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Training Data Export Utilities
Convert conversation history to various training formats
"""
from typing import Dict, Any, List
import json
from datetime import datetime
def export_conversation_as_jsonl(
conversation_history: List[Dict[str, Any]],
session_metadata: Dict[str, Any] = None
) -> str:
"""
Export conversation history as JSONL (one JSON object per line)
Compatible with OpenAI fine-tuning format and standard LLM training
Args:
conversation_history: List of conversation turns with role/content/metadata
session_metadata: Optional session-level metadata
Returns:
JSONL formatted string
"""
lines = []
# Group messages into conversation turns (user + assistant pairs)
i = 0
while i < len(conversation_history):
if conversation_history[i]['role'] == 'user':
turn = {
'messages': [
{
'role': 'user',
'content': conversation_history[i]['content']
}
]
}
# Add assistant response if available
if i + 1 < len(conversation_history) and conversation_history[i + 1]['role'] == 'assistant':
turn['messages'].append({
'role': 'assistant',
'content': conversation_history[i + 1]['content']
})
# Add metadata
turn['metadata'] = {
'timestamp': conversation_history[i]['timestamp'],
'agent_name': conversation_history[i + 1]['metadata'].get('agent_name'),
'action': conversation_history[i + 1]['metadata'].get('action'),
'intent': conversation_history[i + 1]['metadata'].get('intent')
}
i += 2
else:
i += 1
lines.append(json.dumps(turn))
return '\n'.join(lines)
def export_conversation_as_qa_pairs(
conversation_history: List[Dict[str, Any]],
include_metadata: bool = True
) -> List[Dict[str, Any]]:
"""
Export conversation as Q&A pairs for supervised learning
Args:
conversation_history: List of conversation turns
include_metadata: Whether to include metadata in output
Returns:
List of Q&A pairs with optional metadata
"""
qa_pairs = []
i = 0
while i < len(conversation_history):
if conversation_history[i]['role'] == 'user':
qa_pair = {
'question': conversation_history[i]['content'],
'timestamp': conversation_history[i]['timestamp']
}
# Add answer if available
if i + 1 < len(conversation_history) and conversation_history[i + 1]['role'] == 'assistant':
qa_pair['answer'] = conversation_history[i + 1]['content']
if include_metadata:
qa_pair['metadata'] = {
'question_length': conversation_history[i]['metadata'].get('message_length'),
'entities_mentioned': conversation_history[i]['metadata'].get('entities_mentioned', []),
'agent_name': conversation_history[i + 1]['metadata'].get('agent_name'),
'action_type': conversation_history[i + 1]['metadata'].get('action'),
'intent': conversation_history[i + 1]['metadata'].get('intent'),
'recommendations_count': conversation_history[i + 1]['metadata'].get('recommendations_provided', 0),
'clarification_required': conversation_history[i + 1]['metadata'].get('clarification_required', False)
}
i += 2
else:
qa_pair['answer'] = None
i += 1
qa_pairs.append(qa_pair)
return qa_pairs
def export_conversation_as_chat_ml(
conversation_history: List[Dict[str, Any]],
system_prompt: str = "You are a sustainable tourism assistant helping users find eco-friendly travel destinations."
) -> List[Dict[str, str]]:
"""
Export conversation in ChatML format (OpenAI/Anthropic style)
Args:
conversation_history: List of conversation turns
system_prompt: System prompt to prepend
Returns:
List of messages in ChatML format
"""
messages = [
{'role': 'system', 'content': system_prompt}
]
for turn in conversation_history:
messages.append({
'role': turn['role'],
'content': turn['content']
})
return messages
def export_session_for_training(
session_data: Dict[str, Any],
format: str = 'qa_pairs'
) -> Any:
"""
Export entire session in specified format for model training
Args:
session_data: Complete session state dictionary
format: Output format ('qa_pairs', 'jsonl', 'chatml', or 'full')
Returns:
Formatted training data
"""
conversation_history = session_data.get('conversation_history', [])
if format == 'qa_pairs':
return export_conversation_as_qa_pairs(conversation_history)
elif format == 'jsonl':
return export_conversation_as_jsonl(conversation_history, session_data.get('metadata'))
elif format == 'chatml':
return export_conversation_as_chat_ml(conversation_history)
elif format == 'full':
# Full session data including all metadata for analysis
return {
'session_id': session_data.get('id'),
'created_at': session_data.get('created_at'),
'user_type': session_data.get('user_type'),
'user_type_confidence': session_data.get('user_type_confidence'),
'preferences': session_data.get('preferences'),
'collected_entities': session_data.get('collected_entities'),
'conversation_history': conversation_history,
'metadata': session_data.get('metadata'),
'statistics': {
'total_turns': len(conversation_history) // 2,
'total_messages': len(conversation_history),
'clarifications_needed': session_data.get('metadata', {}).get('clarification_count', 0),
'intents_detected': session_data.get('metadata', {}).get('intents', [])
}
}
else:
raise ValueError(f"Unknown format: {format}. Use 'qa_pairs', 'jsonl', 'chatml', or 'full'")
def batch_export_sessions(
sessions: List[Dict[str, Any]],
output_file: str,
format: str = 'jsonl'
):
"""
Batch export multiple sessions to a file
Args:
sessions: List of session data dictionaries
output_file: Path to output file
format: Export format
"""
with open(output_file, 'w') as f:
for session in sessions:
exported = export_session_for_training(session, format)
if format == 'jsonl':
# Already in JSONL format (multiple lines)
f.write(exported + '\n')
else:
# Write as single JSON object per session
f.write(json.dumps(exported) + '\n')
print(f"✅ Exported {len(sessions)} sessions to {output_file}")
# Example usage
if __name__ == "__main__":
# Example conversation history
sample_history = [
{
'role': 'user',
'content': 'I want to find a sustainable destination',
'timestamp': '2024-01-01T10:00:00',
'metadata': {'message_length': 40, 'entities_mentioned': []}
},
{
'role': 'assistant',
'content': 'I can help you find eco-friendly destinations! What type of environment do you prefer?',
'timestamp': '2024-01-01T10:00:01',
'metadata': {
'agent_name': 'clarification',
'action': 'CLARIFY',
'intent': 'FIND_DESTINATION',
'recommendations_provided': 0,
'clarification_required': True
}
},
{
'role': 'user',
'content': 'I love tropical beaches and nature',
'timestamp': '2024-01-01T10:00:30',
'metadata': {'message_length': 35, 'entities_mentioned': ['interests']}
},
{
'role': 'assistant',
'content': 'Here are my top sustainable recommendations: Costa Rica Eco-Lodge...',
'timestamp': '2024-01-01T10:00:32',
'metadata': {
'agent_name': 'recommendation',
'action': 'RECOMMEND',
'intent': 'FIND_DESTINATION',
'recommendations_provided': 2,
'clarification_required': False
}
}
]
print("=== Q&A Pairs Format ===")
qa_pairs = export_conversation_as_qa_pairs(sample_history)
print(json.dumps(qa_pairs, indent=2))
print("\n=== JSONL Format ===")
jsonl = export_conversation_as_jsonl(sample_history)
print(jsonl)
print("\n=== ChatML Format ===")
chatml = export_conversation_as_chat_ml(sample_history)
print(json.dumps(chatml, indent=2))
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