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https://github.com/agentscope-ai/ReMe.git
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96 lines
4.1 KiB
Python
96 lines
4.1 KiB
Python
import datetime
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from typing import List
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from uuid import uuid4
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from pydantic import BaseModel, Field
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from experiencemaker.schema.vector_store_node import VectorStoreNode
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class ExperienceFunctionArg(BaseModel):
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arg_name: str = Field(default=..., description="argument name")
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arg_type: str = Field(default=..., description="argument type, like: 'str', 'int', 'bool'")
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required: bool = Field(default=True, description="whether the argument is required")
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class ExperienceFunction(BaseModel):
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func_code: str = Field(default=..., description="function code")
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func_name: str = Field(default=..., description="function name")
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func_args: List[ExperienceFunctionArg] = Field(default_factory=list, description="function arguments")
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class FunctionExperience(BaseModel):
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experience_function: ExperienceFunction | None = Field(default=None, description="experience function(optional)")
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# 前期作为文本放进来，后期会转换成function
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class TextExperience(BaseModel):
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experience_id: str = Field(default_factory=lambda: uuid4().hex, description="experience unique id")
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experience_workspace_id: str = Field(default="", description="unique workspace id")
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when_to_use_experience: str = Field(default="", description="use condition/purpose. It will be used in vector matching")
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experience_content: str | bytes = Field(default="", description="content of the experience")
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experience_score: float = Field(default=0.0, description="score of the experience")
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metadata: dict = Field(default_factory=dict, description="additional metadata")
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"""
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metadata
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experience_created_time: str = Field(default_factory=lambda: datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
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experience_modified_time: str = Field(default_factory=lambda: datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
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experience_role: str = Field(default="", description="experience role")
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"""
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def to_vector_store_node(self) -> VectorStoreNode:
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metadata: dict = {
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"experience_role": self.experience_role,
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"experience_content": self.experience_content,
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"experience_score": self.experience_score,
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"experience_created_time": self.experience_created_time,
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"experience_modified_time": self.experience_modified_time,
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"metadata": self.metadata,
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}
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if self.experience_function:
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metadata["experience_function"] = self.experience_function.model_dump(),
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return VectorStoreNode(
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unique_id=self.experience_id,
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workspace_id=self.experience_workspace_id,
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content=self.experience_desc,
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metadata=metadata)
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@classmethod
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def from_vector_store_node(cls, node: VectorStoreNode) -> "Experience":
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return cls(
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experience_id=node.unique_id,
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experience_workspace_id=node.workspace_id,
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experience_role=node.metadata.get("experience_role", ""),
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experience_desc=node.content,
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experience_content=node.metadata.get("experience_content", ""),
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experience_function=node.metadata.get("experience_function", None),
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experience_score=node.metadata.get("experience_score", 0.0),
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experience_created_time=node.metadata.get("experience_created_time", ""),
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experience_modified_time=node.metadata.get("experience_modified_time", ""),
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metadata=node.metadata.get("metadata", {}))
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if __name__ == "__main__":
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e1 = Experience(
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experience_workspace_id="w_1024",
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experience_role="qwen3",
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experience_desc="test desc",
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experience_content="test content",
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experience_function=ExperienceFunction(
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func_code="def a():\n return",
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func_name="a",
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func_args=[ExperienceFunctionArg(arg_name="x", arg_type="str", required=True)]
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),
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experience_score=0.99,
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metadata={"haha": 1}
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)
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print(e1.model_dump_json(indent=2))
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v1 = e1.to_vector_store_node()
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print(v1.model_dump_json(indent=2))
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e2 = Experience.from_vector_store_node(v1)
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print(e2.model_dump_json(indent=2))
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