mirror of
https://github.com/dograh-hq/dograh.git
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ef5b9e40a9
* feat: upload file and store embedding * feat: add documents in nodes * feat: add openai embedding service
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
"""
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Embeddings pricing models for different providers.
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Prices are per token for embedding models.
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"""
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from decimal import Decimal
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from typing import Dict
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from api.services.configuration.registry import ServiceProviders
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from .models import PricingModel
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class EmbeddingPricingModel(PricingModel):
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"""Pricing model for token-based embedding services."""
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def __init__(self, token_price: Decimal):
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"""Initialize with price per token.
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Args:
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token_price: Cost per token for embedding
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"""
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self.token_price = token_price
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def calculate_cost(self, token_count: int) -> Decimal:
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"""Calculate cost for embedding token usage."""
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return Decimal(token_count) * self.token_price
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# Embeddings pricing registry
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EMBEDDINGS_PRICING: Dict[str, Dict[str, EmbeddingPricingModel]] = {
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ServiceProviders.OPENAI: {
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"text-embedding-3-small": EmbeddingPricingModel(
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token_price=Decimal("0.02") / 1_000_000, # $0.02 per 1M tokens
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),
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"text-embedding-3-large": EmbeddingPricingModel(
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token_price=Decimal("0.13") / 1_000_000, # $0.13 per 1M tokens
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),
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"text-embedding-ada-002": EmbeddingPricingModel(
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token_price=Decimal("0.10") / 1_000_000, # $0.10 per 1M tokens (legacy)
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),
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},
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}
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