mirror of
https://github.com/aljazceru/enclava.git
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133 lines
4.0 KiB
Python
133 lines
4.0 KiB
Python
"""
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Database models for chatbot module
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"""
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from sqlalchemy import (
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Column,
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Integer,
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String,
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Text,
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Boolean,
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DateTime,
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JSON,
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ForeignKey,
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)
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from sqlalchemy.orm import relationship
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from sqlalchemy.ext.declarative import declarative_base
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from datetime import datetime
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import uuid
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from app.db.database import Base
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class ChatbotInstance(Base):
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"""Configured chatbot instance"""
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__tablename__ = "chatbot_instances"
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id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
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name = Column(String(255), nullable=False)
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description = Column(Text)
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# Configuration stored as JSON
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config = Column(JSON, nullable=False)
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# Metadata
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created_by = Column(String, nullable=False) # User ID
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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is_active = Column(Boolean, default=True)
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# Relationships
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conversations = relationship(
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"ChatbotConversation", back_populates="chatbot", cascade="all, delete-orphan"
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)
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def __repr__(self):
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return f"<ChatbotInstance(id='{self.id}', name='{self.name}')>"
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class ChatbotConversation(Base):
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"""Conversation state and history"""
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__tablename__ = "chatbot_conversations"
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id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
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chatbot_id = Column(String, ForeignKey("chatbot_instances.id"), nullable=False)
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user_id = Column(String, nullable=False) # User ID
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# Conversation metadata
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title = Column(String(255)) # Auto-generated or user-defined title
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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is_active = Column(Boolean, default=True)
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# Conversation context and settings
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context_data = Column(JSON, default=dict) # Additional context
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# Relationships
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chatbot = relationship("ChatbotInstance", back_populates="conversations")
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messages = relationship(
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"ChatbotMessage", back_populates="conversation", cascade="all, delete-orphan"
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)
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def __repr__(self):
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return f"<ChatbotConversation(id='{self.id}', chatbot_id='{self.chatbot_id}')>"
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class ChatbotMessage(Base):
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"""Individual chat messages in conversations"""
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__tablename__ = "chatbot_messages"
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id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
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conversation_id = Column(
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String, ForeignKey("chatbot_conversations.id"), nullable=False
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)
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# Message content
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role = Column(String(20), nullable=False) # 'user', 'assistant', 'system'
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content = Column(Text, nullable=False)
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# Metadata
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timestamp = Column(DateTime, default=datetime.utcnow)
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message_metadata = Column(JSON, default=dict) # Token counts, model used, etc.
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# RAG sources if applicable
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sources = Column(JSON) # RAG sources used for this message
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# Relationships
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conversation = relationship("ChatbotConversation", back_populates="messages")
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def __repr__(self):
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return f"<ChatbotMessage(id='{self.id}', role='{self.role}')>"
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class ChatbotAnalytics(Base):
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"""Analytics and metrics for chatbot usage"""
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__tablename__ = "chatbot_analytics"
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id = Column(Integer, primary_key=True, autoincrement=True)
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chatbot_id = Column(String, ForeignKey("chatbot_instances.id"), nullable=False)
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user_id = Column(String, nullable=False)
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# Event tracking
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event_type = Column(
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String(50), nullable=False
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) # 'message_sent', 'response_generated', etc.
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event_data = Column(JSON, default=dict)
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# Performance metrics
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response_time_ms = Column(Integer)
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token_count = Column(Integer)
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cost_cents = Column(Integer)
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# Context
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model_used = Column(String(100))
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rag_used = Column(Boolean, default=False)
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timestamp = Column(DateTime, default=datetime.utcnow)
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def __repr__(self):
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return f"<ChatbotAnalytics(id={self.id}, event_type='{self.event_type}')>"
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