329 lines
13 KiB
Python
329 lines
13 KiB
Python
# AI history and template models
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from sqlalchemy import BigInteger, Column, DateTime, Integer, Numeric, String, Text, Boolean, Float
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from sqlalchemy.dialects import postgresql
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from sqlalchemy.ext.compiler import compiles
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from sqlalchemy.types import JSON, UserDefinedType
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from database.models import Base
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from datetime import datetime
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class Vector(UserDefinedType):
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"""pgvector column with a SQLite-safe fallback for local metadata tests."""
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cache_ok = True
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def __init__(self, dimensions):
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self.dimensions = dimensions
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def get_col_spec(self, **kw):
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return f"VECTOR({self.dimensions})"
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@compiles(Vector, "sqlite")
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def _compile_vector_sqlite(type_, compiler, **kw):
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return "TEXT"
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def _jsonb_type():
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return JSON().with_variant(postgresql.JSONB, "postgresql")
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def _bigint_array_type():
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return Text().with_variant(postgresql.ARRAY(BigInteger), "postgresql")
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class AIGenerationHistory(Base):
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"""
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AI generation history tracking
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"""
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__tablename__ = 'ai_generation_history'
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id = Column(Integer, primary_key=True, autoincrement=True)
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generation_type = Column(String(50), nullable=False) # product_desc, marketing_copy, etc.
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product_name = Column(String(200))
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input_keywords = Column(Text) # JSON string
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input_trend_topic = Column(String(500))
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output_content = Column(Text)
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generation_duration = Column(Float) # seconds
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is_favorite = Column(Boolean, default=False)
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is_used = Column(Boolean, default=False)
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created_by = Column(String(100))
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created_at = Column(DateTime, default=datetime.now)
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def to_dict(self):
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return {
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'id': self.id,
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'generation_type': self.generation_type,
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'product_name': self.product_name,
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'input_keywords': self.input_keywords,
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'input_trend_topic': self.input_trend_topic,
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'output_content': self.output_content,
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'generation_duration': self.generation_duration,
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'is_favorite': self.is_favorite,
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'is_used': self.is_used,
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'created_by': self.created_by,
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'created_at': self.created_at.isoformat() if self.created_at else None
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}
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class AIPromptTemplate(Base):
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"""
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AI prompt templates
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"""
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__tablename__ = 'ai_prompt_templates'
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id = Column(Integer, primary_key=True, autoincrement=True)
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name = Column(String(200), nullable=False)
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description = Column(Text)
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template_type = Column(String(50), nullable=False) # product_desc, marketing_copy, etc.
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prompt_content = Column(Text, nullable=False)
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variables = Column(Text) # JSON string of variable definitions
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is_active = Column(Boolean, default=True)
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is_system = Column(Boolean, default=False)
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created_by = Column(String(100))
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created_at = Column(DateTime, default=datetime.now)
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updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
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def to_dict(self):
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return {
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'id': self.id,
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'name': self.name,
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'description': self.description,
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'template_type': self.template_type,
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'prompt_content': self.prompt_content,
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'variables': self.variables,
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'is_active': self.is_active,
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'is_system': self.is_system,
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'created_by': self.created_by,
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'created_at': self.created_at.isoformat() if self.created_at else None,
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'updated_at': self.updated_at.isoformat() if self.updated_at else None
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}
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class AIUsageTracking(Base):
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"""
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AI usage tracking for analytics and billing
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"""
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__tablename__ = 'ai_usage_tracking'
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id = Column(Integer, primary_key=True, autoincrement=True)
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user_id = Column(String(100))
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session_id = Column(String(100))
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service_type = Column(String(50), nullable=False) # openai, ollama, etc.
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model_name = Column(String(100))
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prompt_tokens = Column(Integer, default=0)
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completion_tokens = Column(Integer, default=0)
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total_tokens = Column(Integer, default=0)
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cost_usd = Column(Float, default=0.0)
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request_type = Column(String(50)) # generation, analysis, etc.
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status = Column(String(20), default='success') # success, failed
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error_message = Column(Text)
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duration_ms = Column(Float)
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created_at = Column(DateTime, default=datetime.now)
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def to_dict(self):
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return {
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'id': self.id,
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'user_id': self.user_id,
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'session_id': self.session_id,
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'service_type': self.service_type,
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'model_name': self.model_name,
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'prompt_tokens': self.prompt_tokens,
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'completion_tokens': self.completion_tokens,
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'total_tokens': self.total_tokens,
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'cost_usd': self.cost_usd,
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'request_type': self.request_type,
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'status': self.status,
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'error_message': self.error_message,
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'duration_ms': self.duration_ms,
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'created_at': self.created_at.isoformat() if self.created_at else None
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}
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class AIInsight(Base):
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"""
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AI 洞察知識庫（ai_insights 表）— KM 沉澱核心，支援 RAG 向量化。
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對應 openclaw_learning_service / scheduler 各任務寫入。
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"""
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__tablename__ = 'ai_insights'
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id = Column(Integer, primary_key=True, autoincrement=True)
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insight_type = Column(String(50), nullable=False) # backup_status / human_review / auto_heal_playbook 等
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period = Column(String(50)) # 分析週期，e.g. "2026-04-20"
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product_sku = Column(String(50))
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content = Column(Text, nullable=False)
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metadata_json = Column(Text) # JSON extra payload
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avg_quality = Column(Float, default=0.5)
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status = Column(String(20), default='approved') # approved / pending / rejected / archived
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decay_exempt = Column(Boolean, default=False)
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ai_model = Column(String(50))
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feedback_up = Column(Integer, default=0)
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feedback_down = Column(Integer, default=0)
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confidence = Column(Float, default=0.5)
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created_by = Column(String(50), default='system')
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created_at = Column(DateTime, default=datetime.now)
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updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
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# embedding 欄位為 pgvector 型別，透過 raw SQL 寫入，此處不聲明以避免型別衝突
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def to_dict(self):
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return {
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'id': self.id,
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'insight_type': self.insight_type,
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'period': self.period,
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'product_sku': self.product_sku,
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'content': self.content,
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'avg_quality': self.avg_quality,
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'status': self.status,
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'ai_model': self.ai_model,
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'feedback_up': self.feedback_up,
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'feedback_down': self.feedback_down,
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'confidence': self.confidence,
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'created_by': self.created_by,
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'created_at': self.created_at.isoformat() if self.created_at else None,
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}
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class AICall(Base):
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"""ai_calls unified LLM call telemetry table (migration 024)."""
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__tablename__ = 'ai_calls'
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__table_args__ = {'extend_existing': True}
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id = Column(BigInteger, primary_key=True, autoincrement=True)
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called_at = Column(DateTime(timezone=True), default=datetime.now, nullable=False)
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caller = Column(String(64), nullable=False)
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provider = Column(String(32), nullable=False)
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model = Column(String(128), nullable=False)
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input_tokens = Column(Integer, default=0, nullable=False)
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output_tokens = Column(Integer, default=0, nullable=False)
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duration_ms = Column(Integer)
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status = Column(String(16), nullable=False)
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fallback_to = Column(String(64))
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cost_usd = Column(Numeric(10, 6), default=0, nullable=False)
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cache_hit = Column(Boolean, default=False, nullable=False)
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rag_hit = Column(Boolean, default=False, nullable=False)
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request_id = Column(String(64))
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error = Column(Text)
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meta = Column(_jsonb_type())
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class MCPCall(Base):
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"""mcp_calls MCP server call telemetry table (migration 025)."""
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__tablename__ = 'mcp_calls'
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__table_args__ = {'extend_existing': True}
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id = Column(BigInteger, primary_key=True, autoincrement=True)
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called_at = Column(DateTime(timezone=True), default=datetime.now, nullable=False)
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caller = Column(String(64), nullable=False)
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server = Column(String(64), nullable=False)
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tool = Column(String(128), nullable=False)
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input_args = Column(_jsonb_type())
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output_size = Column(Integer)
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duration_ms = Column(Integer)
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status = Column(String(16), nullable=False)
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error = Column(Text)
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cost_usd = Column(Numeric(10, 6), default=0, nullable=False)
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cache_hit = Column(Boolean, default=False, nullable=False)
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request_id = Column(String(64))
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insight_id = Column(BigInteger)
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class AICallBudget(Base):
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"""ai_call_budgets budget guardrail table (migration 025)."""
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__tablename__ = 'ai_call_budgets'
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__table_args__ = {'extend_existing': True}
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id = Column(Integer, primary_key=True, autoincrement=True)
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period = Column(String(16), nullable=False)
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provider = Column(String(32))
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budget_usd = Column(Numeric(10, 2), nullable=False)
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alert_pct = Column(Integer, default=80, nullable=False)
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updated_at = Column(DateTime(timezone=True), default=datetime.now)
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class RAGQueryLog(Base):
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"""rag_query_log RAG recall telemetry table (migration 027)."""
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__tablename__ = 'rag_query_log'
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__table_args__ = {'extend_existing': True}
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id = Column(BigInteger, primary_key=True, autoincrement=True)
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queried_at = Column(DateTime(timezone=True), default=datetime.now, nullable=False)
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caller = Column(String(64), nullable=False)
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query_text = Column(Text, nullable=False)
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query_embedding = Column(Vector(1024))
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embedding_signature = Column(String(64))
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top_k = Column(Integer, default=5, nullable=False)
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threshold = Column(Numeric(4, 3), default=0.85, nullable=False)
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hit_count = Column(Integer, default=0, nullable=False)
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used_results = Column(_bigint_array_type())
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saved_call = Column(Boolean, default=False, nullable=False)
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feedback_score = Column(Integer)
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request_id = Column(String(64))
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class LearningEpisode(Base):
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"""learning_episodes PromotionGate staging table (migration 028)."""
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__tablename__ = 'learning_episodes'
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__table_args__ = {'extend_existing': True}
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id = Column(BigInteger, primary_key=True, autoincrement=True)
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created_at = Column(DateTime(timezone=True), default=datetime.now, nullable=False)
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episode_type = Column(String(32), nullable=False)
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source_table = Column(String(32))
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source_id = Column(BigInteger)
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distilled_text = Column(Text, nullable=False)
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embedding = Column(Vector(1024))
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embedding_signature = Column(String(64))
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quality_score = Column(Numeric(4, 3), default=0.0, nullable=False)
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weight = Column(Numeric(4, 3), default=0.5, nullable=False)
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promotion_status = Column(String(32), default='pending', nullable=False)
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insight_id = Column(BigInteger)
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rejected_reason = Column(Text)
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human_approver = Column(String(64))
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reviewed_at = Column(DateTime(timezone=True))
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class HostHealthProbe(Base):
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"""host_health_probes Ollama failover health history table (migration 029)."""
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__tablename__ = 'host_health_probes'
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__table_args__ = {'extend_existing': True}
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id = Column(BigInteger, primary_key=True, autoincrement=True)
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probed_at = Column(DateTime(timezone=True), default=datetime.now, nullable=False)
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host_label = Column(String(64), nullable=False)
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host_url = Column(String(256), nullable=False)
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healthy = Column(Boolean, nullable=False)
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unhealthy_mark = Column(Boolean, default=False, nullable=False)
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models_count = Column(Integer, default=0)
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response_ms = Column(Integer)
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error_msg = Column(Text)
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class PPTAuditResult(Base):
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"""ppt_audit_results PPT vision audit history table (migration 030)."""
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__tablename__ = 'ppt_audit_results'
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__table_args__ = {'extend_existing': True}
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id = Column(BigInteger, primary_key=True, autoincrement=True)
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audited_at = Column(DateTime(timezone=True), default=datetime.now, nullable=False)
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pptx_filename = Column(String(256), nullable=False)
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pptx_size_kb = Column(Integer)
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pptx_mtime = Column(DateTime(timezone=True))
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vision_enabled = Column(Boolean, nullable=False)
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audit_status = Column(String(32), nullable=False)
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issues_count = Column(Integer, default=0)
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issues_found = Column(_jsonb_type())
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confidence = Column(Numeric(4, 3))
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duration_ms = Column(Integer)
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error_msg = Column(Text)
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reviewer_notes = Column(Text)
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__all__ = [
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"AIGenerationHistory", "AIPromptTemplate", "AIUsageTracking", "AIInsight",
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"AICall", "MCPCall", "AICallBudget", "RAGQueryLog", "LearningEpisode",
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"HostHealthProbe", "PPTAuditResult",
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]
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