CAMEL - Build Multi-Agent AI Systems

SelfInstructGenerator

class SelfInstructGenerator(BaseGenerator):

A generator for creating synthetic datapoints using self-instruct. It utilizes both a human-provided dataset (seed_dataset) and generated machine instructions (machine_instructions) to produce new, synthetic datapoints that include a question, a computed rationale (code), and a final answer (from a verifier).

init

def __init__(
    self,
    seed_dataset: StaticDataset,
    verifier: BaseVerifier,
    instruction_agent: Optional[ChatAgent] = None,
    rationale_agent: Optional[ChatAgent] = None,
    seed: int = 42,
    **kwargs
):

Initialize the self-instruct generator. Parameters:

seed_dataset (StaticDataset): Dataset containing seed instructions.
verifier (BaseVerifier): Verifier instance to validate generated solutions.
instruction_agent (Optional[ChatAgent]): Agent for generating instructions. If not provided, a default agent will be created.
rationale_agent (Optional[ChatAgent]): Agent for generating rationales. If not provided, a default agent will be created.
seed (int): Random seed for reproducibility. (default: :obj:42) **kwargs: Additional keyword arguments passed to the BaseGenerator. (default: 42)

default_instruction_agent

def default_instruction_agent(self):

Returns: ChatAgent: An agent with the default instruction prompt.

default_rationale_agent

def default_rationale_agent(self):

Returns: ChatAgent: An agent with the rationale prompt

format_support_block

def format_support_block(dp: DataPoint):

Format a DataPoint into a few-shot example block. Parameters:

dp (DataPoint): A data point.

Returns: str: A formatted string containing the question and its corresponding code block in Markdown-style Python format.

generate_new_instruction

def generate_new_instruction(
    self,
    agent: ChatAgent,
    support_human_dps: list[DataPoint],
    support_machine_dps: list[DataPoint]
):

Generate a new instruction using self-instruct prompting. Parameters:

agent (ChatAgent): The agent to use for generating the instruction.
support_human_dps (list[DataPoint]): List of human examples to sample.
support_machine_dps (list[DataPoint]): List of machine examples to sample.

Returns: str: The newly generated question.

generate_rationale

def generate_rationale(
    self,
    question: str,
    agent: Optional[ChatAgent] = None,
    support_human_dps: Optional[list[DataPoint]] = None
):

Generate rationale code (solution) for the given question. Parameters:

question (str): The question to be solved.
agent (Optional[ChatAgent]): The agent to use for generating the rationale. If None is provided, the default rationale agent will be used. (default: :obj:None)
support_human_dps (Optional[list[DataPoint]]): List of human examples to sample. (default: :obj:None)

Returns: str: The generated code solution as a string.

QuestionSchema

class QuestionSchema(BaseModel):

Schema for the generated question. Parameters:

question (str): The question generated by the model.

RationaleSchema

class RationaleSchema(BaseModel):

Schema for the generated rationale code. Parameters:

code (str): The generated code without any formatting.

​SelfInstructGenerator

​init

​default_instruction_agent

​default_rationale_agent

​format_support_block

​generate_new_instruction

​generate_rationale

​QuestionSchema

​RationaleSchema