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Consistency Agent Documentation

The SelfConsistencyAgent is a specialized agent designed for generating multiple independent responses to a given task and aggregating them into a single, consistent final answer. It leverages concurrent processing to enhance efficiency and employs a majority voting mechanism to ensure the reliability of the aggregated response.

Purpose

The primary objective of the SelfConsistencyAgent is to provide a robust mechanism for decision-making and problem-solving by generating diverse responses and synthesizing them into a coherent final answer. This approach is particularly useful in scenarios where consistency and reliability are critical.

Class: `SelfConsistencyAgent`

Initialization

__init__: Initializes the SelfConsistencyAgent with specified parameters.

Arguments

Argument	Type	Default	Description
`num_samples`	`int`	`5`	Number of independent responses to sample.
`return_list`	`bool`	`False`	Whether to return the conversation as a list.
`max_loops`	`int`	`1`	Maximum number of loops for the agent to run.
`return_dict`	`bool`	`False`	Whether to return the conversation as a dictionary.
`return_json`	`bool`	`False`	Whether to return the conversation as JSON.
`majority_voting_prompt`	`str`	`None`	Custom prompt for majority voting.

Methods

run: Generates multiple responses for the given task and aggregates them.
- Arguments:
  - task (str): The input prompt.
  - answer (str, optional): The expected answer to validate responses against.
- Returns: str - The aggregated final answer.
aggregate: Aggregates a list of responses into a single final answer using majority voting.
- Arguments:
  - responses (List[str]): The list of responses.
- Returns: str - The aggregated answer.
check_responses_for_answer: Checks if a specified answer is present in any of the provided responses.
- Arguments:
  - responses (List[str]): A list of responses to check.
  - answer (str): The answer to look for in the responses.
- Returns: bool - True if the answer is found, False otherwise.

Examples

Example 1: Basic Usage

from swarms.agents.consistency_agent import SelfConsistencyAgent

# Initialize the agent
agent = SelfConsistencyAgent(
    agent_name="Reasoning-Agent",
    model_name="gpt-4o-mini",
    max_loops=1,
    num_samples=5
)

# Define a task
task = "What is the 40th prime number?"

# Run the agent
final_answer = agent.run(task)

# Print the final aggregated answer
print("Final aggregated answer:", final_answer)

Example 2: Using Custom Majority Voting Prompt

from swarms.agents.consistency_agent import SelfConsistencyAgent

# Initialize the agent with a custom majority voting prompt
agent = SelfConsistencyAgent(
    agent_name="Reasoning-Agent",
    model_name="gpt-4o-mini",
    max_loops=1,
    num_samples=5,
    majority_voting_prompt="Please provide the most common response."
)

# Define a task
task = "Explain the theory of relativity in simple terms."

# Run the agent
final_answer = agent.run(task)

# Print the final aggregated answer
print("Final aggregated answer:", final_answer)

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