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`Flow` Documentation

Overview

The Flow class is a Python module designed to facilitate interactions with a language model, particularly one that operates as an autonomous agent. This class is part of a larger framework aimed at creating conversational agents using advanced language models like GPT-3. It enables you to establish a conversational loop with the model, generate responses, collect feedback, and control the flow of the conversation.

In this documentation, you will learn how to use the Flow class effectively, its purpose, and how it can be integrated into your projects.

Purpose

The Flow class serves several key purposes:

Conversational Loop: It establishes a conversational loop with a language model. This means it allows you to interact with the model in a back-and-forth manner, taking turns in the conversation.
Feedback Collection: The class allows users to provide feedback on the responses generated by the model. This feedback can be valuable for training and improving the model's responses over time.
Stoppable Conversation: You can define custom stopping conditions for the conversation, allowing you to stop the interaction based on specific criteria. For example, you can stop the conversation if a certain keyword is detected in the responses.
Retry Mechanism: The class includes a retry mechanism that can be helpful if there are issues generating responses from the model. It attempts to generate a response multiple times before raising an error.

Class Definition

The Flow class has the following constructor:

class Flow:
    def __init__(
        self,
        llm: Any,
        max_loops: int = 5,
        stopping_condition: Optional[Callable[[str], bool]] = None,
        loop_interval: int = 1,
        retry_attempts: int = 3,
        retry_interval: int = 1,
        interactive: bool = False,
        **kwargs: Any,
    ):

Parameters

llm (Any): The language model with which you want to interact.
max_loops (int): The maximum number of conversation loops. Default is 5.
stopping_condition (Optional[Callablestr], bool): A custom stopping condition function. Default is None.
loop_interval (int): The time interval (in seconds) between conversation loops. Default is 1 second.
retry_attempts (int): The number of retry attempts if response generation fails. Default is 3.
retry_interval (int): The time interval (in seconds) between retry attempts. Default is 1 second.
interactive (bool): Set to True if the conversation is interactive, meaning the user is involved. Default is False.

Usage

The Flow class can be used to create a conversational loop with the language model. Here's how you can use it:

from swarms.structs import Flow

flow = Flow(llm=my_language_model, max_loops=5)

# Define a starting task or message
initial_task = "Hello, can you provide me with some information?"

# Run the conversation loop
final_response = flow.run(initial_task)

Feedback

You can collect feedback during the conversation using the provide_feedback method:

flow.provide_feedback("The response was not accurate.")

Stopping Condition

You can define a custom stopping condition using a function. For example, you can stop the conversation if the response contains the word "Stop":

from swarms.structs import Flow

def stop_when_repeats(response: str) -> bool:
    return "Stop" in response.lower()

flow = Flow(llm=my_language_model, max_loops=5, stopping_condition=stop_when_repeats)

Retry Mechanism

If the response generation fails, the class will retry up to the specified number of attempts:

flow = Flow(llm=my_language_model, max_loops=5, retry_attempts=3)

Additional Information

To save the conversation history to a file, you can use the save method.
To load a previously saved conversation history, you can use the load method.
The class includes methods for bulk running conversations with multiple input sets.

Examples

Here are three usage examples:

Example 1: Simple Conversation

from swarms.structs import Flow

flow = Flow(llm=my_language_model, max_loops=5)

# Define a starting task or message
initial_task = "Hello, can you provide me with some information?"

# Run the conversation loop
final_response = flow.run(initial_task)

Example 2: Custom Stopping Condition

from swarms.structs import Flow

def stop_when_repeats(response: str) -> bool:
    return "Stop" in response.lower()

flow = Flow(llm=my_language_model, max_loops=5, stopping_condition=stop_when_repeats)

Example 3: Interactive Conversation

from swarms.structs import Flow

flow = Flow(llm=my_language_model, max_loops=5, interactive=True)

# Provide initial task
initial_task = "Hello, can you tell me a joke?"

# Run the conversation loop
final_response = flow.run(initial_task)

References and Resources

GitHub Repository

Conclusion

The Flow class provides a powerful way to interact with language models in a conversational manner. By defining custom stopping conditions, collecting feedback, and controlling the flow of the conversation, you can create engaging and interactive applications that make use of advanced language models.

5.3 KiB Raw Blame History

Flow Documentation