ractogateway.rag._models.retrieval

Retrieval and RAG response models.

class ractogateway.rag._models.retrieval.RetrievalConfig(**data)

Bases: BaseModel

Input parameters for a vector-store search.

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.

query: str

top_k: int

filters: dict[str, Any]

model_config: ClassVar[ConfigDict] = {}

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

class ractogateway.rag._models.retrieval.RetrievalResult(**data)

Bases: BaseModel

A single retrieved chunk together with its relevance score.

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.

chunk: Chunk

score: float

rank: int

model_config: ClassVar[ConfigDict] = {}

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

class ractogateway.rag._models.retrieval.RAGResponse(**data)

Bases: BaseModel

Combined output from a RAG query (retrieval + generation).

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.

answer: LLMResponse

sources: list[RetrievalResult]

query: str

context_used: str

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

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