262 lines
9.3 KiB
Python
262 lines
9.3 KiB
Python
"""
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سرویس توصیه آبیاری — بدون API، قابل فراخوانی از سایر سرویسها
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از RAG با پایگاه دانش irrigation و لحن مخصوص آبیاری استفاده میکند.
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"""
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import json
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import logging
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from django.db import transaction
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from irrigation.models import IrrigationMethod
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from irrigation.evapotranspiration import calculate_forecast_water_needs, resolve_crop_profile, resolve_kc
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from farm_data.models import SensorData
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from rag.api_provider import get_chat_client
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from rag.chat import (
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_complete_audit_log,
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_create_audit_log,
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_fail_audit_log,
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_load_service_tone,
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build_rag_context,
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)
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from rag.config import load_rag_config, RAGConfig, get_service_config
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from rag.user_data import build_plant_text, build_irrigation_method_text
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from weather.models import WeatherForecast
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logger = logging.getLogger(__name__)
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KB_NAME = "irrigation"
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SERVICE_ID = "irrigation"
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DEFAULT_IRRIGATION_PROMPT = (
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"بر اساس محاسبات نهایی تبخیر-تعرق و نیاز آبی که در ورودی آمده، "
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"یک برنامه آبیاری قابلفهم برای کشاورز تولید کن. "
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"پاسخ حتماً به فرمت JSON با ساختار زیر باشد:\n"
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'{\n'
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' "plan": {\n'
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' "frequencyPerWeek": <int>,\n'
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' "durationMinutes": <int>,\n'
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' "bestTimeOfDay": "<str>",\n'
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' "moistureLevel": <int>,\n'
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' "warning": "<str>"\n'
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' }\n'
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'}\n'
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"فقط JSON خروجی بده، بدون توضیح اضافی. "
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"از انجام هرگونه محاسبه عددی جدید خودداری کن و فقط از دادههای ساختاریافته ورودی استفاده کن."
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)
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def _resolve_irrigation_method(
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sensor: SensorData | None,
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irrigation_method_name: str | None,
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) -> IrrigationMethod | None:
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if irrigation_method_name:
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return IrrigationMethod.objects.filter(name=irrigation_method_name).first()
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if sensor is not None:
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return sensor.irrigation_method
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return None
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def _persist_irrigation_method_on_farm(
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sensor: SensorData | None,
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irrigation_method: IrrigationMethod | None,
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) -> None:
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if sensor is None or irrigation_method is None:
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return
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if sensor.irrigation_method_id == irrigation_method.id:
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return
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with transaction.atomic():
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sensor.irrigation_method = irrigation_method
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sensor.save(update_fields=["irrigation_method", "updated_at"])
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def get_irrigation_recommendation(
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sensor_uuid: str,
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plant_name: str | None = None,
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growth_stage: str | None = None,
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irrigation_method_name: str | None = None,
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query: str | None = None,
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config: RAGConfig | None = None,
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limit: int = 8,
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) -> dict:
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"""
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توصیه آبیاری برای یک سنسور (کاربر).
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از RAG با پایگاه دانش irrigation استفاده میکند.
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Args:
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sensor_uuid: شناسه سنسور کاربر
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plant_name: نام گیاه (برای بارگذاری مشخصات از جدول Plant)
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growth_stage: مرحله رشد گیاه
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irrigation_method_name: نام روش آبیاری (برای بارگذاری از جدول IrrigationMethod)
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query: سوال اختیاری
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config: تنظیمات RAG
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limit: تعداد چانکهای بازیابیشده
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Returns:
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dict با کلیدهای irrigation_needed, amount_mm, reason, next_check_date, raw_response
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"""
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cfg = config or load_rag_config()
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service = get_service_config(SERVICE_ID, cfg)
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service_cfg = RAGConfig(
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embedding=cfg.embedding,
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qdrant=cfg.qdrant,
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chunking=cfg.chunking,
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llm=service.llm,
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knowledge_bases=cfg.knowledge_bases,
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services=cfg.services,
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chromadb=cfg.chromadb,
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)
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client = get_chat_client(service_cfg)
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model = service.llm.model
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user_query = query or "توصیه آبیاری برای مزرعه من چیست؟"
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sensor = (
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SensorData.objects.select_related("center_location", "irrigation_method")
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.prefetch_related("plants")
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.filter(farm_uuid=sensor_uuid)
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.first()
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)
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irrigation_method = _resolve_irrigation_method(sensor, irrigation_method_name)
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_persist_irrigation_method_on_farm(sensor, irrigation_method)
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plant = None
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resolved_plant_name = plant_name
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if sensor is not None and plant_name:
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plant = sensor.plants.filter(name=plant_name).first()
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elif sensor is not None:
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plant = sensor.plants.first()
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if plant is not None:
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resolved_plant_name = plant.name
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crop_profile = resolve_crop_profile(plant, growth_stage=growth_stage)
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active_kc = resolve_kc(crop_profile, growth_stage=growth_stage)
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forecasts = []
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daily_water_needs = []
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if sensor is not None:
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forecasts = list(
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WeatherForecast.objects.filter(location=sensor.center_location, forecast_date__isnull=False)
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.order_by("forecast_date")[:7]
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)
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efficiency_percent = (
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getattr(irrigation_method, "water_efficiency_percent", None)
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if irrigation_method
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else None
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)
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daily_water_needs = calculate_forecast_water_needs(
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forecasts=forecasts,
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latitude_deg=float(sensor.center_location.latitude),
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crop_profile=crop_profile,
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growth_stage=growth_stage,
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irrigation_efficiency_percent=efficiency_percent,
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)
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context = build_rag_context(
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user_query, sensor_uuid, config=cfg, limit=limit, kb_name=KB_NAME, service_id=SERVICE_ID,
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)
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extra_parts: list[str] = []
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resolved_irrigation_method_name = (
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irrigation_method.name if irrigation_method is not None else None
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)
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if resolved_plant_name and growth_stage:
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plant_text = build_plant_text(resolved_plant_name, growth_stage)
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if plant_text:
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extra_parts.append("[اطلاعات گیاه]\n" + plant_text)
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if resolved_irrigation_method_name:
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method_text = build_irrigation_method_text(resolved_irrigation_method_name)
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if method_text:
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extra_parts.append("[روش آبیاری انتخابی]\n" + method_text)
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if daily_water_needs:
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total_mm = round(sum(item["gross_irrigation_mm"] for item in daily_water_needs), 2)
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schedule_lines = [
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f"- {item['forecast_date']}: ET0={item['et0_mm']} mm, ETc={item['etc_mm']} mm, "
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f"بارش مؤثر={item['effective_rainfall_mm']} mm, نیاز آبی={item['gross_irrigation_mm']} mm, "
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f"زمان پیشنهادی={item['irrigation_timing']}"
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for item in daily_water_needs
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]
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extra_parts.append(
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"[خروجی قطعی محاسبات FAO-56]\n"
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f"کل نیاز آبی ۷ روز آینده: {total_mm} mm\n"
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f"Kc مورد استفاده: {active_kc}\n"
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+ "\n".join(schedule_lines)
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)
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if extra_parts:
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context = "\n\n---\n\n".join(extra_parts) + ("\n\n---\n\n" + context if context else "")
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tone = _load_service_tone(service, cfg)
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system_parts = [tone] if tone else []
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if service.system_prompt:
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system_parts.append(service.system_prompt)
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system_parts.append(DEFAULT_IRRIGATION_PROMPT)
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if context:
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system_parts.append("\n\n" + context)
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system_content = "\n".join(system_parts)
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messages = [
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{"role": "system", "content": system_content},
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{"role": "user", "content": user_query},
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]
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audit_log = _create_audit_log(
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farm_uuid=sensor_uuid,
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service_id=SERVICE_ID,
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model=model,
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query=user_query,
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system_prompt=system_content,
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messages=messages,
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)
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try:
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response = client.chat.completions.create(
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model=model,
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messages=messages,
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)
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raw = response.choices[0].message.content.strip()
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except Exception as exc:
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logger.error("Irrigation recommendation error for %s: %s", sensor_uuid, exc)
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result = {
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"irrigation_needed": None,
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"amount_mm": None,
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"reason": f"خطا در دریافت توصیه: {exc}",
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"next_check_date": None,
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"raw_response": None,
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}
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_fail_audit_log(
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audit_log,
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str(exc),
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response_text=json.dumps(result, ensure_ascii=False, default=str),
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)
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return result
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try:
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cleaned = raw
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if cleaned.startswith("```"):
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cleaned = cleaned.strip("`").removeprefix("json").strip()
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result = json.loads(cleaned)
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except (json.JSONDecodeError, ValueError):
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result = {
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"plan": {
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"warning": raw,
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},
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}
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result["raw_response"] = raw
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result["water_balance"] = {
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"daily": daily_water_needs,
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"crop_profile": crop_profile,
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"active_kc": active_kc,
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}
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result["selected_irrigation_method"] = (
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{
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"id": irrigation_method.id,
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"name": irrigation_method.name,
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"category": irrigation_method.category,
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"water_efficiency_percent": irrigation_method.water_efficiency_percent,
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}
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if irrigation_method is not None
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else None
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)
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_complete_audit_log(
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audit_log,
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json.dumps(result, ensure_ascii=False, default=str),
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)
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return result
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