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#!/usr/bin/env python3
"""
Medical Transcription Retriever from Langfuse
Retrieves medical transcriptions from Langfuse traces and saves them locally.
"""
import os
import json
import time
from datetime import datetime, timedelta
from dotenv import load_dotenv
from langfuse import Langfuse
# Load environment variables
load_dotenv()
class MedicalTranscriptionRetriever:
"""Retrieves medical transcriptions from Langfuse traces."""
def __init__(self):
"""Initialize the retriever with Langfuse credentials."""
self.public_key = os.getenv('LANGFUSE_PUBLIC_KEY')
self.secret_key = os.getenv('LANGFUSE_SECRET_KEY')
self.host = os.getenv('LANGFUSE_HOST', 'https://cloud.langfuse.com')
if not self.public_key or not self.secret_key:
raise ValueError("Missing Langfuse keys in .env file")
self.client = Langfuse(
public_key=self.public_key,
secret_key=self.secret_key,
host=self.host
)
def extract_transcription_from_input(self, input_data):
"""Extract transcription from document input data."""
if isinstance(input_data, str):
if "Voici le document:" in input_data:
parts = input_data.split("Voici le document:")
if len(parts) > 1:
return parts[1].strip()
elif isinstance(input_data, dict):
# Search in messages if it's a dict with messages
if 'messages' in input_data:
for message in input_data['messages']:
if isinstance(message, dict) and message.get('role') == 'user':
content = message.get('content', '')
if isinstance(content, str) and "Voici le document:" in content:
parts = content.split("Voici le document:")
if len(parts) > 1:
return parts[1].strip()
# Search in other dict keys
for key, value in input_data.items():
if isinstance(value, str) and "Voici le document:" in value:
parts = value.split("Voici le document:")
if len(parts) > 1:
return parts[1].strip()
elif isinstance(input_data, list):
for message in input_data:
if isinstance(message, dict):
content = message.get('content', '')
if isinstance(content, str) and "Voici le document:" in content:
parts = content.split("Voici le document:")
if len(parts) > 1:
return parts[1].strip()
return None
def get_traces_with_transcriptions(self, limit=50, days_back=7):
"""Retrieve traces containing medical transcriptions."""
print(f"π Searching for transcriptions in the last {limit} traces...")
try:
# Retrieve traces
traces = self.client.get_traces(limit=limit)
print(f"β
{len(traces.data)} traces retrieved")
transcriptions = []
for i, trace in enumerate(traces.data):
print(
f"π Analyzing trace {i+1}/{len(traces.data)}: {trace.id}")
try:
# Check if trace.input contains a transcription
if hasattr(trace, 'input') and trace.input is not None:
transcription = self.extract_transcription_from_input(
trace.input)
if transcription:
trans_info = {
'trace_id': trace.id,
'trace_name': trace.name,
'user_id': trace.user_id,
'trace_timestamp': trace.timestamp.isoformat() if trace.timestamp else None,
'transcription': transcription,
'extracted_at': datetime.now().isoformat()
}
transcriptions.append(trans_info)
print(f" β
Transcription found and extracted!")
else:
print(f" β No transcription found in trace.input")
else:
print(f" β οΈ No input available for this trace")
except Exception as e:
print(f" β οΈ Error analyzing trace {trace.id}: {e}")
continue
# Delay between requests to avoid rate limiting
if i < len(traces.data) - 1: # Don't wait after the last trace
time.sleep(1) # Wait 1 second between each trace
print(f"\nπ Summary: {len(transcriptions)} transcriptions found")
return transcriptions
except Exception as e:
print(f"β Error retrieving traces: {e}")
return []
def save_transcriptions(self, transcriptions, filename=None):
"""Save transcriptions to a JSON file."""
if not filename:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"medical_transcriptions_{timestamp}.json"
try:
# Concatenate all transcriptions into a single string
transcription_texts = [trans['transcription']
for trans in transcriptions]
concatenated_transcription = "\n\n".join(transcription_texts)
# Save as an object with transcription as a single string
data_to_save = {
"extracted_at": datetime.now().isoformat(),
"total_transcriptions": len(transcriptions),
"transcription": concatenated_transcription
}
with open(filename, 'w', encoding='utf-8') as f:
json.dump(data_to_save, f, ensure_ascii=False, indent=2)
print(f"πΎ Transcriptions saved to {filename}")
return filename
except Exception as e:
print(f"β Error during save: {e}")
return None
def save_transcriptions_by_user(self, transcriptions):
"""Save transcriptions by user in separate files."""
if not transcriptions:
print("π No transcriptions to save")
return
# Create transcriptions directory if it doesn't exist
transcriptions_dir = "transcriptions"
if not os.path.exists(transcriptions_dir):
os.makedirs(transcriptions_dir)
print(f"π Directory '{transcriptions_dir}' created")
# Group transcriptions by user_id
user_transcriptions = {}
for trans in transcriptions:
user_id = trans.get('user_id', 'unknown')
if user_id not in user_transcriptions:
user_transcriptions[user_id] = []
user_transcriptions[user_id].append(trans)
# Save one file per user (only if user_id contains .rtf)
saved_files = []
for user_id, user_trans in user_transcriptions.items():
# Check if user_id contains .rtf
if '.rtf' not in user_id:
print(f"βοΈ Skipped {user_id} (no .rtf)")
continue
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"transcriptions_{user_id}_{timestamp}.json"
filepath = os.path.join(transcriptions_dir, filename)
try:
# Concatenate all transcriptions into a single string
transcription_texts = [trans['transcription']
for trans in user_trans]
concatenated_transcription = "\n\n".join(transcription_texts)
# Save as an object with transcription as a single string
data_to_save = {
"user_id": user_id,
"extracted_at": datetime.now().isoformat(),
"total_transcriptions": len(user_trans),
"transcription": concatenated_transcription
}
with open(filepath, 'w', encoding='utf-8') as f:
json.dump(data_to_save, f, ensure_ascii=False, indent=2)
saved_files.append(filepath)
print(f"πΎ Saved transcriptions for {user_id}: {filename}")
except Exception as e:
print(f"β Error saving transcriptions for {user_id}: {e}")
print(f"\nπ Summary: {len(saved_files)} files saved")
return saved_files
def display_transcriptions_summary(self, transcriptions):
"""Display a summary of retrieved transcriptions."""
if not transcriptions:
print("π No transcriptions to display")
return
print("\nπ TRANSCRIPTIONS SUMMARY")
print("=" * 50)
print(f"Total transcriptions: {len(transcriptions)}")
# Group by user
user_counts = {}
for trans in transcriptions:
user_id = trans.get('user_id', 'unknown')
user_counts[user_id] = user_counts.get(user_id, 0) + 1
print(f"Unique users: {len(user_counts)}")
for user_id, count in user_counts.items():
print(f" - {user_id}: {count} transcriptions")
def run(self, limit=50, save_to_file=True, save_by_user=True):
"""Run the complete transcription retrieval process."""
print("π Starting medical transcription retrieval...")
print("=" * 60)
# Retrieve transcriptions
transcriptions = self.get_traces_with_transcriptions(limit=limit)
if not transcriptions:
print("β No transcriptions found")
return None
# Display summary
self.display_transcriptions_summary(transcriptions)
# Save transcriptions
saved_files = []
if save_to_file:
saved_file = self.save_transcriptions(transcriptions)
if saved_file:
saved_files.append(saved_file)
if save_by_user:
user_files = self.save_transcriptions_by_user(transcriptions)
saved_files.extend(user_files)
print(f"\nβ
Retrieval completed! {len(saved_files)} files saved")
return saved_files
def main():
"""Main function to run the transcription retriever."""
print("π₯ Medical Transcription Retriever")
print("=" * 40)
try:
retriever = MedicalTranscriptionRetriever()
saved_files = retriever.run(
limit=50, save_to_file=True, save_by_user=True)
if saved_files:
print(f"\nπ Success! Files saved: {len(saved_files)}")
else:
print("\nβ No files were saved")
except Exception as e:
print(f"β Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
main()
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