ractogateway.rag._models

RAG Pydantic models.

class ractogateway.rag._models.Chunk(**data)

Bases: BaseModel

A single embeddable slice of a document.

Produced by a BaseChunker and enriched with an embedding vector by a BaseEmbedder.

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_id: str

doc_id: str

content: str

embedding: list[float] | None

metadata: ChunkMetadata

model_config: ClassVar[ConfigDict] = {}

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

class ractogateway.rag._models.ChunkMetadata(**data)

Bases: BaseModel

Provenance and positional data attached to every chunk.

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.

source: str

page: int | None

chunk_index: int

total_chunks: int

start_char: int

end_char: int

doc_id: str

extra: 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.Document(**data)

Bases: BaseModel

A raw document loaded from a file or supplied as plain text.

Parameters:

• content (str) – The full extracted text of the document.

• source (str) – Absolute file path, URL, or a descriptive label (e.g. "manual").

• metadata (dict[str, Any]) – Free-form key/value pairs (file size, author, MIME type, …).

• doc_id (str) – Auto-generated UUID; override only when you need stable IDs.

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.

doc_id: str

content: str

source: str

metadata: 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.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].

class ractogateway.rag._models.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.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].