Utils API Reference¶
- lmitf.utils.print_conversation(msgs)[source]¶
Print the conversation in a readable format.
Parameters:¶
- msg: list
The conversation messages to print.
Overview¶
The utils module provides utility functions for common tasks when working with LMITF, including conversation formatting, data processing, and helper functions.
Function Reference¶
print_conversation()¶
- lmitf.utils.print_conversation(msgs)[source]¶
Print the conversation in a readable format.
Parameters:¶
- msg: list
The conversation messages to print.
A utility function to display conversation messages in a readable format with visual icons.
Parameters:
msgs(list): List of conversation messages in OpenAI format
Usage:
from lmitf.utils import print_conversation
# Format: [{"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}]
conversation = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What's the weather like today?"},
{"role": "assistant", "content": "I don't have access to real-time weather data, but I can help you find weather information."},
{"role": "user", "content": "How can I check the weather?"}
]
print_conversation(conversation)
Output:
⚙️ ════════════════════════════════════════════════════════════════════════════════
You are a helpful assistant.
👤 ════════════════════════════════════════════════════════════════════════════════
What's the weather like today?
🤖 ════════════════════════════════════════════════════════════════════════════════
I don't have access to real-time weather data, but I can help you find weather information.
👤 ════════════════════════════════════════════════════════════════════════════════
How can I check the weather?
Icon Legend¶
🤖 Assistant: Messages from the AI assistant
👤 User: Messages from the user
⚙️ System: System messages and prompts
Integration with BaseLLM¶
The utils module integrates seamlessly with LMITF classes:
from lmitf import BaseLLM
from lmitf.utils import print_conversation
llm = BaseLLM()
# Build a conversation
messages = [
{"role": "system", "content": "You are a coding assistant."},
{"role": "user", "content": "How do I create a Python list?"}
]
# Get response
response = llm.call(messages)
# Add response to conversation
messages.append({"role": "assistant", "content": response})
# Display the full conversation nicely
print_conversation(messages)
Alternative Import¶
The print_conversation function is also available as print_turn in the main module:
from lmitf import print_turn
# These are equivalent
print_turn(messages)
print_conversation(messages)
Custom Formatting¶
For custom conversation formatting, you can extend the function:
from wasabi import msg
def custom_print_conversation(msgs, show_timestamps=False):
"""Custom conversation printer with optional timestamps."""
import datetime
for i, turn in enumerate(msgs):
icon = '🤖' if turn['role'] == 'assistant' else (
'⚙️' if turn['role'] == 'system' else '👤'
)
header = f"{icon} Turn {i+1}"
if show_timestamps:
header += f" ({datetime.datetime.now().strftime('%H:%M:%S')})"
msg.divider(header)
print(turn['content'])
print() # Extra spacing
# Usage
custom_print_conversation(messages, show_timestamps=True)
Best Practices¶
Debug Conversations: Use
print_conversation()to debug multi-turn conversationsLog Interactions: Capture conversation flows for analysis
User Interface: Display conversations in CLI applications
Development: Monitor conversation state during development
Future Utilities¶
The utils module may be extended with additional helper functions:
Message validation
Token counting utilities
Conversation saving/loading
Format conversion helpers