Instruction Generators

class afterimage.ContextualInstructionGeneratorCallback(api_key: str | SmartKeyPool, documents: list[str] | DocumentProvider, prompt: str | None = None, model_name: str | None = None, model_provider_name: Literal['gemini', 'openai', 'deepseek'] = 'gemini', num_random_contexts: int = 1, n_instructions: int = 3, separator_text: str = '\n--------------------------------------------------------------------------------\n\n', safety_settings: dict | None = None, monitor: GenerationMonitor | None = None)[source]

Bases: BaseInstructionGeneratorCallback

Generates instructions based on randomly sampled contexts.

Parameters:

  • api_key – API key for the generative AI service.

  • documents – Either a list of texts or a DocumentProvider instance providing context to ground the instructions. For each round of generation num_random_contexts documents are sampled from this collection.

  • prompt – Prompt that guides the instruction generation. If None, uses the default instruction generation prompt.

  • model_name – Model name to use.

  • model_provider_name – Model provider name to use.

  • num_random_contexts – Number of contexts to sample for each round of generation.

  • n_instructions – Number of instructions to generate in each round of generation.

  • separator_text – Separator text for merging contexts if more than one context is sampled.

  • safety_settings – Safety settings for the model. Mainly intended for Gemini models. Deprecated and may be removed in the future.

  • monitor – GenerationMonitor instance to use for tracking. If None, Conversation and/or structured generators will set their own monitor.

async acreate_correspondent_prompt(respondent_prompt: str) → str[source]

Create a correspondent prompt based on the respondent prompt asynchronously.

async agenerate(original_prompt)[source]

Generates instructions based on the provided prompt and sampled context asynchronously.

create_correspondent_prompt(respondent_prompt: str) → str[source]

Create a correspondent prompt based on the respondent prompt.

generate(original_prompt)[source]

Generates instructions based on the provided prompt and sampled context.

Parameters:

original_prompt (str) – The correspondent prompt if any.

Returns:

The instructions generated along with the context used.

Return type:

GeneratedInstructions

set_monitor(monitor: GenerationMonitor) → None[source]
class afterimage.PersonaInstructionGeneratorCallback(api_key: str | SmartKeyPool, documents: list[str] | DocumentProvider, prompt: str | None = None, model_name: str | None = None, model_provider_name: Literal['gemini', 'openai', 'deepseek'] = 'gemini', num_random_contexts: int = 1, n_instructions: int = 3, separator_text: str = '\n--------------------------------------------------------------------------------\n\n', safety_settings: dict | None = None, monitor: GenerationMonitor | None = None)[source]

Bases: ContextualInstructionGeneratorCallback

Generates instructions based on randomly sampled contexts and personas.

It works very similarly to ~ContextualInstructionGeneratorCallback but it also samples a persona from the sampled documents.

This usually results in more diverse yet still contextually relevant instructions.

Parameters:

  • api_key – API key for the generative AI service.

  • documents – Either a list of texts or a DocumentProvider instance providing context to ground the instructions. For each round of generation num_random_contexts documents are sampled from this collection.

  • prompt – Prompt that guides the instruction generation. If None, uses the default instruction generation prompt.

  • model_name – Model name to use.

  • model_provider_name – Model provider name to use.

  • num_random_contexts – Number of contexts to sample for each round of generation.

  • n_instructions – Number of instructions to generate in each round of generation.

  • separator_text – Separator text for merging contexts if more than one context is sampled.

  • safety_settings – Safety settings for the model. Mainly intended for Gemini models. Deprecated and may be removed in the future.

  • monitor – GenerationMonitor instance to use for tracking. If None, Conversation and/or structured generators will set their own monitor

async agenerate(original_prompt)[source]

Generates instructions based on the provided prompt, sampled context and persona asynchronously.

configure_persona_sampling(num_requested: int | None = None) → None[source]
generate(original_prompt)[source]

Generates instructions based on the provided prompt, sampled context and persona.

Parameters:

original_prompt (str) – The prompt guiding instruction generation.

Returns:

The instructions generated along with the context and persona used.

Return type:

GeneratedInstructions

class afterimage.ToolCallingInstructionGeneratorCallback(api_key: str | SmartKeyPool, tools: List[dict | Type[BaseModel]], documents: list[str] | DocumentProvider, prompt: str | None = None, model_name: str | None = None, model_provider_name: Literal['gemini', 'openai', 'deepseek'] = 'gemini', num_random_contexts: int = 1, n_instructions: int = 3, num_tools_to_sample: int = 2, separator_text: str = '\n--------------------------------------------------------------------------------\n\n', safety_settings: dict | None = None, monitor: GenerationMonitor | None = None)[source]

Bases: PersonaInstructionGeneratorCallback

Generates instructions that specifically require calling provided tools, optionally using personas.

Parameters:

  • api_key – API key for the generative AI service.

  • tools – List of tools to use. each item of this list should be an OpenAI-style tool description as a dictionary or a pydantic model.

  • documents – Either a list of texts or a DocumentProvider instance providing context to ground the instructions. For each round of generation num_random_contexts documents are sampled from this collection.

  • prompt – Prompt that guides the instruction generation. If None, uses the default instruction generation prompt.

  • model_name – Model name to use.

  • model_provider_name – Model provider name to use.

  • num_random_contexts – Number of contexts to sample for each round of generation.

  • n_instructions – Number of instructions to generate in each round of generation.

  • num_tools_to_sample – Number of tools to sample as the targets for each round of generation.

  • separator_text – Separator text for merging contexts if more than one context is sampled.

  • safety_settings – Safety settings for the model. Mainly intended for Gemini models. Deprecated and may be removed in the future.

  • monitor – GenerationMonitor instance to use for tracking. If None, Conversation and/or structured generators will set their own monitor

async acreate_correspondent_prompt(respondent_prompt: str) → str[source]

Create a prompt for the correspondent asynchronously.

async agenerate(original_prompt)[source]

Generates instructions that require tool calls asynchronously.

create_correspondent_prompt(respondent_prompt: str) → str[source]

Create a prompt for the correspondent.

generate(original_prompt)[source]

Generates instructions that require tool calls.