Données d'utilisation de Paradigm

import requests
import os

# Récupérer la clé API et l'URL de base depuis l'environnement
api_key = os.getenv("PARADIGM_API_KEY")
base_url = os.getenv("PARADIGM_BASE_URL", "https://paradigm.lighton.ai/api/v2")

url = f"{base_url}/reporting/chat-sessions/"

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

params = {
    "company_id": 2,
    "start_date": "2025-01-01",  # Requis
    # "end_date": "2025-04-01"   # Optionnel, par défaut aujourd'hui
}

response = requests.get(url, headers=headers, params=params)

if response.status_code == 200:
    try:
        data = response.json()
    except requests.JSONDecodeError:
        print("Réponse non-JSON reçue:", response.text)
else:
    print("Erreur:", response.status_code, response.text)

import pandas as pd

# Aplatir la structure
flattened_data = []
for entry in data["reporting"][0]["dates"]:
    flat_entry = {
        'date': entry['date'],
        'nb_sessions': entry['sessions']['sessions_count'],
        'unique_models': entry['sessions']['unique_models_count'],
        'average_session': entry['sessions']['average_session_length'],
        'user_queries': entry['chats']['user_queries_count'],
        'model_responses': entry['chats']['model_responses_count'],
        'model_responses_with_docs': entry['chats']['model_responses_with_docs_count'],
    }
    flattened_data.append(flat_entry)

# Créer le DataFrame
df_sessions = pd.DataFrame(flattened_data)