mirror of
https://github.com/langgenius/dify-plugin-sdks.git
synced 2026-07-22 18:35:29 -04:00
Yeuoly
f902504af0
* Inline multimodal entities into existing models * apply ruff * bump to 0.7.0b1 * fix: remove tenant_id from invoke_multimodal_embedding * tests: add rerank * apply ruff * fix * fix: typing
72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
from decimal import Decimal
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from enum import StrEnum
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from pydantic import BaseModel, ConfigDict, Field
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from dify_plugin.entities.model import BaseModelConfig, ModelType, ModelUsage
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class EmbeddingUsage(ModelUsage):
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"""
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Model class for embedding usage.
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"""
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tokens: int
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total_tokens: int
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unit_price: Decimal
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price_unit: Decimal
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total_price: Decimal
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currency: str
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latency: float
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class TextEmbeddingResult(BaseModel):
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"""
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Model class for text embedding result.
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"""
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model: str
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embeddings: list[list[float]]
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usage: EmbeddingUsage
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class TextEmbeddingModelConfig(BaseModelConfig):
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"""
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Model class for text embedding model config.
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"""
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model_type: ModelType = ModelType.TEXT_EMBEDDING
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model_config = ConfigDict(protected_namespaces=())
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class MultiModalContentType(StrEnum):
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"""Supported content types for multimodal inputs."""
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TEXT = "text"
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IMAGE = "image"
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class MultiModalContent(BaseModel):
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"""A multimodal content payload provided by the caller."""
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content: str = Field(..., description="The payload content, plain text or base64 encoded file data.")
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content_type: MultiModalContentType = Field(..., description="The modality of the provided content.")
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class MultiModalEmbeddingResult(BaseModel):
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"""Embedding response produced by a multimodal embedding model."""
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model: str = Field(..., description="Identifier of the model generating embeddings.")
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embeddings: list[list[float]] = Field(..., description="Embedding vectors for provided contents.")
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usage: EmbeddingUsage = Field(..., description="Usage metrics associated with the inference.")
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class MultiModalEmbeddingModelConfig(BaseModelConfig):
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"""Configuration payload for invoking a multimodal embedding model."""
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model_type: ModelType = ModelType.TEXT_EMBEDDING
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tenant_id: str = Field(..., description="Vendor tenant identifier associated with the dataset.")
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model_config = ConfigDict(protected_namespaces=())
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