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¶
- Getting Started
- Installation and Setup
- Creating a New Prompt
- Managing Prompt Content
- Editing Prompts
- Retrieving Prompt Content
- Version Control
- Tracking Edits and History
- Rolling Back to Previous Versions
- Autosaving Prompts
- Enabling and Configuring Autosave
- Manually Triggering Autosave
- Logging and Telemetry
- Handling Errors
- Extending the Prompt Class
- Customizing the Save Mechanism
- Integrating with Databases
1. Getting Started¶
Installation and Setup¶
Before diving into how to use the Prompt
class, ensure that you have the required dependencies installed:
Creating a New Prompt¶
To create a new instance of a Prompt
, simply initialize it with the required attributes such as content
:
from swarms 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.
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.
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.
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:
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:
The rollback operation is thread-safe, and any rollback also triggers a telemetry log.
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):
Autosaves are stored as JSON files in the folder specified by autosave_folder
under the workspace directory (WORKSPACE_DIR
environment variable).
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:
2024-10-10 10:12:34.567 | INFO | Editing prompt a7b8f9. Current content: 'This is my first prompt!'
2024-10-10 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:
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.
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 swarms.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}")
8. Conclusion¶
This guide covered how to effectively use the Prompt
class in production environments, including core features like editing, version control, autosaving, and logging. By following the best practices outlined here, you can ensure that your prompts are managed efficiently, with minimal overhead and maximum flexibility.
The Prompt
class is designed with scalability and robustness in mind, making it a great choice for managing prompt content in multi-agent architectures or any application where dynamic prompt management is required. Feel free to extend the functionality to suit your needs, whether it's integrating with persistent storage or enhancing logging mechanisms.
By using this architecture, you'll be able to scale your system effortlessly while maintaining detailed version control and history of every interaction with your prompts.