ractogateway.prompts

ractogateway.prompts — RACTO Prompt Engine.

Provides the RactoPrompt Pydantic model that enforces structured prompt definition via the RACTO principle (Role, Aim, Constraints, Tone, Output) and compiles it into an optimized, anti-hallucination system prompt.

class ractogateway.prompts.RactoPrompt(**data)[source]

Bases: BaseModel

A strictly validated RACTO prompt definition.

Parameters:

  • role (str) – A sentence (or short paragraph) describing who the LLM is.

  • aim (str) – A clear statement of the task objective.

  • constraints (list[str]) – Hard rules the model must obey. At least one is required.

  • tone (str) – The desired communication style (e.g. “Professional and concise”).

  • output_format (str | type[BaseModel]) – Either a format keyword ("json", "text", "markdown"), a free-form format description, or a Pydantic model class whose JSON Schema will be embedded in the prompt.

  • context (str | None) – Optional extra context paragraph injected between AIM and CONSTRAINTS. Useful for passing domain-specific background knowledge that the model needs to reason about.

  • examples (list[dict[str, str]] | None) – Optional list of example input/output pairs that are included in the prompt to steer the model via few-shot learning.

  • anti_hallucination (bool) – When True (the default), the compiler appends explicit anti-hallucination directives at the end of the prompt.

Notes

Legacy compatibility:

Older RactoGateway versions accepted instructions without the full RACTO field set. That shape is still accepted and mapped to aim with sensible defaults for missing RACTO fields.

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.

role: str
aim: str
constraints: list[str]
tone: str
output_format: str | type[BaseModel]
context: str | None
examples: list[dict[str, str]] | None
anti_hallucination: bool
model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

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

compile()[source]

Compile the RACTO fields into an optimized system prompt string.

The resulting prompt is structured into clearly delimited sections so that the LLM can parse each instruction block unambiguously.

Return type:

str

Returns:

str – A ready-to-use system prompt.

to_messages(user_message, *, attachments=None, provider='generic')[source]

Return a ready-to-send message list for a given LLM provider.

Parameters:

  • user_message (str) – The end-user’s query or input.

  • attachments (list[RactoFile] | None) –

    Optional list of RactoFile objects to send alongside the text message. Accepted inputs per file:

    • File path — use RactoFile.from_path():

      RactoFile.from_path("/tmp/diagram.png")

    • Raw bytes — use RactoFile.from_bytes():

      RactoFile.from_bytes(img_bytes, "image/png")

    Each file is re-encoded into the content-block schema expected by the target provider (image_url for OpenAI, image / document for Anthropic, inline_data for Google, images list for Ollama).

  • provider (str) – One of "openai", "anthropic", "google", "ollama", or "generic". Controls the system-role key name and the content-block format used for attachments.

Return type:

list[dict[str, Any]]

Returns:

list[dict[str, Any]] – A list of message dicts suitable for the provider’s API.