c15f35a1df
- Update token pricing with actual gpt-5-nano-2025-08-07 prices: * Input: $0.05 per 1M = $0.00005 per 1K * Cached: $0.005 per 1M = $0.000005 per 1K * Output: $0.40 per 1M = $0.0004 per 1K - Add cached_tokens support in OpenAI service - Update cost calculation to use cached token pricing - Add cached_tokens column to token_usage table (migration) - Fix chat interface keyboard handling: * Send message on Enter key * New line on Shift+Enter * Change onKeyPress to onKeyDown for better support - Add textarea auto-resize with maxHeight limit - Improve responsive styles for mobile devices - Add iOS-specific fixes (prevent zoom on input focus)
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
"""
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TokenUsage model for tracking OpenAI API token consumption
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"""
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from sqlalchemy import Column, String, Integer, Numeric, DateTime, JSON, ForeignKey
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from sqlalchemy.dialects.postgresql import UUID
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from sqlalchemy.orm import relationship
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from sqlalchemy.sql import func
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import uuid
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from app.database import Base
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class TokenUsage(Base):
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"""
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TokenUsage model for tracking and analyzing token consumption and costs
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"""
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__tablename__ = "token_usage"
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True)
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user_id = Column(
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UUID(as_uuid=True),
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ForeignKey("users.id", ondelete="CASCADE"),
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nullable=False,
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index=True
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)
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conversation_id = Column(
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UUID(as_uuid=True),
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ForeignKey("conversations.id", ondelete="SET NULL"),
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index=True
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)
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message_id = Column(
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UUID(as_uuid=True),
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ForeignKey("messages.id", ondelete="SET NULL"),
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index=True
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)
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# Token counts
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prompt_tokens = Column(Integer, default=0, nullable=False)
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cached_tokens = Column(Integer, default=0, nullable=False) # Cached input tokens (charged at lower rate)
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completion_tokens = Column(Integer, default=0, nullable=False)
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total_tokens = Column(Integer, default=0, nullable=False)
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# Model and cost information
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model = Column(String(100), nullable=False) # e.g., gpt-5-nano-2025-08-07
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cost_usd = Column(Numeric(10, 6), default=0.0, nullable=False) # Cost in USD
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# Operation type (chat, search, etc.)
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operation_type = Column(String(50))
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# Timestamp
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created_at = Column(DateTime(timezone=True), server_default=func.now(), nullable=False, index=True)
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# JSON field for additional metadata
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meta_data = Column(JSON, default=dict, nullable=False)
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# Relationships
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user = relationship("User", back_populates="token_usage")
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conversation = relationship("Conversation", back_populates="token_usage")
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message = relationship("Message", back_populates="token_usage_records")
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def __repr__(self):
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return f"<TokenUsage {self.id} - {self.total_tokens} tokens>"
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