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clean project

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jinli.yl 2025-06-18 12:23:43 +08:00
parent 556e8afb5d
commit 40bb3b1052
21 changed files with 26 additions and 404 deletions
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4
cookbook/simple_agent/your_own_agent_enhanced.py
View file

@ -20,7 +20,7 @@ class YourOwnAgentEnhanced(YourOwnAgent):
def summary_experience(self, query: str):
messages = self.run(query)
trajectory: Trajectory = Trajectory(query=query, steps=messages, answer=messages[-1].content, done=True)
trajectory: Trajectory = Trajectory(query=query, steps=messages, answer=messages[-1].content, is_terminated=True)
request: SummarizerRequest = SummarizerRequest(trajectories=[trajectory], workspace_id=self.workspace_id)
response: SummarizerResponse = self.em_client.call_summarizer(request)
@ -40,7 +40,7 @@ class YourOwnAgentEnhanced(YourOwnAgent):
trajectory.steps = messages
trajectory.answer = messages[-1].content
trajectory.done = True
trajectory.is_terminated = True
trajectory.metadata["experience"] = response.context_msg.content
return trajectory

3
cookbook/step_agent/examples.py
View file

@ -21,8 +21,7 @@ def create_sample_trajectory(query: str, steps: List[Message], done: bool = True
trajectory = Trajectory(
query=query,
steps=steps,
done=done,
current_step=len(steps) - 1,
is_terminated=done,
metadata={
"domain": "coding",
"task_type": "problem_solving"

78
experiencemaker/module/agent_wrapper/base_agent_wrapper.py
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@ -1,78 +0,0 @@
from typing import List
from pydantic import Field
from experiencemaker.module.agent_wrapper.agent_wrapper_mixin import AgentWrapperMixin
from experiencemaker.module.environment.base_environment import BaseEnvironment
from experiencemaker.schema.trajectory import Trajectory, Message, StateMessage, ActionMessage, ContextMessage
class BaseAgentWrapper(AgentWrapperMixin):
max_steps: int = Field(default=10)
enable_exploration: bool = Field(default=False)
trajectory: Trajectory | None = Field(default_factory=Trajectory)
def reset(self):
self.trajectory.reset()
def after_step_hook(self, **kwargs):
raise NotImplementedError
def build_messages(self,
state: StateMessage,
context_msg: ContextMessage | None,
env: BaseEnvironment, **kwargs) -> List[Message]:
raise NotImplementedError
def explore_messages(self, messages: List[Message], **kwargs) -> List[Message]:
raise NotImplementedError
def action_parser(self, action_msg: ActionMessage) -> ActionMessage: # noqa
return action_msg
def generate_action(self,
state: StateMessage,
context_msg: ContextMessage | None,
env: BaseEnvironment,
**kwargs) -> ActionMessage:
messages: List[Message] = self.build_messages(state, context_msg, env, **kwargs)
if self.enable_exploration:
messages = self.explore_messages(messages, **kwargs)
action_msg: ActionMessage = self.llm.chat(messages, tools=env.tools)
return self.action_parser(action_msg)
def execute(self, query: str, env: BaseEnvironment = None, **kwargs) -> Trajectory:
self.trajectory.query = query
current_state = env.current_state
for i in range(self.max_steps):
self.trajectory.current_step = i
# generate context
context_msg: ContextMessage | None = None
if self.context_generator:
context_msg = self.context_generator.execute(trajectory=self.trajectory, **kwargs)
# generate action
action_msg = self.generate_action(state=current_state, context_msg=context_msg, env=env, **kwargs)
# generate next state
next_state, reward, done, info = env.step(action_msg, trajectory=self.trajectory, **kwargs)
self.after_step_hook(step_index=i,
current_state=current_state,
action_msg=action_msg,
reward=reward,
next_state=next_state,
done=done,
info=info,
**kwargs)
if done:
break
current_state = next_state
return self.trajectory

107
experiencemaker/module/agent_wrapper/mcp_react_agent.py
View file

@ -1,107 +0,0 @@
# import os
# import time
# import json
# import best_logger
# import agentscope
# from experiencemaker.module.base_module import BaseModule
# from experiencemaker.schema.trajectory import Trajectory as OutputTrajectory
# from experiencemaker.schema.trajectory import Message as OutputTrajectoryMessage
# from datetime import datetime
# from pydantic import BaseModel, Field
# import uuid
# from typing import (
# Literal,
# Union,
# List,
# Optional,
# Dict,
# Any,
# Sequence,
# )
# from experiencemaker.module.agent_wrapper.base_agent_wrapper import BaseAgentWrapper
# from agentscope.agents import ReActAgent, DialogAgent
# from beyond.trajectory import Trajectory as TrajectoryOperation
# from beyond.solver import TaskExecutor
# from agentscope.message import Msg
# from beyond.planner import *
# from beyond.debug import *
# from best_logger import *
# from loguru import logger
# def run_agent_and_extract_memory(msg_question, traj, agent):
# if not isinstance(msg_question, list):
# raise ValueError("msg_question should be a list of Msg objects")
# agent_ret = agent(msg_question)
# latest_agent_memory_buffer = msg_sort(agent.memory.get_memory())
# traj.add_steps(latest_agent_memory_buffer)
# return latest_agent_memory_buffer, agent_ret
# def msg_sort(msg_list: List[Msg]) -> List[Msg]:
# """
# Sort the message list by timestamp.
# """
# sorted_msg = sorted(
# msg_list,
# key=lambda msg: datetime.strptime(msg.timestamp, "%Y-%m-%d %H:%M:%S.%f")
# )
# return sorted_msg
# class MainAgent(BaseAgentWrapper):
# mcp_url: str = Field(
# default=os.getenv('MCP_URL', 'http://localhost:33333/sse'),
# description="The URL of the MCP server.",
# )
# def __init__(self, *args, **kwargs):
# return super().__init__(*args, **kwargs)
# def execute(self, query, **kwargs):
# question = query.strip()
# ref_answer = "not available"
# print_dict({
# 'question': question,
# 'ref_answer': ref_answer,
# }, mod='gaia_result')
# try:
# except Exception as e:
# logger.exception(f"Error in solving task {question}: {e}")
# raise RuntimeError(f"Error in solving task {question}: {e}")
# print_dict({
# 'question': question,
# 'ref_answer': ref_answer,
# 'predicted_result': final_answer,
# }, mod='gaia_result')
# role_mapping = {
# 'system': 'system',
# 'end-user': 'user',
# 'commander': 'user',
# 'assistant': 'assistant',
# 'tool-agent': 'user',
# 'tool': 'tool',
# }
# output_trajectory = OutputTrajectory(
# steps=[OutputTrajectoryMessage(
# role=role_mapping[step.executor],
# content=step.content,
# timestamp=step.timestamp
# ) for step in traj.raw_steps],
# done=True,
# query=question,
# answer=final_answer,
# current_step=len(traj.raw_steps),
# )
# return output_trajectory

2
experiencemaker/module/agent_wrapper/simple_agent_wrapper.py
View file

@ -15,5 +15,5 @@ class SimpleAgentWrapper(SimpleAgent, AgentWrapperMixin):
trajectory.steps = messages
trajectory.answer = messages[-1].content
trajectory.done = True
trajectory.is_terminated = True
return trajectory

5
experiencemaker/module/context_generator/simple_context_generator.py
View file

@ -11,10 +11,7 @@ from experiencemaker.schema.vector_store_node import VectorStoreNode
class SimpleContextGenerator(BaseContextGenerator):
def _build_retrieve_query(self, trajectory: Trajectory, **kwargs) -> str:
query = ""
if trajectory.current_step == 0:
query = trajectory.query
return query
return trajectory.query
def _retrieve_by_query(self, trajectory: Trajectory, query: str, workspace_id: str, retrieve_top_k: int,
**kwargs) -> List[VectorStoreNode]:

1
experiencemaker/module/context_generator/traj_context_generator.py
View file

@ -1 +0,0 @@
from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator

0
experiencemaker/module/environment/__init__.py
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56
experiencemaker/module/environment/base_environment.py
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@ -1,56 +0,0 @@
from abc import ABC
from typing import List
from pydantic import Field, BaseModel
from experiencemaker.module.reward_fn.base_reward_fn import BaseRewardFn
from experiencemaker.schema.reward import Reward
from experiencemaker.schema.trajectory import StateMessage, ActionMessage, ToolCall
from experiencemaker.tool.base_tool import BaseTool
class BaseEnvironment(BaseModel, ABC):
tools: List[BaseTool] = Field(default_factory=list)
reward_fns: List[BaseRewardFn] = Field(default_factory=list)
current_state: StateMessage = Field(default_factory=StateMessage)
metadata: dict = Field(default_factory=dict, description="add query / answer and etc for reward calculating!")
def reset(self):
self.current_state = StateMessage()
self.metadata.clear()
def step(self, action_msg: ActionMessage, **kwargs):
next_state: StateMessage = self.transition(action_msg=action_msg, **kwargs)
reward: Reward = self.calculate_reward(action_msg=action_msg, next_state=next_state, **kwargs)
done: bool = self.is_terminated(action_msg=action_msg, next_state=next_state, reward=reward, **kwargs)
info: dict = self.build_info(action_msg=action_msg, next_state=next_state, reward=reward, done=done, **kwargs)
self.current_state = next_state
return next_state, reward, done, info
def transition(self, action_msg: ActionMessage, **kwargs) -> StateMessage:
tool_dict = {tool.name: tool for tool in self.tools}
new_tool_calls: List[ToolCall] = []
for tool_call in action_msg.tool_calls:
if tool_call.name not in tool_dict:
continue
new_tool_call = tool_call.model_copy(deep=True)
tool = tool_dict[tool_call.name]
new_tool_call.result = tool.execute(**tool_call.argument_dict)
new_tool_calls.append(new_tool_call)
return StateMessage(tool_calls=new_tool_calls)
def calculate_reward(self, **kwargs) -> Reward:
return Reward()
def is_terminated(self, **kwargs) -> bool:
raise NotImplementedError
def build_info(self, **kwargs):
return {}

0
experiencemaker/module/evaluator/__init__.py
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19
experiencemaker/module/evaluator/base_evaluator.py
View file

@ -1,19 +0,0 @@
from abc import ABC
from pydantic import BaseModel, Field
from experiencemaker.module.agent_wrapper.agent_wrapper_mixin import AgentWrapperMixin
from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator
from experiencemaker.module.environment.base_environment import BaseEnvironment
from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer
class BaseEvaluator(BaseModel, ABC):
data_path: str = Field(default="")
agent_wrapper: AgentWrapperMixin | None = Field(default=None)
context_generator: BaseContextGenerator | None = Field(default=None)
summarizer: BaseSummarizer | None = Field(default=None)
env: BaseEnvironment | None = Field(default=None)
def evaluate(self, **kwargs):
raise NotImplementedError

6
experiencemaker/module/reward_fn/simple_compare_reward_fn.py
View file

@ -55,7 +55,7 @@ class SimpleCompareRewardFn(BaseRewardFn, PromptMixin):
else:
logger.warning(f"no plan exists in result={result}")
reward_value = 0
outcome = 0
if valid_cnt > 0:
reward_value = better_cnt / valid_cnt
return Reward(reward_value=reward_value)
outcome = better_cnt / valid_cnt
return Reward(outcome=outcome)

0
experiencemaker/module/runner/__init__.py
View file

27
experiencemaker/module/runner/base_runner.py
View file

@ -1,27 +0,0 @@
from typing import List
from pydantic import BaseModel, Field
from experiencemaker.module.agent_wrapper.agent_wrapper_mixin import AgentWrapperMixin
from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator
from experiencemaker.module.environment.base_environment import BaseEnvironment
from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer
from experiencemaker.schema.trajectory import Trajectory
class BaseRunner(BaseModel):
agent_wrapper: AgentWrapperMixin | None = Field(default=None)
context_generator: BaseContextGenerator | None = Field(default=None)
summarizer: BaseSummarizer | None = Field(default=None)
env: BaseEnvironment | None = Field(default=None)
traj_buffer: List[Trajectory] = Field(default_factory=list)
def reset(self):
self.traj_buffer.clear()
self.env.reset()
def rollout_trajectory(self, query: str, **kwargs):
raise NotImplementedError
def summary(self, **kwargs):
raise NotImplementedError

4
experiencemaker/module/summarizer/step_summarizer.py
View file

@ -80,8 +80,8 @@ class StepSummarizer(BaseSummarizer, PromptMixin):
all_experiences = []
# Classify trajectories based on trajectory.done
success_trajectories = [traj for traj in trajectories if traj.done]
failure_trajectories = [traj for traj in trajectories if not traj.done]
success_trajectories = [traj for traj in trajectories if traj.is_terminated]
failure_trajectories = [traj for traj in trajectories if not traj.is_terminated]
# Process success and failure samples concurrently
tasks = []

34
experiencemaker/module/summarizer/traj_summarizer.py
View file

@ -1,34 +0,0 @@
import os
from typing import List
from pydantic import Field
from experiencemaker.schema.trajectory import Trajectory, Sample, SummaryMessage
from experiencemaker.storage.base_vector_store import BaseVectorStore
from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer
from beyond.trajectory import Trajectory as TrajectoryOperation
from beyond.solver import TaskExecutor
class TrajectorySummarizer(BaseSummarizer):
vector_store: BaseVectorStore | None = Field(default=None)
samples: List[Sample] = Field(default=[])
def extract_samples(self, trajectories: List[Trajectory], **kwargs) -> List[Sample]:
raise NotImplementedError
def insert_into_vector_store(self, samples: List[Sample], **kwargs):
raise NotImplementedError
def process_trajectory(self, traj: Trajectory):
traj_operation = TrajectoryOperation()
for step in traj.steps:
step.executor = step.role.value
traj_operation.raw_steps += [step]
traj_description = traj_operation.chain_work_steps()
mcp_url = os.getenv('MCP_URL', 'http://localhost:33333/sse')
world_summary = traj_operation.generate_failure_ask_for_internet_help_raj_abs_post_level_3(mcp_url=mcp_url)
self.samples += []
def execute(self, trajectories: List[Trajectory], return_samples: bool = True, **kwargs) -> List[Sample]:
for traj in trajectories:
self.process_trajectory(traj)
return self.samples

0
experiencemaker/module/trainner/__init__.py
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34
experiencemaker/module/trainner/base_trainner.py
View file

@ -1,34 +0,0 @@
from abc import ABC
from typing import List
from pydantic import BaseModel, Field
from experiencemaker.schema.trajectory import Trajectory
class BaseTrainner(BaseModel, ABC):
traj_buffer: List[Trajectory] = Field()
def fit(self):
return
def save_module_state(self):
return
class BaseContextTrainner(BaseTrainner):
"""
load model/prompt/db/buffer -> new cpt
"""
class BaseSummaryTrainner(BaseTrainner):
"""
load model/prompt/db/buffer -> new cpt
"""
class BasePolicyTrainner(BaseTrainner):
"""
load model/prompt/db/buffer -> new cpt
"""

7
experiencemaker/schema/reward.py
View file

@ -2,5 +2,10 @@ from pydantic import Field, BaseModel
class Reward(BaseModel):
reward_value: float | None = Field(default=None)
outcome: float = Field(default=0)
description: str = Field(default="Outcome 1 denotes success, and 0 denotes failure.")
metadata: dict = Field(default_factory=dict)
@property
def success(self) -> bool:
return self.outcome > 0

26
experiencemaker/schema/trajectory.py
View file

@ -6,6 +6,7 @@ from uuid import uuid4
from pydantic import BaseModel, Field, model_validator
from experiencemaker.enumeration.role import Role
from experiencemaker.schema.reward import Reward
class ToolCall(BaseModel):
@ -45,12 +46,7 @@ class ToolCall(BaseModel):
@property
def tool_text_result(self):
return f"""
tool.{self.index}
name={self.name}
arguments={self.arguments}
result={str(self.result)}
""".strip()
return f"tool.{self.index}\nname={self.name}\narguments={self.arguments}\nresult={str(self.result)}".strip()
class Message(BaseModel):
@ -117,21 +113,19 @@ class Sample(BaseModel):
class Trajectory(BaseModel):
id: str = Field(default_factory=lambda: uuid4().hex)
steps: List[Message] = Field(default_factory=list)
current_step: int = Field(default=0)
is_terminated: bool = Field(default=False)
reward: Reward = Field(default_factory=Reward)
done: bool = Field(default=False)
query: str = Field(default="")
answer: Any = Field(default=None)
answer: str = Field(default="")
metadata: dict = Field(default_factory=dict)
def add_step(self, step: Message):
self.steps.append(step)
def reset(self):
self.id = uuid4().hex
self.steps.clear()
self.current_step = 0
self.done = False
self.query = ""
self.answer = ""
self.metadata.clear()
for name, field in self.model_fields.items():
if field.default is not None:
setattr(self, name, field.default)
elif field.default_factory is not None:
setattr(self, name, field.default_factory())

17
experiencemaker/storage/base_sample_buffer.py
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@ -1,17 +0,0 @@
from typing import List
from pydantic import BaseModel
from experiencemaker.schema.trajectory import Sample
class BaseSampleBuffer(BaseModel):
def add(self, samples: Sample | List[Sample]):
raise NotImplementedError
def get_all(self):
raise NotImplementedError
def clear(self):
raise NotImplementedError