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"""
Test script for LLM interface with conversation history.
"""
from utils import SocialGraphManager
from llm_interface import LLMInterface
# Initialize the social graph manager
graph_manager = SocialGraphManager("social_graph.json")
# Get a person with conversation history
person_id = "emma" # Emma has conversation history
person_context = graph_manager.get_person_context(person_id)
# Initialize the LLM interface
llm_interface = LLMInterface()
# Store the original generate_suggestion method
original_method = llm_interface.generate_suggestion
# Create a mock method to print the prompt
def mock_generate_suggestion(*args, **kwargs):
"""Mock method to print the prompt instead of sending it to the LLM."""
# Extract the prompt that would be sent to the LLM
person_context = args[0]
user_input = args[1] if len(args) > 1 else kwargs.get("user_input")
# Build the prompt as the original method would
# Extract context information
name = person_context.get("name", "")
role = person_context.get("role", "")
topics = person_context.get("topics", [])
context = person_context.get("context", "")
selected_topic = person_context.get("selected_topic", "")
frequency = person_context.get("frequency", "")
mood = person_context.get("mood", 3)
# Get mood description
mood_descriptions = {
1: "I'm feeling quite down and sad today. My responses might be more subdued.",
2: "I'm feeling a bit low today. I might be less enthusiastic than usual.",
3: "I'm feeling okay today - neither particularly happy nor sad.",
4: "I'm feeling pretty good today. I'm in a positive mood.",
5: "I'm feeling really happy and upbeat today! I'm in a great mood.",
}
mood_description = mood_descriptions.get(mood, mood_descriptions[3])
# Get current date and time
import datetime
current_datetime = datetime.datetime.now()
current_time = current_datetime.strftime("%I:%M %p")
current_day = current_datetime.strftime("%A")
current_date = current_datetime.strftime("%B %d, %Y")
# Build enhanced prompt
prompt = f"""I am Will, a 38-year-old with MND (Motor Neuron Disease) from Manchester.
I am talking to {name}, who is my {role}.
About {name}: {context}
We typically talk about: {', '.join(topics)}
We communicate {frequency}.
Current time: {current_time}
Current day: {current_day}
Current date: {current_date}
My current mood: {mood_description}
"""
# Add communication style based on relationship
if role in ["wife", "son", "daughter", "mother", "father"]:
prompt += "I communicate with my family in a warm, loving way, sometimes using inside jokes.\n"
elif role in ["doctor", "therapist", "nurse"]:
prompt += "I communicate with healthcare providers in a direct, informative way.\n"
elif role in ["best mate", "friend"]:
prompt += "I communicate with friends casually, often with humor and sometimes swearing.\n"
elif role in ["work colleague", "boss"]:
prompt += "I communicate with colleagues professionally but still friendly.\n"
# Add topic information if provided
if selected_topic:
prompt += f"\nWe are currently discussing {selected_topic}.\n"
# Add conversation history if available
conversation_history = person_context.get("conversation_history", [])
if conversation_history:
# Get the two most recent conversations
recent_conversations = sorted(
conversation_history,
key=lambda x: x.get("timestamp", ""),
reverse=True
)[:2]
if recent_conversations:
prompt += "\nOur recent conversations:\n"
for conversation in recent_conversations:
# Format the timestamp
timestamp = conversation.get("timestamp", "")
try:
dt = datetime.datetime.fromisoformat(timestamp)
formatted_date = dt.strftime("%B %d at %I:%M %p")
except (ValueError, TypeError):
formatted_date = timestamp
prompt += f"\nConversation on {formatted_date}:\n"
# Add the messages
messages = conversation.get("messages", [])
for message in messages:
speaker = message.get("speaker", "Unknown")
text = message.get("text", "")
prompt += f'{speaker}: "{text}"\n'
# Add the user's message if provided, or set up for conversation initiation
if user_input:
# If user input is provided, we're responding to something
prompt += f'\n{name} just said to me: "{user_input}"\n'
prompt += f"I want to respond directly to what {name} just said.\n"
else:
# No user input means we're initiating a conversation
if selected_topic:
# If a topic is selected, initiate conversation about that topic
prompt += f"\nI'm about to start a conversation with {name} about {selected_topic}.\n"
prompt += f"I want to initiate a conversation about {selected_topic} in a natural way.\n"
else:
# Generic conversation starter
prompt += f"\nI'm about to start a conversation with {name}.\n"
prompt += "I want to initiate a conversation in a natural way based on our relationship.\n"
# Print the prompt
print("\n=== PROMPT WITH CONVERSATION HISTORY ===")
print(prompt)
print("=======================================\n")
# Return a mock response
return "This is a mock response to test conversation history inclusion in the prompt."
# Replace the original method with our mock method
llm_interface.generate_suggestion = mock_generate_suggestion
# Test with a user input
user_input = "Do you think you'll be up for dinner with the kids tonight?"
llm_interface.generate_suggestion(person_context, user_input=user_input)
# Restore the original method
llm_interface.generate_suggestion = original_method
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