ADD = "add" + RETRIEVAL = "retrieval" + + +@PublicAPI(stability="beta") +class MemoryFragment(ABC): + """Memory fragment interface. + + It is the interface of memory fragment, which is the basic unit of memory, which + contains the basic information of memory, such as observation, importance, whether + it is insight, last access time, etc + """ + + @classmethod + @abstractmethod + def build_from( + cls: Type[T], + observation: str, + embeddings: Optional[List[float]] = None, + memory_id: Optional[int] = None, + importance: Optional[float] = None, + is_insight: bool = False, + last_accessed_time: Optional[datetime] = None, + **kwargs, + ) -> T: + """Build a memory fragment from memory id and observation. + + Args: + observation(str): Observation + embeddings(List[float], optional): Embeddings of the memory fragment. + memory_id(int): Memory id + importance(float): Importance + is_insight(bool): Whether the memory fragment is an insight + last_accessed_time(datetime): Last accessed time + + Returns: + MemoryFragment: Memory fragment + """ + raise NotImplementedError + + @property + @abstractmethod + def id(self) -> int: + """Return the id of the memory fragment. + + Commonly, the id is generated by Snowflake algorithm. So we can parse the + timestamp of when the memory fragment is created. + + Returns: + int: id + """ + + @property + def metadata(self) -> Dict[str, Any]: + """Return the metadata of the memory fragment. + + Returns: + Dict[str, Any]: Metadata + """ + return {} + + @property + def importance(self) -> Optional[float]: + """Return the importance of the memory fragment. + + It should be noted that importance only reflects the characters of the memory + itself. + + Returns: + Optional[float]: importance, None means the importance is not available. + """ + return None + + @abstractmethod + def update_importance(self, importance: float) -> Optional[float]: + """Update the importance of the memory fragment. + + Args: + importance(float): importance + + Returns: + Optional[float]: importance + """ + + @property + @abstractmethod + def raw_observation(self) -> str: + """Return the raw observation. + + Raw observation is the original observation data, it can be an observation from + environment or an observation after executing an action. + + Returns: + str: raw observation + """ + + @property + def embeddings(self) -> Optional[List[float]]: + """Return the embeddings of the memory fragment. + + Returns: + Optional[List[float]]: embeddings + """ + return None + + @abstractmethod + def update_embeddings(self, embeddings: List[float]) -> None: + """Update the embeddings of the memory fragment. + + Args: + embeddings(List[float]): embeddings + """ + + def calculate_current_embeddings( + self, embedding_func: Callable[[List[str]], List[List[float]]] + ) -> List[float]: + """Calculate the embeddings of the memory fragment. + + Args: + embedding_func(Callable[[List[str]], List[List[float]]]): Function to + compute embeddings + + Returns: + List[float]: Embeddings of the memory fragment + """ + raise NotImplementedError + + @property + @abstractmethod + def is_insight(self) -> bool: + """Return whether the memory fragment is an insight. + + Returns: + bool: whether the memory fragment is an insight. + """ + + @property + @abstractmethod + def last_accessed_time(self) -> Optional[datetime]: + """Return the last accessed time of the memory fragment. + + Returns: + Optional[datetime]: last accessed time + """ + + @abstractmethod + def update_accessed_time(self, now: datetime) -> Optional[datetime]: + """Update the last accessed time of the memory fragment. + + Args: + now(datetime): The current time + + Returns: + Optional[datetime]: The last accessed time + """ + + @abstractmethod + def copy(self: T) -> T: + """Copy the memory fragment.""" + + def reduce(self, memory_fragments: List[T], **kwargs) -> T: + """Reduce memory fragments to a single memory fragment. + + Args: + memory_fragments(List[T]): Memory fragments + + Returns: + T: The reduced memory fragment + """ + obs = [] + for memory_fragment in memory_fragments: + obs.append(memory_fragment.raw_observation) + new_observation = ";".join(obs) + return self.current_class.build_from(new_observation, **kwargs) # type: ignore + + @property + def current_class(self: T) -> Type[T]: + """Return the current class.""" + return self.__class__ + + +class InsightMemoryFragment(Generic[T]): + """Insight memory fragment. + + Insight memory fragment is a memory fragment that contains insights. + """ + + def __init__( + self, + original_memory_fragment: Union[T, List[T]], + insights: Union[List[T], List[str]], + ): + """Create an insight memory fragment. + + Insight is also a memory fragment. + """ + if insights and isinstance(insights[0], str): + mf = ( + original_memory_fragment[0] + if isinstance(original_memory_fragment, list) + else original_memory_fragment + ) + insights = [ + mf.current_class.build_from(i, is_insight=True) for i in insights # type: ignore # noqa + ] + self._original_memory_fragment = original_memory_fragment + self._insights: List[T] = cast(List[T], insights) + + @property + def original_memory_fragment(self) -> Union[T, List[T]]: + """Return the original memory fragment.""" + return self._original_memory_fragment + + @property + def insights(self) -> List[T]: + """Return the insights.""" + return self._insights + + +class DiscardedMemoryFragments(Generic[T]): + """Discarded memory fragments. + + Sometimes, we need to discard some memory fragments, there are following cases: + 1. Memory duplicated, the same/similar action is executed multiple times and the + same/similar observation from environment is received. + 2. Memory overflow. The memory is full and the new memory fragment needs to be + written. + 3. The memory fragment is not important enough. + 4. Simulation of forgetting mechanism. + + The discarded memory fragments may be transferred to another memory. + """ + + def __init__( + self, + discarded_memory_fragments: List[T], + discarded_insights: Optional[List[InsightMemoryFragment[T]]] = None, + ): + """Create a discarded memory fragments.""" + if discarded_insights is None: + discarded_insights = [] + self._discarded_memory_fragments = discarded_memory_fragments + self._discarded_insights = discarded_insights + + @property + def discarded_memory_fragments(self) -> List[T]: + """Return the discarded memory fragments.""" + return self._discarded_memory_fragments + + @property + def discarded_insights(self) -> List[InsightMemoryFragment[T]]: + """Return the discarded insights.""" + return self._discarded_insights + + +class InsightExtractor(ABC, Generic[T]): + """Insight extractor interface. + + Obtain high-level insights from memories. + """ + + @abstractmethod + async def extract_insights( + self, + memory_fragment: T, + llm_client: Optional[LLMClient] = None, + ) -> InsightMemoryFragment[T]: + """Extract insights from memory fragments. + + Args: + memory_fragment(T): Memory fragment + llm_client(Optional[LLMClient]): LLM client + + Returns: + InsightMemoryFragment: The insights of the memory fragment. + """ + + +class ImportanceScorer(ABC, Generic[T]): + """Importance scorer interface. + + Score the importance of memories. + """ + + @abstractmethod + async def score_importance( + self, + memory_fragment: T, + llm_client: Optional[LLMClient] = None, + ) -> float: + """Score the importance of memory fragment. + + Args: + memory_fragment(T): Memory fragment. + llm_client(Optional[LLMClient]): LLM client + + Returns: + float: The importance of the memory fragment. + """ + + +@PublicAPI(stability="beta") +class Memory(ABC, Generic[T]): + """Memory interface.""" + + name: Optional[str] = None + llm_client: Optional[LLMClient] = None + importance_scorer: Optional[ImportanceScorer] = None + insight_extractor: Optional[InsightExtractor] = None + _real_memory_fragment_class: Optional[Type[T]] = None + importance_weight: float = 0.15 + + @mutable + def initialize( + self, + name: Optional[str] = None, + llm_client: Optional[LLMClient] = None, + importance_scorer: Optional[ImportanceScorer] = None, + insight_extractor: Optional[InsightExtractor] = None, + real_memory_fragment_class: Optional[Type[T]] = None, + ) -> None: + """Initialize memory. + + Some agent may need to initialize memory before using it. + """ + self.name = name + self.llm_client = llm_client + self.importance_scorer = importance_scorer + self.insight_extractor = insight_extractor + self._real_memory_fragment_class = real_memory_fragment_class + + @abstractmethod + @immutable + def structure_clone(self: M, now: Optional[datetime] = None) -> M: + """Return a structure clone of the memory. + + Sometimes, we need to clone the structure of the memory, but not the content. + + There some cases: + + 1. When we need to reset the memory, we can use this method to create a new + one, and the new memory has the same structure as the old one. + 2. Create a new agent, the new agent has the same memory structure as the + planner. + + Args: + now(Optional[datetime]): The current time + + Returns: + M: The structure clone of the memory + + """ + raise NotImplementedError + + @mutable + def _copy_from(self, memory: "Memory") -> None: + """Copy memory from another memory. + + Args: + memory(Memory): Another memory + """ + self.name = memory.name + self.llm_client = memory.llm_client + self.importance_scorer = memory.importance_scorer + self.insight_extractor = memory.insight_extractor + self._real_memory_fragment_class = memory._real_memory_fragment_class + + @abstractmethod + @mutable + async def write( + self, + memory_fragment: T, + now: Optional[datetime] = None, + op: WriteOperation = WriteOperation.ADD, + ) -> Optional[DiscardedMemoryFragments[T]]: + """Write a memory fragment to memory. + + Two situations need to be noted here: + 1. Memory duplicated, the same/similar action is executed multiple times and + the same/similar observation from environment is received. + + 2.Memory overflow. The memory is full and the new memory fragment needs to be + written to memory, the common strategy is to discard some memory fragments. + + Args: + memory_fragment(T): Memory fragment + now(Optional[datetime]): The current time + op(WriteOperation): Write operation + + Returns: + Optional[DiscardedMemoryFragments]: The discarded memory fragments, None + means no memory fragments are discarded. + """ + + @mutable + async def write_batch( + self, memory_fragments: List[T], now: Optional[datetime] = None + ) -> Optional[DiscardedMemoryFragments[T]]: + """Write a batch of memory fragments to memory. + + Args: + memory_fragments(List[T]): Memory fragments + now(Optional[datetime]): The current time + + Returns: + Optional[DiscardedMemoryFragments]: The discarded memory fragments, None + means no memory fragments are discarded. + """ + raise NotImplementedError + + @abstractmethod + @immutable + async def read( + self, + observation: str, + alpha: Optional[float] = None, + beta: Optional[float] = None, + gamma: Optional[float] = None, + ) -> List[T]: + r"""Read memory fragments by observation. + + Usually, there three commonly used criteria for information extraction, that is, + the recency, relevance, and importance + + Memories that are more recent, relevant, and important are more likely to be + extracted. Formally, we conclude the following equation from existing + literature for memory information extraction: + + .. math:: + + m^* = \arg\min_{m \in M} \alpha s^{\text{rec}}(q, m) + \\ + \beta s^{\text{rel}}(q, m) + \gamma s^{\text{imp}}(m), \tag{1} + + Args: + observation(str): observation(Query) + alpha(float, optional): Recency coefficient. Default is None. + beta(float, optional): Relevance coefficient. Default is None. + gamma(float, optional): Importance coefficient. Default is None. + + Returns: + List[T]: memory fragments + """ + + @immutable + async def reflect(self, memory_fragments: List[T]) -> List[T]: + """Reflect memory fragments by observation. + + Args: + memory_fragments(List[T]): memory fragments to be reflected. + + Returns: + List[T]: memory fragments after reflection. + """ + return memory_fragments + + @immutable + async def handle_duplicated( + self, memory_fragments: List[T], new_memory_fragments: List[T] + ) -> List[T]: + """Handle duplicated memory fragments. + + Args: + memory_fragments(List[T]): Existing memory fragments + new_memory_fragments(List[T]): New memory fragments + + Returns: + List[T]: The new memory fragments after handling duplicated memory + fragments. + """ + return memory_fragments + new_memory_fragments + + @mutable + async def handle_overflow( + self, memory_fragments: List[T] + ) -> Tuple[List[T], List[T]]: + """Handle memory overflow. + + Args: + memory_fragments(List[T]): Existing memory fragments + + Returns: + Tuple[List[T], List[T]]: The memory fragments after handling overflow and + the discarded memory fragments. + """ + return memory_fragments, [] + + @abstractmethod + @mutable + async def clear(self) -> List[T]: + """Clear all memory fragments. + + Returns: + List[T]: The all cleared memory fragments. + """ + + @immutable + async def get_insights( + self, memory_fragments: List[T] + ) -> List[InsightMemoryFragment[T]]: + """Get insights from memory fragments. + + Args: + memory_fragments(List[T]): Memory fragments + + Returns: + List[InsightMemoryFragment]: The insights of the memory fragments. + """ + if not self.insight_extractor: + return [] + # Obtain insights in parallel from memory fragments parallel + tasks = [] + for memory_fragment in memory_fragments: + tasks.append( + self.insight_extractor.extract_insights( + memory_fragment, self.llm_client + ) + ) + insights = await asyncio.gather(*tasks) + result = [] + for insight in insights: + if not insight: + continue + result.append(insight) + if len(result) != len(insights): + raise ValueError( + "The number of insights is not equal to the number of memory fragments." + ) + return result + + @immutable + async def score_memory_importance(self, memory_fragments: List[T]) -> List[float]: + """Score the importance of memory fragments. + + Args: + memory_fragments(List[T]): Memory fragments + + Returns: + List[float]: The importance of memory fragments. + """ + if not self.importance_scorer: + return [5 * self.importance_weight for _ in memory_fragments] + tasks = [] + for memory_fragment in memory_fragments: + tasks.append( + self.importance_scorer.score_importance( + memory_fragment, self.llm_client + ) + ) + result = [] + for importance in await asyncio.gather(*tasks): + real_score = importance * self.importance_weight + result.append(real_score) + return result + + @property + @immutable + def real_memory_fragment_class(self) -> Type[T]: + """Return the real memory fragment class.""" + if not self._real_memory_fragment_class: + raise ValueError("The real memory fragment class is not set.") + return self._real_memory_fragment_class + + +class SensoryMemory(Memory, Generic[T]): + """Sensory memory.""" + + importance_weight: float = 0.9 + threshold_to_short_term: float = 0.1 + + def __init__(self, buffer_size: int = 0): + """Create a sensory memory.""" + self._buffer_size = buffer_size + self._fragments: List[T] = [] + self._lock = asyncio.Lock() + + def structure_clone( + self: "SensoryMemory[T]", now: Optional[datetime] = None + ) -> "SensoryMemory[T]": + """Return a structure clone of the memory.""" + m: SensoryMemory[T] = SensoryMemory(buffer_size=self._buffer_size) + m._copy_from(self) + return m + + @mutable + async def write( + self, + memory_fragment: T, + now: Optional[datetime] = None, + op: WriteOperation = WriteOperation.ADD, + ) -> Optional[DiscardedMemoryFragments[T]]: + """Write a memory fragment to sensory memory.""" + fragments = await self.handle_duplicated(self._fragments, [memory_fragment]) + discarded_fragments: List[T] = [] + if len(fragments) > self._buffer_size: + fragments, discarded_fragments = await self.handle_overflow(fragments) + + async with self._lock: + await self.clear() + self._fragments = fragments + if not discarded_fragments: + return None + return DiscardedMemoryFragments(discarded_fragments, []) + + @immutable + async def read( + self, + observation: str, + alpha: Optional[float] = None, + beta: Optional[float] = None, + gamma: Optional[float] = None, + ) -> List[T]: + """Read memory fragments by observation.""" + return self._fragments + + @mutable + async def handle_overflow( + self, memory_fragments: List[T] + ) -> Tuple[List[T], List[T]]: + """Handle memory overflow. + + For sensory memory, the overflow strategy is to transfer all memory fragments + to short-term memory. + + Args: + memory_fragments(List[T]): Existing memory fragments + + Returns: + Tuple[List[T], List[T]]: The memory fragments after handling overflow and + the discarded memory fragments, the discarded memory fragments should + be transferred to short-term memory. + """ + scores = await self.score_memory_importance(memory_fragments) + result = [] + for i, memory in enumerate(memory_fragments): + if scores[i] >= self.threshold_to_short_term: + memory.update_importance(scores[i]) + result.append(memory) + return [], result + + @mutable + async def clear(self) -> List[T]: + """Clear all memory fragments.""" + # async with self._lock: + fragments = self._fragments + self._fragments = [] + return fragments + + +class ShortTermMemory(Memory, Generic[T]): + """Short term memory. + + All memories are stored in computer memory. + """ + + def __init__(self, buffer_size: int = 5): + """Create a short-term memory.""" + self._buffer_size = buffer_size + self._fragments: List[T] = [] + self._lock = asyncio.Lock() + + def structure_clone( + self: "ShortTermMemory[T]", now: Optional[datetime] = None + ) -> "ShortTermMemory[T]": + """Return a structure clone of the memory.""" + m: ShortTermMemory[T] = ShortTermMemory(buffer_size=self._buffer_size) + m._copy_from(self) + return m + + @mutable + async def write( + self, + memory_fragment: T, + now: Optional[datetime] = None, + op: WriteOperation = WriteOperation.ADD, + ) -> Optional[DiscardedMemoryFragments[T]]: + """Write a memory fragment to short-term memory. + + Args: + memory_fragment(T): New memory fragment + now(Optional[datetime]): The current time + op(WriteOperation): Write operation + + Returns: + Optional[DiscardedMemoryFragments]: The discarded memory fragments, None + means no memory fragments are discarded. The discarded memory fragments + should be transferred and stored in long-term memory. + """ + fragments = await self.handle_duplicated(self._fragments, [memory_fragment]) + + async with self._lock: + await self.clear() + self._fragments = fragments + discarded_memories = await self.transfer_to_long_term(memory_fragment) + fragments, discarded_fragments = await self.handle_overflow(self._fragments) + self._fragments = fragments + return discarded_memories + + @immutable + async def read( + self, + observation: str, + alpha: Optional[float] = None, + beta: Optional[float] = None, + gamma: Optional[float] = None, + ) -> List[T]: + """Read memory fragments by observation.""" + return self._fragments + + @mutable + async def transfer_to_long_term( + self, memory_fragment: T + ) -> Optional[DiscardedMemoryFragments[T]]: + """Transfer the oldest memories to long-term memory. + + This is a very simple strategy, just transfer the oldest memories to long-term + memory. + """ + if len(self._fragments) > self._buffer_size: + overflow_cnt = len(self._fragments) - self._buffer_size + # Just keep the most recent memories in short-term memory + self._fragments = self._fragments[overflow_cnt:] + # Transfer the oldest memories to long-term memory + overflow_fragments = self._fragments[:overflow_cnt] + insights = await self.get_insights(overflow_fragments) + return DiscardedMemoryFragments(overflow_fragments, insights) + else: + return None + + @mutable + async def clear(self) -> List[T]: + """Clear all memory fragments.""" + # async with self._lock: + fragments = self._fragments + self._fragments = [] + return fragments + + @property + @immutable + def short_term_memories(self) -> List[T]: + """Return short-term memories.""" + return self._fragments diff --git a/dbgpt/agent/core/memory/gpts/__init__.py b/dbgpt/agent/core/memory/gpts/__init__.py new file mode 100644 index 000000000..fe338f1fa --- /dev/null +++ b/dbgpt/agent/core/memory/gpts/__init__.py @@ -0,0 +1,19 @@ +"""Memory module for GPTS messages and plans. + +It stores the messages and plans generated of multiple agents in the conversation. + +It is different from the agent memory as it is a formatted structure to store the +messages and plans, and it can be stored in a database or a file. +""" + +from .base import ( # noqa: F401 + GptsMessage, + GptsMessageMemory, + GptsPlan, + GptsPlansMemory, +) +from .default_gpts_memory import ( # noqa: F401 + DefaultGptsMessageMemory, + DefaultGptsPlansMemory, +) +from .gpts_memory import GptsMemory # noqa: F401 diff --git a/dbgpt/agent/memory/base.py b/dbgpt/agent/core/memory/gpts/base.py similarity index 99% rename from dbgpt/agent/memory/base.py rename to dbgpt/agent/core/memory/gpts/base.py index 7d4077e69..c407bf8df 100644 --- a/dbgpt/agent/memory/base.py +++ b/dbgpt/agent/core/memory/gpts/base.py @@ -1,10 +1,11 @@ """Base memory interface for agents.""" + import dataclasses from abc import ABC, abstractmethod from datetime import datetime from typing import Any, Dict, List, Optional -from dbgpt.agent.core.schema import Status +from ...schema import Status @dataclasses.dataclass diff --git a/dbgpt/agent/memory/default_gpts_memory.py b/dbgpt/agent/core/memory/gpts/default_gpts_memory.py similarity index 99% rename from dbgpt/agent/memory/default_gpts_memory.py rename to dbgpt/agent/core/memory/gpts/default_gpts_memory.py index d21eeb35f..f636df634 100644 --- a/dbgpt/agent/memory/default_gpts_memory.py +++ b/dbgpt/agent/core/memory/gpts/default_gpts_memory.py @@ -5,7 +5,7 @@ import pandas as pd -from ..core.schema import Status +from ...schema import Status from .base import GptsMessage, GptsMessageMemory, GptsPlan, GptsPlansMemory diff --git a/dbgpt/agent/memory/gpts_memory.py b/dbgpt/agent/core/memory/gpts/gpts_memory.py similarity index 99% rename from dbgpt/agent/memory/gpts_memory.py rename to dbgpt/agent/core/memory/gpts/gpts_memory.py index bbad0ad2c..da65fb865 100644 --- a/dbgpt/agent/memory/gpts_memory.py +++ b/dbgpt/agent/core/memory/gpts/gpts_memory.py @@ -1,11 +1,12 @@ """GPTs memory.""" + import json from collections import OrderedDict, defaultdict from typing import Dict, List, Optional from dbgpt.vis.client import VisAgentMessages, VisAgentPlans, vis_client -from ..actions.action import ActionOutput +from ...action.base import ActionOutput from .base import GptsMessage, GptsMessageMemory, GptsPlansMemory from .default_gpts_memory import DefaultGptsMessageMemory, DefaultGptsPlansMemory diff --git a/dbgpt/agent/core/memory/hybrid.py b/dbgpt/agent/core/memory/hybrid.py new file mode 100644 index 000000000..0d7add35e --- /dev/null +++ b/dbgpt/agent/core/memory/hybrid.py @@ -0,0 +1,288 @@ +"""Hybrid memory module. + +This structure explicitly models the human short-term and long-term memories. The +short-term memory temporarily buffers recent perceptions, while long-term memory +consolidates important information over time. +""" + +import os.path +from concurrent.futures import Executor, ThreadPoolExecutor +from datetime import datetime +from typing import TYPE_CHECKING, Generic, List, Optional, Tuple, Type + +from dbgpt.core import Embeddings, LLMClient +from dbgpt.util.annotations import immutable, mutable + +from .base import ( + DiscardedMemoryFragments, + ImportanceScorer, + InsightExtractor, + Memory, + SensoryMemory, + ShortTermMemory, + T, + WriteOperation, +) +from .long_term import LongTermMemory +from .short_term import EnhancedShortTermMemory + +if TYPE_CHECKING: + from dbgpt.storage.vector_store.connector import VectorStoreConnector + + +class HybridMemory(Memory, Generic[T]): + """Hybrid memory for the agent.""" + + importance_weight: float = 0.9 + + def __init__( + self, + now: datetime, + sensory_memory: SensoryMemory[T], + short_term_memory: ShortTermMemory[T], + long_term_memory: LongTermMemory[T], + default_insight_extractor: Optional[InsightExtractor] = None, + default_importance_scorer: Optional[ImportanceScorer] = None, + ): + """Create a hybrid memory.""" + self.now = now + self._sensory_memory = sensory_memory + self._short_term_memory = short_term_memory + self._long_term_memory = long_term_memory + self._default_insight_extractor = default_insight_extractor + self._default_importance_scorer = default_importance_scorer + + def structure_clone( + self: "HybridMemory[T]", now: Optional[datetime] = None + ) -> "HybridMemory[T]": + """Return a structure clone of the memory.""" + now = now or self.now + m = HybridMemory( + now=now, + sensory_memory=self._sensory_memory.structure_clone(now), + short_term_memory=self._short_term_memory.structure_clone(now), + long_term_memory=self._long_term_memory.structure_clone(now), + ) + m._copy_from(self) + return m + + @classmethod + def from_chroma( + cls, + vstore_name: Optional[str] = "_chroma_agent_memory_", + vstore_path: Optional[str] = None, + embeddings: Optional[Embeddings] = None, + executor: Optional[Executor] = None, + now: Optional[datetime] = None, + sensory_memory: Optional[SensoryMemory[T]] = None, + short_term_memory: Optional[ShortTermMemory[T]] = None, + long_term_memory: Optional[LongTermMemory[T]] = None, + **kwargs + ): + """Create a hybrid memory from Chroma vector store.""" + from dbgpt.configs.model_config import DATA_DIR + from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig + from dbgpt.storage.vector_store.connector import VectorStoreConnector + + if not embeddings: + from dbgpt.rag.embedding import DefaultEmbeddingFactory + + embeddings = DefaultEmbeddingFactory.openai() + + vstore_path = vstore_path or os.path.join(DATA_DIR, "agent_memory") + + vector_store_connector = VectorStoreConnector.from_default( + vector_store_type="Chroma", + embedding_fn=embeddings, + vector_store_config=ChromaVectorConfig( + name=vstore_name, + persist_path=vstore_path, + ), + ) + return cls.from_vstore( + vector_store_connector=vector_store_connector, + embeddings=embeddings, + executor=executor, + now=now, + sensory_memory=sensory_memory, + short_term_memory=short_term_memory, + long_term_memory=long_term_memory, + **kwargs + ) + + @classmethod + def from_vstore( + cls, + vector_store_connector: "VectorStoreConnector", + embeddings: Optional[Embeddings] = None, + executor: Optional[Executor] = None, + now: Optional[datetime] = None, + sensory_memory: Optional[SensoryMemory[T]] = None, + short_term_memory: Optional[ShortTermMemory[T]] = None, + long_term_memory: Optional[LongTermMemory[T]] = None, + **kwargs + ): + """Create a hybrid memory from vector store.""" + if not embeddings: + embeddings = vector_store_connector.current_embeddings + if not executor: + executor = ThreadPoolExecutor() + if not now: + now = datetime.now() + + if not sensory_memory: + sensory_memory = SensoryMemory() + if not short_term_memory: + if not embeddings: + raise ValueError("embeddings is required.") + short_term_memory = EnhancedShortTermMemory(embeddings, executor) + if not long_term_memory: + long_term_memory = LongTermMemory( + executor, + vector_store_connector, + now=now, + ) + return cls(now, sensory_memory, short_term_memory, long_term_memory, **kwargs) + + def initialize( + self, + name: Optional[str] = None, + llm_client: Optional[LLMClient] = None, + importance_scorer: Optional[ImportanceScorer[T]] = None, + insight_extractor: Optional[InsightExtractor[T]] = None, + real_memory_fragment_class: Optional[Type[T]] = None, + ) -> None: + """Initialize the memory. + + It will initialize all the memories. + """ + memories = [ + self._sensory_memory, + self._short_term_memory, + self._long_term_memory, + ] + kwargs = { + "name": name, + "llm_client": llm_client, + "importance_scorer": importance_scorer, + "insight_extractor": insight_extractor, + "real_memory_fragment_class": real_memory_fragment_class, + } + for memory in memories: + memory.initialize(**kwargs) + super().initialize(**kwargs) + + @mutable + async def write( + self, + memory_fragment: T, + now: Optional[datetime] = None, + op: WriteOperation = WriteOperation.ADD, + ) -> Optional[DiscardedMemoryFragments[T]]: + """Write a memory fragment to the memory.""" + # First write to sensory memory + sen_discarded_memories = await self._sensory_memory.write(memory_fragment) + if not sen_discarded_memories: + return None + short_term_discarded_memories = [] + discarded_memory_fragments = [] + discarded_insights = [] + for sen_memory in sen_discarded_memories.discarded_memory_fragments: + # Write to short term memory + short_discarded_memory = await self._short_term_memory.write(sen_memory) + if short_discarded_memory: + short_term_discarded_memories.append(short_discarded_memory) + discarded_memory_fragments.extend( + short_discarded_memory.discarded_memory_fragments + ) + for insight in short_discarded_memory.discarded_insights: + # Just keep the first insight + discarded_insights.append(insight.insights[0]) + # Obtain the importance of insights + insight_scores = await self.score_memory_importance(discarded_insights) + # Get the importance of insights + for i, ins in enumerate(discarded_insights): + ins.update_importance(insight_scores[i]) + all_memories = discarded_memory_fragments + discarded_insights + if self._long_term_memory: + # Write to long term memory + await self._long_term_memory.write_batch(all_memories, self.now) + return None + + @immutable + async def read( + self, + observation: str, + alpha: Optional[float] = None, + beta: Optional[float] = None, + gamma: Optional[float] = None, + ) -> List[T]: + """Read memories from the memory.""" + ( + retrieved_long_term_memories, + short_term_discarded_memories, + ) = await self.fetch_memories(observation, self._short_term_memory) + + await self.save_memories_after_retrieval(short_term_discarded_memories) + return retrieved_long_term_memories + + @immutable + async def fetch_memories( + self, + observation: str, + short_term_memory: Optional[ShortTermMemory[T]] = None, + ) -> Tuple[List[T], List[DiscardedMemoryFragments[T]]]: + """Fetch memories from long term memory. + + If short_term_memory is provided, write the fetched memories to the short term + memory. + """ + retrieved_long_term_memories = await self._long_term_memory.fetch_memories( + observation + ) + if not short_term_memory: + return retrieved_long_term_memories, [] + short_term_discarded_memories: List[DiscardedMemoryFragments[T]] = [] + discarded_memory_fragments: List[T] = [] + for ltm in retrieved_long_term_memories: + short_discarded_memory = await short_term_memory.write( + ltm, op=WriteOperation.RETRIEVAL + ) + if short_discarded_memory: + short_term_discarded_memories.append(short_discarded_memory) + discarded_memory_fragments.extend( + short_discarded_memory.discarded_memory_fragments + ) + for stm in short_term_memory.short_term_memories: + retrieved_long_term_memories.append( + stm.current_class.build_from( + observation=stm.raw_observation, + importance=stm.importance, + ) + ) + return retrieved_long_term_memories, short_term_discarded_memories + + async def save_memories_after_retrieval( + self, fragments: List[DiscardedMemoryFragments[T]] + ): + """Save memories after retrieval.""" + discarded_memory_fragments = [] + discarded_memory_insights: List[T] = [] + for f in fragments: + discarded_memory_fragments.extend(f.discarded_memory_fragments) + for fi in f.discarded_insights: + discarded_memory_insights.append(fi.insights[0]) + insights_importance = await self.score_memory_importance( + discarded_memory_insights + ) + for i, ins in enumerate(discarded_memory_insights): + ins.update_importance(insights_importance[i]) + all_memories = discarded_memory_fragments + discarded_memory_insights + await self._long_term_memory.write_batch(all_memories, self.now) + + async def clear(self) -> List[T]: + """Clear the memory. + + # TODO + """ + return [] diff --git a/dbgpt/agent/core/memory/llm.py b/dbgpt/agent/core/memory/llm.py new file mode 100644 index 000000000..619211d25 --- /dev/null +++ b/dbgpt/agent/core/memory/llm.py @@ -0,0 +1,174 @@ +"""LLM Utility For Agent Memory.""" + +import re +from typing import List, Optional, Union + +from dbgpt._private.pydantic import BaseModel +from dbgpt.core import ( + ChatPromptTemplate, + HumanPromptTemplate, + LLMClient, + ModelMessage, + ModelRequest, +) + +from .base import ImportanceScorer, InsightExtractor, InsightMemoryFragment, T + + +class BaseLLMCaller(BaseModel): + """Base class for LLM caller.""" + + prompt: str = "" + model: Optional[str] = None + + async def call_llm( + self, + prompt: Union[ChatPromptTemplate, str], + llm_client: Optional[LLMClient] = None, + **kwargs, + ) -> str: + """Call LLM client to generate response. + + Args: + llm_client(LLMClient): LLM client + prompt(ChatPromptTemplate): prompt + **kwargs: other keyword arguments + + Returns: + str: response + """ + if not llm_client: + raise ValueError("LLM client is required.") + if isinstance(prompt, str): + prompt = ChatPromptTemplate( + messages=[HumanPromptTemplate.from_template(prompt)] + ) + model = self.model + if not model: + model = await self.get_model(llm_client) + prompt_kwargs = {} + prompt_kwargs.update(kwargs) + pass_kwargs = { + k: v for k, v in prompt_kwargs.items() if k in prompt.input_variables + } + messages = prompt.format_messages(**pass_kwargs) + model_messages = ModelMessage.from_base_messages(messages) + model_request = ModelRequest.build_request(model, messages=model_messages) + model_output = await llm_client.generate(model_request) + if not model_output.success: + raise ValueError("Call LLM failed.") + return model_output.text + + async def get_model(self, llm_client: LLMClient) -> str: + """Get the model. + + Args: + llm_client(LLMClient): LLM client + + Returns: + str: model + """ + models = await llm_client.models() + if not models: + raise ValueError("No models available.") + self.model = models[0].model + return self.model + + @staticmethod + def _parse_list(text: str) -> List[str]: + """Parse a newline-separated string into a list of strings. + + 1. First, split by newline + 2. Remove whitespace from each line + """ + lines = re.split(r"\n", text.strip()) + lines = [line for line in lines if line.strip()] # remove empty lines + # Use regular expression to remove the numbers and dots at the beginning of + # each line + return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines] + + @staticmethod + def _parse_number(text: str, importance_weight: Optional[float] = None) -> float: + """Parse a number from a string.""" + match = re.search(r"^\D*(\d+)", text) + if match: + score = float(match.group(1)) + if importance_weight is not None: + score = (score / 10) * importance_weight + return score + else: + return 0.0 + + +class LLMInsightExtractor(BaseLLMCaller, InsightExtractor[T]): + """LLM Insight Extractor. + + Get high-level insights from memories. + """ + + prompt: str = ( + "There are some memories: {content}\nCan you infer from the " + "above memories the high-level insight for this person's character? The insight" + " needs to be significantly different from the content and structure of the " + "original memories.Respond in one sentence.\n\n" + "Results:" + ) + + async def extract_insights( + self, + memory_fragment: T, + llm_client: Optional[LLMClient] = None, + ) -> InsightMemoryFragment[T]: + """Extract insights from memory fragments. + + Args: + memory_fragment(T): Memory fragment + llm_client(Optional[LLMClient]): LLM client + + Returns: + InsightMemoryFragment: The insights of the memory fragment. + """ + insights_str: str = await self.call_llm( + self.prompt, llm_client, content=memory_fragment.raw_observation + ) + insights_list = self._parse_list(insights_str) + return InsightMemoryFragment(memory_fragment, insights_list) + + +class LLMImportanceScorer(BaseLLMCaller, ImportanceScorer[T]): + """LLM Importance Scorer. + + Score the importance of memories. + """ + + prompt: str = ( + "Please give an importance score between 1 to 10 for the following " + "observation. Higher score indicates the observation is more important. More " + "rules that should be followed are:" + "\n(1): Learning experience of a certain skill is important" + "\n(2): The occurrence of a particular event is important" + "\n(3): User thoughts and emotions matter" + "\n(4): More informative indicates more important." + "Please respond with a single integer." + "\nObservation:{content}" + "\nRating:" + ) + + async def score_importance( + self, + memory_fragment: T, + llm_client: Optional[LLMClient] = None, + ) -> float: + """Score the importance of memory fragments. + + Args: + memory_fragment(T): Memory fragment + llm_client(Optional[LLMClient]): LLM client + + Returns: + float: The importance score of the memory fragment. + """ + score: str = await self.call_llm( + self.prompt, llm_client, content=memory_fragment.raw_observation + ) + return self._parse_number(score) diff --git a/dbgpt/agent/core/memory/long_term.py b/dbgpt/agent/core/memory/long_term.py new file mode 100644 index 000000000..b479f86df --- /dev/null +++ b/dbgpt/agent/core/memory/long_term.py @@ -0,0 +1,192 @@ +"""Long-term memory module.""" + +from concurrent.futures import Executor +from datetime import datetime +from typing import Generic, List, Optional + +from dbgpt.core import Chunk +from dbgpt.rag.retriever.time_weighted import TimeWeightedEmbeddingRetriever +from dbgpt.storage.vector_store.connector import VectorStoreConnector +from dbgpt.storage.vector_store.filters import MetadataFilters +from dbgpt.util.annotations import immutable, mutable +from dbgpt.util.executor_utils import blocking_func_to_async + +from .base import DiscardedMemoryFragments, Memory, T, WriteOperation + +_FORGET_PLACEHOLDER = "[FORGET]" +_MERGE_PLACEHOLDER = "[MERGE]" +_METADATA_BUFFER_IDX = "buffer_idx" +_METADATA_LAST_ACCESSED_AT = "last_accessed_at" +_METADAT_IMPORTANCE = "importance" + + +class LongTermRetriever(TimeWeightedEmbeddingRetriever): + """Long-term retriever.""" + + def __init__(self, now: datetime, **kwargs): + """Create a long-term retriever.""" + self.now = now + super().__init__(**kwargs) + + @mutable + def _retrieve( + self, query: str, filters: Optional[MetadataFilters] = None + ) -> List[Chunk]: + """Retrieve memories.""" + current_time = self.now + docs_and_scores = { + doc.metadata[_METADATA_BUFFER_IDX]: (doc, self.default_salience) + # Calculate for all memories. + for doc in self.memory_stream + } + # If a doc is considered salient, update the salience score + docs_and_scores.update(self.get_salient_docs(query)) + rescored_docs = [ + (doc, self._get_combined_score(doc, relevance, current_time)) + for doc, relevance in docs_and_scores.values() + ] + rescored_docs.sort(key=lambda x: x[1], reverse=True) + result = [] + # Ensure frequently accessed memories aren't forgotten + retrieved_num = 0 + for doc, _ in rescored_docs: + if ( + retrieved_num < self._k + and doc.content.find(_FORGET_PLACEHOLDER) == -1 + and doc.content.find(_MERGE_PLACEHOLDER) == -1 + ): + retrieved_num += 1 + buffered_doc = self.memory_stream[doc.metadata[_METADATA_BUFFER_IDX]] + buffered_doc.metadata[_METADATA_LAST_ACCESSED_AT] = current_time + result.append(buffered_doc) + return result + + +class LongTermMemory(Memory, Generic[T]): + """Long-term memory.""" + + importance_weight: float = 0.15 + + def __init__( + self, + executor: Executor, + vector_store_connector: VectorStoreConnector, + now: Optional[datetime] = None, + reflection_threshold: Optional[float] = None, + ): + """Create a long-term memory.""" + self.now = now or datetime.now() + self.executor = executor + self.reflecting: bool = False + self.forgetting: bool = False + self.reflection_threshold: Optional[float] = reflection_threshold + self.aggregate_importance: float = 0.0 + self._vector_store_connector = vector_store_connector + self.memory_retriever = LongTermRetriever( + now=self.now, vector_store_connector=vector_store_connector + ) + + @immutable + def structure_clone( + self: "LongTermMemory[T]", now: Optional[datetime] = None + ) -> "LongTermMemory[T]": + """Create a structure clone of the long-term memory.""" + new_name = self.name + if not new_name: + raise ValueError("name is required.") + m: LongTermMemory[T] = LongTermMemory( + now=now, + executor=self.executor, + vector_store_connector=self._vector_store_connector.new_connector(new_name), + reflection_threshold=self.reflection_threshold, + ) + m._copy_from(self) + return m + + @mutable + async def write( + self, + memory_fragment: T, + now: Optional[datetime] = None, + op: WriteOperation = WriteOperation.ADD, + ) -> Optional[DiscardedMemoryFragments[T]]: + """Write a memory fragment to the memory.""" + importance = memory_fragment.importance + last_accessed_time = memory_fragment.last_accessed_time + if importance is None: + raise ValueError("importance is required.") + if not self.reflecting: + self.aggregate_importance += importance + + memory_idx = len(self.memory_retriever.memory_stream) + document = Chunk( + page_content="[{}] ".format(memory_idx) + + str(memory_fragment.raw_observation), + metadata={ + _METADAT_IMPORTANCE: importance, + _METADATA_LAST_ACCESSED_AT: last_accessed_time, + }, + ) + await blocking_func_to_async( + self.executor, + self.memory_retriever.load_document, + [document], + current_time=now, + ) + + return None + + @mutable + async def write_batch( + self, memory_fragments: List[T], now: Optional[datetime] = None + ) -> Optional[DiscardedMemoryFragments[T]]: + """Write a batch of memory fragments to the memory.""" + current_datetime = self.now + if not now: + raise ValueError("Now time is required.") + for short_term_memory in memory_fragments: + short_term_memory.update_accessed_time(now=now) + await self.write(short_term_memory, now=current_datetime) + # TODO(fangyinc): Reflect on the memories and get high-level insights. + # TODO(fangyinc): Forget memories that are not important. + return None + + @immutable + async def read( + self, + observation: str, + alpha: Optional[float] = None, + beta: Optional[float] = None, + gamma: Optional[float] = None, + ) -> List[T]: + """Read memory fragments related to the observation.""" + return await self.fetch_memories(observation=observation, now=self.now) + + @immutable + async def fetch_memories( + self, observation: str, now: Optional[datetime] = None + ) -> List[T]: + """Fetch memories related to the observation.""" + # TODO: Mock now? + retrieved_memories = [] + retrieved_list = await blocking_func_to_async( + self.executor, + self.memory_retriever.retrieve, + observation, + ) + for retrieved_chunk in retrieved_list: + retrieved_memories.append( + self.real_memory_fragment_class.build_from( + observation=retrieved_chunk.content, + importance=retrieved_chunk.metadata[_METADAT_IMPORTANCE], + ) + ) + return retrieved_memories + + @mutable + async def clear(self) -> List[T]: + """Clear the memory. + + TODO: Implement this method. + """ + return [] diff --git a/dbgpt/agent/core/memory/short_term.py b/dbgpt/agent/core/memory/short_term.py new file mode 100644 index 000000000..e5a11a175 --- /dev/null +++ b/dbgpt/agent/core/memory/short_term.py @@ -0,0 +1,203 @@ +"""Short term memory module.""" + +import random +from concurrent.futures import Executor +from datetime import datetime +from typing import Dict, List, Optional, Tuple + +from dbgpt.core import Embeddings +from dbgpt.util.annotations import immutable, mutable +from dbgpt.util.executor_utils import blocking_func_to_async +from dbgpt.util.similarity_util import cosine_similarity, sigmoid_function + +from .base import ( + DiscardedMemoryFragments, + InsightMemoryFragment, + ShortTermMemory, + T, + WriteOperation, +) + + +class EnhancedShortTermMemory(ShortTermMemory[T]): + """Enhanced short term memory.""" + + def __init__( + self, + embeddings: Embeddings, + executor: Executor, + buffer_size: int = 2, + enhance_similarity_threshold: float = 0.7, + enhance_threshold: int = 3, + ): + """Initialize enhanced short term memory.""" + super().__init__(buffer_size=buffer_size) + self._executor = executor + self._embeddings = embeddings + self.short_embeddings: List[List[float]] = [] + self.enhance_cnt: List[int] = [0 for _ in range(self._buffer_size)] + self.enhance_memories: List[List[T]] = [[] for _ in range(self._buffer_size)] + self.enhance_similarity_threshold = enhance_similarity_threshold + self.enhance_threshold = enhance_threshold + + @immutable + def structure_clone( + self: "EnhancedShortTermMemory[T]", now: Optional[datetime] = None + ) -> "EnhancedShortTermMemory[T]": + """Return a structure clone of the memory.""" + m: EnhancedShortTermMemory[T] = EnhancedShortTermMemory( + embeddings=self._embeddings, + executor=self._executor, + buffer_size=self._buffer_size, + enhance_similarity_threshold=self.enhance_similarity_threshold, + enhance_threshold=self.enhance_threshold, + ) + m._copy_from(self) + return m + + @mutable + async def write( + self, + memory_fragment: T, + now: Optional[datetime] = None, + op: WriteOperation = WriteOperation.ADD, + ) -> Optional[DiscardedMemoryFragments[T]]: + """Write memory fragment to short term memory. + + Reference: https://github.com/RUC-GSAI/YuLan-Rec/blob/main/agents/recagent_memory.py#L336 # noqa + """ + # Calculate current embeddings of current memory fragment + memory_fragment_embeddings = await blocking_func_to_async( + self._executor, + memory_fragment.calculate_current_embeddings, + self._embeddings.embed_documents, + ) + memory_fragment.update_embeddings(memory_fragment_embeddings) + for idx, memory_embedding in enumerate(self.short_embeddings): + similarity = await blocking_func_to_async( + self._executor, + cosine_similarity, + memory_embedding, + memory_fragment_embeddings, + ) + # Sigmoid probability, transform similarity to [0, 1] + sigmoid_prob: float = await blocking_func_to_async( + self._executor, sigmoid_function, similarity + ) + if ( + sigmoid_prob >= self.enhance_similarity_threshold + and random.random() < sigmoid_prob + ): + self.enhance_cnt[idx] += 1 + self.enhance_memories[idx].append(memory_fragment) + discard_memories = await self.transfer_to_long_term(memory_fragment) + if op == WriteOperation.ADD: + self._fragments.append(memory_fragment) + self.short_embeddings.append(memory_fragment_embeddings) + await self.handle_overflow(self._fragments) + return discard_memories + + @mutable + async def transfer_to_long_term( + self, memory_fragment: T + ) -> Optional[DiscardedMemoryFragments[T]]: + """Transfer memory fragment to long term memory.""" + transfer_flag = False + existing_memory = [True for _ in range(len(self.short_term_memories))] + + enhance_memories: List[T] = [] + to_get_insight_memories: List[T] = [] + for idx, memory in enumerate(self.short_term_memories): + # if exceed the enhancement threshold + if ( + self.enhance_cnt[idx] >= self.enhance_threshold + and existing_memory[idx] is True + ): + existing_memory[idx] = False + transfer_flag = True + # + # short-term memories + content = [memory] + # do not repeatedly add observation memory to summary, so use [:-1]. + for enhance_memory in self.enhance_memories[idx][:-1]: + content.append(enhance_memory) + content.append(memory_fragment) + # Merge the enhanced memories to single memory + merged_enhance_memory: T = memory.reduce( + content, merged_memory=memory.importance + ) + to_get_insight_memories.append(merged_enhance_memory) + enhance_memories.append(merged_enhance_memory) + # Get insights for the every enhanced memory + enhance_insights: List[InsightMemoryFragment] = await self.get_insights( + to_get_insight_memories + ) + + if transfer_flag: + # re-construct the indexes of short-term memories after removing summarized + # memories + new_memories: List[T] = [] + new_embeddings: List[List[float]] = [] + new_enhance_memories: List[List[T]] = [[] for _ in range(self._buffer_size)] + new_enhance_cnt: List[int] = [0 for _ in range(self._buffer_size)] + for idx, memory in enumerate(self.short_term_memories): + if existing_memory[idx]: + # Remove not enhanced memories to new memories + new_enhance_memories[len(new_memories)] = self.enhance_memories[idx] + new_enhance_cnt[len(new_memories)] = self.enhance_cnt[idx] + new_memories.append(memory) + new_embeddings.append(self.short_embeddings[idx]) + self._fragments = new_memories + self.short_embeddings = new_embeddings + self.enhance_memories = new_enhance_memories + self.enhance_cnt = new_enhance_cnt + return DiscardedMemoryFragments(enhance_memories, enhance_insights) + + @mutable + async def handle_overflow( + self, memory_fragments: List[T] + ) -> Tuple[List[T], List[T]]: + """Handle overflow of short term memory. + + Discard the least important memory fragment if the buffer size exceeds. + """ + if len(self.short_term_memories) > self._buffer_size: + id2fragments: Dict[int, Dict] = {} + for idx in range(len(self.short_term_memories) - 1): + # Not discard the last one + memory = self.short_term_memories[idx] + id2fragments[idx] = { + "enhance_count": self.enhance_cnt[idx], + "importance": memory.importance, + } + # Sort by importance and enhance count, first discard the least important + sorted_ids = sorted( + id2fragments.keys(), + key=lambda x: ( + id2fragments[x]["importance"], + id2fragments[x]["enhance_count"], + ), + ) + pop_id = sorted_ids[0] + pop_raw_observation = self.short_term_memories[pop_id].raw_observation + self.enhance_cnt.pop(pop_id) + self.enhance_cnt.append(0) + self.enhance_memories.pop(pop_id) + self.enhance_memories.append([]) + + discard_memory = self._fragments.pop(pop_id) + self.short_embeddings.pop(pop_id) + + # remove the discard_memory from other short-term memory's enhanced list + for idx in range(len(self.short_term_memories)): + current_enhance_memories: List[T] = self.enhance_memories[idx] + to_remove_idx = [] + for i, ehf in enumerate(current_enhance_memories): + if ehf.raw_observation == pop_raw_observation: + to_remove_idx.append(i) + for i in to_remove_idx: + current_enhance_memories.pop(i) + self.enhance_cnt[idx] -= len(to_remove_idx) + + return memory_fragments, [discard_memory] + return memory_fragments, [] diff --git a/dbgpt/agent/plan/__init__.py b/dbgpt/agent/core/plan/__init__.py similarity index 100% rename from dbgpt/agent/plan/__init__.py rename to dbgpt/agent/core/plan/__init__.py diff --git a/dbgpt/agent/plan/awel/__init__.py b/dbgpt/agent/core/plan/awel/__init__.py similarity index 100% rename from dbgpt/agent/plan/awel/__init__.py rename to dbgpt/agent/core/plan/awel/__init__.py diff --git a/dbgpt/agent/plan/awel/agent_operator.py b/dbgpt/agent/core/plan/awel/agent_operator.py similarity index 92% rename from dbgpt/agent/plan/awel/agent_operator.py rename to dbgpt/agent/core/plan/awel/agent_operator.py index bea9259eb..43189cbad 100644 --- a/dbgpt/agent/plan/awel/agent_operator.py +++ b/dbgpt/agent/core/plan/awel/agent_operator.py @@ -1,4 +1,5 @@ """Agent Operator for AWEL.""" + from abc import ABC from typing import List, Optional, Type @@ -16,10 +17,10 @@ # TODO: Don't dependent on MixinLLMOperator from dbgpt.model.operators.llm_operator import MixinLLMOperator -from ...core.agent import Agent, AgentGenerateContext, AgentMessage -from ...core.agent_manage import get_agent_manager -from ...core.base_agent import ConversableAgent -from ...core.llm.llm import LLMConfig +from ....util.llm.llm import LLMConfig +from ...agent import Agent, AgentGenerateContext, AgentMessage +from ...agent_manage import get_agent_manager +from ...base_agent import ConversableAgent from .agent_operator_resource import AWELAgent @@ -61,9 +62,7 @@ async def map(self, input_value: AgentGenerateContext) -> AgentGenerateContext: input_message = input_value.message.copy() # Isolate the message delivery mechanism and pass it to the operator - _goal = ( - self.agent.get_name() if self.agent.get_name() else self.agent.get_profile() - ) + _goal = self.agent.name if self.agent.name else self.agent.role current_goal = f"[{_goal}]:" if input_message.content: @@ -95,7 +94,7 @@ async def map(self, input_value: AgentGenerateContext) -> AgentGenerateContext: if not is_success: raise ValueError( - f"The task failed at step {self.agent.get_profile()} and the attempt " + f"The task failed at step {self.agent.role} and the attempt " f"to repair it failed. The final reason for " f"failure:{agent_reply_message.content}!" ) @@ -170,18 +169,14 @@ async def map( agent = await self.get_agent(input_value) if agent.fixed_subgoal and len(agent.fixed_subgoal) > 0: # Isolate the message delivery mechanism and pass it to the operator - current_goal = ( - f"[{agent.get_name() if agent.get_name() else agent.get_profile()}]:" - ) + current_goal = f"[{agent.name if agent.name else agent.role}]:" if agent.fixed_subgoal: current_goal += agent.fixed_subgoal input_message.current_goal = current_goal input_message.content = agent.fixed_subgoal else: # Isolate the message delivery mechanism and pass it to the operator - current_goal = ( - f"[{agent.get_name() if agent.get_name() else agent.get_profile()}]:" - ) + current_goal = f"[{agent.name if agent.name else agent.role}]:" if input_message.content: current_goal += input_message.content input_message.current_goal = current_goal @@ -213,7 +208,7 @@ async def map( if not is_success: raise ValueError( - f"The task failed at step {agent.get_profile()} and the attempt to " + f"The task failed at step {agent.role} and the attempt to " f"repair it failed. The final reason for " f"failure:{agent_reply_message.content}!" ) @@ -231,7 +226,7 @@ async def map( # Default single step transfer of information rely_messages=now_rely_messages, silent=input_value.silent, - memory=input_value.memory, + memory=input_value.memory.structure_clone() if input_value.memory else None, agent_context=input_value.agent_context, resource_loader=input_value.resource_loader, llm_client=input_value.llm_client, diff --git a/dbgpt/agent/plan/awel/agent_operator_resource.py b/dbgpt/agent/core/plan/awel/agent_operator_resource.py similarity index 89% rename from dbgpt/agent/plan/awel/agent_operator_resource.py rename to dbgpt/agent/core/plan/awel/agent_operator_resource.py index 94577b249..b7252d36a 100644 --- a/dbgpt/agent/plan/awel/agent_operator_resource.py +++ b/dbgpt/agent/core/plan/awel/agent_operator_resource.py @@ -1,4 +1,5 @@ """The AWEL Agent Operator Resource.""" + from typing import Any, Dict, List, Optional from dbgpt._private.pydantic import BaseModel, ConfigDict, Field, model_validator @@ -11,9 +12,9 @@ register_resource, ) -from ...core.agent_manage import get_agent_manager -from ...core.llm.llm import LLMConfig, LLMStrategyType -from ...resource.resource_api import AgentResource, ResourceType +from ....resource.resource_api import AgentResource, ResourceType +from ....util.llm.llm import LLMConfig, LLMStrategyType +from ...agent_manage import get_agent_manager @register_resource( @@ -50,7 +51,10 @@ description="The agent resource value.", ), ], - alias=["dbgpt.serve.agent.team.layout.agent_operator_resource.AwelAgentResource"], + alias=[ + "dbgpt.serve.agent.team.layout.agent_operator_resource.AwelAgentResource", + "dbgpt.agent.plan.awel.agent_operator_resource.AWELAgentResource", + ], ) class AWELAgentResource(AgentResource): """AWEL Agent Resource.""" @@ -107,7 +111,10 @@ def pre_fill(cls, values: Dict[str, Any]) -> Dict[str, Any]: description="The agent LLM Strategy Value.", ), ], - alias=["dbgpt.serve.agent.team.layout.agent_operator_resource.AwelAgentConfig"], + alias=[ + "dbgpt.serve.agent.team.layout.agent_operator_resource.AwelAgentConfig", + "dbgpt.agent.plan.awel.agent_operator_resource.AWELAgentConfig", + ], ) class AWELAgentConfig(LLMConfig): """AWEL Agent Config.""" @@ -168,7 +175,10 @@ def _agent_resource_option_values() -> List[OptionValue]: description="The agent llm config.", ), ], - alias=["dbgpt.serve.agent.team.layout.agent_operator_resource.AwelAgent"], + alias=[ + "dbgpt.serve.agent.team.layout.agent_operator_resource.AwelAgent", + "dbgpt.agent.plan.awel.agent_operator_resource.AWELAgent", + ], ) class AWELAgent(BaseModel): """AWEL Agent.""" diff --git a/dbgpt/agent/plan/awel/team_awel_layout.py b/dbgpt/agent/core/plan/awel/team_awel_layout.py similarity index 89% rename from dbgpt/agent/plan/awel/team_awel_layout.py rename to dbgpt/agent/core/plan/awel/team_awel_layout.py index 681d554ef..d25ea737a 100644 --- a/dbgpt/agent/plan/awel/team_awel_layout.py +++ b/dbgpt/agent/core/plan/awel/team_awel_layout.py @@ -2,7 +2,7 @@ import logging from abc import ABC, abstractmethod -from typing import List, Optional, cast +from typing import Optional, cast from dbgpt._private.config import Config from dbgpt._private.pydantic import ( @@ -15,9 +15,10 @@ from dbgpt.core.awel import DAG from dbgpt.core.awel.dag.dag_manager import DAGManager -from ...actions.action import ActionOutput -from ...core.agent import Agent, AgentGenerateContext, AgentMessage -from ...core.base_team import ManagerAgent +from ...action.base import ActionOutput +from ...agent import Agent, AgentGenerateContext, AgentMessage +from ...base_team import ManagerAgent +from ...profile import DynConfig, ProfileConfig from .agent_operator import AWELAgentOperator, WrappedAgentOperator logger = logging.getLogger(__name__) @@ -84,11 +85,24 @@ class AWELBaseManager(ManagerAgent, ABC): model_config = ConfigDict(arbitrary_types_allowed=True) - goal: str = ( - "Promote and solve user problems according to the process arranged by AWEL." + profile: ProfileConfig = ProfileConfig( + name="AWELBaseManager", + role=DynConfig( + "PlanManager", category="agent", key="dbgpt_agent_plan_awel_profile_name" + ), + goal=DynConfig( + "Promote and solve user problems according to the process arranged " + "by AWEL.", + category="agent", + key="dbgpt_agent_plan_awel_profile_goal", + ), + desc=DynConfig( + "Promote and solve user problems according to the process arranged " + "by AWEL.", + category="agent", + key="dbgpt_agent_plan_awel_profile_desc", + ), ) - constraints: List[str] = [] - desc: str = goal async def _a_process_received_message(self, message: AgentMessage, sender: Agent): """Process the received message.""" @@ -116,7 +130,7 @@ async def act( message=AgentMessage(content=message, current_goal=message), sender=sender, reviewer=reviewer, - memory=self.memory, + memory=self.memory.structure_clone(), agent_context=self.agent_context, resource_loader=self.resource_loader, llm_client=self.not_null_llm_config.llm_client, @@ -162,8 +176,6 @@ class WrappedAWELLayoutManager(AWELBaseManager): model_config = ConfigDict(arbitrary_types_allowed=True) - profile: str = "WrappedAWELLayoutManager" - dag: Optional[DAG] = Field(None, description="The DAG of the manager") def get_dag(self) -> DAG: @@ -238,8 +250,6 @@ class DefaultAWELLayoutManager(AWELBaseManager): model_config = ConfigDict(arbitrary_types_allowed=True) - profile: str = "DefaultAWELLayoutManager" - dag: AWELTeamContext = Field(...) @validator("dag") diff --git a/dbgpt/agent/plan/plan_action.py b/dbgpt/agent/core/plan/plan_action.py similarity index 94% rename from dbgpt/agent/plan/plan_action.py rename to dbgpt/agent/core/plan/plan_action.py index a25ab3241..381de1907 100644 --- a/dbgpt/agent/plan/plan_action.py +++ b/dbgpt/agent/core/plan/plan_action.py @@ -6,12 +6,12 @@ from dbgpt._private.pydantic import BaseModel, Field from dbgpt.vis.tags.vis_agent_plans import Vis, VisAgentPlans -from ..actions.action import Action, ActionOutput -from ..core.agent import AgentContext -from ..core.schema import Status -from ..memory.base import GptsPlan -from ..memory.gpts_memory import GptsPlansMemory -from ..resource.resource_api import AgentResource +from ...resource.resource_api import AgentResource +from ..action.base import Action, ActionOutput +from ..agent import AgentContext +from ..memory.gpts.base import GptsPlan +from ..memory.gpts.gpts_memory import GptsPlansMemory +from ..schema import Status logger = logging.getLogger(__name__) diff --git a/dbgpt/agent/core/plan/planner_agent.py b/dbgpt/agent/core/plan/planner_agent.py new file mode 100644 index 000000000..c64cf0a42 --- /dev/null +++ b/dbgpt/agent/core/plan/planner_agent.py @@ -0,0 +1,165 @@ +"""Planner Agent.""" + +from typing import Any, Dict, List + +from dbgpt._private.pydantic import Field + +from ..agent import AgentMessage +from ..base_agent import ConversableAgent +from ..plan.plan_action import PlanAction +from ..profile import DynConfig, ProfileConfig + + +class PlannerAgent(ConversableAgent): + """Planner Agent. + + Planner agent, realizing task goal planning decomposition through LLM. + """ + + agents: List[ConversableAgent] = Field(default_factory=list) + + profile: ProfileConfig = ProfileConfig( + name=DynConfig( + "Planner", + category="agent", + key="dbgpt_agent_plan_planner_agent_profile_name", + ), + role=DynConfig( + "Planner", + category="agent", + key="dbgpt_agent_plan_planner_agent_profile_role", + ), + goal=DynConfig( + "Understand each of the following intelligent agents and their " + "capabilities, using the provided resources, solve user problems by " + "coordinating intelligent agents. Please utilize your LLM's knowledge " + "and understanding ability to comprehend the intent and goals of the " + "user's problem, generating a task plan that can be completed through" + " the collaboration of intelligent agents without user assistance.", + category="agent", + key="dbgpt_agent_plan_planner_agent_profile_goal", + ), + expand_prompt=DynConfig( + "Available Intelligent Agents:\n {{ agents }}", + category="agent", + key="dbgpt_agent_plan_planner_agent_profile_expand_prompt", + ), + constraints=DynConfig( + [ + "Every step of the task plan should exist to advance towards solving " + "the user's goals. Do not generate meaningless task steps; ensure " + "that each step has a clear goal and its content is complete.", + "Pay attention to the dependencies and logic of each step in the task " + "plan. For the steps that are depended upon, consider the data they " + "depend on and whether it can be obtained based on the current goal. " + "If it cannot be obtained, please indicate in the goal that the " + "dependent data needs to be generated.", + "Each step must be an independently achievable goal. Ensure that the " + "logic and information are complete. Avoid steps with unclear " + "objectives, like 'Analyze the retrieved issues data,' where it's " + "unclear what specific content needs to be analyzed.", + "Please ensure that only the intelligent agents mentioned above are " + "used, and you may use only the necessary parts of them. Allocate " + "them to appropriate steps strictly based on their described " + "capabilities and limitations. Each intelligent agent can be reused.", + "Utilize the provided resources to assist in generating the plan " + "steps according to the actual needs of the user's goals. Do not use " + "unnecessary resources.", + "Each step should ideally use only one type of resource to accomplish " + "a sub-goal. If the current goal can be broken down into multiple " + "subtasks of the same type, you can create mutually independent " + "parallel tasks.", + "Data resources can be loaded and utilized by the appropriate " + "intelligent agents without the need to consider the issues related " + "to data loading links.", + "Try to merge continuous steps that have sequential dependencies. If " + "the user's goal does not require splitting, you can create a " + "single-step task with content that is the user's goal.", + "Carefully review the plan to ensure it comprehensively covers all " + "information involved in the user's problem and can ultimately " + "achieve the goal. Confirm whether each step includes the necessary " + "resource information, such as URLs, resource names, etc.", + ], + category="agent", + key="dbgpt_agent_plan_planner_agent_profile_constraints", + ), + desc=DynConfig( + "You are a task planning expert! You can coordinate intelligent agents" + " and allocate resources to achieve complex task goals.", + category="agent", + key="dbgpt_agent_plan_planner_agent_profile_desc", + ), + examples=DynConfig( + """ +user:help me build a sales report summarizing our key metrics and trends +assistants:[ + {{ + "serial_number": "1", + "agent": "DataScientist", + "content": "Retrieve total sales, average sales, and number of transactions grouped by "product_category"'.", + "rely": "" + }}, + {{ + "serial_number": "2", + "agent": "DataScientist", + "content": "Retrieve monthly sales and transaction number trends.", + "rely": "" + }}, + {{ + "serial_number": "3", + "agent": "Reporter", + "content": "Integrate analytical data into the format required to build sales reports.", + "rely": "1,2" + }} +]""", # noqa: E501 + category="agent", + key="dbgpt_agent_plan_planner_agent_profile_examples", + ), + ) + _goal_zh: str = ( + "理解下面每个智能体(agent)和他们的能力，使用给出的资源，通过协调智能体来解决" + "用户问题。 请发挥你LLM的知识和理解能力，理解用户问题的意图和目标，生成一个可以在没有用户帮助" + "下，由智能体协作完成目标的任务计划。" + ) + _expand_prompt_zh: str = "可用智能体(agent):\n {{ agents }}" + + _constraints_zh: List[str] = [ + "任务计划的每个步骤都应该是为了推进解决用户目标而存在，不要生成无意义的任务步骤，确保每个步骤内目标明确内容完整。", + "关注任务计划每个步骤的依赖关系和逻辑，被依赖步骤要考虑被依赖的数据，是否能基于当前目标得到，如果不能请在目标中提示要生成被依赖数据。", + "每个步骤都是一个独立可完成的目标，一定要确保逻辑和信息完整，不要出现类似:" + "'Analyze the retrieved issues data'这样目标不明确，不知道具体要分析啥内容的步骤", + "请确保只使用上面提到的智能体，并且可以只使用其中需要的部分，严格根据描述能力和限制分配给合适的步骤，每个智能体都可以重复使用。", + "根据用户目标的实际需要使用提供的资源来协助生成计划步骤，不要使用不需要的资源。", + "每个步骤最好只使用一种资源完成一个子目标，如果当前目标可以分解为同类型的多个子任务，可以生成相互不依赖的并行任务。", + "数据资源可以被合适的智能体加载使用，不用考虑数据资源的加载链接问题", + "尽量合并有顺序依赖的连续相同步骤,如果用户目标无拆分必要，可以生成内容为用户目标的单步任务。", + "仔细检查计划，确保计划完整的包含了用户问题所涉及的所有信息，并且最终能完成目标，确认每个步骤是否包含了需要用到的资源信息,如URL、资源名等. ", + ] + _desc_zh: str = "你是一个任务规划专家！可以协调智能体，分配资源完成复杂的任务目标。" + + def __init__(self, **kwargs): + """Create a new PlannerAgent instance.""" + super().__init__(**kwargs) + self._init_actions([PlanAction]) + + def _init_reply_message(self, received_message: AgentMessage): + reply_message = super()._init_reply_message(received_message) + reply_message.context = { + "agents": "\n".join([f"- {item.role}:{item.desc}" for item in self.agents]), + } + return reply_message + + def bind_agents(self, agents: List[ConversableAgent]) -> ConversableAgent: + """Bind the agents to the planner agent.""" + self.agents = agents + for agent in self.agents: + if agent.resources and len(agent.resources) > 0: + self.resources.extend(agent.resources) + return self + + def prepare_act_param(self) -> Dict[str, Any]: + """Prepare the parameters for the act method.""" + return { + "context": self.not_null_agent_context, + "plans_memory": self.memory.plans_memory, + } diff --git a/dbgpt/agent/plan/team_auto_plan.py b/dbgpt/agent/core/plan/team_auto_plan.py similarity index 87% rename from dbgpt/agent/plan/team_auto_plan.py rename to dbgpt/agent/core/plan/team_auto_plan.py index f79621d4a..c409736a9 100644 --- a/dbgpt/agent/plan/team_auto_plan.py +++ b/dbgpt/agent/core/plan/team_auto_plan.py @@ -5,14 +5,15 @@ from dbgpt.core.interface.message import ModelMessageRoleType -from ..actions.action import ActionOutput -from ..core.agent import Agent, AgentMessage -from ..core.agent_manage import mentioned_agents, participant_roles -from ..core.base_agent import ConversableAgent -from ..core.base_team import ManagerAgent -from ..core.schema import Status -from ..memory.base import GptsPlan -from .planner_agent import PlannerAgent +from ..action.base import ActionOutput +from ..agent import Agent, AgentMessage +from ..agent_manage import mentioned_agents, participant_roles +from ..base_agent import ConversableAgent +from ..base_team import ManagerAgent +from ..memory.gpts.base import GptsPlan +from ..plan.planner_agent import PlannerAgent +from ..profile import DynConfig, ProfileConfig +from ..schema import Status logger = logging.getLogger(__name__) @@ -20,14 +21,30 @@ class AutoPlanChatManager(ManagerAgent): """A chat manager agent that can manage a team chat of multiple agents.""" - profile: str = "PlanManager" - goal: str = ( - "Advance the task plan generated by the planning agent. If the plan " - "does not pre-allocate an agent, it needs to be coordinated with the " - "appropriate agent to complete." + profile: ProfileConfig = ProfileConfig( + name=DynConfig( + "AutoPlanChatManager", + category="agent", + key="dbgpt_agent_plan_team_auto_plan_profile_name", + ), + role=DynConfig( + "PlanManager", + category="agent", + key="dbgpt_agent_plan_team_auto_plan_profile_role", + ), + goal=DynConfig( + "Advance the task plan generated by the planning agent. If the plan " + "does not pre-allocate an agent, it needs to be coordinated with the " + "appropriate agent to complete.", + category="agent", + key="dbgpt_agent_plan_team_auto_plan_profile_goal", + ), + desc=DynConfig( + "Advance the task plan generated by the planning agent.", + category="agent", + key="dbgpt_agent_plan_team_auto_plan_profile_desc", + ), ) - constraints: List[str] = [] - desc: str = "Advance the task plan generated by the planning agent." def __init__(self, **kwargs): """Create a new AutoPlanChatManager instance.""" @@ -56,19 +73,21 @@ async def process_rely_message( { "content": rely_task.sub_task_content, "role": ModelMessageRoleType.HUMAN, + "name": rely_task.sub_task_agent, } ) rely_messages.append( { "content": rely_task.result, "role": ModelMessageRoleType.AI, + "name": rely_task.sub_task_agent, } ) return rely_prompt, rely_messages def select_speaker_msg(self, agents: List[Agent]) -> str: """Return the message for selecting the next speaker.""" - agent_names = [agent.get_name() for agent in agents] + agent_names = [agent.name for agent in agents] return ( "You are in a role play game. The following roles are available:\n" f" {participant_roles(agents)}.\n" @@ -95,7 +114,7 @@ async def select_speaker( else: # auto speaker selection # TODO selector a_thinking It has been overwritten and cannot be used. - agent_names = [agent.get_name() for agent in agents] + agent_names = [agent.name for agent in agents] fina_name, model = await selector.thinking( messages=[ AgentMessage( @@ -104,7 +123,7 @@ async def select_speaker( " assign the appropriate role to complete the task.\n" f"Task content: {now_goal_context},\n" f"Select the role from: {agent_names},\n" - f"Please only return the role, such as: {agents[0].get_name()}", + f"Please only return the role, such as: {agents[0].name}", ) ], prompt=self.select_speaker_msg(agents), @@ -269,7 +288,7 @@ async def act( now_plan.sub_task_num, Status.FAILED.value, now_plan.retry_times + 1, - speaker.get_name(), + speaker.name, "", plan_result, ) diff --git a/dbgpt/agent/core/profile/__init__.py b/dbgpt/agent/core/profile/__init__.py new file mode 100644 index 000000000..3aac36b2e --- /dev/null +++ b/dbgpt/agent/core/profile/__init__.py @@ -0,0 +1,31 @@ +"""Profiling module. + +Autonomous agents typically perform tasks by assuming specific roles, such as coders, +teachers and domain experts. + +The profiling module aims to indicate the profiles of the agent roles, which are usually + written into the prompt to influence the LLM behaviors. + +Agent profiles typically encompass basic information such as age, gender, and career, +as well as psychology information, reflecting the personalities of the agent, and social + information, detailing the relationships between agents. + +The choice of analysis information depends heavily on the application scenario. + +How to create a profile: +1. Handcrafting method +2. LLM-generation method +3. Dataset alignment method +""" + +from dbgpt.util.configure import DynConfig # noqa: F401 + +from .base import ( # noqa: F401 + CompositeProfileFactory, + DatasetProfileFactory, + DefaultProfile, + LLMProfileFactory, + Profile, + ProfileConfig, + ProfileFactory, +) diff --git a/dbgpt/agent/core/profile/base.py b/dbgpt/agent/core/profile/base.py new file mode 100644 index 000000000..ca7f59986 --- /dev/null +++ b/dbgpt/agent/core/profile/base.py @@ -0,0 +1,413 @@ +"""Profile module.""" + +from abc import ABC, abstractmethod +from typing import List, Optional + +import cachetools + +from dbgpt._private.pydantic import BaseModel, ConfigDict, Field, model_validator +from dbgpt.util.configure import ConfigInfo, DynConfig + +VALID_TEMPLATE_KEYS = { + "role", + "name", + "goal", + "resource_prompt", + "expand_prompt", + "language", + "constraints", + "examples", + "out_schema", + "most_recent_memories", + "question", +} + +_DEFAULT_SYSTEM_TEMPLATE = """ +You are a {{ role }}, {% if name %}named {{ name }}, {% endif %}your goal is {{ goal }}. +Please think step by step to achieve the goal. You can use the resources given below. +At the same time, please strictly abide by the constraints and specifications in IMPORTANT REMINDER. +{% if resource_prompt %} {{ resource_prompt }} {% endif %} +{% if expand_prompt %} {{ expand_prompt }} {% endif %} + +*** IMPORTANT REMINDER *** +{% if language == 'zh' %} +Please answer in simplified Chinese. +{% else %} +Please answer in English. +{% endif %} + +{% if constraints %} +{% for constraint in constraints %} +{{ loop.index }}. {{ constraint }} +{% endfor %} +{% endif %} + +{% if examples %} +You can refer to the following examples: +{{ examples }} +{% endif %} + +{% if out_schema %} {{ out_schema }} {% endif %} +""" # noqa + +_DEFAULT_USER_TEMPLATE = """ +{% if most_recent_memories %} +Most recent observations: +{{ most_recent_memories }} +{% endif %} + +{% if question %} +Question: {{ question }} +{% endif %} +""" + +_DEFAULT_SAVE_MEMORY_TEMPLATE = """ +{% if question %}Question: {{ question }} {% endif %} +{% if thought %}Thought: {{ thought }} {% endif %} +{% if action %}Action: {{ action }} {% endif %} +{% if observation %}Observation: {{ observation }} {% endif %} +""" + + +class Profile(ABC): + """Profile interface.""" + + @abstractmethod + def get_name(self) -> str: + """Return the name of current agent.""" + + @abstractmethod + def get_role(self) -> str: + """Return the role of current agent.""" + + def get_goal(self) -> Optional[str]: + """Return the goal of current agent.""" + return None + + def get_constraints(self) -> Optional[List[str]]: + """Return the constraints of current agent.""" + return None + + def get_description(self) -> Optional[str]: + """Return the description of current agent. + + It will not be used to generate prompt. + """ + return None + + def get_expand_prompt(self) -> Optional[str]: + """Return the expand prompt of current agent.""" + return None + + def get_examples(self) -> Optional[str]: + """Return the examples of current agent.""" + return None + + @abstractmethod + def get_system_prompt_template(self) -> str: + """Return the prompt template of current agent.""" + + @abstractmethod + def get_user_prompt_template(self) -> str: + """Return the user prompt template of current agent.""" + + @abstractmethod + def get_save_memory_template(self) -> str: + """Return the save memory template of current agent.""" + + +class DefaultProfile(BaseModel, Profile): + """Default profile.""" + + name: str = Field("", description="The name of the agent.") + role: str = Field("", description="The role of the agent.") + goal: Optional[str] = Field(None, description="The goal of the agent.") + constraints: Optional[List[str]] = Field( + None, description="The constraints of the agent." + ) + + desc: Optional[str] = Field( + None, description="The description of the agent, not used to generate prompt." + ) + + expand_prompt: Optional[str] = Field( + None, description="The expand prompt of the agent." + ) + + examples: Optional[str] = Field( + None, description="The examples of the agent prompt." + ) + + system_prompt_template: str = Field( + _DEFAULT_SYSTEM_TEMPLATE, description="The system prompt template of the agent." + ) + user_prompt_template: str = Field( + _DEFAULT_USER_TEMPLATE, description="The user prompt template of the agent." + ) + + save_memory_template: str = Field( + _DEFAULT_SAVE_MEMORY_TEMPLATE, + description="The save memory template of the agent.", + ) + + def get_name(self) -> str: + """Return the name of current agent.""" + return self.name + + def get_role(self) -> str: + """Return the role of current agent.""" + return self.role + + def get_goal(self) -> Optional[str]: + """Return the goal of current agent.""" + return self.goal + + def get_constraints(self) -> Optional[List[str]]: + """Return the constraints of current agent.""" + return self.constraints + + def get_description(self) -> Optional[str]: + """Return the description of current agent. + + It will not be used to generate prompt. + """ + return self.desc + + def get_expand_prompt(self) -> Optional[str]: + """Return the expand prompt of current agent.""" + return self.expand_prompt + + def get_examples(self) -> Optional[str]: + """Return the examples of current agent.""" + return self.examples + + def get_system_prompt_template(self) -> str: + """Return the prompt template of current agent.""" + return self.system_prompt_template + + def get_user_prompt_template(self) -> str: + """Return the user prompt template of current agent.""" + return self.user_prompt_template + + def get_save_memory_template(self) -> str: + """Return the save memory template of current agent.""" + return self.save_memory_template + + +class ProfileFactory: + """Profile factory interface. + + It is used to create a profile. + """ + + @abstractmethod + def create_profile( + self, + profile_id: int, + name: Optional[str] = None, + role: Optional[str] = None, + goal: Optional[str] = None, + prefer_prompt_language: Optional[str] = None, + prefer_model: Optional[str] = None, + ) -> Optional[Profile]: + """Create a profile.""" + + +class LLMProfileFactory(ProfileFactory): + """Create a profile by LLM. + + Based on LLM automatic generation, it usually specifies the rules of the generation + configuration first, clarifies the composition and attributes of the agent + configuration in the target population, and then gives a small number of samples, + and finally LLM generates the configuration of all agents. + """ + + def create_profile( + self, + profile_id: int, + name: Optional[str] = None, + role: Optional[str] = None, + goal: Optional[str] = None, + prefer_prompt_language: Optional[str] = None, + prefer_model: Optional[str] = None, + ) -> Optional[Profile]: + """Create a profile by LLM. + + TODO: Implement this method. + """ + pass + + +class DatasetProfileFactory(ProfileFactory): + """Create a profile by dataset. + + Use existing data sets to generate agent configurations. + + In some cases, the data set contains a large amount of information about real people + , first organize the information about real people in the data set into a natural + language prompt, which is then used to generate the agent configuration. + """ + + def create_profile( + self, + profile_id: int, + name: Optional[str] = None, + role: Optional[str] = None, + goal: Optional[str] = None, + prefer_prompt_language: Optional[str] = None, + prefer_model: Optional[str] = None, + ) -> Optional[Profile]: + """Create a profile by dataset. + + TODO: Implement this method. + """ + pass + + +class CompositeProfileFactory(ProfileFactory): + """Create a profile by combining multiple profile factories.""" + + def __init__(self, factories: List[ProfileFactory]): + """Create a composite profile factory.""" + self.factories = factories + + def create_profile( + self, + profile_id: int, + name: Optional[str] = None, + role: Optional[str] = None, + goal: Optional[str] = None, + prefer_prompt_language: Optional[str] = None, + prefer_model: Optional[str] = None, + ) -> Optional[Profile]: + """Create a profile by combining multiple profile factories. + + TODO: Implement this method. + """ + pass + + +class ProfileConfig(BaseModel): + """Profile configuration. + + If factory is not specified, name and role must be specified. + If factory is specified and name and role are also specified, the factory will be + preferred. + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) + + profile_id: int = Field(0, description="The profile ID.") + name: str | ConfigInfo | None = DynConfig(..., description="The name of the agent.") + role: str | ConfigInfo | None = DynConfig(..., description="The role of the agent.") + goal: str | ConfigInfo | None = DynConfig(None, description="The goal.") + constraints: List[str] | ConfigInfo | None = DynConfig(None, is_list=True) + desc: str | ConfigInfo | None = DynConfig( + None, description="The description of the agent." + ) + expand_prompt: str | ConfigInfo | None = DynConfig( + None, description="The expand prompt." + ) + examples: str | ConfigInfo | None = DynConfig(None, description="The examples.") + + system_prompt_template: str | ConfigInfo | None = DynConfig( + _DEFAULT_SYSTEM_TEMPLATE, description="The prompt template." + ) + user_prompt_template: str | ConfigInfo | None = DynConfig( + _DEFAULT_USER_TEMPLATE, description="The user prompt template." + ) + save_memory_template: str | ConfigInfo | None = DynConfig( + _DEFAULT_SAVE_MEMORY_TEMPLATE, description="The save memory template." + ) + factory: ProfileFactory | None = Field(None, description="The profile factory.") + + @model_validator(mode="before") + @classmethod + def check_before(cls, values): + """Check before validation.""" + if isinstance(values, dict): + return values + if values["factory"] is None: + if values["name"] is None: + raise ValueError("name must be specified if factory is not specified") + if values["role"] is None: + raise ValueError("role must be specified if factory is not specified") + return values + + @cachetools.cached(cachetools.TTLCache(maxsize=100, ttl=10)) + def create_profile( + self, + profile_id: Optional[int] = None, + prefer_prompt_language: Optional[str] = None, + prefer_model: Optional[str] = None, + ) -> Profile: + """Create a profile. + + If factory is specified, use the factory to create the profile. + """ + factory_profile = None + if profile_id is None: + profile_id = self.profile_id + name = self.name + role = self.role + goal = self.goal + constraints = self.constraints + desc = self.desc + expand_prompt = self.expand_prompt + system_prompt_template = self.system_prompt_template + user_prompt_template = self.user_prompt_template + save_memory_template = self.save_memory_template + examples = self.examples + call_args = { + "prefer_prompt_language": prefer_prompt_language, + "prefer_model": prefer_model, + } + if isinstance(name, ConfigInfo): + name = name.query(**call_args) + if isinstance(role, ConfigInfo): + role = role.query(**call_args) + if isinstance(goal, ConfigInfo): + goal = goal.query(**call_args) + if isinstance(constraints, ConfigInfo): + constraints = constraints.query(**call_args) + if isinstance(desc, ConfigInfo): + desc = desc.query(**call_args) + if isinstance(expand_prompt, ConfigInfo): + expand_prompt = expand_prompt.query(**call_args) + if isinstance(examples, ConfigInfo): + examples = examples.query(**call_args) + if isinstance(system_prompt_template, ConfigInfo): + system_prompt_template = system_prompt_template.query(**call_args) + if isinstance(user_prompt_template, ConfigInfo): + user_prompt_template = user_prompt_template.query(**call_args) + if isinstance(save_memory_template, ConfigInfo): + save_memory_template = save_memory_template.query(**call_args) + + if self.factory is not None: + factory_profile = self.factory.create_profile( + profile_id, + name, + role, + goal, + prefer_prompt_language, + prefer_model, + ) + + if factory_profile is not None: + return factory_profile + return DefaultProfile( + name=name, + role=role, + goal=goal, + constraints=constraints, + desc=desc, + expand_prompt=expand_prompt, + examples=examples, + system_prompt_template=system_prompt_template, + user_prompt_template=user_prompt_template, + save_memory_template=save_memory_template, + ) + + def __hash__(self): + """Return the hash value.""" + return hash(self.profile_id) diff --git a/dbgpt/agent/core/role.py b/dbgpt/agent/core/role.py index 04d50c204..99078bb27 100644 --- a/dbgpt/agent/core/role.py +++ b/dbgpt/agent/core/role.py @@ -1,125 +1,217 @@ """Role class for role-based conversation.""" + from abc import ABC -from typing import List, Optional +from typing import Any, Dict, List, Optional, Set + +from jinja2.meta import find_undeclared_variables +from jinja2.sandbox import SandboxedEnvironment from dbgpt._private.pydantic import BaseModel, ConfigDict, Field +from .action.base import ActionOutput +from .memory.agent_memory import AgentMemory, AgentMemoryFragment +from .memory.llm import LLMImportanceScorer, LLMInsightExtractor +from .profile import Profile, ProfileConfig + class Role(ABC, BaseModel): """Role class for role-based conversation.""" model_config = ConfigDict(arbitrary_types_allowed=True) - profile: str = "" - name: str = "" - resource_introduction: str = "" - goal: str = "" - - expand_prompt: str = "" + profile: ProfileConfig = Field( + ..., + description="The profile of the role.", + ) + memory: AgentMemory = Field(default_factory=AgentMemory) fixed_subgoal: Optional[str] = Field(None, description="Fixed subgoal") - constraints: List[str] = Field(default_factory=list, description="Constraints") - examples: str = "" - desc: str = "" language: str = "en" is_human: bool = False is_team: bool = False - def prompt_template( + template_env: SandboxedEnvironment = Field(default_factory=SandboxedEnvironment) + + async def build_prompt( self, - specified_prompt: Optional[str] = None, + question: Optional[str] = None, + is_system: bool = True, + most_recent_memories: Optional[str] = None, + **kwargs ) -> str: """Return the prompt template for the role. - Args: - specified_prompt (str, optional): The specified prompt. Defaults to None. - Returns: str: The prompt template. """ - if specified_prompt: - return specified_prompt - - expand_prompt = self.expand_prompt if len(self.expand_prompt) > 0 else "" - examples_prompt = ( - "You can refer to the following examples:\n" - if len(self.examples) > 0 - else "" + prompt_template = ( + self.system_prompt_template if is_system else self.user_prompt_template ) - examples = self.examples if len(self.examples) > 0 else "" - template = ( - f"{self.role_prompt}\n" - "Please think step by step to achieve the goal. You can use the resources " - "given below. At the same time, please strictly abide by the constraints " - "and specifications in IMPORTANT REMINDER.\n\n" - f"{{resource_prompt}}\n\n" - f"{expand_prompt}\n\n" - "*** IMPORTANT REMINDER ***\n" - f"{self.language_require_prompt}\n" - f"{self.constraints_prompt}\n" - f"{examples_prompt}{examples}\n\n" - f"{{out_schema}}" - ) - return template + template_vars = self._get_template_variables(prompt_template) + _sub_render_keys = {"role", "name", "goal", "expand_prompt", "constraints"} + pass_vars = { + "role": self.role, + "name": self.name, + "goal": self.goal, + "expand_prompt": self.expand_prompt, + "language": self.language, + "constraints": self.constraints, + "most_recent_memories": ( + most_recent_memories if most_recent_memories else None + ), + "examples": self.examples, + # "out_schema": out_schema if out_schema else None, + # "resource_prompt": resource_prompt if resource_prompt else None, + "question": question, + } + resource_vars = await self.generate_resource_variables(question) + pass_vars.update(resource_vars) + pass_vars.update(kwargs) + filtered_data = { + key: pass_vars[key] for key in template_vars if key in pass_vars + } + for key in filtered_data.keys(): + value = filtered_data[key] + if key in _sub_render_keys and value: + if isinstance(value, str): + # Render the sub-template + filtered_data[key] = self._render_template(value, **pass_vars) + elif isinstance(value, list): + for i, item in enumerate(value): + if isinstance(item, str): + value[i] = self._render_template(item, **pass_vars) + return self._render_template(prompt_template, **filtered_data) + + async def generate_resource_variables( + self, question: Optional[str] = None + ) -> Dict[str, Any]: + """Generate the resource variables.""" + return {} + + def identity_check(self) -> None: + """Check the identity of the role.""" + pass + + def get_name(self) -> str: + """Get the name of the role.""" + return self.current_profile.get_name() @property - def role_prompt(self) -> str: - """Return the role prompt. + def current_profile(self) -> Profile: + """Return the current profile.""" + profile = self.profile.create_profile() + return profile - You are a {self.profile}, named {self.name}, your goal is {self.goal}. + @property + def name(self) -> str: + """Return the name of the role.""" + return self.current_profile.get_name() - Returns: - str: The role prompt. - """ - profile_prompt = f"You are a {self.profile}," - name_prompt = f"named {self.name}," if len(self.name) > 0 else "" - goal_prompt = f"your goal is {self.goal}" - prompt = f"""{profile_prompt}{name_prompt}{goal_prompt}""" - return prompt + @property + def examples(self) -> Optional[str]: + """Return the examples of the role.""" + return self.current_profile.get_examples() @property - def constraints_prompt(self) -> str: - """Return the constraints prompt. + def role(self) -> str: + """Return the role of the role.""" + return self.current_profile.get_role() - Return: - str: The constraints prompt. - """ - if len(self.constraints) > 0: - return "\n".join( - f"{i + 1}. {item}" for i, item in enumerate(self.constraints) - ) - return "" + @property + def goal(self) -> Optional[str]: + """Return the goal of the role.""" + return self.current_profile.get_goal() @property - def language_require_prompt(self) -> str: - """Return the language requirement prompt. + def constraints(self) -> Optional[List[str]]: + """Return the constraints of the role.""" + return self.current_profile.get_constraints() - Returns: - str: The language requirement prompt. - """ - if self.language == "zh": - return "Please answer in simplified Chinese." - else: - return "Please answer in English." + @property + def desc(self) -> Optional[str]: + """Return the description of the role.""" + return self.current_profile.get_description() @property - def introduce(self) -> str: - """Introduce the role.""" - return self.desc + def expand_prompt(self) -> Optional[str]: + """Return the expand prompt of the role.""" + return self.current_profile.get_expand_prompt() - def identity_check(self) -> None: - """Check the identity of the role.""" - pass + @property + def system_prompt_template(self) -> str: + """Return the current system prompt template.""" + return self.current_profile.get_system_prompt_template() - def get_name(self) -> str: - """Get the name of the role.""" - return self.name + @property + def user_prompt_template(self) -> str: + """Return the current user prompt template.""" + return self.current_profile.get_user_prompt_template() + + @property + def save_memory_template(self) -> str: + """Return the current save memory template.""" + return self.current_profile.get_save_memory_template() + + def _get_template_variables(self, template: str) -> Set[str]: + parsed_content = self.template_env.parse(template) + return find_undeclared_variables(parsed_content) + + def _render_template(self, template: str, **kwargs): + r_template = self.template_env.from_string(template) + return r_template.render(**kwargs) - def get_profile(self) -> str: - """Get the profile of the role.""" - return self.profile + @property + def memory_importance_scorer(self) -> Optional[LLMImportanceScorer]: + """Create the memory importance scorer. + + The memory importance scorer is used to score the importance of a memory + fragment. + """ + return None - def get_describe(self) -> str: - """Get the describe of the role.""" - return self.desc + @property + def memory_insight_extractor(self) -> Optional[LLMInsightExtractor]: + """Create the memory insight extractor. + + The memory insight extractor is used to extract a high-level insight from a + memory fragment. + """ + return None + + async def read_memories( + self, + question: str, + ) -> str: + """Read the memories from the memory.""" + memories = await self.memory.read(question) + recent_messages = [m.raw_observation for m in memories] + return "".join(recent_messages) + + async def save_to_memory( + self, + question: str, + ai_message: str, + action_output: Optional[ActionOutput] = None, + check_pass: bool = True, + check_fail_reason: Optional[str] = None, + ) -> None: + """Save the role to the memory.""" + if not action_output: + raise ValueError("Action output is required to save to memory.") + + mem_thoughts = action_output.thoughts or ai_message + observation = action_output.observations or action_output.content + if not check_pass and check_fail_reason: + observation += "\n" + check_fail_reason + + memory_map = { + "question": question, + "thought": mem_thoughts, + "action": action_output.action, + "observation": observation, + } + save_memory_template = self.save_memory_template + memory_content = self._render_template(save_memory_template, **memory_map) + fragment = AgentMemoryFragment(memory_content) + await self.memory.write(fragment) diff --git a/dbgpt/agent/core/user_proxy_agent.py b/dbgpt/agent/core/user_proxy_agent.py index 61087ae12..c6baa5e19 100644 --- a/dbgpt/agent/core/user_proxy_agent.py +++ b/dbgpt/agent/core/user_proxy_agent.py @@ -1,5 +1,6 @@ """A proxy agent for the user.""" from .base_agent import ConversableAgent +from .profile import ProfileConfig class UserProxyAgent(ConversableAgent): @@ -8,12 +9,13 @@ class UserProxyAgent(ConversableAgent): That can execute code and provide feedback to the other agents. """ - name: str = "User" - profile: str = "Human" - - desc: str = ( - "A human admin. Interact with the planner to discuss the plan. " - "Plan execution needs to be approved by this admin." + profile: ProfileConfig = ProfileConfig( + name="User", + role="Human", + description=( + "A human admin. Interact with the planner to discuss the plan. " + "Plan execution needs to be approved by this admin." + ), ) is_human: bool = True diff --git a/dbgpt/agent/expand/Indicator_assistant_agent.py b/dbgpt/agent/expand/Indicator_assistant_agent.py index 91ae4c965..eff891cd5 100644 --- a/dbgpt/agent/expand/Indicator_assistant_agent.py +++ b/dbgpt/agent/expand/Indicator_assistant_agent.py @@ -1,9 +1,10 @@ """Indicator Assistant Agent.""" + import logging -from typing import List -from ..actions.indicator_action import IndicatorAction from ..core.base_agent import ConversableAgent +from ..core.profile import DynConfig, ProfileConfig +from .actions.indicator_action import IndicatorAction logger = logging.getLogger(__name__) @@ -11,29 +12,48 @@ class IndicatorAssistantAgent(ConversableAgent): """Indicator Assistant Agent.""" - name = "Indicator" - profile: str = "Indicator" - goal: str = ( - "Summarize answer summaries based on user questions from provided " - "resource information or from historical conversation memories." - ) - - constraints: List[str] = [ - "Prioritize the summary of answers to user questions from the improved resource" - " text. If no relevant information is found, summarize it from the historical" - " dialogue memory given. It is forbidden to make up your own.", - "You need to first detect user's question that you need to answer with your " - "summarization.", - "Extract the provided text content used for summarization.", - "Then you need to summarize the extracted text content.", - "Output the content of summarization ONLY related to user's question. The " - "output language must be the same to user's question language.", - "If you think the provided text content is not related to user questions at " - "all, ONLY output 'Did not find the information you want.'!!.", - ] - desc: str = ( - "You can summarize provided text content according to user's questions " - "and output the summarization." + profile: ProfileConfig = ProfileConfig( + name=DynConfig( + "Indicator", + category="agent", + key="dbgpt_agent_expand_indicator_assistant_agent_profile_name", + ), + role=DynConfig( + "Indicator", + category="agent", + key="dbgpt_agent_expand_indicator_assistant_agent_profile_role", + ), + goal=DynConfig( + "Summarize answer summaries based on user questions from provided " + "resource information or from historical conversation memories.", + category="agent", + key="dbgpt_agent_expand_indicator_assistant_agent_profile_goal", + ), + constraints=DynConfig( + [ + "Prioritize the summary of answers to user questions from the " + "improved resource text. If no relevant information is found, " + "summarize it from the historical dialogue memory given. It is " + "forbidden to make up your own.", + "You need to first detect user's question that you need to answer " + "with your summarization.", + "Extract the provided text content used for summarization.", + "Then you need to summarize the extracted text content.", + "Output the content of summarization ONLY related to user's question. " + "The output language must be the same to user's question language.", + "If you think the provided text content is not related to user " + "questions at all, ONLY output 'Did not find the information you " + "want.'!!.", + ], + category="agent", + key="dbgpt_agent_expand_indicator_assistant_agent_profile_constraints", + ), + desc=DynConfig( + "You can summarize provided text content according to user's questions " + "and output the summarization.", + category="agent", + key="dbgpt_agent_expand_indicator_assistant_agent_profile_desc", + ), ) def __init__(self, **kwargs): diff --git a/dbgpt/agent/expand/actions/__init__.py b/dbgpt/agent/expand/actions/__init__.py new file mode 100644 index 000000000..391269477 --- /dev/null +++ b/dbgpt/agent/expand/actions/__init__.py @@ -0,0 +1 @@ +"""Actions of expand Agents.""" diff --git a/dbgpt/agent/actions/chart_action.py b/dbgpt/agent/expand/actions/chart_action.py similarity index 95% rename from dbgpt/agent/actions/chart_action.py rename to dbgpt/agent/expand/actions/chart_action.py index 76df3a77a..1899a8ef5 100644 --- a/dbgpt/agent/actions/chart_action.py +++ b/dbgpt/agent/expand/actions/chart_action.py @@ -1,4 +1,5 @@ """Chart Action for SQL execution and rendering.""" + import json import logging from typing import Optional @@ -6,9 +7,9 @@ from dbgpt._private.pydantic import BaseModel, Field, model_to_json from dbgpt.vis.tags.vis_chart import Vis, VisChart -from ..resource.resource_api import AgentResource, ResourceType -from ..resource.resource_db_api import ResourceDbClient -from .action import Action, ActionOutput +from ...core.action.base import Action, ActionOutput +from ...resource.resource_api import AgentResource, ResourceType +from ...resource.resource_db_api import ResourceDbClient logger = logging.getLogger(__name__) diff --git a/dbgpt/agent/actions/code_action.py b/dbgpt/agent/expand/actions/code_action.py similarity index 93% rename from dbgpt/agent/actions/code_action.py rename to dbgpt/agent/expand/actions/code_action.py index 659c23a08..42443327c 100644 --- a/dbgpt/agent/actions/code_action.py +++ b/dbgpt/agent/expand/actions/code_action.py @@ -1,4 +1,5 @@ """Code Action Module.""" + import logging from typing import Optional, Union @@ -6,8 +7,8 @@ from dbgpt.util.utils import colored from dbgpt.vis.tags.vis_code import Vis, VisCode -from ..resource.resource_api import AgentResource -from .action import Action, ActionOutput +from ...core.action.base import Action, ActionOutput +from ...resource.resource_api import AgentResource logger = logging.getLogger(__name__) @@ -73,7 +74,13 @@ async def run( if not self.render_protocol: raise NotImplementedError("The render_protocol should be implemented.") view = await self.render_protocol.display(content=param) - return ActionOutput(is_exe_success=exit_success, content=content, view=view) + return ActionOutput( + is_exe_success=exit_success, + content=content, + view=view, + thoughts=ai_message, + observations=content, + ) except Exception as e: logger.exception("Code Action Run Failed！") return ActionOutput( diff --git a/dbgpt/agent/actions/dashboard_action.py b/dbgpt/agent/expand/actions/dashboard_action.py similarity index 96% rename from dbgpt/agent/actions/dashboard_action.py rename to dbgpt/agent/expand/actions/dashboard_action.py index 098f81835..2bc1d210b 100644 --- a/dbgpt/agent/actions/dashboard_action.py +++ b/dbgpt/agent/expand/actions/dashboard_action.py @@ -1,4 +1,5 @@ """Dashboard Action Module.""" + import json import logging from typing import List, Optional @@ -6,9 +7,9 @@ from dbgpt._private.pydantic import BaseModel, Field, model_to_dict from dbgpt.vis.tags.vis_dashboard import Vis, VisDashboard -from ..resource.resource_api import AgentResource, ResourceType -from ..resource.resource_db_api import ResourceDbClient -from .action import Action, ActionOutput +from ...core.action.base import Action, ActionOutput +from ...resource.resource_api import AgentResource, ResourceType +from ...resource.resource_db_api import ResourceDbClient logger = logging.getLogger(__name__) diff --git a/dbgpt/agent/actions/indicator_action.py b/dbgpt/agent/expand/actions/indicator_action.py similarity index 97% rename from dbgpt/agent/actions/indicator_action.py rename to dbgpt/agent/expand/actions/indicator_action.py index 1df0e9d39..6ebd67a36 100644 --- a/dbgpt/agent/actions/indicator_action.py +++ b/dbgpt/agent/expand/actions/indicator_action.py @@ -7,9 +7,9 @@ from dbgpt._private.pydantic import BaseModel, Field from dbgpt.vis.tags.vis_plugin import Vis, VisPlugin -from ..core.schema import Status -from ..resource.resource_api import AgentResource, ResourceType -from .action import Action, ActionOutput +from ...core.action.base import Action, ActionOutput +from ...core.schema import Status +from ...resource.resource_api import AgentResource, ResourceType logger = logging.getLogger(__name__) diff --git a/dbgpt/agent/actions/plugin_action.py b/dbgpt/agent/expand/actions/plugin_action.py similarity index 92% rename from dbgpt/agent/actions/plugin_action.py rename to dbgpt/agent/expand/actions/plugin_action.py index 025f1d52b..3a2a7e600 100644 --- a/dbgpt/agent/actions/plugin_action.py +++ b/dbgpt/agent/expand/actions/plugin_action.py @@ -1,4 +1,5 @@ """Plugin Action Module.""" + import json import logging from typing import Optional @@ -6,11 +7,11 @@ from dbgpt._private.pydantic import BaseModel, Field from dbgpt.vis.tags.vis_plugin import Vis, VisPlugin -from ..core.schema import Status -from ..plugin.generator import PluginPromptGenerator -from ..resource.resource_api import AgentResource, ResourceType -from ..resource.resource_plugin_api import ResourcePluginClient -from .action import Action, ActionOutput +from ...core.action.base import Action, ActionOutput +from ...core.schema import Status +from ...plugin.generator import PluginPromptGenerator +from ...resource.resource_api import AgentResource, ResourceType +from ...resource.resource_plugin_api import ResourcePluginClient logger = logging.getLogger(__name__) @@ -144,7 +145,10 @@ async def run( view = await self.render_protocol.display(content=plugin_param) return ActionOutput( - is_exe_success=response_success, content=tool_result, view=view + is_exe_success=response_success, + content=tool_result, + view=view, + observations=tool_result, ) except Exception as e: logger.exception("Tool Action Run Failed！") diff --git a/dbgpt/agent/expand/code_assistant_agent.py b/dbgpt/agent/expand/code_assistant_agent.py index 7c00b6226..1e3d9af4e 100644 --- a/dbgpt/agent/expand/code_assistant_agent.py +++ b/dbgpt/agent/expand/code_assistant_agent.py @@ -1,12 +1,14 @@ """Code Assistant Agent.""" -from typing import List, Optional, Tuple + +from typing import Optional, Tuple from dbgpt.core import ModelMessageRoleType from dbgpt.util.string_utils import str_to_bool -from ..actions.code_action import CodeAction from ..core.agent import AgentMessage from ..core.base_agent import ConversableAgent +from ..core.profile import DynConfig, ProfileConfig +from .actions.code_action import CodeAction CHECK_RESULT_SYSTEM_MESSAGE = ( "You are an expert in analyzing the results of task execution. Your responsibility " @@ -42,54 +44,75 @@ class CodeAssistantAgent(ConversableAgent): """Code Assistant Agent.""" - name: str = "Turing" - profile: str = "CodeEngineer" - goal: str = ( - "Solve tasks using your coding and language skills.\n" - "In the following cases, suggest python code (in a python coding block) or " - "shell script (in a sh coding block) for the user to execute.\n" - " 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.\n" - " 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." - ) - constraints: List[str] = [ - "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.Don't ask users to copy and " - "paste results. Instead, the 'Print' function must be used for output when " - "relevant.", - "When using code, you must indicate the script type in the code block. Please " - "don't include multiple code blocks in one response.", - "If you want the user to save the code in a file before executing it, put " - "# filename: inside the code block as the first line.", - "If you receive user input that indicates an error in the code execution, fix " - "the error and output the complete code again. It is recommended to use the " - "complete code rather than partial code or code changes. If the error cannot be" - " fixed, or the task is not resolved even after the code executes successfully," - " analyze the problem, revisit your assumptions, gather additional information" - " you need from historical conversation records, and consider trying a " - "different approach.", - "Unless necessary, give priority to solving problems with python code. If it " - "involves downloading files or storing data locally, please use 'Print' to " - "output the full file path of the stored data and a brief introduction to the " - "data.", - "The output content of the 'print' function will be passed to other LLM agents " - "as dependent data. Please control the length of the output content of the " - "'print' function. The 'print' function only outputs part of the key data " - "information that is relied on, and is as concise as possible.", - "The code is executed without user participation. It is forbidden to use " - "methods that will block the process or need to be shut down, such as the " - "plt.show() method of matplotlib.pyplot as plt.", - "It is prohibited to fabricate non-existent data to achieve goals.", - ] - desc: str = ( - "Can independently write and execute python/shell code to solve various" - " problems" + profile: ProfileConfig = ProfileConfig( + name=DynConfig( + "Turing", + category="agent", + key="dbgpt_agent_expand_code_assistant_agent_profile_name", + ), + role=DynConfig( + "CodeEngineer", + category="agent", + key="dbgpt_agent_expand_code_assistant_agent_profile_role", + ), + goal=DynConfig( + "Solve tasks using your coding and language skills.\n" + "In the following cases, suggest python code (in a python coding block) or " + "shell script (in a sh coding block) for the user to execute.\n" + " 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.\n" + " 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.", + category="agent", + key="dbgpt_agent_expand_code_assistant_agent_profile_goal", + ), + constraints=DynConfig( + [ + "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.Don't ask users to copy and paste results. Instead, " + "the 'Print' function must be used for output when relevant.", + "When using code, you must indicate the script type in the code block. " + "Please don't include multiple code blocks in one response.", + "If you want the user to save the code in a file before executing it, " + "put # filename: inside the code block as the first line.", + "If you receive user input that indicates an error in the code " + "execution, fix the error and output the complete code again. It is " + "recommended to use the complete code rather than partial code or " + "code changes. If the error cannot be fixed, or the task is not " + "resolved even after the code executes successfully, analyze the " + "problem, revisit your assumptions, gather additional information you " + "need from historical conversation records, and consider trying a " + "different approach.", + "Unless necessary, give priority to solving problems with python " + "code. If it involves downloading files or storing data locally, " + "please use 'Print' to output the full file path of the stored data " + "and a brief introduction to the data.", + "The output content of the 'print' function will be passed to other " + "LLM agents as dependent data. Please control the length of the " + "output content of the 'print' function. The 'print' function only " + "outputs part of the key data information that is relied on, " + "and is as concise as possible.", + "The code is executed without user participation. It is forbidden to " + "use methods that will block the process or need to be shut down, " + "such as the plt.show() method of matplotlib.pyplot as plt.", + "It is prohibited to fabricate non-existent data to achieve goals.", + ], + category="agent", + key="dbgpt_agent_expand_code_assistant_agent_profile_constraints", + ), + desc=DynConfig( + "Can independently write and execute python/shell code to solve various" + " problems", + category="agent", + key="dbgpt_agent_expand_code_assistant_agent_profile_desc", + ), ) def __init__(self, **kwargs): diff --git a/dbgpt/agent/expand/dashboard_assistant_agent.py b/dbgpt/agent/expand/dashboard_assistant_agent.py index 337ead0e4..396eeaee8 100644 --- a/dbgpt/agent/expand/dashboard_assistant_agent.py +++ b/dbgpt/agent/expand/dashboard_assistant_agent.py @@ -1,37 +1,54 @@ """Dashboard Assistant Agent.""" -from typing import List - -from ..actions.dashboard_action import DashboardAction from ..core.agent import AgentMessage from ..core.base_agent import ConversableAgent +from ..core.profile import DynConfig, ProfileConfig from ..resource.resource_db_api import ResourceDbClient +from .actions.dashboard_action import DashboardAction class DashboardAssistantAgent(ConversableAgent): """Dashboard Assistant Agent.""" - name: str = "Visionary" - - profile: str = "Reporter" - goal: str = ( - "Read the provided historical messages, collect various analysis SQLs " - "from them, and assemble them into professional reports." - ) - constraints: List[str] = [ - "You are only responsible for collecting and sorting out the analysis SQL that" - " already exists in historical messages, and do not generate any analysis sql " - "yourself.", - "In order to build a report with rich display types, you can appropriately " - "adjust the display type of the charts you collect so that you can build a " - "better report. Of course, you can choose from the following available " - "display types: {display_type}", - "Please read and completely collect all analysis sql in the historical " - "conversation, and do not omit or modify the content of the analysis sql.", - ] - desc: str = ( - "Observe and organize various analysis results and construct " - "professional reports" + profile: ProfileConfig = ProfileConfig( + name=DynConfig( + "Visionary", + category="agent", + key="dbgpt_agent_expand_dashboard_assistant_agent_profile_name", + ), + role=DynConfig( + "Reporter", + category="agent", + key="dbgpt_agent_expand_dashboard_assistant_agent_profile_role", + ), + goal=DynConfig( + "Read the provided historical messages, collect various analysis SQLs " + "from them, and assemble them into professional reports.", + category="agent", + key="dbgpt_agent_expand_dashboard_assistant_agent_profile_goal", + ), + constraints=DynConfig( + [ + "You are only responsible for collecting and sorting out the analysis " + "SQL that already exists in historical messages, and do not generate " + "any analysis sql yourself.", + "In order to build a report with rich display types, you can " + "appropriately adjust the display type of the charts you collect so " + "that you can build a better report. Of course, you can choose from " + "the following available display types: {{ display_type }}", + "Please read and completely collect all analysis sql in the " + "historical conversation, and do not omit or modify the content of " + "the analysis sql.", + ], + category="agent", + key="dbgpt_agent_expand_dashboard_assistant_agent_profile_constraints", + ), + desc=DynConfig( + "Observe and organize various analysis results and construct " + "professional reports", + category="agent", + key="dbgpt_agent_expand_dashboard_assistant_agent_profile_desc", + ), ) def __init__(self, **kwargs): diff --git a/dbgpt/agent/expand/data_scientist_agent.py b/dbgpt/agent/expand/data_scientist_agent.py index cbe8150c8..b9bd50f7f 100644 --- a/dbgpt/agent/expand/data_scientist_agent.py +++ b/dbgpt/agent/expand/data_scientist_agent.py @@ -2,14 +2,15 @@ import json import logging -from typing import List, Optional, Tuple, cast +from typing import Optional, Tuple, cast -from ..actions.action import ActionOutput -from ..actions.chart_action import ChartAction +from ..core.action.base import ActionOutput from ..core.agent import AgentMessage from ..core.base_agent import ConversableAgent +from ..core.profile import DynConfig, ProfileConfig from ..resource.resource_api import ResourceType from ..resource.resource_db_api import ResourceDbClient +from .actions.chart_action import ChartAction logger = logging.getLogger(__name__) @@ -17,31 +18,53 @@ class DataScientistAgent(ConversableAgent): """Data Scientist Agent.""" - name: str = "Edgar" - profile: str = "DataScientist" - goal: str = ( - "Use correct {dialect} SQL to analyze and solve tasks based on the data" - " structure information of the database given in the resource." - ) - constraints: List[str] = [ - "Please check the generated SQL carefully. Please strictly abide by the data " - "structure definition given. It is prohibited to use non-existent fields and " - "data values. Do not use fields from table A to table B. You can perform " - "multi-table related queries.", - "If the data and fields that need to be analyzed in the target are in different" - " tables, it is recommended to use multi-table correlation queries first, and " - "pay attention to the correlation between multiple table structures.", - "It is forbidden to construct data by yourself as a query condition. If you " - "want to query a specific field, if the value of the field is provided, then " - "you can perform a group statistical query on the field.", - "Please select an appropriate one from the supported display methods for data " - "display. If no suitable display type is found, table display is used by " - "default. Supported display types: \n {display_type}", - ] - desc: str = ( - "Use database resources to conduct data analysis, analyze SQL, and " - "provide recommended rendering methods." + profile: ProfileConfig = ProfileConfig( + name=DynConfig( + "Edgar", + category="agent", + key="dbgpt_agent_expand_dashboard_assistant_agent_profile_name", + ), + role=DynConfig( + "DataScientist", + category="agent", + key="dbgpt_agent_expand_dashboard_assistant_agent_profile_role", + ), + goal=DynConfig( + "Use correct {{ dialect }} SQL to analyze and solve tasks based on the data" + " structure information of the database given in the resource.", + category="agent", + key="dbgpt_agent_expand_dashboard_assistant_agent_profile_goal", + ), + constraints=DynConfig( + [ + "Please check the generated SQL carefully. Please strictly abide by " + "the data structure definition given. It is prohibited to use " + "non-existent fields and data values. Do not use fields from table A " + "to table B. You can perform multi-table related queries.", + "If the data and fields that need to be analyzed in the target are in " + "different tables, it is recommended to use multi-table correlation " + "queries first, and pay attention to the correlation between multiple " + "table structures.", + "It is forbidden to construct data by yourself as a query condition. " + "If you want to query a specific field, if the value of the field is " + "provided, then you can perform a group statistical query on the " + "field.", + "Please select an appropriate one from the supported display methods " + "for data display. If no suitable display type is found, " + "table display is used by default. Supported display types: \n" + "{{ display_type }}", + ], + category="agent", + key="dbgpt_agent_expand_dashboard_assistant_agent_profile_constraints", + ), + desc=DynConfig( + "Use database resources to conduct data analysis, analyze SQL, and provide " + "recommended rendering methods.", + category="agent", + key="dbgpt_agent_expand_dashboard_assistant_agent_profile_desc", + ), ) + max_retry_count: int = 5 def __init__(self, **kwargs): diff --git a/dbgpt/agent/expand/plugin_assistant_agent.py b/dbgpt/agent/expand/plugin_assistant_agent.py index 9c7137d66..35e13eb87 100644 --- a/dbgpt/agent/expand/plugin_assistant_agent.py +++ b/dbgpt/agent/expand/plugin_assistant_agent.py @@ -1,12 +1,14 @@ """Plugin Assistant Agent.""" + import logging -from typing import Any, Dict, List, Optional +from typing import Any, Dict, Optional -from ..actions.plugin_action import PluginAction from ..core.base_agent import ConversableAgent +from ..core.profile import DynConfig, ProfileConfig from ..plugin.generator import PluginPromptGenerator from ..resource.resource_api import ResourceType from ..resource.resource_plugin_api import ResourcePluginClient +from .actions.plugin_action import PluginAction logger = logging.getLogger(__name__) @@ -16,23 +18,42 @@ class PluginAssistantAgent(ConversableAgent): plugin_generator: Optional[PluginPromptGenerator] = None - name: str = "LuBan" - profile: str = "ToolExpert" - goal: str = ( - "Read and understand the tool information given in the resources below to " - "understand their capabilities and how to use them,and choosing the right tools" - " to achieve the user's goals." - ) - constraints: List[str] = [ - "Please read the parameter definition of the tool carefully and extract the " - "specific parameters required to execute the tool from the user goal.", - "Please output the selected tool name and specific parameter information in " - "json format according to the following required format. If there is an " - "example, please refer to the sample format output.", - ] - desc: str = ( - "You can use the following tools to complete the task objectives, tool " - "information: {tool_infos}" + profile: ProfileConfig = ProfileConfig( + name=DynConfig( + "LuBan", + category="agent", + key="dbgpt_agent_expand_plugin_assistant_agent_name", + ), + role=DynConfig( + "ToolExpert", + category="agent", + key="dbgpt_agent_expand_plugin_assistant_agent_role", + ), + goal=DynConfig( + "Read and understand the tool information given in the resources " + "below to understand their capabilities and how to use them,and choosing " + "the right tools to achieve the user's goals.", + category="agent", + key="dbgpt_agent_expand_plugin_assistant_agent_goal", + ), + constraints=DynConfig( + [ + "Please read the parameter definition of the tool carefully and extract" + " the specific parameters required to execute the tool from the user " + "goal.", + "Please output the selected tool name and specific parameter " + "information in json format according to the following required format." + " If there is an example, please refer to the sample format output.", + ], + category="agent", + key="dbgpt_agent_expand_plugin_assistant_agent_constraints", + ), + desc=DynConfig( + "You can use the following tools to complete the task objectives, " + "tool information: {tool_infos}", + category="agent", + key="dbgpt_agent_expand_plugin_assistant_agent_desc", + ), ) def __init__(self, **kwargs): @@ -40,14 +61,14 @@ def __init__(self, **kwargs): super().__init__(**kwargs) self._init_actions([PluginAction]) - @property - def introduce(self, **kwargs) -> str: - """Introduce the agent.""" - if not self.plugin_generator: - raise ValueError("PluginGenerator is not loaded.") - return self.desc.format( - tool_infos=self.plugin_generator.generate_commands_string() - ) + # @property + # def introduce(self, **kwargs) -> str: + # """Introduce the agent.""" + # if not self.plugin_generator: + # raise ValueError("PluginGenerator is not loaded.") + # return self.desc.format( + # tool_infos=self.plugin_generator.generate_commands_string() + # ) async def preload_resource(self): """Preload the resource.""" diff --git a/dbgpt/agent/expand/retrieve_summary_assistant_agent.py b/dbgpt/agent/expand/retrieve_summary_assistant_agent.py index 9e1df8954..96005d5ca 100644 --- a/dbgpt/agent/expand/retrieve_summary_assistant_agent.py +++ b/dbgpt/agent/expand/retrieve_summary_assistant_agent.py @@ -1,4 +1,5 @@ """Retrieve Summary Assistant Agent.""" + import glob import json import logging @@ -9,9 +10,10 @@ from dbgpt.configs.model_config import PILOT_PATH from dbgpt.core import ModelMessageRoleType -from ..actions.action import Action, ActionOutput +from ..core.action.base import Action, ActionOutput from ..core.agent import Agent, AgentMessage, AgentReviewInfo from ..core.base_agent import ConversableAgent +from ..core.profile import ProfileConfig from ..resource.resource_api import AgentResource from ..util.cmp import cmp_string_equal @@ -86,18 +88,7 @@ class RetrieveSummaryAssistantAgent(ConversableAgent): including suggesting python code blocks and debugging. """ - goal = ( - "You're an extraction expert. You need to extract Please complete this task " - "step by step following instructions below:\n" - " 1. You need to first ONLY extract user's question that you need to answer " - "without ANY file paths and URLs. \n" - " 2. Extract the provided file paths and URLs.\n" - " 3. Construct the extracted file paths and URLs as a list of strings.\n" - " 4. ONLY output the extracted results with the following json format: " - "{response}." - ) - - PROMPT_QA = ( + PROMPT_QA: str = ( "You are a great summary writer to summarize the provided text content " "according to user questions.\n" "User's Question is: {input_question}\n\n" @@ -118,7 +109,7 @@ class RetrieveSummaryAssistantAgent(ConversableAgent): "If the provided text content CAN NOT ANSWER user's question, ONLY output " "'NO RELATIONSHIP.UPDATE TEXT CONTENT.'!!." ) - CHECK_RESULT_SYSTEM_MESSAGE = ( + CHECK_RESULT_SYSTEM_MESSAGE: str = ( "You are an expert in analyzing the results of a summary task." "Your responsibility is to check whether the summary results can summarize the " "input provided by the user, and then make a judgment. You need to answer " @@ -131,20 +122,30 @@ class RetrieveSummaryAssistantAgent(ConversableAgent): "not summarized. TERMINATE" ) - DEFAULT_DESCRIBE = ( + DEFAULT_DESCRIBE: str = ( "Summarize provided content according to user's questions and " "the provided file paths." ) - - name = "RetrieveSummarizer" - desc = DEFAULT_DESCRIBE + profile: ProfileConfig = ProfileConfig( + name="RetrieveSummarizer", + role="Assistant", + goal="You're an extraction expert. You need to extract Please complete this " + "task step by step following instructions below:\n" + " 1. You need to first ONLY extract user's question that you need to answer " + "without ANY file paths and URLs. \n" + " 2. Extract the provided file paths and URLs.\n" + " 3. Construct the extracted file paths and URLs as a list of strings.\n" + " 4. ONLY output the extracted results with the following json format: " + "{{ response }}.", + desc=DEFAULT_DESCRIBE, + ) chunk_token_size: int = 4000 chunk_mode: str = "multi_lines" - _model = "gpt-3.5-turbo-16k" - _max_tokens = _get_max_tokens(_model) - context_max_tokens = _max_tokens * 0.8 + _model: str = "gpt-3.5-turbo-16k" + _max_tokens: int = _get_max_tokens(_model) + context_max_tokens: int = int(_max_tokens * 0.8) def __init__( self, @@ -174,12 +175,14 @@ async def generate_reply( reply_message: AgentMessage = self._init_reply_message( received_message=received_message ) - await self._system_message_assembly( - received_message.content, reply_message.context - ) # 1.Think about how to do things llm_reply, model_name = await self.thinking( - self._load_thinking_messages(received_message, sender, rely_messages) + await self._load_thinking_messages( + received_message, + sender, + rely_messages, + context=reply_message.get_dict_context(), + ) ) if not llm_reply: @@ -454,16 +457,16 @@ async def _split_text_to_chunks( " set to False." ) must_break_at_empty_line = False - chunks.append(prev) if len( - prev - ) > 10 else None # don't add chunks less than 10 characters + ( + chunks.append(prev) if len(prev) > 10 else None + ) # don't add chunks less than 10 characters lines = lines[cnt:] lines_tokens = lines_tokens[cnt:] sum_tokens = sum(lines_tokens) text_to_chunk = "\n".join(lines) - chunks.append(text_to_chunk) if len( - text_to_chunk - ) > 10 else None # don't add chunks less than 10 characters + ( + chunks.append(text_to_chunk) if len(text_to_chunk) > 10 else None + ) # don't add chunks less than 10 characters return chunks def _extract_text_from_pdf(self, file: str) -> str: diff --git a/dbgpt/agent/expand/summary_assistant_agent.py b/dbgpt/agent/expand/summary_assistant_agent.py index 8f6521d81..5f2d8e423 100644 --- a/dbgpt/agent/expand/summary_assistant_agent.py +++ b/dbgpt/agent/expand/summary_assistant_agent.py @@ -1,9 +1,10 @@ """Summary Assistant Agent.""" + import logging -from typing import List -from ..actions.blank_action import BlankAction +from ..core.action.blank_action import BlankAction from ..core.base_agent import ConversableAgent +from ..core.profile import DynConfig, ProfileConfig logger = logging.getLogger(__name__) @@ -11,29 +12,48 @@ class SummaryAssistantAgent(ConversableAgent): """Summary Assistant Agent.""" - name: str = "Aristotle" - profile: str = "Summarizer" - goal: str = ( - "Summarize answer summaries based on user questions from provided " - "resource information or from historical conversation memories." - ) - - constraints: List[str] = [ - "Prioritize the summary of answers to user questions from the improved resource" - " text. If no relevant information is found, summarize it from the historical " - "dialogue memory given. It is forbidden to make up your own.", - "You need to first detect user's question that you need to answer with your" - " summarization.", - "Extract the provided text content used for summarization.", - "Then you need to summarize the extracted text content.", - "Output the content of summarization ONLY related to user's question. The " - "output language must be the same to user's question language.", - "If you think the provided text content is not related to user questions at " - "all, ONLY output 'Did not find the information you want.'!!.", - ] - desc: str = ( - "You can summarize provided text content according to user's questions" - " and output the summarization." + profile: ProfileConfig = ProfileConfig( + name=DynConfig( + "Aristotle", + category="agent", + key="dbgpt_agent_expand_summary_assistant_agent_profile_name", + ), + role=DynConfig( + "Summarizer", + category="agent", + key="dbgpt_agent_expand_summary_assistant_agent_profile_role", + ), + goal=DynConfig( + "Summarize answer summaries based on user questions from provided " + "resource information or from historical conversation memories.", + category="agent", + key="dbgpt_agent_expand_summary_assistant_agent_profile_goal", + ), + constraints=DynConfig( + [ + "Prioritize the summary of answers to user questions from the improved " + "resource text. If no relevant information is found, summarize it from " + "the historical dialogue memory given. It is forbidden to make up your " + "own.", + "You need to first detect user's question that you need to answer with " + "your summarization.", + "Extract the provided text content used for summarization.", + "Then you need to summarize the extracted text content.", + "Output the content of summarization ONLY related to user's question. " + "The output language must be the same to user's question language.", + "If you think the provided text content is not related to user " + "questions at all, ONLY output 'Did not find the information you " + "want.'!!.", + ], + category="agent", + key="dbgpt_agent_expand_summary_assistant_agent_profile_constraints", + ), + desc=DynConfig( + "You can summarize provided text content according to user's questions" + " and output the summarization.", + category="agent", + key="dbgpt_agent_expand_summary_assistant_agent_profile_desc", + ), ) def __init__(self, **kwargs): diff --git a/dbgpt/agent/memory/__init__.py b/dbgpt/agent/memory/__init__.py deleted file mode 100644 index 48d1d49b2..000000000 --- a/dbgpt/agent/memory/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Memory module for agents.""" diff --git a/dbgpt/agent/plan/planner_agent.py b/dbgpt/agent/plan/planner_agent.py deleted file mode 100644 index e9c7e6254..000000000 --- a/dbgpt/agent/plan/planner_agent.py +++ /dev/null @@ -1,138 +0,0 @@ -"""Planner Agent.""" - -from typing import Any, Dict, List - -from dbgpt._private.pydantic import Field - -from ..core.agent import AgentMessage -from ..core.base_agent import ConversableAgent -from .plan_action import PlanAction - - -class PlannerAgent(ConversableAgent): - """Planner Agent. - - Planner agent, realizing task goal planning decomposition through LLM. - """ - - agents: List[ConversableAgent] = Field(default_factory=list) - - profile: str = "Planner" - goal_zh: str = ( - "理解下面每个智能体(agent)和他们的能力，使用给出的资源，通过协调智能体来解决" - "用户问题。 请发挥你LLM的知识和理解能力，理解用户问题的意图和目标，生成一个可以在没有用户帮助" - "下，由智能体协作完成目标的任务计划。" - ) - goal: str = ( - "Understand each of the following intelligent agents and their " - "capabilities, using the provided resources, solve user problems by " - "coordinating intelligent agents. Please utilize your LLM's knowledge " - "and understanding ability to comprehend the intent and goals of the " - "user's problem, generating a task plan that can be completed through" - " the collaboration of intelligent agents without user assistance." - ) - expand_prompt_zh: str = "可用智能体(agent):\n {agents}" - expand_prompt: str = "Available Intelligent Agents:\n {agents}" - - constraints_zh: List[str] = [ - "任务计划的每个步骤都应该是为了推进解决用户目标而存在，不要生成无意义的任务步骤，确保每个步骤内目标明确内容完整。", - "关注任务计划每个步骤的依赖关系和逻辑，被依赖步骤要考虑被依赖的数据，是否能基于当前目标得到，如果不能请在目标中提示要生成被依赖数据。", - "每个步骤都是一个独立可完成的目标，一定要确保逻辑和信息完整，不要出现类似:" - "'Analyze the retrieved issues data'这样目标不明确，不知道具体要分析啥内容的步骤", - "请确保只使用上面提到的智能体，并且可以只使用其中需要的部分，严格根据描述能力和限制分配给合适的步骤，每个智能体都可以重复使用。", - "根据用户目标的实际需要使用提供的资源来协助生成计划步骤，不要使用不需要的资源。", - "每个步骤最好只使用一种资源完成一个子目标，如果当前目标可以分解为同类型的多个子任务，可以生成相互不依赖的并行任务。", - "数据资源可以被合适的智能体加载使用，不用考虑数据资源的加载链接问题", - "尽量合并有顺序依赖的连续相同步骤,如果用户目标无拆分必要，可以生成内容为用户目标的单步任务。", - "仔细检查计划，确保计划完整的包含了用户问题所涉及的所有信息，并且最终能完成目标，确认每个步骤是否包含了需要用到的资源信息,如URL、资源名等. ", - ] - constraints: List[str] = [ - "Every step of the task plan should exist to advance towards solving the user's" - " goals. Do not generate meaningless task steps; ensure that each step has a " - "clear goal and its content is complete.", - "Pay attention to the dependencies and logic of each step in the task plan. " - "For the steps that are depended upon, consider the data they depend on and " - "whether it can be obtained based on the current goal. If it cannot be obtained" - ", please indicate in the goal that the dependent data needs to be generated.", - "Each step must be an independently achievable goal. Ensure that the logic and" - " information are complete. Avoid steps with unclear objectives, like " - "'Analyze the retrieved issues data,' where it's unclear what specific content" - " needs to be analyzed.", - "Please ensure that only the intelligent agents mentioned above are used, and" - " you may use only the necessary parts of them. Allocate them to appropriate " - "steps strictly based on their described capabilities and limitations. Each " - "intelligent agent can be reused.", - "Utilize the provided resources to assist in generating the plan steps " - "according to the actual needs of the user's goals. Do not use unnecessary " - "resources.", - "Each step should ideally use only one type of resource to accomplish a " - "sub-goal. If the current goal can be broken down into multiple subtasks of the" - " same type, you can create mutually independent parallel tasks.", - "Data resources can be loaded and utilized by the appropriate intelligent " - "agents without the need to consider the issues related to data loading links.", - "Try to merge continuous steps that have sequential dependencies. If the " - "user's goal does not require splitting, you can create a single-step task with" - " content that is the user's goal.", - "Carefully review the plan to ensure it comprehensively covers all information" - " involved in the user's problem and can ultimately achieve the goal. Confirm" - " whether each step includes the necessary resource information, such as URLs," - " resource names, etc.", - ] - desc_zh: str = "你是一个任务规划专家！可以协调智能体，分配资源完成复杂的任务目标。" - desc: str = ( - "You are a task planning expert! You can coordinate intelligent agents" - " and allocate resources to achieve complex task goals." - ) - - examples: str = """ - user:help me build a sales report summarizing our key metrics and trends - assistants:[ - {{ - "serial_number": "1", - "agent": "DataScientist", - "content": "Retrieve total sales, average sales, and number of transactions grouped by "product_category"'.", - "rely": "" - }}, - {{ - "serial_number": "2", - "agent": "DataScientist", - "content": "Retrieve monthly sales and transaction number trends.", - "rely": "" - }}, - {{ - "serial_number": "3", - "agent": "Reporter", - "content": "Integrate analytical data into the format required to build sales reports.", - "rely": "1,2" - }} - ] - """ # noqa: E501 - - def __init__(self, **kwargs): - """Create a new PlannerAgent instance.""" - super().__init__(**kwargs) - self._init_actions([PlanAction]) - - def _init_reply_message(self, received_message: AgentMessage): - reply_message = super()._init_reply_message(received_message) - reply_message.context = { - "agents": "\n".join( - [f"- {item.profile}:{item.desc}" for item in self.agents] - ), - } - return reply_message - - def bind_agents(self, agents: List[ConversableAgent]) -> ConversableAgent: - """Bind the agents to the planner agent.""" - self.agents = agents - for agent in self.agents: - if agent.resources and len(agent.resources) > 0: - self.resources.extend(agent.resources) - return self - - def prepare_act_param(self) -> Dict[str, Any]: - """Prepare the parameters for the act method.""" - return { - "context": self.not_null_agent_context, - "plans_memory": self.memory.plans_memory, - } diff --git a/dbgpt/agent/plugin/commands/command.py b/dbgpt/agent/plugin/commands/command.py index a9fc08498..f3f79b114 100644 --- a/dbgpt/agent/plugin/commands/command.py +++ b/dbgpt/agent/plugin/commands/command.py @@ -70,7 +70,7 @@ def execute_ai_response_json( def execute_command( command_name: str, - arguments, + arguments: Dict[str, Any], plugin_generator: PluginPromptGenerator, ) -> Any: """Execute the command and return the result. @@ -78,6 +78,7 @@ def execute_command( Args: command_name (str): The name of the command to execute arguments (dict): The arguments for the command + plugin_generator (PluginPromptGenerator): The plugin generator Returns: str: The result of the command @@ -103,18 +104,23 @@ def execute_command( else: for command in plugin_generator.commands: if ( - command_name == command["label"].lower() - or command_name == command["name"].lower() + command_name == command.label.lower() + or command_name == command.name.lower() ): try: - # 删除非定义参数 + # Delete non-defined parameters diff_ags = list( - set(arguments.keys()).difference(set(command["args"].keys())) + set(arguments.keys()).difference(set(command.args.keys())) ) for arg_name in diff_ags: del arguments[arg_name] print(str(arguments)) - return command["function"](**arguments) + func = command.function + if not func: + raise ExecutionCommandException( + f"Function not found for command: {command_name}" + ) + return func(**arguments) except Exception as e: raise ExecutionCommandException(f"Execution error: {str(e)}") raise NotCommandException("Invalid command: " + command_name) diff --git a/dbgpt/agent/plugin/generator.py b/dbgpt/agent/plugin/generator.py index ebd6e1562..709f7c455 100644 --- a/dbgpt/agent/plugin/generator.py +++ b/dbgpt/agent/plugin/generator.py @@ -1,5 +1,34 @@ """A module for generating custom prompt strings.""" -from typing import Any, Callable, Dict, List, Optional +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional + +from dbgpt._private.pydantic import BaseModel, Field + +if TYPE_CHECKING: + from .commands.command_manage import CommandRegistry + + +class CommandEntry(BaseModel): + """CommandEntry class. + + A class for storing information about a command. + """ + + label: str = Field( + ..., + description="The label of the command.", + ) + name: str = Field( + ..., + description="The name of the command.", + ) + args: Dict[str, Any] = Field( + default_factory=dict, + description="A dictionary containing argument names and their values.", + ) + function: Optional[Callable] = Field( + None, + description="A callable function to be called when the command is executed.", + ) class PluginPromptGenerator: @@ -9,7 +38,7 @@ class PluginPromptGenerator: resources, and performance evaluations. """ - def __init__(self) -> None: + def __init__(self): """Create a new PromptGenerator object. Initialize the PromptGenerator object with empty lists of constraints, @@ -17,11 +46,44 @@ def __init__(self) -> None: """ from .commands.command_manage import CommandRegistry - self.constraints: List[str] = [] - self.commands: List[Dict[str, Any]] = [] - self.resources: List[str] = [] - self.performance_evaluation: List[str] = [] - self.command_registry: CommandRegistry = CommandRegistry() + self._constraints: List[str] = [] + self._commands: List[CommandEntry] = [] + self._resources: List[str] = [] + self._performance_evaluation: List[str] = [] + self._command_registry: CommandRegistry = CommandRegistry() + + @property + def constraints(self) -> List[str]: + """Return the list of constraints.""" + return self._constraints + + @property + def commands(self) -> List[CommandEntry]: + """Return the list of commands.""" + return self._commands + + @property + def resources(self) -> List[str]: + """Return the list of resources.""" + return self._resources + + @property + def performance_evaluation(self) -> List[str]: + """Return the list of performance evaluations.""" + return self._performance_evaluation + + @property + def command_registry(self) -> "CommandRegistry": + """Return the command registry.""" + return self._command_registry + + def set_command_registry(self, command_registry: "CommandRegistry") -> None: + """Set the command registry. + + Args: + command_registry: CommandRegistry + """ + self._command_registry = command_registry def add_constraint(self, constraint: str) -> None: """Add a constraint to the constraints list. @@ -29,13 +91,13 @@ def add_constraint(self, constraint: str) -> None: Args: constraint (str): The constraint to be added. """ - self.constraints.append(constraint) + self._constraints.append(constraint) def add_command( self, command_label: str, command_name: str, - args=None, + args: Optional[Dict[str, Any]] = None, function: Optional[Callable] = None, ) -> None: """Add a command to the commands. @@ -55,16 +117,15 @@ def add_command( command_args = {arg_key: arg_value for arg_key, arg_value in args.items()} - command = { - "label": command_label, - "name": command_name, - "args": command_args, - "function": function, - } - - self.commands.append(command) + command = CommandEntry( + label=command_label, + name=command_name, + args=command_args, + function=function, + ) + self._commands.append(command) - def _generate_command_string(self, command: Dict[str, Any]) -> str: + def _generate_command_string(self, command: CommandEntry) -> str: """ Generate a formatted string representation of a command. @@ -75,9 +136,9 @@ def _generate_command_string(self, command: Dict[str, Any]) -> str: str: The formatted command string. """ args_string = ", ".join( - f'"{key}": "{value}"' for key, value in command["args"].items() + f'"{key}": "{value}"' for key, value in command.args.items() ) - return f'"{command["name"]}": {command["label"]} , args: {args_string}' + return f'"{command.name}": {command.label} , args: {args_string}' def add_resource(self, resource: str) -> None: """ @@ -86,7 +147,7 @@ def add_resource(self, resource: str) -> None: Args: resource (str): The resource to be added. """ - self.resources.append(resource) + self._resources.append(resource) def add_performance_evaluation(self, evaluation: str) -> None: """ @@ -95,7 +156,7 @@ def add_performance_evaluation(self, evaluation: str) -> None: Args: evaluation (str): The evaluation item to be added. """ - self.performance_evaluation.append(evaluation) + self._performance_evaluation.append(evaluation) def _generate_numbered_list(self, items: List[Any], item_type="list") -> str: """ @@ -111,10 +172,10 @@ def _generate_numbered_list(self, items: List[Any], item_type="list") -> str: """ if item_type == "command": command_strings = [] - if self.command_registry: + if self._command_registry: command_strings += [ str(item) - for item in self.command_registry.commands.values() + for item in self._command_registry.commands.values() if item.enabled ] # terminate command is added manually @@ -125,4 +186,4 @@ def _generate_numbered_list(self, items: List[Any], item_type="list") -> str: def generate_commands_string(self) -> str: """Return a formatted string representation of the commands list.""" - return f"{self._generate_numbered_list(self.commands, item_type='command')}" + return f"{self._generate_numbered_list(self._commands, item_type='command')}" diff --git a/dbgpt/agent/resource/resource_plugin_api.py b/dbgpt/agent/resource/resource_plugin_api.py index e373fb156..e6c69210a 100644 --- a/dbgpt/agent/resource/resource_plugin_api.py +++ b/dbgpt/agent/resource/resource_plugin_api.py @@ -1,7 +1,7 @@ """Resource plugin client API.""" import logging import os -from typing import List, Optional, Union, cast +from typing import Any, Dict, List, Optional, Union, cast from ..plugin.commands.command_manage import execute_command from ..plugin.generator import PluginPromptGenerator @@ -48,8 +48,8 @@ async def plugins_prompt( async def execute_command( self, command_name: str, - arguments: Optional[dict], - plugin_generator: Optional[PluginPromptGenerator], + arguments: Dict[str, Any], + plugin_generator: PluginPromptGenerator, ): """Execute the command.""" if plugin_generator is None: diff --git a/dbgpt/agent/core/llm/__init__.py b/dbgpt/agent/util/llm/__init__.py similarity index 100% rename from dbgpt/agent/core/llm/__init__.py rename to dbgpt/agent/util/llm/__init__.py diff --git a/dbgpt/agent/core/llm/llm.py b/dbgpt/agent/util/llm/llm.py similarity index 100% rename from dbgpt/agent/core/llm/llm.py rename to dbgpt/agent/util/llm/llm.py diff --git a/dbgpt/agent/core/llm/llm_client.py b/dbgpt/agent/util/llm/llm_client.py similarity index 100% rename from dbgpt/agent/core/llm/llm_client.py rename to dbgpt/agent/util/llm/llm_client.py diff --git a/dbgpt/agent/core/llm/strategy/__init__.py b/dbgpt/agent/util/llm/strategy/__init__.py similarity index 100% rename from dbgpt/agent/core/llm/strategy/__init__.py rename to dbgpt/agent/util/llm/strategy/__init__.py diff --git a/dbgpt/agent/core/llm/strategy/priority.py b/dbgpt/agent/util/llm/strategy/priority.py similarity index 100% rename from dbgpt/agent/core/llm/strategy/priority.py rename to dbgpt/agent/util/llm/strategy/priority.py diff --git a/dbgpt/app/scene/chat_agent/chat.py b/dbgpt/app/scene/chat_agent/chat.py index a41782c54..64be6c34f 100644 --- a/dbgpt/app/scene/chat_agent/chat.py +++ b/dbgpt/app/scene/chat_agent/chat.py @@ -35,8 +35,8 @@ def __init__(self, chat_param: Dict): chat_param["chat_mode"] = ChatScene.ChatAgent super().__init__(chat_param=chat_param) - self.plugins_prompt_generator = PluginPromptGenerator() - self.plugins_prompt_generator.command_registry = CFG.command_registry + self.plugins_prompt_generator: PluginPromptGenerator = PluginPromptGenerator() + self.plugins_prompt_generator.set_command_registry(CFG.command_registry) # load select plugin agent_module = CFG.SYSTEM_APP.get_component( diff --git a/dbgpt/app/scene/chat_execution/chat.py b/dbgpt/app/scene/chat_execution/chat.py index 44b9e586e..a7dc5c3b2 100644 --- a/dbgpt/app/scene/chat_execution/chat.py +++ b/dbgpt/app/scene/chat_execution/chat.py @@ -29,7 +29,7 @@ def __init__(self, chat_param: Dict): chat_param["chat_mode"] = ChatScene.ChatExecution super().__init__(chat_param=chat_param) self.plugins_prompt_generator = PluginPromptGenerator() - self.plugins_prompt_generator.command_registry = CFG.command_registry + self.plugins_prompt_generator.set_command_registry(CFG.command_registry) # 加载插件中可用命令 self.select_plugin = self.plugin_selector if self.select_plugin: diff --git a/dbgpt/core/awel/flow/compat.py b/dbgpt/core/awel/flow/compat.py index 1fa7df10b..435139181 100644 --- a/dbgpt/core/awel/flow/compat.py +++ b/dbgpt/core/awel/flow/compat.py @@ -5,8 +5,9 @@ _COMPAT_FLOW_MAPPING: Dict[str, str] = {} -_OLD_AGENT_RESOURCE_MODULE = "dbgpt.serve.agent.team.layout.agent_operator_resource" -_NEW_AGENT_RESOURCE_MODULE = "dbgpt.agent.plan.awel.agent_operator_resource" +_OLD_AGENT_RESOURCE_MODULE_1 = "dbgpt.serve.agent.team.layout.agent_operator_resource" +_OLD_AGENT_RESOURCE_MODULE_2 = "dbgpt.agent.plan.awel.agent_operator_resource" +_NEW_AGENT_RESOURCE_MODULE = "dbgpt.agent.core.plan.awel.agent_operator_resource" def _register( @@ -24,17 +25,32 @@ def get_new_class_name(old_class_name: str) -> Optional[str]: _register( - _OLD_AGENT_RESOURCE_MODULE, + _OLD_AGENT_RESOURCE_MODULE_1, _NEW_AGENT_RESOURCE_MODULE, "AwelAgentResource", "AWELAgentResource", ) _register( - _OLD_AGENT_RESOURCE_MODULE, + _OLD_AGENT_RESOURCE_MODULE_2, + _NEW_AGENT_RESOURCE_MODULE, + "AWELAgentResource", +) +_register( + _OLD_AGENT_RESOURCE_MODULE_1, _NEW_AGENT_RESOURCE_MODULE, "AwelAgentConfig", "AWELAgentConfig", ) _register( - _OLD_AGENT_RESOURCE_MODULE, _NEW_AGENT_RESOURCE_MODULE, "AwelAgent", "AWELAgent" + _OLD_AGENT_RESOURCE_MODULE_2, + _NEW_AGENT_RESOURCE_MODULE, + "AWELAgentConfig", + "AWELAgentConfig", +) +_register( + _OLD_AGENT_RESOURCE_MODULE_1, _NEW_AGENT_RESOURCE_MODULE, "AwelAgent", "AWELAgent" +) + +_register( + _OLD_AGENT_RESOURCE_MODULE_2, _NEW_AGENT_RESOURCE_MODULE, "AWELAgent", "AWELAgent" ) diff --git a/dbgpt/rag/retriever/base.py b/dbgpt/rag/retriever/base.py index e74fde706..14647c089 100644 --- a/dbgpt/rag/retriever/base.py +++ b/dbgpt/rag/retriever/base.py @@ -1,7 +1,7 @@ """Base retriever module.""" from abc import ABC, abstractmethod from enum import Enum -from typing import List, Optional +from typing import Any, Dict, List, Optional from dbgpt.core import Chunk from dbgpt.storage.vector_store.filters import MetadataFilters @@ -24,6 +24,15 @@ class RetrieverStrategy(str, Enum): class BaseRetriever(ABC): """Base retriever.""" + def load_document(self, chunks: List[Chunk], **kwargs: Dict[str, Any]) -> List[str]: + """Load document in vector database. + + Args: + - chunks: document chunks. + Return chunk ids. + """ + raise NotImplementedError + def retrieve( self, query: str, filters: Optional[MetadataFilters] = None ) -> List[Chunk]: diff --git a/dbgpt/rag/retriever/embedding.py b/dbgpt/rag/retriever/embedding.py index a8a784190..e2be7b7be 100644 --- a/dbgpt/rag/retriever/embedding.py +++ b/dbgpt/rag/retriever/embedding.py @@ -1,6 +1,6 @@ """Embedding retriever.""" from functools import reduce -from typing import List, Optional, cast +from typing import Any, Dict, List, Optional, cast from dbgpt.core import Chunk from dbgpt.rag.retriever.base import BaseRetriever @@ -66,6 +66,16 @@ def __init__( self._vector_store_connector = vector_store_connector self._rerank = rerank or DefaultRanker(self._top_k) + def load_document(self, chunks: List[Chunk], **kwargs: Dict[str, Any]) -> List[str]: + """Load document in vector database. + + Args: + chunks (List[Chunk]): document chunks. + Return: + List[str]: chunk ids. + """ + return self._vector_store_connector.load_document(chunks) + def _retrieve( self, query: str, filters: Optional[MetadataFilters] = None ) -> List[Chunk]: diff --git a/dbgpt/rag/retriever/time_weighted.py b/dbgpt/rag/retriever/time_weighted.py new file mode 100644 index 000000000..722db6c69 --- /dev/null +++ b/dbgpt/rag/retriever/time_weighted.py @@ -0,0 +1,137 @@ +"""Time weighted retriever.""" + +import datetime +from copy import deepcopy +from typing import Any, Dict, List, Optional, Tuple + +from dbgpt.core import Chunk +from dbgpt.rag.retriever.rerank import Ranker +from dbgpt.rag.retriever.rewrite import QueryRewrite +from dbgpt.storage.vector_store.connector import VectorStoreConnector +from dbgpt.storage.vector_store.filters import MetadataFilters + +from .embedding import EmbeddingRetriever + + +def _get_hours_passed(time: datetime.datetime, ref_time: datetime.datetime) -> float: + """Get the hours passed between two datetime objects.""" + return (time - ref_time).total_seconds() / 3600 + + +class TimeWeightedEmbeddingRetriever(EmbeddingRetriever): + """Time weighted embedding retriever.""" + + def __init__( + self, + vector_store_connector: VectorStoreConnector, + top_k: int = 100, + query_rewrite: Optional[QueryRewrite] = None, + rerank: Optional[Ranker] = None, + decay_rate: float = 0.01, + ): + """Initialize TimeWeightedEmbeddingRetriever. + + Args: + vector_store_connector (VectorStoreConnector): vector store connector + top_k (int): top k + query_rewrite (Optional[QueryRewrite]): query rewrite + rerank (Ranker): rerank + """ + super().__init__( + vector_store_connector=vector_store_connector, + top_k=top_k, + query_rewrite=query_rewrite, + rerank=rerank, + ) + self.memory_stream: List[Chunk] = [] + self.other_score_keys: List[str] = [] + self.decay_rate: float = decay_rate + self.default_salience: Optional[float] = None + self._top_k = top_k + self._k = 4 + + def load_document(self, chunks: List[Chunk], **kwargs: Dict[str, Any]) -> List[str]: + """Load document in vector database. + + Args: + - chunks: document chunks. + Return chunk ids. + """ + current_time: Optional[datetime.datetime] = kwargs.get("current_time") # type: ignore # noqa + if current_time is None: + current_time = datetime.datetime.now() + # Avoid mutating input documents + dup_docs = [deepcopy(d) for d in chunks] + for i, doc in enumerate(dup_docs): + if "last_accessed_at" not in doc.metadata: + doc.metadata["last_accessed_at"] = current_time + if "created_at" not in doc.metadata: + doc.metadata["created_at"] = current_time + doc.metadata["buffer_idx"] = len(self.memory_stream) + i + self.memory_stream.extend(dup_docs) + return self._vector_store_connector.load_document(dup_docs) + + def _retrieve( + self, query: str, filters: Optional[MetadataFilters] = None + ) -> List[Chunk]: + """Retrieve knowledge chunks. + + Args: + query (str): query text + filters: metadata filters. + Return: + List[Chunk]: list of chunks + """ + current_time = datetime.datetime.now() + docs_and_scores = { + doc.metadata["buffer_idx"]: (doc, self.default_salience) + for doc in self.memory_stream[-self._k :] + } + # If a doc is considered salient, update the salience score + docs_and_scores.update(self.get_salient_docs(query)) + rescored_docs = [ + (doc, self._get_combined_score(doc, relevance, current_time)) + for doc, relevance in docs_and_scores.values() + ] + rescored_docs.sort(key=lambda x: x[1], reverse=True) + result = [] + # Ensure frequently accessed memories aren't forgotten + for doc, _ in rescored_docs[: self._k]: + # TODO: Update vector store doc once `update` method is exposed. + buffered_doc = self.memory_stream[doc.metadata["buffer_idx"]] + buffered_doc.metadata["last_accessed_at"] = current_time + result.append(buffered_doc) + return result + + def _get_combined_score( + self, + chunk: Chunk, + vector_relevance: Optional[float], + current_time: datetime.datetime, + ) -> float: + """Return the combined score for a document.""" + hours_passed = _get_hours_passed( + current_time, + chunk.metadata["last_accessed_at"], + ) + score = (1.0 - self.decay_rate) ** hours_passed + for key in self.other_score_keys: + if key in chunk.metadata: + score += chunk.metadata[key] + if vector_relevance is not None: + score += vector_relevance + return score + + def get_salient_docs(self, query: str) -> Dict[int, Tuple[Chunk, float]]: + """Return documents that are salient to the query.""" + docs_and_scores: List[Chunk] + docs_and_scores = self._vector_store_connector.similar_search_with_scores( + query, topk=self._top_k, score_threshold=0 + ) + results = {} + for ck in docs_and_scores: + if "buffer_idx" in ck.metadata: + buffer_idx = ck.metadata["buffer_idx"] + doc = self.memory_stream[buffer_idx] + results[buffer_idx] = (doc, ck.score) + return results diff --git a/dbgpt/serve/agent/agents/controller.py b/dbgpt/serve/agent/agents/controller.py index 0dbad248e..a483b3a04 100644 --- a/dbgpt/serve/agent/agents/controller.py +++ b/dbgpt/serve/agent/agents/controller.py @@ -12,13 +12,13 @@ from dbgpt.agent.core.agent import Agent, AgentContext from dbgpt.agent.core.agent_manage import get_agent_manager from dbgpt.agent.core.base_agent import ConversableAgent -from dbgpt.agent.core.llm.llm import LLMConfig, LLMStrategyType +from dbgpt.agent.core.memory.agent_memory import AgentMemory +from dbgpt.agent.core.memory.gpts.gpts_memory import GptsMemory +from dbgpt.agent.core.plan import AutoPlanChatManager, DefaultAWELLayoutManager from dbgpt.agent.core.schema import Status from dbgpt.agent.core.user_proxy_agent import UserProxyAgent -from dbgpt.agent.memory.gpts_memory import GptsMemory -from dbgpt.agent.plan.awel.team_awel_layout import DefaultAWELLayoutManager -from dbgpt.agent.plan.team_auto_plan import AutoPlanChatManager from dbgpt.agent.resource.resource_loader import ResourceLoader +from dbgpt.agent.util.llm.llm import LLMConfig, LLMStrategyType from dbgpt.app.openapi.api_view_model import Result from dbgpt.app.scene.base import ChatScene from dbgpt.component import BaseComponent, ComponentType, SystemApp @@ -82,6 +82,39 @@ def __init__(self): plans_memory=MetaDbGptsPlansMemory(), message_memory=MetaDbGptsMessageMemory(), ) + self.agent_memory_map = {} + super().__init__() + + def get_or_build_agent_memory(self, conv_id: str, dbgpts_name: str) -> AgentMemory: + from dbgpt.agent.core.memory.agent_memory import ( + AgentMemory, + AgentMemoryFragment, + ) + from dbgpt.agent.core.memory.hybrid import HybridMemory + from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG + from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory + from dbgpt.storage.vector_store.base import VectorStoreConfig + from dbgpt.storage.vector_store.connector import VectorStoreConnector + + memory_key = f"{dbgpts_name}_{conv_id}" + if memory_key in self.agent_memory_map: + return self.agent_memory_map[memory_key] + + embedding_factory = EmbeddingFactory.get_instance(CFG.SYSTEM_APP) + embedding_fn = embedding_factory.create( + model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] + ) + vstore_name = f"_chroma_agent_memory_{dbgpts_name}_{conv_id}" + vector_store_connector = VectorStoreConnector( + vector_store_type=CFG.VECTOR_STORE_TYPE, + vector_store_config=VectorStoreConfig( + name=vstore_name, embedding_fn=embedding_fn + ), + ) + memory = HybridMemory[AgentMemoryFragment].from_vstore(vector_store_connector) + agent_memory = AgentMemory(memory, gpts_memory=self.memory) + self.agent_memory_map[memory_key] = agent_memory + return agent_memory def gpts_create(self, entity: GptsInstanceEntity): self.gpts_intance.add(entity) @@ -101,6 +134,7 @@ async def agent_chat( user_query: str, user_code: str = None, sys_code: str = None, + agent_memory: Optional[AgentMemory] = None, ): gpt_app: GptsApp = self.gpts_app.app_detail(gpts_name) @@ -124,7 +158,7 @@ async def agent_chat( task = asyncio.create_task( multi_agents.agent_team_chat_new( - user_query, agent_conv_id, gpt_app, is_retry_chat + user_query, agent_conv_id, gpt_app, is_retry_chat, agent_memory ) ) @@ -170,9 +204,15 @@ async def app_agent_chat( agent_conv_id = conv_uid + "_" + str(current_message.chat_order) agent_task = None try: + agent_memory = self.get_or_build_agent_memory(conv_uid, gpts_name) agent_conv_id = conv_uid + "_" + str(current_message.chat_order) async for task, chunk in multi_agents.agent_chat( - agent_conv_id, gpts_name, user_query, user_code, sys_code + agent_conv_id, + gpts_name, + user_query, + user_code, + sys_code, + agent_memory, ): agent_task = task yield chunk @@ -200,6 +240,7 @@ async def agent_team_chat_new( conv_uid: str, gpts_app: GptsApp, is_retry_chat: bool = False, + agent_memory: Optional[AgentMemory] = None, ): employees: List[Agent] = [] # Prepare resource loader @@ -235,10 +276,10 @@ async def agent_team_chat_new( agent = ( await cls() .bind(context) - .bind(self.memory) .bind(llm_config) .bind(record.resources) .bind(resource_loader) + .bind(agent_memory) .build() ) employees.append(agent) @@ -256,9 +297,9 @@ async def agent_team_chat_new( raise ValueError(f"Unknown Agent Team Mode!{team_mode}") manager = ( await manager.bind(context) - .bind(self.memory) .bind(llm_config) .bind(resource_loader) + .bind(agent_memory) .build() ) manager.hire(employees) @@ -267,8 +308,8 @@ async def agent_team_chat_new( user_proxy: UserProxyAgent = ( await UserProxyAgent() .bind(context) - .bind(self.memory) .bind(resource_loader) + .bind(agent_memory) .build() ) if is_retry_chat: diff --git a/dbgpt/serve/agent/agents/db_gpts_memory.py b/dbgpt/serve/agent/agents/db_gpts_memory.py index 58af325ce..a73fca0d7 100644 --- a/dbgpt/serve/agent/agents/db_gpts_memory.py +++ b/dbgpt/serve/agent/agents/db_gpts_memory.py @@ -1,7 +1,7 @@ from typing import List, Optional -from dbgpt.agent.memory.base import GptsPlan -from dbgpt.agent.memory.gpts_memory import ( +from dbgpt.agent.core.memory.gpts.base import GptsPlan +from dbgpt.agent.core.memory.gpts.gpts_memory import ( GptsMessage, GptsMessageMemory, GptsPlansMemory, diff --git a/dbgpt/serve/agent/app/controller.py b/dbgpt/serve/agent/app/controller.py index 4a914bc34..8a56361d5 100644 --- a/dbgpt/serve/agent/app/controller.py +++ b/dbgpt/serve/agent/app/controller.py @@ -4,8 +4,8 @@ from dbgpt._private.config import Config from dbgpt.agent.core.agent_manage import get_agent_manager -from dbgpt.agent.core.llm.llm import LLMStrategyType from dbgpt.agent.resource.resource_api import ResourceType +from dbgpt.agent.util.llm.llm import LLMStrategyType from dbgpt.app.knowledge.api import knowledge_space_service from dbgpt.app.knowledge.request.request import KnowledgeSpaceRequest from dbgpt.app.openapi.api_view_model import Result @@ -69,11 +69,11 @@ async def all_agents(): return Result.failed(code="E000X", msg=f"query agents error: {ex}") -@router.post("/v1/app/remove", response_model=Result[str]) +@router.post("/v1/app/remove") async def delete(gpts_app: GptsApp): try: gpts_dao.delete(gpts_app.app_code, gpts_app.user_code, gpts_app.sys_code) - return Result.succ([]) + return Result.succ(None) except Exception as ex: return Result.failed(code="E000X", msg=f"delete app error: {ex}") diff --git a/dbgpt/serve/agent/db/gpts_app.py b/dbgpt/serve/agent/db/gpts_app.py index b2d00a69d..a563b65a9 100644 --- a/dbgpt/serve/agent/db/gpts_app.py +++ b/dbgpt/serve/agent/db/gpts_app.py @@ -14,7 +14,7 @@ model_to_json, model_validator, ) -from dbgpt.agent.plan.awel.team_awel_layout import AWELTeamContext +from dbgpt.agent.core.plan import AWELTeamContext from dbgpt.agent.resource.resource_api import AgentResource from dbgpt.serve.agent.team.base import TeamMode from dbgpt.storage.metadata import BaseDao, Model diff --git a/dbgpt/serve/agent/resource_loader/plugin_hub_load_client.py b/dbgpt/serve/agent/resource_loader/plugin_hub_load_client.py index f98650896..009fc69aa 100644 --- a/dbgpt/serve/agent/resource_loader/plugin_hub_load_client.py +++ b/dbgpt/serve/agent/resource_loader/plugin_hub_load_client.py @@ -28,7 +28,7 @@ async def load_plugin( logger.info(f"PluginHubLoadClient load plugin:{value}") if plugin_generator is None: plugin_generator = PluginPromptGenerator() - plugin_generator.command_registry = CFG.command_registry + plugin_generator.set_command_registry(CFG.command_registry) agent_module = CFG.SYSTEM_APP.get_component( ComponentType.PLUGIN_HUB, ModulePlugin diff --git a/dbgpt/storage/vector_store/connector.py b/dbgpt/storage/vector_store/connector.py index 3bc062c4b..5ba0a8a39 100644 --- a/dbgpt/storage/vector_store/connector.py +++ b/dbgpt/storage/vector_store/connector.py @@ -1,9 +1,10 @@ """Connector for vector store.""" +import copy import os from typing import Any, Dict, List, Optional, Type, cast -from dbgpt.core import Chunk +from dbgpt.core import Chunk, Embeddings from dbgpt.core.awel.flow import ( FunctionDynamicOptions, OptionValue, @@ -11,7 +12,6 @@ ResourceCategory, register_resource, ) -from dbgpt.storage import vector_store from dbgpt.storage.vector_store.base import VectorStoreBase, VectorStoreConfig from dbgpt.storage.vector_store.filters import MetadataFilters from dbgpt.util.i18n_utils import _ @@ -20,6 +20,8 @@ def _load_vector_options() -> List[OptionValue]: + from dbgpt.storage import vector_store + return [ OptionValue(label=cls, name=cls, value=cls) for cls in vector_store.__all__ @@ -88,6 +90,10 @@ def __init__( raise Exception(f"Vector Store Type Not support. {0}", vector_store_type) print(self.connector_class) + self._vector_store_type = vector_store_type + self._embeddings = ( + vector_store_config.embedding_fn if vector_store_config else None + ) self.client = self.connector_class(vector_store_config) @classmethod @@ -195,10 +201,30 @@ def delete_by_ids(self, ids): """ return self.client.delete_by_ids(ids=ids) + @property + def current_embeddings(self) -> Optional[Embeddings]: + """Return the current embeddings.""" + return self._embeddings + + def new_connector(self, name: str, **kwargs) -> "VectorStoreConnector": + """Create a new connector. + + New connector based on the current connector. + """ + config = copy.copy(self.vector_store_config) + for k, v in kwargs.items(): + if v is not None: + setattr(config, k, v) + config.name = name + + return self.__class__(self._vector_store_type, config) + def _match(self, vector_store_type) -> bool: return bool(connector.get(vector_store_type)) def _register(self): + from dbgpt.storage import vector_store + for cls in vector_store.__all__: if issubclass(getattr(vector_store, cls), VectorStoreBase): _k, _v = cls, getattr(vector_store, cls) diff --git a/dbgpt/util/annotations.py b/dbgpt/util/annotations.py index d20f223db..6726d8ea9 100644 --- a/dbgpt/util/annotations.py +++ b/dbgpt/util/annotations.py @@ -67,6 +67,44 @@ def decorator(obj): return decorator +def mutable(func): + """Decorator to mark a method of an instance will change the instance state. + + Examples: + >>> from dbgpt.util.annotations import mutable + >>> class Foo: + ... def __init__(self): + ... self.a = 1 + ... + ... @mutable + ... def change_a(self): + ... self.a = 2 + ... + + """ + _modify_mutability(func, mutability=True) + return func + + +def immutable(func): + """Decorator to mark a method of an instance will not change the instance state. + + Examples: + >>> from dbgpt.util.annotations import immutable + >>> class Foo: + ... def __init__(self): + ... self.a = 1 + ... + ... @immutable + ... def get_a(self): + ... return self.a + ... + + """ + _modify_mutability(func, mutability=False) + return func + + def _modify_docstring(obj, message: Optional[str] = None): if not message: return @@ -94,3 +132,7 @@ def _modify_annotation(obj, stability) -> None: obj._public_stability = stability if hasattr(obj, "__name__"): obj._annotated = obj.__name__ + + +def _modify_mutability(obj, mutability) -> None: + obj._mutability = mutability diff --git a/dbgpt/util/code_utils.py b/dbgpt/util/code_utils.py index d05dfc2ae..55f63fc3f 100644 --- a/dbgpt/util/code_utils.py +++ b/dbgpt/util/code_utils.py @@ -70,6 +70,7 @@ def extract_code( text: Union[str, List], pattern: str = CODE_BLOCK_PATTERN, detect_single_line_code: bool = False, + default_lang: str = "python", ) -> List[Tuple[str, str]]: """Extract code from a text. @@ -80,6 +81,7 @@ def extract_code( code block. Defaults to CODE_BLOCK_PATTERN. detect_single_line_code (bool, optional): Enable the new feature for extracting single line code. Defaults to False. + default_lang (str, optional): The default language to use when the language Returns: list: A list of tuples, each containing the language and the code. @@ -89,7 +91,7 @@ def extract_code( text = content_str(text) if not detect_single_line_code: match = re.findall(pattern, text, flags=re.DOTALL) - return match if match else [(UNKNOWN, text)] + return match if match else [(default_lang, text)] # Extract both multi-line and single-line code block, separated by the | operator # `([^`]+)`: Matches inline code. diff --git a/dbgpt/util/configure/__init__.py b/dbgpt/util/configure/__init__.py new file mode 100644 index 000000000..8471d1f1f --- /dev/null +++ b/dbgpt/util/configure/__init__.py @@ -0,0 +1 @@ +from .base import ConfigInfo, ConfigProvider, DynConfig diff --git a/dbgpt/util/configure/base.py b/dbgpt/util/configure/base.py new file mode 100644 index 000000000..93dc07a4a --- /dev/null +++ b/dbgpt/util/configure/base.py @@ -0,0 +1,163 @@ +"""Configuration base module.""" + +import logging +from abc import ABC, abstractmethod +from enum import Enum +from typing import Any, Optional, Union + +logger = logging.getLogger(__name__) + + +class _MISSING_TYPE: + pass + + +_MISSING = _MISSING_TYPE() + + +class ConfigCategory(str, Enum): + """The configuration category.""" + + AGENT = "agent" + + +class ProviderType(str, Enum): + """The provider type.""" + + ENV = "env" + PROMPT_MANAGER = "prompt_manager" + + +class ConfigProvider(ABC): + """The configuration provider.""" + + name: ProviderType + + @abstractmethod + def query(self, key: str, **kwargs) -> Any: + """Query the configuration value by key.""" + + +class EnvironmentConfigProvider(ConfigProvider): + """Environment configuration provider. + + Obtain the configuration value from the environment variable. + """ + + name: ProviderType = ProviderType.ENV + + def query(self, key: str, **kwargs) -> Any: + import os + + return os.environ.get(key, None) + + +class PromptManagerConfigProvider(ConfigProvider): + """Prompt manager configuration provider. + + Obtain the configuration value from the prompt manager. + + It is valid only when DB-GPT web server is running for now. + """ + + name: ProviderType = ProviderType.PROMPT_MANAGER + + def query(self, key: str, **kwargs) -> Any: + from dbgpt._private.config import Config + + try: + from dbgpt.serve.prompt.serve import Serve + except ImportError: + logger.warning("Prompt manager is not available.") + return None + + cfg = Config() + sys_app = cfg.SYSTEM_APP + if not sys_app: + return None + prompt_serve = Serve.get_instance(sys_app) + if not prompt_serve or not prompt_serve.prompt_manager: + return None + prompt_manager = prompt_serve.prompt_manager + value = prompt_manager.prefer_query(key, **kwargs) + if not value: + return None + # Just return the first value + return value[0].to_prompt_template().template + + +class ConfigInfo: + def __init__( + self, + default: Any, + key: Optional[str] = None, + provider: Optional[Union[str, ConfigProvider]] = None, + is_list: bool = False, + separator: str = "[LIST_SEP]", + description: Optional[str] = None, + ): + self.default = default + self.key = key + self.provider = provider + self.is_list = is_list + self.separator = separator + self.description = description + + def query(self, **kwargs) -> Any: + if self.key is None: + return self.default + value: Any = None + if isinstance(self.provider, ConfigProvider): + value = self.provider.query(self.key, **kwargs) + elif self.provider == ProviderType.ENV: + value = EnvironmentConfigProvider().query(self.key, **kwargs) + elif self.provider == ProviderType.PROMPT_MANAGER: + value = PromptManagerConfigProvider().query(self.key, **kwargs) + if value is None: + value = self.default + if value and self.is_list and isinstance(value, str): + value = value.split(self.separator) + return value + + +def DynConfig( + default: Any = _MISSING, + *, + category: str | ConfigCategory | None = None, + key: str | None = None, + provider: str | ProviderType | ConfigProvider | None = None, + is_list: bool = False, + separator: str = "[LIST_SEP]", + description: str | None = None, +) -> Any: + """Dynamic configuration. + + It allows to query the configuration value dynamically. + It can obtain the configuration value from the specified provider. + + **Note**: Now just support obtaining string value or string list value. + + Args: + default (Any): The default value. + category (str | ConfigCategory | None): The configuration category. + key (str | None): The configuration key. + provider (str | ProviderType | ConfigProvider | None): The configuration + provider. + is_list (bool): Whether the value is a list. + separator (str): The separator to split the list value. + description (str | None): The configuration description. + """ + if provider is None and category == ConfigCategory.AGENT: + provider = ProviderType.PROMPT_MANAGER + if default == _MISSING and key is None: + raise ValueError("Default value or key is required.") + if default != _MISSING and isinstance(default, list): + is_list = True + return ConfigInfo( + default=default, + key=key, + provider=provider, + is_list=is_list, + separator=separator, + description=description, + ) diff --git a/dbgpt/util/dbgpts/template.py b/dbgpt/util/dbgpts/template.py index d427ce77f..b07d7e447 100644 --- a/dbgpt/util/dbgpts/template.py +++ b/dbgpt/util/dbgpts/template.py @@ -252,11 +252,12 @@ def _write_agent_init_file(working_directory: str, name: str, mod_name: str): from typing import Optional, Tuple from dbgpt.agent import ( - AgentMessage, Action, ActionOutput, + AgentMessage, AgentResource, ConversableAgent, + ProfileConfig, ) from dbgpt.agent.util import cmp_string_equal @@ -264,21 +265,25 @@ def _write_agent_init_file(working_directory: str, name: str, mod_name: str): class HelloWorldSpeakerAgent(ConversableAgent): - name: str = "Hodor" - profile: str = "HelloWorldSpeaker" - goal: str = f"answer any question from user with '{_HELLO_WORLD}'" - desc: str = f"You can answer any question from user with '{_HELLO_WORLD}'" - constraints: list[str] = [ - "You can only answer with '{fix_message}'", - "You can't use any other words", - ] - examples: str = ( - f"user: What's your name?\\nassistant: {_HELLO_WORLD}\\n\\n", - f"user: What's the weather today?\\nassistant: {_HELLO_WORLD}\\n\\n", - f"user: Can you help me?\\nassistant: {_HELLO_WORLD}\\n\\n", - f"user: Please tell me a joke.\\nassistant: {_HELLO_WORLD}\\n\\n", - f"user: Please answer me without '{_HELLO_WORLD}'.\\nassistant: {_HELLO_WORLD}" - "\\n\\n", + + profile: ProfileConfig = ProfileConfig( + name="Hodor", + role="HelloWorldSpeaker", + goal=f"answer any question from user with '{_HELLO_WORLD}'", + desc=f"You can answer any question from user with '{_HELLO_WORLD}'", + constraints=[ + "You can only answer with '{{ fix_message }}'", + f"You can't use any other words", + ], + examples=( + f"user: What's your name?\\nassistant: {_HELLO_WORLD}\\n\\n" + f"user: What's the weather today?\\nassistant: {_HELLO_WORLD}\\n\\n" + f"user: Can you help me?\\nassistant: {_HELLO_WORLD}\\n\\n" + f"user: Please tell me a joke.\\nassistant: {_HELLO_WORLD}\\n\\n" + f"user: Please answer me without '{_HELLO_WORLD}'.\\nassistant: " + f"{_HELLO_WORLD}" + "\\n\\n" + ), ) def __init__(self, **kwargs): @@ -330,28 +335,28 @@ async def _test_agent(): It will not run in the production environment. \"\"\" from dbgpt.model.proxy import OpenAILLMClient - from dbgpt.agent import AgentContext, GptsMemory, UserProxyAgent, LLMConfig + from dbgpt.agent import AgentContext, AgentMemory, UserProxyAgent, LLMConfig llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="summarize") - default_memory: GptsMemory = GptsMemory() + agent_memory: AgentMemory = AgentMemory() speaker = ( await HelloWorldSpeakerAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) + .bind(agent_memory) .build() ) - user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build() + user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build() await user_proxy.initiate_chat( recipient=speaker, reviewer=user_proxy, message="What's your name?", ) - print(await default_memory.one_chat_completions("summarize")) + print(await agent_memory.gpts_memory.one_chat_completions("summarize")) if __name__ == "__main__": diff --git a/dbgpt/util/id_generator.py b/dbgpt/util/id_generator.py new file mode 100644 index 000000000..cc7f7c27c --- /dev/null +++ b/dbgpt/util/id_generator.py @@ -0,0 +1,73 @@ +from typing import Optional + +from snowflake import Snowflake, SnowflakeGenerator + +_GLOBAL_GENERATOR = SnowflakeGenerator(42) + + +def initialize_id_generator( + instance: int, *, seq: int = 0, epoch: int = 0, timestamp: Optional[int] = None +): + """Initialize the global ID generator. + + Args: + instance (int): The identifier combining both data center and machine ID in + traditional Snowflake algorithm. This single value serves to uniquely + identify the source of the ID generation request within distributed + environments. In standard Snowflake, this would be split into datacenter_id + and worker_id, but here it is combined into one for simplicity. + + seq (int, optional): The initial sequence number for the generator. Default is + 0. The sequence number increments within the same millisecond to allow + multiple IDs to be generated in quick succession. It resets when the + timestamp advances. + + epoch (int, optional): The epoch time in milliseconds that acts as an offset + for the generator. This value helps to reduce the length of the generated + number by setting a custom "start time" for the timestamp component. + Default is 0. + + timestamp (int, optional): The initial timestamp for the generator in + milliseconds since epoch. If not provided, the generator will use the + current system time. This can be used for testing or in scenarios where a + fixed start time is required. + """ + global _GLOBAL_GENERATOR + _GLOBAL_GENERATOR = SnowflakeGenerator( + instance, seq=seq, epoch=epoch, timestamp=timestamp + ) + + +def new_id() -> int: + """Generate a new Snowflake ID. + + Returns: + int: A new Snowflake ID. + """ + return next(_GLOBAL_GENERATOR) + + +def parse(snowflake_id: int, epoch: int = 0) -> Snowflake: + """Parse a Snowflake ID into its components. + + Example: + .. code-block:: python + + from dbgpt.util.id_generator import parse, new_id + + snowflake_id = new_id() + snowflake = parse(snowflake_id) + print(snowflake.timestamp) + print(snowflake.instance) + print(snowflake.seq) + print(snowflake.datetime) + + Args: + snowflake_id (int): The Snowflake ID to parse. + epoch (int, optional): The epoch time in milliseconds that acts as an offset + for the generator. + + Returns: + Snowflake: The parsed Snowflake object. + """ + return Snowflake.parse(snowflake_id, epoch=epoch) diff --git a/dbgpt/util/similarity_util.py b/dbgpt/util/similarity_util.py index 1f382dd27..32ee97f41 100644 --- a/dbgpt/util/similarity_util.py +++ b/dbgpt/util/similarity_util.py @@ -1,10 +1,53 @@ """Utility functions for calculating similarity.""" -from typing import TYPE_CHECKING, Any, Sequence +from typing import TYPE_CHECKING, Any, List, Sequence if TYPE_CHECKING: from dbgpt.core.interface.embeddings import Embeddings +def cosine_similarity(embedding1: List[float], embedding2: List[float]) -> float: + """Calculate the cosine similarity between two vectors. + + Args: + embedding1(List[float]): The first vector. + embedding2(List[float]): The second vector. + + Returns: + float: The cosine similarity. + """ + try: + import numpy as np + except ImportError: + raise ImportError("numpy is required for SimilarityMetric") + dot_product = np.dot(embedding1, embedding2) + norm1 = np.linalg.norm(embedding1) + norm2 = np.linalg.norm(embedding2) + similarity = dot_product / (norm1 * norm2) + return similarity + + +def sigmoid_function(x: float) -> float: + """Calculate the sigmoid function. + + The sigmoid function is defined as: + .. math:: + f(x) = \\frac{1}{1 + e^{-x}} + + It is used to map the input to a value between 0 and 1. + + Args: + x(float): The input to the sigmoid function. + + Returns: + float: The output of the sigmoid function. + """ + try: + import numpy as np + except ImportError: + raise ImportError("numpy is required for sigmoid_function") + return 1 / (1 + np.exp(-x)) + + def calculate_cosine_similarity( embeddings: "Embeddings", prediction: str, contexts: Sequence[str] ) -> Any: diff --git a/dbgpt/util/time_utils.py b/dbgpt/util/time_utils.py new file mode 100644 index 000000000..ec3f53eeb --- /dev/null +++ b/dbgpt/util/time_utils.py @@ -0,0 +1,36 @@ +import contextlib +from datetime import datetime + + +@contextlib.contextmanager +def mock_now(dt_value): # type: ignore + """Context manager for mocking out datetime.now() in unit tests. + + Adapted from langchain.utils.mock_now. + + Example: + with mock_now(datetime.datetime(2011, 2, 3, 10, 11)): + assert datetime.datetime.now() == datetime.datetime(2011, 2, 3, 10, 11) + """ + + class MockDateTime(datetime.datetime): + @classmethod + def now(cls): # type: ignore + # Create a copy of dt_value. + return datetime.datetime( + dt_value.year, + dt_value.month, + dt_value.day, + dt_value.hour, + dt_value.minute, + dt_value.second, + dt_value.microsecond, + dt_value.tzinfo, + ) + + real_datetime = datetime.datetime + datetime.datetime = MockDateTime + try: + yield datetime.datetime + finally: + datetime.datetime = real_datetime diff --git a/docs/docs/agents/cookbook/calculator_with_agents.md b/docs/docs/agents/cookbook/calculator_with_agents.md index 4d217d286..24ab7d4ef 100644 --- a/docs/docs/agents/cookbook/calculator_with_agents.md +++ b/docs/docs/agents/cookbook/calculator_with_agents.md @@ -7,7 +7,7 @@ In this example, we will show you how to use an agent as your calculator. Install the required packages by running the following command: ```bash -pip install "dbgpt[agent]>=0.5.4rc0" -U +pip install "dbgpt[agent]>=0.5.6rc1" -U pip install openai ``` @@ -18,36 +18,39 @@ Create a new Python file and add the following code: ```python import asyncio -from dbgpt.agent import AgentContext, GptsMemory, LLMConfig, UserProxyAgent +from dbgpt.agent import AgentContext, AgentMemory, LLMConfig, UserProxyAgent from dbgpt.agent.expand.code_assistant_agent import CodeAssistantAgent from dbgpt.model.proxy import OpenAILLMClient + async def main(): llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="test123") - default_memory: GptsMemory = GptsMemory() + # Create an agent memory, default memory is ShortTermMemory + agent_memory: AgentMemory = AgentMemory() # Create a code assistant agent coder = ( await CodeAssistantAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) + .bind(agent_memory) .build() ) # Create a user proxy agent - user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build() + user_proxy = await UserProxyAgent().bind(context).bind(agent_memory).build() # Initiate a chat with the user proxy agent await user_proxy.initiate_chat( recipient=coder, reviewer=user_proxy, - message="calculate the result of 321 * 123" + message="calculate the result of 321 * 123", ) # Obtain conversation history messages between agents - print(await default_memory.one_chat_completions("test123")) - + print(await agent_memory.gpts_memory.one_chat_completions("test123")) + + if __name__ == "__main__": asyncio.run(main()) ``` @@ -55,6 +58,12 @@ if __name__ == "__main__": You will see the following output: ````bash +Prompt manager is not available. +Prompt manager is not available. +Prompt manager is not available. +Prompt manager is not available. +Prompt manager is not available. + -------------------------------------------------------------------------------- User (to Turing)-[]: @@ -62,7 +71,8 @@ User (to Turing)-[]: -------------------------------------------------------------------------------- un_stream ai response: ```python -# Calculate the result of 321 * 123 +# filename: calculate_multiplication.py + result = 321 * 123 print(result) ``` @@ -74,7 +84,7 @@ un_stream ai response: True -------------------------------------------------------------------------------- Turing (to User)-[gpt-3.5-turbo]: -"```python\n# Calculate the result of 321 * 123\nresult = 321 * 123\nprint(result)\n```" +"```python\n# filename: calculate_multiplication.py\n\nresult = 321 * 123\nprint(result)\n```" >>>>>>>>Turing Review info: Pass(None) >>>>>>>>Turing Action report: @@ -85,7 +95,6 @@ execution succeeded, -------------------------------------------------------------------------------- ```agent-plans -[{"name": "calculate the result of 321 * 123", "num": 1, "status": "complete", "agent": "Human", "markdown": "```agent-messages\n[{\"sender\": \"CodeEngineer\", \"receiver\": \"Human\", \"model\": \"gpt-3.5-turbo\", \"markdown\": \"```vis-code\\n{\\\"exit_success\\\": true, \\\"language\\\": \\\"python\\\", \\\"code\\\": [[\\\"python\\\", \\\"# Calculate the result of 321 * 123\\\\nresult = 321 * 123\\\\nprint(result)\\\"]], \\\"log\\\": \\\"\\\\n39483\\\\n\\\"}\\n```\"}]\n```"}] +[{"name": "calculate the result of 321 * 123", "num": 1, "status": "complete", "agent": "Human", "markdown": "```agent-messages\n[{\"sender\": \"CodeEngineer\", \"receiver\": \"Human\", \"model\": \"gpt-3.5-turbo\", \"markdown\": \"```vis-code\\n{\\\"exit_success\\\": true, \\\"language\\\": \\\"python\\\", \\\"code\\\": [[\\\"python\\\", \\\"# filename: calculate_multiplication.py\\\\n\\\\nresult = 321 * 123\\\\nprint(result)\\\"]], \\\"log\\\": \\\"\\\\n39483\\\\n\\\"}\\n```\"}]\n```"}] ``` -(dbgpt-agents-py3.11) (base) ➜ dbgpt-agents ```` \ No newline at end of file diff --git a/docs/docs/agents/custom_agents.md b/docs/docs/agents/custom_agents.md index 53e1d34cd..acbfc9cdb 100644 --- a/docs/docs/agents/custom_agents.md +++ b/docs/docs/agents/custom_agents.md @@ -10,7 +10,7 @@ summarizer. Install the required packages by running the following command: ```bash -pip install "dbgpt[agent]>=0.5.4rc0" -U +pip install "dbgpt[agent]>=0.5.6rc1" -U pip install openai ``` @@ -35,23 +35,25 @@ Before designing each Agent, it is necessary to define its role, identity, and functional role. The specific definitions are as follows: ```python -from dbgpt.agent import ConversableAgent +from dbgpt.agent import ConversableAgent, ProfileConfig class MySummarizerAgent(ConversableAgent): - # The name of the agent - name = "Aristotle" - # The profile of the agent - profile: str = "Summarizer" - # The core functional goals of the agent tell LLM what it can do with it. - goal: str = ( - "Summarize answer summaries based on user questions from provided " - "resource information or from historical conversation memories." - ) - # Introduction and description of the agent, used for task assignment and display. - # If it is empty, the goal content will be used. - desc: str = ( - "You can summarize provided text content according to user's questions" - " and output the summarization." + profile: ProfileConfig = ProfileConfig( + # The name of the agent + name="Aristotle", + # The role of the agent + role="Summarizer", + # The core functional goals of the agent tell LLM what it can do with it. + goal=( + "Summarize answer summaries based on user questions from provided " + "resource information or from historical conversation memories." + ), + # Introduction and description of the agent, used for task assignment and display. + # If it is empty, the goal content will be used. + desc=( + "You can summarize provided text content according to user's questions" + " and output the summarization." + ), ) def __init__(self, **kwargs): super().__init__(**kwargs) @@ -70,39 +72,41 @@ be bound if there are some special requirements). which mainly includes: So, we can define the constraints of the agent's prompt as follows: ```python -from dbgpt.agent import ConversableAgent +from dbgpt.agent import ConversableAgent, ProfileConfig class MySummarizerAgent(ConversableAgent): - # The name of the agent - name = "Aristotle" - # The profile of the agent - profile: str = "Summarizer" - # The core functional goals of the agent tell LLM what it can do with it. - goal: str = ( - "Summarize answer summaries based on user questions from provided " - "resource information or from historical conversation memories." - ) - # Introduction and description of the agent, used for task assignment and display. - # If it is empty, the goal content will be used. - desc: str = ( - "You can summarize provided text content according to user's questions" - " and output the summarization." + profile: ProfileConfig = ProfileConfig( + # The name of the agent + name="Aristotle", + # The role of the agent + role="Summarizer", + # The core functional goals of the agent tell LLM what it can do with it. + goal=( + "Summarize answer summaries based on user questions from provided " + "resource information or from historical conversation memories." + ), + # Introduction and description of the agent, used for task assignment and display. + # If it is empty, the goal content will be used. + desc=( + "You can summarize provided text content according to user's questions" + " and output the summarization." + ), + # Refer to the following. It can contain multiple constraints and reasoning + # restriction logic, and supports the use of parameter template {{ param_name }}. + constraints=[ + "Prioritize the summary of answers to user questions from the improved resource" + " text. If no relevant information is found, summarize it from the historical " + "dialogue memory given. It is forbidden to make up your own.", + "You need to first detect user's question that you need to answer with your" + " summarization.", + "Extract the provided text content used for summarization.", + "Then you need to summarize the extracted text content.", + "Output the content of summarization ONLY related to user's question. The " + "output language must be the same to user's question language.", + "If you think the provided text content is not related to user questions at " + "all, ONLY output '{{ not_related_message }}'!!.", + ] ) - # Refer to the following. It can contain multiple constraints and reasoning - # restriction logic, and supports the use of parameter template {param_name}. - constraints: list[str] = [ - "Prioritize the summary of answers to user questions from the improved resource" - " text. If no relevant information is found, summarize it from the historical " - "dialogue memory given. It is forbidden to make up your own.", - "You need to first detect user's question that you need to answer with your" - " summarization.", - "Extract the provided text content used for summarization.", - "Then you need to summarize the extracted text content.", - "Output the content of summarization ONLY related to user's question. The " - "output language must be the same to user's question language.", - "If you think the provided text content is not related to user questions at " - "all, ONLY output '{not_related_message}'!!.", - ] def __init__(self, **kwargs): super().__init__(**kwargs) ``` @@ -114,50 +118,52 @@ to assemble the values, and the following interface (`_init_reply_message`) need overloaded and implemented: ```python -from dbgpt.agent import ConversableAgent, AgentMessage +from dbgpt.agent import AgentMessage, ConversableAgent, ProfileConfig NOT_RELATED_MESSAGE = "Did not find the information you want." + class MySummarizerAgent(ConversableAgent): - # The name of the agent - name = "Aristotle" - # The profile of the agent - profile: str = "Summarizer" - # The core functional goals of the agent tell LLM what it can do with it. - goal: str = ( - "Summarize answer summaries based on user questions from provided " - "resource information or from historical conversation memories." - ) - # Introduction and description of the agent, used for task assignment and display. - # If it is empty, the goal content will be used. - desc: str = ( - "You can summarize provided text content according to user's questions" - " and output the summarization." + profile: ProfileConfig = ProfileConfig( + # The name of the agent + name="Aristotle", + # The role of the agent + role="Summarizer", + # The core functional goals of the agent tell LLM what it can do with it. + goal=( + "Summarize answer summaries based on user questions from provided " + "resource information or from historical conversation memories." + ), + # Introduction and description of the agent, used for task assignment and display. + # If it is empty, the goal content will be used. + desc=( + "You can summarize provided text content according to user's questions" + " and output the summarization." + ), + # Refer to the following. It can contain multiple constraints and reasoning + # restriction logic, and supports the use of parameter template {{ param_name }}. + constraints=[ + "Prioritize the summary of answers to user questions from the improved resource" + " text. If no relevant information is found, summarize it from the historical " + "dialogue memory given. It is forbidden to make up your own.", + "You need to first detect user's question that you need to answer with your" + " summarization.", + "Extract the provided text content used for summarization.", + "Then you need to summarize the extracted text content.", + "Output the content of summarization ONLY related to user's question. The " + "output language must be the same to user's question language.", + "If you think the provided text content is not related to user questions at " + "all, ONLY output '{{ not_related_message }}'!!.", + ], ) - # Refer to the following. It can contain multiple constraints and reasoning - # restriction logic, and supports the use of parameter template {param_name}. - constraints: list[str] = [ - "Prioritize the summary of answers to user questions from the improved resource" - " text. If no relevant information is found, summarize it from the historical " - "dialogue memory given. It is forbidden to make up your own.", - "You need to first detect user's question that you need to answer with your" - " summarization.", - "Extract the provided text content used for summarization.", - "Then you need to summarize the extracted text content.", - "Output the content of summarization ONLY related to user's question. The " - "output language must be the same to user's question language.", - "If you think the provided text content is not related to user questions at " - "all, ONLY output '{not_related_message}'!!.", - ] + def __init__(self, **kwargs): super().__init__(**kwargs) def _init_reply_message(self, received_message: AgentMessage) -> AgentMessage: reply_message = super()._init_reply_message(received_message) # Fill in the dynamic parameters in the prompt template - reply_message.context = { - "not_related_message": NOT_RELATED_MESSAGE - } + reply_message.context = {"not_related_message": NOT_RELATED_MESSAGE} return reply_message ``` @@ -343,7 +349,7 @@ class SummaryAction(Action[SummaryActionInput]): ### Binding Action to Agent -After the development and definition of agent and cction are completed, +After the development and definition of agent and action are completed, bind the action to the corresponding agent. ```python @@ -405,7 +411,7 @@ After the custom agent is created, you can use it in the following way: import asyncio -from dbgpt.agent import AgentContext, ConversableAgent, GptsMemory, LLMConfig, UserProxyAgent +from dbgpt.agent import AgentContext, ConversableAgent, AgentMemory, LLMConfig, UserProxyAgent from dbgpt.model.proxy import OpenAILLMClient class MySummarizerAgent(ConversableAgent): @@ -415,17 +421,17 @@ async def main(): llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="summarize") - default_memory: GptsMemory = GptsMemory() + agent_memory: AgentMemory = AgentMemory() summarizer = ( await MySummarizerAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) + .bind(agent_memory) .build() ) - user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build() + user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build() await user_proxy.initiate_chat( @@ -451,7 +457,7 @@ async def main(): Nuclear electric rocket """, ) - print(await default_memory.one_chat_completions("summarize")) + print(await agent_memory.gpts_memory.one_chat_completions("summarize")) if __name__ == "__main__": asyncio.run(main()) @@ -467,11 +473,12 @@ from dbgpt.agent import ( Action, ActionOutput, AgentContext, + AgentMemory, AgentMessage, AgentResource, ConversableAgent, - GptsMemory, LLMConfig, + ProfileConfig, ResourceType, UserProxyAgent, ) @@ -481,7 +488,6 @@ from dbgpt.model.proxy import OpenAILLMClient from dbgpt.vis import Vis from pydantic import BaseModel, Field - NOT_RELATED_MESSAGE = "Did not find the information you want." CHECK_RESULT_SYSTEM_MESSAGE = ( @@ -496,37 +502,42 @@ CHECK_RESULT_SYSTEM_MESSAGE = ( "by TERMINATE. For instance: False|Some important concepts in the input are " "not summarized. TERMINATE" ) + + class MySummarizerAgent(ConversableAgent): - # The name of the agent - name = "Aristotle" - # The profile of the agent - profile: str = "Summarizer" - # The core functional goals of the agent tell LLM what it can do with it. - goal: str = ( - "Summarize answer summaries based on user questions from provided " - "resource information or from historical conversation memories." - ) - # Introduction and description of the agent, used for task assignment and display. - # If it is empty, the goal content will be used. - desc: str = ( - "You can summarize provided text content according to user's questions" - " and output the summarization." + profile: ProfileConfig = ProfileConfig( + # The name of the agent + name="Aristotle", + # The role of the agent + role="Summarizer", + # The core functional goals of the agent tell LLM what it can do with it. + goal=( + "Summarize answer summaries based on user questions from provided " + "resource information or from historical conversation memories." + ), + # Introduction and description of the agent, used for task assignment and display. + # If it is empty, the goal content will be used. + desc=( + "You can summarize provided text content according to user's questions" + " and output the summarization." + ), + # Refer to the following. It can contain multiple constraints and reasoning + # restriction logic, and supports the use of parameter template {{ param_name }}. + constraints=[ + "Prioritize the summary of answers to user questions from the improved resource" + " text. If no relevant information is found, summarize it from the historical " + "dialogue memory given. It is forbidden to make up your own.", + "You need to first detect user's question that you need to answer with your" + " summarization.", + "Extract the provided text content used for summarization.", + "Then you need to summarize the extracted text content.", + "Output the content of summarization ONLY related to user's question. The " + "output language must be the same to user's question language.", + "If you think the provided text content is not related to user questions at " + "all, ONLY output '{{ not_related_message }}'!!.", + ], ) - # Refer to the following. It can contain multiple constraints and reasoning - # restriction logic, and supports the use of parameter template {param_name}. - constraints: list[str] = [ - "Prioritize the summary of answers to user questions from the improved resource" - " text. If no relevant information is found, summarize it from the historical " - "dialogue memory given. It is forbidden to make up your own.", - "You need to first detect user's question that you need to answer with your" - " summarization.", - "Extract the provided text content used for summarization.", - "Then you need to summarize the extracted text content.", - "Output the content of summarization ONLY related to user's question. The " - "output language must be the same to user's question language.", - "If you think the provided text content is not related to user questions at " - "all, ONLY output '{not_related_message}'!!.", - ] + def __init__(self, **kwargs): super().__init__(**kwargs) self._init_actions([SummaryAction]) @@ -534,11 +545,9 @@ class MySummarizerAgent(ConversableAgent): def _init_reply_message(self, received_message: AgentMessage) -> AgentMessage: reply_message = super()._init_reply_message(received_message) # Fill in the dynamic parameters in the prompt template - reply_message.context = { - "not_related_message": NOT_RELATED_MESSAGE - } + reply_message.context = {"not_related_message": NOT_RELATED_MESSAGE} return reply_message - + def prepare_act_param(self) -> Dict[str, Any]: return {"action_extra_param_key": "this is extra param"} @@ -565,7 +574,7 @@ class MySummarizerAgent(ConversableAgent): ], prompt=CHECK_RESULT_SYSTEM_MESSAGE, ) - + fail_reason = "" if check_result and ( "true" in check_result.lower() or "yes" in check_result.lower() @@ -595,6 +604,7 @@ class SummaryActionInput(BaseModel): description="The summary content", ) + class SummaryAction(Action[SummaryActionInput]): def __init__(self): super().__init__() @@ -604,13 +614,13 @@ class SummaryAction(Action[SummaryActionInput]): # The resource type that the current Agent needs to use # here we do not need to use resources, just return None return None - + @property def render_protocol(self) -> Optional[Vis]: # The visualization rendering protocol that the current Agent needs to use # here we do not need to use visualization rendering, just return None return None - + @property def out_model_type(self): return SummaryActionInput @@ -624,14 +634,16 @@ class SummaryAction(Action[SummaryActionInput]): **kwargs, ) -> ActionOutput: """Perform the action. - - The entry point for actual execution of Action. Action execution will be - automatically initiated after model inference. + + The entry point for actual execution of Action. Action execution will be + automatically initiated after model inference. """ extra_param = kwargs.get("action_extra_param_key", None) try: # Parse the input message - param: SummaryActionInput = self._input_convert(ai_message, SummaryActionInput) + param: SummaryActionInput = self._input_convert( + ai_message, SummaryActionInput + ) except Exception: return ActionOutput( is_exe_success=False, @@ -640,7 +652,7 @@ class SummaryAction(Action[SummaryActionInput]): ) # Check if the summary content is not related to user questions if param.summary and cmp_string_equal( - param.summary, + param.summary, NOT_RELATED_MESSAGE, ignore_case=True, ignore_punctuation=True, @@ -662,18 +674,17 @@ async def main(): llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="summarize") - default_memory: GptsMemory = GptsMemory() + agent_memory: AgentMemory = AgentMemory() summarizer = ( await MySummarizerAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) + .bind(agent_memory) .build() ) - user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build() - + user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build() await user_proxy.initiate_chat( recipient=summarizer, @@ -698,7 +709,8 @@ async def main(): Nuclear electric rocket """, ) - print(await default_memory.one_chat_completions("summarize")) + print(await agent_memory.gpts_memory.one_chat_completions("summarize")) + if __name__ == "__main__": asyncio.run(main()) diff --git a/examples/agents/auto_plan_agent_dialogue_example.py b/examples/agents/auto_plan_agent_dialogue_example.py index 10de22111..0340f92de 100644 --- a/examples/agents/auto_plan_agent_dialogue_example.py +++ b/examples/agents/auto_plan_agent_dialogue_example.py @@ -14,27 +14,33 @@ python examples/agents/auto_plan_agent_dialogue_example.py """ - import asyncio from dbgpt.agent import ( AgentContext, - GptsMemory, + AgentMemory, + AutoPlanChatManager, LLMConfig, ResourceLoader, UserProxyAgent, ) from dbgpt.agent.expand.code_assistant_agent import CodeAssistantAgent -from dbgpt.agent.plan import AutoPlanChatManager +from dbgpt.util.tracer import initialize_tracer + +initialize_tracer( + "/tmp/agent_auto_plan_agent_dialogue_example_trace.jsonl", create_system_app=True +) async def main(): from dbgpt.model.proxy import OpenAILLMClient - llm_client = OpenAILLMClient(model_alias="gpt-4") - context: AgentContext = AgentContext(conv_id="test456", gpts_app_name="代码分析助手") + agent_memory = AgentMemory() - default_memory = GptsMemory() + llm_client = OpenAILLMClient(model_alias="gpt-4") + context: AgentContext = AgentContext( + conv_id="test456", gpts_app_name="代码分析助手", max_new_tokens=2048 + ) resource_loader = ResourceLoader() @@ -42,21 +48,21 @@ async def main(): await CodeAssistantAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) .bind(resource_loader) + .bind(agent_memory) .build() ) manager = ( await AutoPlanChatManager() .bind(context) - .bind(default_memory) + .bind(agent_memory) .bind(LLMConfig(llm_client=llm_client)) .build() ) manager.hire([coder]) - user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build() + user_proxy = await UserProxyAgent().bind(context).bind(agent_memory).build() await user_proxy.initiate_chat( recipient=manager, @@ -66,7 +72,7 @@ async def main(): # message="find papers on LLM applications from arxiv in the last month, create a markdown table of different domains.", ) - print(await default_memory.one_chat_completions("test456")) + print(await agent_memory.gpts_memory.one_chat_completions("test456")) if __name__ == "__main__": diff --git a/examples/agents/awel_layout_agents_chat_examples.py b/examples/agents/awel_layout_agents_chat_examples.py index 65df5938d..e12ca4a2b 100644 --- a/examples/agents/awel_layout_agents_chat_examples.py +++ b/examples/agents/awel_layout_agents_chat_examples.py @@ -19,16 +19,16 @@ from dbgpt.agent import ( AgentContext, + AgentMemory, AgentResource, - GptsMemory, LLMConfig, ResourceLoader, ResourceType, UserProxyAgent, + WrappedAWELLayoutManager, ) from dbgpt.agent.expand.plugin_assistant_agent import PluginAssistantAgent from dbgpt.agent.expand.summary_assistant_agent import SummaryAssistantAgent -from dbgpt.agent.plan import WrappedAWELLayoutManager from dbgpt.agent.resource import PluginFileLoadClient from dbgpt.configs.model_config import ROOT_PATH from dbgpt.util.tracer import initialize_tracer @@ -44,8 +44,7 @@ async def main(): llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="test456", gpts_app_name="信息析助手") - default_memory = GptsMemory() - + agent_memory = AgentMemory() resource_loader = ResourceLoader() plugin_file_loader = PluginFileLoadClient() resource_loader.register_resource_api(plugin_file_loader) @@ -60,7 +59,7 @@ async def main(): await PluginAssistantAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) + .bind(agent_memory) .bind([plugin_resource]) .bind(resource_loader) .build() @@ -68,7 +67,7 @@ async def main(): summarizer = ( await SummaryAssistantAgent() .bind(context) - .bind(default_memory) + .bind(agent_memory) .bind(LLMConfig(llm_client=llm_client)) .build() ) @@ -76,13 +75,13 @@ async def main(): manager = ( await WrappedAWELLayoutManager() .bind(context) - .bind(default_memory) + .bind(agent_memory) .bind(LLMConfig(llm_client=llm_client)) .build() ) manager.hire([tool_engineer, summarizer]) - user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build() + user_proxy = await UserProxyAgent().bind(context).bind(agent_memory).build() await user_proxy.initiate_chat( recipient=manager, @@ -93,7 +92,7 @@ async def main(): # message="find papers on LLM applications from arxiv in the last month, create a markdown table of different domains.", ) - print(await default_memory.one_chat_completions("test456")) + print(await agent_memory.gpts_memory.one_chat_completions("test456")) if __name__ == "__main__": diff --git a/examples/agents/plugin_agent_dialogue_example.py b/examples/agents/plugin_agent_dialogue_example.py index 4138341ae..233a364e6 100644 --- a/examples/agents/plugin_agent_dialogue_example.py +++ b/examples/agents/plugin_agent_dialogue_example.py @@ -19,8 +19,8 @@ from dbgpt.agent import ( AgentContext, + AgentMemory, AgentResource, - GptsMemory, LLMConfig, ResourceLoader, ResourceType, @@ -40,7 +40,7 @@ async def main(): llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="test456") - default_memory: GptsMemory = GptsMemory() + agent_memory = AgentMemory() plugin_resource = AgentResource( type=ResourceType.Plugin, @@ -52,13 +52,13 @@ async def main(): plugin_file_loader = PluginFileLoadClient() resource_loader.register_resource_api(plugin_file_loader) - user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build() + user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build() tool_engineer = ( await PluginAssistantAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) + .bind(agent_memory) .bind([plugin_resource]) .bind(resource_loader) .build() @@ -70,8 +70,8 @@ async def main(): message="查询今天成都的天气", ) - ## dbgpt-vis message infos - print(await default_memory.one_chat_completions("test456")) + # dbgpt-vis message infos + print(await agent_memory.gpts_memory.one_chat_completions("test456")) if __name__ == "__main__": diff --git a/examples/agents/retrieve_summary_agent_dialogue_example.py b/examples/agents/retrieve_summary_agent_dialogue_example.py index 5e9f706dd..527f09ad2 100644 --- a/examples/agents/retrieve_summary_agent_dialogue_example.py +++ b/examples/agents/retrieve_summary_agent_dialogue_example.py @@ -17,7 +17,7 @@ import asyncio import os -from dbgpt.agent import AgentContext, GptsMemory, LLMConfig, UserProxyAgent +from dbgpt.agent import AgentContext, AgentMemory, LLMConfig, UserProxyAgent from dbgpt.agent.expand.retrieve_summary_assistant_agent import ( RetrieveSummaryAssistantAgent, ) @@ -29,17 +29,16 @@ async def summary_example_with_success(): llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo-16k") context: AgentContext = AgentContext(conv_id="retrieve_summarize") - - default_memory = GptsMemory() + agent_memory = AgentMemory() summarizer = ( await RetrieveSummaryAssistantAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) + .bind(agent_memory) .build() ) - user_proxy = UserProxyAgent(memory=default_memory, agent_context=context) + user_proxy = UserProxyAgent(memory=agent_memory, agent_context=context) paths_urls = [ os.path.join(ROOT_PATH, "examples/agents/example_files/Nuclear_power.pdf"), @@ -56,7 +55,7 @@ async def summary_example_with_success(): ) # dbgpt-vis message infos - print(await default_memory.one_chat_completions("retrieve_summarize")) + print(await agent_memory.gpts_memory.one_chat_completions("retrieve_summarize")) if __name__ == "__main__": diff --git a/examples/agents/single_agent_dialogue_example.py b/examples/agents/single_agent_dialogue_example.py index 8ebae2f19..5cff173de 100644 --- a/examples/agents/single_agent_dialogue_example.py +++ b/examples/agents/single_agent_dialogue_example.py @@ -16,7 +16,14 @@ import asyncio -from dbgpt.agent import AgentContext, GptsMemory, LLMConfig, UserProxyAgent +from dbgpt.agent import ( + AgentContext, + AgentMemory, + AgentMemoryFragment, + HybridMemory, + LLMConfig, + UserProxyAgent, +) from dbgpt.agent.expand.code_assistant_agent import CodeAssistantAgent @@ -25,17 +32,17 @@ async def main(): llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="test123") - default_memory: GptsMemory = GptsMemory() + agent_memory = AgentMemory(HybridMemory[AgentMemoryFragment].from_chroma()) coder = ( await CodeAssistantAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) + .bind(agent_memory) .build() ) - user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build() + user_proxy = await UserProxyAgent().bind(context).bind(agent_memory).build() await user_proxy.initiate_chat( recipient=coder, @@ -44,7 +51,7 @@ async def main(): # message="download data from https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.", ) ## dbgpt-vis message infos - print(await default_memory.one_chat_completions("test123")) + print(await agent_memory.gpts_memory.one_chat_completions("test123")) if __name__ == "__main__": diff --git a/examples/agents/single_summary_agent_dialogue_example.py b/examples/agents/single_summary_agent_dialogue_example.py index 8a7859b1d..81991258f 100644 --- a/examples/agents/single_summary_agent_dialogue_example.py +++ b/examples/agents/single_summary_agent_dialogue_example.py @@ -16,7 +16,7 @@ import asyncio -from dbgpt.agent import AgentContext, GptsMemory, LLMConfig, UserProxyAgent +from dbgpt.agent import AgentContext, AgentMemory, LLMConfig, UserProxyAgent from dbgpt.agent.expand.summary_assistant_agent import SummaryAssistantAgent @@ -26,17 +26,16 @@ async def summary_example_with_success(): llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="summarize") - default_memory: GptsMemory = GptsMemory() - + agent_memory = AgentMemory() summarizer = ( await SummaryAssistantAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) + .bind(agent_memory) .build() ) - user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build() + user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build() await user_proxy.initiate_chat( recipient=summarizer, @@ -71,8 +70,8 @@ async def summary_example_with_success(): """, ) - ## dbgpt-vis message infos - print(await default_memory.one_chat_completions("summarize")) + # dbgpt-vis message infos + print(await agent_memory.gpts_memory.one_chat_completions("summarize")) async def summary_example_with_faliure(): @@ -81,17 +80,16 @@ async def summary_example_with_faliure(): llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="summarize") - default_memory: GptsMemory = GptsMemory() - + agent_memory = AgentMemory() summarizer = ( await SummaryAssistantAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) + .bind(agent_memory) .build() ) - user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build() + user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build() # Test the failure example @@ -112,7 +110,7 @@ async def summary_example_with_faliure(): """, ) - print(await default_memory.one_chat_completions("summarize")) + print(await agent_memory.gpts_memory.one_chat_completions("summarize")) if __name__ == "__main__": diff --git a/examples/agents/sql_agent_dialogue_example.py b/examples/agents/sql_agent_dialogue_example.py index c9bef8ae5..7b4518ec6 100644 --- a/examples/agents/sql_agent_dialogue_example.py +++ b/examples/agents/sql_agent_dialogue_example.py @@ -19,8 +19,8 @@ from dbgpt.agent import ( AgentContext, + AgentMemory, AgentResource, - GptsMemory, LLMConfig, ResourceLoader, ResourceType, @@ -40,11 +40,11 @@ async def main(): from dbgpt.model.proxy.llms.chatgpt import OpenAILLMClient + agent_memory = AgentMemory() + llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo") context: AgentContext = AgentContext(conv_id="test456") - default_memory: GptsMemory = GptsMemory() - db_resource = AgentResource( type=ResourceType.DB, name="TestData", @@ -55,15 +55,15 @@ async def main(): sqlite_file_loader = SqliteLoadClient() resource_loader.register_resource_api(sqlite_file_loader) - user_proxy = await UserProxyAgent().bind(default_memory).bind(context).build() + user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build() sql_boy = ( await DataScientistAgent() .bind(context) .bind(LLMConfig(llm_client=llm_client)) - .bind(default_memory) .bind([db_resource]) .bind(resource_loader) + .bind(agent_memory) .build() ) @@ -74,7 +74,7 @@ async def main(): ) ## dbgpt-vis message infos - print(await default_memory.one_chat_completions("test456")) + print(await agent_memory.gpts_memory.one_chat_completions("test456")) if __name__ == "__main__": diff --git a/setup.py b/setup.py index 1dfe77bf2..397795db7 100644 --- a/setup.py +++ b/setup.py @@ -416,6 +416,8 @@ def core_requires(): "pydantic>=2.6.0", # For AWEL type checking "typeguard", + # Snowflake no additional dependencies. + "snowflake-id", ] # For DB-GPT python client SDK setup_spec.extras["client"] = setup_spec.extras["core"] + [