fix: use mammouth public api and improve model mapping
This commit is contained in:
laurent
100
update_models.py
100
update_models.py
@@ -7,38 +7,33 @@ from dotenv import load_dotenv
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# Charger .env.global depuis le répertoire parent
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load_dotenv("../.env.global")
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MAMMOUTH_APIKEY = os.getenv("MAMMOUTH_APIKEY")
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AIANALASYS_APIKEY = os.getenv("AIANALASYS_APIKEY")
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def get_mammouth_models():
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# Mammouth utilise l'API OpenRouter (revendeur)
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url = "https://openrouter.ai/api/v1/models"
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headers = {"Authorization": f"Bearer {MAMMOUTH_APIKEY}"}
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# Utilisation de l'endpoint public LiteLLM de Mammouth
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url = "https://mammouth.ai/public/models"
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try:
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response = requests.get(url, headers=headers)
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response = requests.get(url)
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response.raise_for_status()
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return response.json()['data']
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# Le format retourné est {'data': [ {id, model_info: {input_cost_per_token, ...}} ]}
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return response.json().get('data', [])
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except Exception as e:
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print(f"Error fetching Mammouth models: {e}")
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print(f"Error fetching Mammouth public models: {e}")
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return []
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def get_aa_data():
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# URL correcte d'après la doc (version v2)
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url = "https://artificialanalysis.ai/api/v2/data/llms/models"
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headers = {"x-api-key": AIANALASYS_APIKEY}
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try:
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response = requests.get(url, headers=headers)
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response.raise_for_status()
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# Le diagnostic a montré que les données sont dans 'data'
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return response.json().get('data', [])
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except Exception as e:
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print(f"Error fetching Artificial Analysis data: {e}")
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return []
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def generate_markdown(models_data):
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# Trier par catégorie (genre)
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categories = {}
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for m in models_data:
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cat = m.get('category', 'General')
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if cat not in categories:
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@@ -46,10 +41,9 @@ def generate_markdown(models_data):
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categories[cat].append(m)
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md = "# Table des Modèles Mammouth.ai\n\n"
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md += "*Mise à jour automatique via Artificial Analysis & Mammouth API*\n\n"
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md += "*Mise à jour automatique via Artificial Analysis & Mammouth Public API*\n\n"
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md += "Dernière mise à jour : " + time.strftime("%Y-%m-%d %H:%M:%S") + "\n\n"
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# Liste des catégories dans un ordre spécifique
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order = ['Coding', 'Agents', 'General']
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sorted_cats = sorted(categories.keys(), key=lambda x: order.index(x) if x in order else 99)
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@@ -58,78 +52,80 @@ def generate_markdown(models_data):
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md += f"## {cat}\n\n"
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md += "| Modèle | Prix (In / Out / 1M) | Performance (AA Index) | Vitesse (TPS) |\n"
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md += "| :--- | :--- | :--- | :--- |\n"
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# Trier par performance (AA index)
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models.sort(key=lambda x: x.get('score') or 0, reverse=True)
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# On trie d'abord par score décroissant, puis par prix croissant
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models.sort(key=lambda x: (x.get('score') or 0, -(x.get('price_in') or 0)), reverse=True)
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for m in models:
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p_in = f"${m['price_in']:.2f}" if m['price_in'] is not None else "N/A"
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p_out = f"${m['price_out']:.2f}" if m['price_out'] is not None else "N/A"
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p_in = f"${m['price_in']:.2f}"
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p_out = f"${m['price_out']:.2f}"
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score = f"**{m['score']:.1f}**" if m['score'] else "N/A"
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speed = f"{m['speed']:.1f}" if m['speed'] else "N/A"
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md += f"| {m['name']} | {p_in} / {p_out} | {score} | {speed} |\n"
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md += "\n"
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return md
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def main():
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print("Fetching Mammouth models...")
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print("Fetching Mammouth public models...")
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mammouth_models = get_mammouth_models()
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if not mammouth_models:
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print("No models found from Mammouth.")
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return
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print("Fetching Artificial Analysis data...")
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aa_data = get_aa_data()
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aa_raw = get_aa_data()
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# Créer un dictionnaire de mapping pour AA (clé: nom du modèle en minuscule)
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# Construction du mapping AA (index par nom et par ID technique)
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aa_map = {}
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for aa_m in aa_data:
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for aa_m in aa_raw:
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name = aa_m.get('model_name', '').lower()
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aa_map[name] = aa_m
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model_id = aa_m.get('model_id', '').lower()
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if name: aa_map[name] = aa_m
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if model_id: aa_map[model_id] = aa_m
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enriched_models = []
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for m in mammouth_models:
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m_id = m['id']
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m_name = m['name'].lower()
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short_name = m_id.split('/')[-1].lower()
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m_id = m.get('id', '')
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info = m.get('model_info', {})
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# Mapping logique plus complet
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aa_info = aa_map.get(m_name) or aa_map.get(short_name)
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# On ignore les modèles sans ID
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if not m_id: continue
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# Normalisation du nom pour le mapping
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m_id_low = m_id.lower()
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short_name = m_id_low.split('/')[-1]
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# Recherche de correspondance dans AA (Précis puis Approché)
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aa_info = aa_map.get(m_id_low) or aa_map.get(short_name)
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# Si pas de match exact, on cherche par sous-chaîne ou flou
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if not aa_info:
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# Recherche par sous-chaîne pour les modèles comme "mistral-large-2407"
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for key in aa_map:
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if key in m_name or m_name in key or key in short_name or short_name in key:
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if key in m_id_low or m_id_low in key:
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aa_info = aa_map[key]
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break
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# Extraction des prix Mammouth (prix pour 1 token chez OpenRouter)
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pricing = m.get('pricing', {})
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# Extraction des prix (LiteLLM: prix par 1 token)
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try:
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price_in = float(pricing.get('prompt', 0)) * 1000000
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price_out = float(pricing.get('completion', 0)) * 1000000
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price_in = float(info.get('input_cost_per_token', 0)) * 1000000
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price_out = float(info.get('output_cost_per_token', 0)) * 1000000
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except (ValueError, TypeError):
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price_in = 0
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price_out = 0
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price_in, price_out = 0, 0
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score = None
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speed = None
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# On essaie d'extraire la catégorie de AA, sinon on devine
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category = "General"
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if aa_info:
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evals = aa_info.get('evaluations', {})
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# On cherche l'intelligence index
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score = evals.get('artificial_analysis_intelligence_index')
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speed = aa_info.get('median_output_tokens_per_second')
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# Détermination de la catégorie (Genre)
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if any(x in m_name or x in short_name for x in ['coding', 'code', 'starcoder', 'stable-code', 'deepseek-coder']):
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# Catégorisation simplifiée
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category = "General"
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if any(x in m_id_low for x in ['coding', 'code', 'starcoder', 'coder']):
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category = "Coding"
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elif any(x in m_name or x in short_name for x in ['agent', 'hermes', 'tool']):
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elif any(x in m_id_low for x in ['agent', 'hermes', 'tool', 'function']):
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category = "Agents"
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else:
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category = "General"
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enriched_models.append({
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'name': m['name'],
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'name': m_id,
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'price_in': price_in,
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'price_out': price_out,
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'score': score,
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@@ -137,12 +133,14 @@ def main():
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'category': category
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})
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# On ne garde que les modèles qui ont un score de performance OU un prix raisonnable
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# (Certains modèles sont gratuits ou ont des prix nuls)
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final_list = [m for m in enriched_models if m['price_in'] > 0 or m['score'] is not None]
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# Filtrer les modèles : prix > 0 (ceux qui sont configurés)
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final_list = [m for m in enriched_models if m['price_in'] > 0 or m['price_out'] > 0]
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if not final_list:
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print("No valid models found after filtering.")
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return
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markdown = generate_markdown(final_list)
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with open("README.md", "w", encoding="utf-8") as f:
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f.write(markdown)
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