Spaces:
Running
Running
File size: 5,212 Bytes
c4d486b a066e5a e5b256c a066e5a e5b256c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | from datetime import datetime, timezone
import uuid
from sqlalchemy import (
Column,
String,
Text,
DateTime,
Float,
Integer,
Boolean,
ForeignKey,
)
from sqlalchemy.orm import relationship
from sqlalchemy.dialects.postgresql import UUID
# Absolute import from project root
from app.models.database import Base
class UserPreference(Base):
__tablename__ = "user_preferences"
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False)
# Preference details
preference_type = Column(
String(50), nullable=False
) # e.g., "name", "product_interest", "communication_style"
preference_key = Column(
String(100), nullable=False
) # e.g., "user_name", "preferred_products", "formality_level"
preference_value = Column(
Text, nullable=False
) # e.g., "John", "toys,gifts", "casual"
# Learning metadata
confidence_score = Column(
Float, default=1.0
) # How confident the model is (0.0-1.0)
learned_from_messages = Column(
Integer, default=1
) # Number of messages that taught us this
first_learned = Column(DateTime, default=lambda: datetime.now(timezone.utc))
last_updated = Column(DateTime, default=lambda: datetime.now(timezone.utc))
# Relationship
user = relationship("User", back_populates="preferences")
def __repr__(self):
return f"<UserPreference(id={self.id} user_id={self.user_id} {self.preference_key}={self.preference_value})>"
class ConversationTopic(Base):
__tablename__ = "conversation_topics"
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
conversation_id = Column(
UUID(as_uuid=True), ForeignKey("conversations.id"), nullable=False
)
# Topic details
topic = Column(
String(100), nullable=False
) # "product_inquiry", "order_management", "support"
subtopic = Column(String(100)) # "toys", "cancellation", "shipping_issue"
keywords = Column(Text) # JSON list of relevant keywords
# Topic metadata
first_mentioned = Column(DateTime, default=lambda: datetime.now(timezone.utc))
message_count = Column(Integer, default=1) # How many messages discussed this topic
importance_score = Column(
Float, default=1.0
) # How important this topic was (0.0-1.0)
# Relationships
conversation = relationship("Conversation", back_populates="topics")
def __repr__(self):
return f"<ConversationTopic(topic={self.topic}, subtopic={self.subtopic})>"
class UserInsight(Base):
__tablename__ = "user_insights"
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), nullable=False)
# Insight details
insight_type = Column(
String(50), nullable=False
) # "behavior_pattern", "preference_trend", "interaction_style"
insight_key = Column(
String(100), nullable=False
) # "most_active_time", "preferred_topics", "question_complexity"
insight_value = Column(Text, nullable=False) # "morning", "toys,gifts", "simple"
insight_description = Column(Text) # Human-readable explanation
# Analytics metadata
confidence_level = Column(Float, default=1.0) # Statistical confidence (0.0-1.0)
based_on_messages = Column(Integer, default=0) # Number of messages analyzed
last_calculated = Column(DateTime, default=lambda: datetime.now(timezone.utc))
is_active = Column(Boolean, default=True) # Is this insight still relevant?
# Relationships
user = relationship("User", back_populates="insights")
def __repr__(self):
return f"<UserInsight(id={self.id} user_id={self.user_id}, {self.insight_key}={self.insight_value})>"
class KnowledgeBase(Base):
__tablename__ = "knowledge_base"
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
created_at = Column(DateTime, default=lambda: datetime.now(timezone.utc))
updated_at = Column(
DateTime,
default=lambda: datetime.now(timezone.utc),
onupdate=lambda: datetime.now(timezone.utc),
)
tenant_id = Column(UUID(as_uuid=True), ForeignKey("tenants.id"), nullable=False)
entry = Column(Text, nullable=False)
def __repr__(self):
return f"<KnowledgeBase(id={self.id} tenant_id={self.tenant_id})>"
class Tenant(Base):
__tablename__ = "tenants"
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
name = Column(String(255), nullable=False)
api_key = Column(String(255), unique=True, nullable=False)
is_active = Column(Boolean, default=True)
created_at = Column(DateTime, default=lambda: datetime.now(timezone.utc))
updated_at = Column(
DateTime,
default=lambda: datetime.now(timezone.utc),
onupdate=lambda: datetime.now(timezone.utc),
)
# Relationships
users = relationship("User", back_populates="tenant")
conversations = relationship("Conversation", back_populates="tenant")
def __repr__(self):
return f"<Tenant(id={self.id} name={self.name})>"
|