ractogateway._models.embedding

Typed input / output models for embedding calls.

class ractogateway._models.embedding.EmbeddingConfig(**data)

Bases: BaseModel

Validated input for embed / aembed calls.

Example:

config = EmbeddingConfig(texts=["Hello world", "Goodbye world"])
response = kit.embed(config)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

texts: list[str]

model: str | None

dimensions: int | None

extra: dict[str, Any]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class ractogateway._models.embedding.EmbeddingVector(**data)

Bases: BaseModel

A single embedding result.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

index: int

text: str

embedding: list[float]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class ractogateway._models.embedding.EmbeddingResponse(**data)

Bases: BaseModel

Unified response from an embedding call.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

vectors: list[EmbeddingVector]

model: str

usage: dict[str, int]

raw: Any

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].