103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
from sqlalchemy import Column, Integer, String, Boolean, Float, ForeignKey, Text, DateTime, JSON
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from sqlalchemy.orm import relationship
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import uuid
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from datetime import datetime
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from .database import Base
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# Compatibility layer for SQLite and PostgreSQL without pgvector
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try:
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from sqlalchemy.dialects.postgresql import UUID, ARRAY
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import os
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if "sqlite" in os.getenv("DATABASE_URL", ""):
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raise ImportError("Using SQLite")
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# Try to import pgvector, if fails use JSON
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try:
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from pgvector.sqlalchemy import Vector
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except ImportError:
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# No pgvector, use JSON instead
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Vector = JSON
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except ImportError:
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from sqlalchemy.types import TypeDecorator, CHAR
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import json
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class UUID(TypeDecorator):
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impl = CHAR(36)
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def __init__(self, as_uuid=True):
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super().__init__()
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def process_bind_param(self, value, dialect):
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if value is None: return None
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return str(value)
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def process_result_value(self, value, dialect):
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if value is None: return None
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return uuid.UUID(value)
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class Vector(TypeDecorator):
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impl = Text
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def __init__(self, dim=None):
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super().__init__()
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def process_bind_param(self, value, dialect):
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if value is None: return None
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return json.dumps(value)
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def process_result_value(self, value, dialect):
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if value is None: return None
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return json.loads(value)
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ARRAY = JSON
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class ContextNode(Base):
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__tablename__ = "context_nodes"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
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content = Column(Text, nullable=False)
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embedding = Column(Vector(768))
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version = Column(Integer, default=1)
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status = Column(String(20), default='active')
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created_by = Column(UUID(as_uuid=True), nullable=True) # Placeholder for user link
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# New fields for AI Insights
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ai_insights = Column(JSON, nullable=True) # Stores 'moat', 'tech_specs', etc.
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class Experiment(Base):
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__tablename__ = "experiments"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
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title = Column(String(255))
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hypothesis = Column(Text)
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status = Column(String(50), default='draft')
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owner_id = Column(UUID(as_uuid=True), nullable=True) # Placeholder
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linked_context_nodes = Column(JSON, nullable=True) # Changed from ARRAY to JSON for compatibility
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target_metric = Column(String(50))
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current_score = Column(Float, nullable=True)
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class Signal(Base):
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__tablename__ = "signals"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
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source = Column(String(50))
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content = Column(Text)
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impact_score = Column(Float)
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priority = Column(String(20), default='low') # critical, high, medium, low
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processed = Column(Boolean, default=False)
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# Store strategic analysis result
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strategic_analysis = Column(Text, nullable=True) # JSON string
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tasks_generated = Column(Text, nullable=True) # JSON string
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created_at = Column(DateTime, default=datetime.utcnow)
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class SignalFeedback(Base):
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"""User feedback on AI strategic analysis of signals"""
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__tablename__ = "signal_feedbacks"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
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signal_id = Column(UUID(as_uuid=True), ForeignKey('signals.id'), nullable=False)
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# User's correction of the AI's judgment
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was_strategic = Column(Boolean, nullable=False) # User's actual judgment
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reason = Column(Text, nullable=True) # Why they agree/disagree
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# Reference to original AI prediction for analysis
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ai_predicted_strategic = Column(Boolean, nullable=False)
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ai_confidence = Column(Float, nullable=True)
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# Optional: user info for tracking
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user_id = Column(String(100), nullable=True) # Simple identifier (email, name, etc.)
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created_at = Column(DateTime, default=datetime.utcnow)
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# Relationship to signal
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signal = relationship("Signal", backref="feedbacks")
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