347 lines
15 KiB
Python
347 lines
15 KiB
Python
"""
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所有的内容是放错误地方了，应该放在models
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"""
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from typing import Any, Optional, List, Dict, Literal
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import time
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import uuid
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from pydantic import BaseModel, Field, ConfigDict, field_validator, model_validator
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# ============================================================================
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# 原 UserInput 相关 Schema (保留原有功能)
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# ============================================================================
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class UserInput(BaseModel):
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message: str
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history: list[dict]
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search_switch: str
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group_id: str
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class Write_UserInput(BaseModel):
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message: str
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group_id: str
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# ============================================================================
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# 从 json_schema.py 迁移的 Schema
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# ============================================================================
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class BaseDataSchema(BaseModel):
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"""Base schema for the data"""
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id: str = Field(..., description="The unique identifier for the data entry.")
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statement: str = Field(..., description="The statement text.")
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group_id: str = Field(..., description="The group identifier.")
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chunk_id: str = Field(..., description="The chunk identifier.")
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created_at: str = Field(..., description="The creation timestamp in ISO 8601 format.")
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expired_at: Optional[str] = Field(None, description="The expiration timestamp in ISO 8601 format.")
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valid_at: Optional[str] = Field(None, description="The validation timestamp in ISO 8601 format.")
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invalid_at: Optional[str] = Field(None, description="The invalidation timestamp in ISO 8601 format.")
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entity_ids: List[str] = Field([], description="The list of entity identifiers.")
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class ConflictResultSchema(BaseModel):
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"""Schema for the conflict result data in the reflexion_data.json file."""
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data: List[BaseDataSchema] = Field(..., description="The conflict memory data.")
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conflict: bool = Field(..., description="Whether the memory is in conflict.")
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conflict_memory: Optional[BaseDataSchema] = Field(None, description="The conflict memory data.")
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@model_validator(mode="before")
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@classmethod
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def _normalize_data(cls, v):
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if isinstance(v, dict):
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d = v.get("data")
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if isinstance(d, dict):
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v["data"] = [d]
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return v
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class ConflictSchema(BaseModel):
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"""Schema for the conflict data in the reflexion_data"""
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data: List[BaseDataSchema] = Field(..., description="The conflict memory data.")
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conflict_memory: Optional[BaseDataSchema] = Field(None, description="The conflict memory data.")
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@model_validator(mode="before")
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@classmethod
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def _normalize_data(cls, v):
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if isinstance(v, dict):
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d = v.get("data")
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if isinstance(d, dict):
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v["data"] = [d]
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return v
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class ReflexionSchema(BaseModel):
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"""Schema for the reflexion data in the reflexion_data"""
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reason: str = Field(..., description="The reason for the reflexion.")
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solution: str = Field(..., description="The solution for the reflexion.")
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class ResolvedSchema(BaseModel):
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"""Schema for the resolved memory data in the reflexion_data"""
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original_memory_id: Optional[str] = Field(None, description="The original memory identifier.")
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resolved_memory: Optional[BaseDataSchema] = Field(None, description="The resolved memory data.")
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class ReflexionResultSchema(BaseModel):
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"""Schema for the reflexion result data in the reflexion_data.json file."""
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# 模型输出中 "conflict" 为单个冲突对象（包含 data 与 conflict_memory），而非字典映射
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conflict: ConflictResultSchema = Field(..., description="The conflict result data.")
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reflexion: Optional[ReflexionSchema] = Field(None, description="The reflexion data.")
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resolved: Optional[ResolvedSchema] = Field(None, description="The resolved memory data.")
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@model_validator(mode="before")
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@classmethod
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def _normalize_resolved(cls, v):
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if isinstance(v, dict):
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conflict = v.get("conflict")
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if isinstance(conflict, dict) and conflict.get("conflict") is False:
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v["resolved"] = None
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else:
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resolved = v.get("resolved")
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if isinstance(resolved, dict):
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orig = resolved.get("original_memory_id")
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mem = resolved.get("resolved_memory")
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if orig is None and (mem is None or mem == {}):
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v["resolved"] = None
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return v
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# ============================================================================
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# 从 messages.py 迁移的 Schema
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# ============================================================================
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# Composite key identifying a config row
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class ConfigKey(BaseModel): # 配置参数键模型
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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config_id: int = Field("config_id", description="配置唯一标识（字符串）")
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user_id: str = Field("user_id", description="用户标识（字符串）")
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apply_id: str = Field("apply_id", description="应用或场景标识（字符串）")
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# Allowed chunking strategies (extendable later)
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ChunkerStrategy = Literal[ # 分块策略枚举
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"RecursiveChunker",
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"TokenChunker",
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"SemanticChunker",
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"NeuralChunker",
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"HybridChunker",
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"LLMChunker",
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"SentenceChunker",
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"LateChunker"
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]
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# 这是 Request body示例
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class ConfigParams(ConfigKey): # 创建配置参数模型 旧
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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# Boolean switches
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enable_llm_dedup_blockwise: bool = Field(True, description="启用LLM决策去重")
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enable_llm_disambiguation: bool = Field(True, description="启用LLM决策消歧")
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deep_retrieval: bool = Field(True, description="深度检索开关（保留既有拼写）")
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# Thresholds in [0, 1]
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t_type_strict: float = Field(0.8, ge=0.0, le=1.0, description="类型严格阈值")
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t_name_strict: float = Field(0.8, ge=0.0, le=1.0, description="名称严格阈值")
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t_overall: float = Field(0.8, ge=0.0, le=1.0, description="综合阈值")
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state: bool = Field(False, description="配置使用状态（True/False）")
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# Chunker strategy selection (must be one of the declared literals)
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chunker_strategy: ChunkerStrategy = Field(
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"RecursiveChunker",
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description=(
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"分块策略：RecursiveChunker/TokenChunker/SemanticChunker/NeuralChunker/"
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"HybridChunker/LLMChunker/SentenceChunker/LateChunker"
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),
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)
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@field_validator("chunker_strategy", mode="before")
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@classmethod
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def map_chunker_aliases(cls, v: str):
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# 允许常见别名并映射到合法枚举
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if isinstance(v, str):
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m = v.strip().lower()
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alias_map = {
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"auto": "RecursiveChunker",
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"by_sentence": "SentenceChunker",
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"by_paragraph": "SemanticChunker",
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"fixed_tokens": "TokenChunker",
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"递归分块": "RecursiveChunker",
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"token 分块": "TokenChunker",
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"token分块": "TokenChunker",
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"语义分块": "SemanticChunker",
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"神经网络分块": "NeuralChunker",
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"混合分块": "HybridChunker",
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"llm 分块": "LLMChunker",
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"llm分块": "LLMChunker",
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"句子分块": "SentenceChunker",
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"延迟分块": "LateChunker",
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}
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if m in alias_map:
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return alias_map[m]
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return v
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@field_validator("config_id", "user_id", "apply_id")
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@classmethod
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def non_empty_str(cls, v: str) -> str:
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s = str(v).strip() if v is not None else ""
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if not s:
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raise ValueError("标识字段必须为非空字符串")
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return s
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class ConfigParamsCreate(BaseModel): # 创建配置参数模型（仅 body，去除主键）
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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config_name: str = Field("配置名称", description="配置名称（字符串）")
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config_desc: str = Field("配置描述", description="配置描述（字符串）")
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workspace_id: Optional[uuid.UUID] = Field(None, description="工作空间ID（UUID）")
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# 模型配置字段（可选，用于手动指定或自动填充）
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llm_id: Optional[str] = Field(None, description="LLM模型配置ID")
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embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID")
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rerank_id: Optional[str] = Field(None, description="重排序模型配置ID")
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class ConfigParamsDelete(BaseModel): # 删除配置参数模型（请求体）
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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# config_name: str = Field("配置名称", description="配置名称（字符串）")
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config_id: int = Field("配置ID", description="配置ID（字符串）")
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class ConfigUpdate(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型
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config_id: Optional[int] = None
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config_name: str = Field("配置名称", description="配置名称（字符串）")
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config_desc: str = Field("配置描述", description="配置描述（字符串）")
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class ConfigUpdateExtracted(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型
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config_id: Optional[int] = None
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llm_id: Optional[str] = Field(None, description="LLM模型配置ID")
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embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID")
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rerank_id: Optional[str] = Field(None, description="重排序模型配置ID")
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enable_llm_dedup_blockwise: Optional[bool] = None
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enable_llm_disambiguation: Optional[bool] = None
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deep_retrieval: Optional[bool] = Field(None, validation_alias="deep_retrieval")
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t_type_strict: Optional[float] = Field(None, ge=0.0, le=1.0)
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t_name_strict: Optional[float] = Field(None, ge=0.0, le=1.0)
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t_overall: Optional[float] = Field(None, ge=0.0, le=1.0)
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state: Optional[bool] = None
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chunker_strategy: Optional[ChunkerStrategy] = None
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# 句子提取
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statement_granularity: Optional[int] = Field(2, ge=1, le=3, description="陈述提取颗粒度，挡位 1/2/3；默认 2")
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include_dialogue_context: Optional[bool] = None
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max_context: Optional[int] = Field(1000, gt=100, description="对话语境中包含字符的最大数量（>100）；默认 1000")
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# 剪枝配置：与 runtime.json 中 pruning 段对应
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pruning_enabled: Optional[bool] = Field(None, description="是否启动智能语义剪枝")
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pruning_scene: Optional[Literal["education", "online_service", "outbound"]] = Field(
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None, description="智能剪枝场景：education/online_service/outbound"
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)
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pruning_threshold: Optional[float] = Field(
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None, ge=0.0, le=0.9, description="智能语义剪枝阈值（0-0.9）"
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)
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# 反思配置
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enable_self_reflexion: Optional[bool] = Field(None, description="是否启用自我反思")
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iteration_period: Optional[Literal["1", "3", "6", "12", "24"]] = Field(
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"3", description="反思迭代周期，单位小时"
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)
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reflexion_range: Optional[Literal["retrieval", "database"]] = Field(
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"retrieval", description="反思范围：部分/全部"
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)
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baseline: Optional[Literal["TIME", "FACT", "TIME-FACT"]] = Field(
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"TIME", description="基线：时间/事实/时间和事实"
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)
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@field_validator("chunker_strategy", mode="before")
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@classmethod
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def map_chunker_aliases_update(cls, v: str):
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if isinstance(v, str):
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m = v.strip().lower()
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alias_map = {
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"auto": "RecursiveChunker",
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"by_sentence": "SentenceChunker",
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"by_paragraph": "SemanticChunker",
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"fixed_tokens": "TokenChunker",
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"递归分块": "RecursiveChunker",
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"token 分块": "TokenChunker",
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"token分块": "TokenChunker",
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"语义分块": "SemanticChunker",
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"神经网络分块": "NeuralChunker",
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"混合分块": "HybridChunker",
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"llm 分块": "LLMChunker",
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"llm分块": "LLMChunker",
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"句子分块": "SentenceChunker",
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"延迟分块": "LateChunker",
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}
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if m in alias_map:
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return alias_map[m]
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return v
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class ConfigUpdateForget(BaseModel): # 更新遗忘引擎配置参数时使用的模型
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# 遗忘引擎配置参数更新模型
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config_id: Optional[int] = None
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lambda_time: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="最低保持度，0-1 小数；默认 0.5")
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lambda_mem: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="遗忘率，0-1 小数；默认 0.5")
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offset: Optional[float] = Field(0.0, ge=0.0, le=1.0, description="偏移度，0-1 小数；默认 0.0")
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class ConfigPilotRun(BaseModel): # 试运行触发请求模型
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config_id: int = Field(..., description="配置ID（唯一）")
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dialogue_text: str = Field(..., description="前端传入的对话文本，格式如 '用户: ...\nAI: ...' 可多行，试运行必填")
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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class ConfigFilter(BaseModel): # 查询配置参数时使用的模型
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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config_id: Optional[int] = None
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user_id: Optional[str] = None
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apply_id: Optional[str] = None
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limit: int = Field(20, ge=1, le=200, description="返回数量上限")
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offset: int = Field(0, ge=0, description="起始偏移")
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class ApiResponse(BaseModel): # 通用API响应模型
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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code: int = Field(..., description="0=成功，非0=各类业务异常")
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msg: str = Field("", description="说明信息")
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data: Optional[Any] = Field(None, description="返回数据载荷")
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error: str = Field("", description="错误信息，失败时有值，成功为空字符串")
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time: Optional[int] = Field(None, description="响应时间（毫秒，Unix 时间戳）")
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def _now_ms() -> int:
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return round(time.time() * 1000)
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def ok(msg: str = "OK", data: Optional[Any] = None, time: Optional[int] = None) -> ApiResponse:
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return ApiResponse(code=0, msg=msg, data=data, error="", time=time or _now_ms())
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def fail(
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msg: str,
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error_code: str = "ERROR",
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data: Optional[Any] = None,
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time: Optional[int] = None,
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query_preview: Optional[str] = None,
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) -> ApiResponse:
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payload = data
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if query_preview is not None:
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if payload is None:
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payload = {"query_preview": query_preview}
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elif isinstance(payload, dict):
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payload = {**payload, "query_preview": query_preview}
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else:
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payload = {"data": payload, "query_preview": query_preview}
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return ApiResponse(
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code=1,
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msg=msg,
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data=payload,
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error=error_code,
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time=time or _now_ms(),
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)
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