⚪Managing Prompts in Production

The Prompt class provides a comprehensive solution for managing prompts, including advanced features like version control, autosaving, and logging. This guide will walk you through how to effectively use this class in a production environment, focusing on its core features, use cases, and best practices.

Table of Contents

1. Getting Started

   • Installation and Setup

   • Creating a New Prompt

2. Managing Prompt Content

   • Editing Prompts

   • Retrieving Prompt Content

3. Version Control

   • Tracking Edits and History

   • Rolling Back to Previous Versions

4. Autosaving Prompts

   • Enabling and Configuring Autosave

   • Manually Triggering Autosave

5. Logging and Telemetry

6. Handling Errors

7. Extending the Prompt Class

   • Customizing the Save Mechanism

   • Integrating with Databases

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1. Getting Started

Installation and Setup

Before diving into how to use the Prompt class, ensure that you have the required dependencies installed:

pip3 install -U zytron

Creating a New Prompt

To create a new instance of a Prompt, simply initialize it with the required attributes such as content:

from zytron import Prompt

prompt = Prompt(
    content="This is my first prompt!",
    name="My First Prompt",
    description="A simple example prompt."
)

print(prompt)

This creates a new prompt with the current timestamp and a unique identifier.

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2. Managing Prompt Content

Editing Prompts

Once you have initialized a prompt, you can edit its content using the edit_prompt method. Each time the content is edited, a new version is stored in the edit_history, and the last_modified_at timestamp is updated.

new_content = "This is an updated version of my prompt."
prompt.edit_prompt(new_content)

Note: If the new content is identical to the current content, an error will be raised to prevent unnecessary edits:

try:
    prompt.edit_prompt("This is my first prompt!")  # Same as initial content
except ValueError as e:
    print(e)  # Output: New content must be different from the current content.

Retrieving Prompt Content

You can retrieve the current prompt content using the get_prompt method:

current_content = prompt.get_prompt()
print(current_content)  # Output: This is an updated version of my prompt.

This method also logs telemetry data, which includes both system information and prompt metadata.

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3. Version Control

Tracking Edits and History

The Prompt class automatically tracks every change made to the prompt. This is stored in the edit_history attribute as a list of previous versions.

print(prompt.edit_history)  # Output: ['This is my first prompt!', 'This is an updated version of my prompt.']

The number of edits is also tracked using the edit_count attribute:

print(prompt.edit_count)  # Output: 2

Rolling Back to Previous Versions

If you want to revert a prompt to a previous version, you can use the rollback method, passing the version index you want to revert to:

prompt.rollback(0)
print(prompt.get_prompt())  # Output: This is my first prompt!

The rollback operation is thread-safe, and any rollback also triggers a telemetry log.

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4. Autosaving Prompts

Enabling and Configuring Autosave

To automatically save prompts to storage after every change, you can enable the autosave feature when initializing the prompt:

prompt = Prompt(
    content="This is my first prompt!",
    autosave=True,
    autosave_folder="my_prompts"  # Specify the folder within WORKSPACE_DIR
)

This will ensure that every edit or rollback action triggers an autosave to the specified folder.

Manually Triggering Autosave

You can also manually trigger an autosave by calling the _autosave method (which is a private method typically used internally):

prompt._autosave()  # Manually triggers autosaving

Autosaves are stored as JSON files in the folder specified by autosave_folder under the workspace directory (WORKSPACE_DIR environment variable).

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5. Logging and Telemetry

The Prompt class integrates with the loguru logging library to provide detailed logs for every major action, such as editing, rolling back, and saving. The log_telemetry method captures and logs system data, including prompt metadata, for each operation.

Here's an example of a log when editing a prompt:

2024-12-24 10:12:34.567 | INFO  | Editing prompt a7b8f9. Current content: 'This is my first prompt!'
2024-12-24 10:12:34.789 | DEBUG | Prompt a7b8f9 updated. Edit count: 1. New content: 'This is an updated version of my prompt.'

You can extend logging by integrating the log_telemetry method with your own telemetry systems or databases:

prompt.log_telemetry()

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6. Handling Errors

Error handling in the Prompt class is robust and prevents common mistakes, such as editing with identical content or rolling back to an invalid version. Here's a common scenario:

Editing with Identical Content

try:
    prompt.edit_prompt("This is an updated version of my prompt.")
except ValueError as e:
    print(e)  # Output: New content must be different from the current content.

Invalid Rollback Version

try:
    prompt.rollback(10)  # Invalid version index
except IndexError as e:
    print(e)  # Output: Invalid version number for rollback.

Always ensure that version numbers passed to rollback are within the valid range of existing versions.

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7. Extending the Prompt Class

Customizing the Save Mechanism

The Prompt class currently includes a placeholder for saving and loading prompts from persistent storage. You can override the save_to_storage and load_from_storage methods to integrate with databases, cloud storage, or other persistent layers.

Here's how you can implement the save functionality:

def save_to_storage(self):
    # Example of saving to a database or cloud storage
    data = self.model_dump()
    save_to_database(data)  # Custom function to save data

Similarly, you can implement a load_from_storage function to load the prompt from a storage location using its unique identifier (id).

Full Example code with all methods

from zytron.prompts.prompt import Prompt

# Example 1: Initializing a Financial Report Prompt
financial_prompt = Prompt(
    content="Q1 2024 Earnings Report: Initial Draft", autosave=True
)

# Output the initial state of the prompt
print("\n--- Example 1: Initializing Prompt ---")
print(f"Prompt ID: {financial_prompt.id}")
print(f"Content: {financial_prompt.content}")
print(f"Created At: {financial_prompt.created_at}")
print(f"Edit Count: {financial_prompt.edit_count}")
print(f"History: {financial_prompt.edit_history}")


# Example 2: Editing a Financial Report Prompt
financial_prompt.edit_prompt(
    "Q1 2024 Earnings Report: Updated Revenue Figures"
)

# Output the updated state of the prompt
print("\n--- Example 2: Editing Prompt ---")
print(f"Content after edit: {financial_prompt.content}")
print(f"Edit Count: {financial_prompt.edit_count}")
print(f"History: {financial_prompt.edit_history}")


# Example 3: Rolling Back to a Previous Version
financial_prompt.edit_prompt("Q1 2024 Earnings Report: Final Version")
financial_prompt.rollback(
    1
)  # Roll back to the second version (index 1)

# Output the state after rollback
print("\n--- Example 3: Rolling Back ---")
print(f"Content after rollback: {financial_prompt.content}")
print(f"Edit Count: {financial_prompt.edit_count}")
print(f"History: {financial_prompt.edit_history}")


# Example 4: Handling Invalid Rollback
print("\n--- Example 4: Invalid Rollback ---")
try:
    financial_prompt.rollback(
        5
    )  # Attempt an invalid rollback (out of bounds)
except IndexError as e:
    print(f"Error: {e}")


# Example 5: Preventing Duplicate Edits
print("\n--- Example 5: Preventing Duplicate Edits ---")
try:
    financial_prompt.edit_prompt(
        "Q1 2024 Earnings Report: Updated Revenue Figures"
    )  # Duplicate content
except ValueError as e:
    print(f"Error: {e}")


# Example 6: Retrieving the Prompt Content as a String
print("\n--- Example 6: Retrieving Prompt as String ---")
current_content = financial_prompt.get_prompt()
print(f"Current Prompt Content: {current_content}")


# Example 7: Simulating Financial Report Changes Over Time
print("\n--- Example 7: Simulating Changes Over Time ---")
# Initialize a new prompt representing an initial financial report draft
financial_prompt = Prompt(
    content="Q2 2024 Earnings Report: Initial Draft"
)

# Simulate several updates over time
financial_prompt.edit_prompt(
    "Q2 2024 Earnings Report: Updated Forecasts"
)
financial_prompt.edit_prompt(
    "Q2 2024 Earnings Report: Revenue Adjustments"
)
financial_prompt.edit_prompt("Q2 2024 Earnings Report: Final Review")

# Display full history
print(f"Final Content: {financial_prompt.content}")
print(f"Edit Count: {financial_prompt.edit_count}")
print(f"Edit History: {financial_prompt.edit_history}")