consultoria-as
b5d62c2812
- System prompt: SIEMPRE devuelve search_query en ingles - Diccionario de traducciones (balatas→Brake Pad, etc.) - Busca directo sin preguntar mas info - Fallback: extrae keywords si AI no da search_query Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
92 lines
3.5 KiB
Python
92 lines
3.5 KiB
Python
# /home/Autopartes/pos/services/ai_chat.py
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"""AI Chat service using OpenRouter for parts lookup assistance."""
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import requests
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import json
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from config import OPENROUTER_API_KEY
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OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"
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MODEL = "anthropic/claude-haiku-4.5" # Fast + cheap for chat
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SYSTEM_PROMPT = """Eres un asistente de refaccionaria automotriz mexicana. Tu trabajo es ayudar a encontrar autopartes.
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IMPORTANTE: Responde SIEMPRE en formato JSON valido con esta estructura:
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{
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"message": "Tu respuesta al usuario en español",
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"search_query": "termino de busqueda EN INGLES para el catalogo",
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"vehicle": {"brand": "TOYOTA", "model": "Corolla", "year": 2020}
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}
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Reglas OBLIGATORIAS:
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1. "search_query" SIEMPRE debe tener un valor cuando el usuario menciona una parte. NUNCA dejes null si el usuario pide algo.
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2. "search_query" debe estar EN INGLES porque el catalogo TecDoc tiene nombres en ingles. Traducciones comunes:
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- Balatas/Pastillas de freno = "Brake Pad"
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- Discos de freno = "Brake Disc"
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- Amortiguador = "Shock Absorber"
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- Filtro de aceite = "Oil Filter"
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- Filtro de aire = "Air Filter"
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- Bujias = "Spark Plug"
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- Banda serpentina = "V-Belt" o "Serpentine Belt"
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- Bomba de agua = "Water Pump"
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- Alternador = "Alternator"
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- Radiador = "Radiator"
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- Sensor de oxigeno = "Oxygen Sensor"
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- Terminal de direccion = "Tie Rod End"
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- Bomba de gasolina = "Fuel Pump"
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- Clutch/Embrague = "Clutch Kit"
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- Mofle/Escape = "Exhaust"
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- Inyector = "Injector"
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3. "vehicle" extrae marca, modelo y ano. La marca en MAYUSCULAS.
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4. Nombres mexicanos: Tsuru = TSURU, Aveo = AVEO, Jetta = JETTA, Pointer = POINTER, Chevy = CORSA, Vocho = BEETLE.
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5. No preguntes mas info si ya puedes buscar. Si el usuario dice "balatas para Tsuru 2015", busca directo.
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6. "message" es breve y directo: "Buscando balatas para Nissan Tsuru 2015..."
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"""
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def chat(user_message, conversation_history=None):
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"""Send a message to the AI and get a response with search suggestions."""
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messages = [{"role": "system", "content": SYSTEM_PROMPT}]
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if conversation_history:
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messages.extend(conversation_history)
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messages.append({"role": "user", "content": user_message})
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try:
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resp = requests.post(
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OPENROUTER_URL,
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headers={
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"Authorization": f"Bearer {OPENROUTER_API_KEY}",
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"Content-Type": "application/json",
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},
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json={
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"model": MODEL,
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"messages": messages,
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"max_tokens": 500,
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"temperature": 0.3,
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},
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timeout=15,
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)
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resp.raise_for_status()
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data = resp.json()
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content = data["choices"][0]["message"]["content"]
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# Try to parse JSON response
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try:
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# Handle markdown-wrapped JSON (```json ... ```)
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stripped = content.strip()
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if stripped.startswith("```"):
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lines = stripped.split("\n")
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# Remove first and last lines (``` markers)
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json_str = "\n".join(lines[1:-1])
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parsed = json.loads(json_str)
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else:
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parsed = json.loads(stripped)
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return parsed
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except (json.JSONDecodeError, IndexError):
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return {"message": content, "search_query": None, "vehicle": None}
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except Exception as e:
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return {
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"message": f"Error de conexion: {str(e)}",
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"search_query": None,
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"vehicle": None,
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}
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