Ai/rag/services/fertilization.py

"""
سرویس توصیه کودهی — بدون API، قابل فراخوانی از سایر سرویس‌ها
از RAG با پایگاه دانش fertilization و لحن مخصوص کودهی استفاده می‌کند.
"""
import json
import logging

from django.apps import apps

from farm_data.models import SensorData
from rag.api_provider import get_chat_client
from rag.chat import (
    _complete_audit_log,
    _create_audit_log,
    _fail_audit_log,
    _load_service_tone,
    build_rag_context,
)
from rag.config import load_rag_config, RAGConfig, get_service_config
from rag.user_data import build_plant_text

logger = logging.getLogger(__name__)

KB_NAME = "fertilization"
SERVICE_ID = "fertilization"

DEFAULT_FERTILIZATION_PROMPT = (
    "از داده های خاک، مرحله رشد و خروجی بهینه ساز شبیه سازی برای ساخت توصیه کودهی استفاده کن. "
    "اگر بلوک [خروجی بهینه ساز شبیه سازی] وجود داشت، همان را مرجع اصلی فرمول، مقدار، روش مصرف و اعتبار قرار بده. "
    "پاسخ فقط JSON معتبر با کلید sections باشد."
)


def _get_optimizer():
    return apps.get_app_config("crop_simulation").get_recommendation_optimizer()


def _validate_fertilization_response(parsed_result: dict) -> dict:
    if not isinstance(parsed_result, dict):
        raise ValueError("Fertilization recommendation response is not a JSON object.")

    sections = parsed_result.get("sections")
    if not isinstance(sections, list) or not sections:
        raise ValueError("Fertilization recommendation response is missing sections.")

    for index, section in enumerate(sections):
        if not isinstance(section, dict):
            raise ValueError(f"Fertilization recommendation section {index} is invalid.")
        missing = [key for key in ("type", "title", "icon") if key not in section]
        if missing:
            raise ValueError(
                f"Fertilization recommendation section {index} is missing fields: {', '.join(missing)}"
            )

    return parsed_result


def get_fertilization_recommendation(
    farm_uuid: str | None = None,
    plant_name: str | None = None,
    growth_stage: str | None = None,
    query: str | None = None,
    config: RAGConfig | None = None,
    limit: int = 8,
    sensor_uuid: str | None = None,
) -> dict:
    """
    توصیه کودهی برای یک مزرعه.
    از RAG با پایگاه دانش fertilization استفاده می‌کند.

    Args:
        farm_uuid: شناسه مزرعه
        plant_name: نام گیاه (برای بارگذاری مشخصات از جدول Plant)
        growth_stage: مرحله رشد گیاه
        query: سوال اختیاری
        config: تنظیمات RAG
        limit: تعداد چانک‌های بازیابی‌شده

    Returns:
        dict ساختاریافته برای توصیه کودهی
    """
    cfg = config or load_rag_config()
    service = get_service_config(SERVICE_ID, cfg)
    service_cfg = RAGConfig(
        embedding=cfg.embedding,
        qdrant=cfg.qdrant,
        chunking=cfg.chunking,
        llm=service.llm,
        knowledge_bases=cfg.knowledge_bases,
        services=cfg.services,
        chromadb=cfg.chromadb,
    )
    client = get_chat_client(service_cfg)
    model = service.llm.model

    resolved_farm_uuid = str(farm_uuid or sensor_uuid or "").strip()
    if not resolved_farm_uuid:
        raise ValueError("farm_uuid is required.")

    user_query = query or "توصیه کودهی برای مزرعه من چیست؟"

    sensor = (
        SensorData.objects.select_related("center_location")
        .prefetch_related("plants")
        .filter(farm_uuid=resolved_farm_uuid)
        .first()
    )
    resolved_plant_name = plant_name
    plant = None
    if not resolved_plant_name and sensor is not None:
        plant = sensor.plants.first()
        if plant is not None:
            resolved_plant_name = plant.name
    elif sensor is not None and plant_name:
        plant = sensor.plants.filter(name=plant_name).first() or sensor.plants.first()

    forecasts = []
    optimized_result = None
    if sensor is not None and getattr(sensor, "center_location", None) is not None:
        from weather.models import WeatherForecast

        forecasts = list(
            WeatherForecast.objects.filter(
                location=sensor.center_location,
                forecast_date__isnull=False,
            ).order_by("forecast_date")[:7]
        )
    if sensor is not None and plant is not None:
        optimized_result = _get_optimizer().optimize_fertilization(
            sensor=sensor,
            plant=plant,
            forecasts=forecasts,
            growth_stage=growth_stage,
        )

    context = build_rag_context(
        user_query, resolved_farm_uuid, config=cfg, limit=limit, kb_name=KB_NAME, service_id=SERVICE_ID,
    )

    extra_parts: list[str] = []
    if resolved_plant_name and growth_stage:
        plant_text = build_plant_text(resolved_plant_name, growth_stage)
        if plant_text:
            extra_parts.append("[اطلاعات گیاه]\n" + plant_text)
    if optimized_result is not None:
        extra_parts.append(
            "[خروجی بهینه ساز شبیه سازی]\n"
            + optimized_result["context_text"]
        )
    if extra_parts:
        context = "\n\n---\n\n".join(extra_parts) + ("\n\n---\n\n" + context if context else "")

    tone = _load_service_tone(service, cfg)
    system_parts = [tone] if tone else []
    if service.system_prompt:
        system_parts.append(service.system_prompt)
    system_parts.append(DEFAULT_FERTILIZATION_PROMPT)
    if context:
        system_parts.append("\n\n" + context)
    system_content = "\n".join(system_parts)

    messages = [
        {"role": "system", "content": system_content},
        {"role": "user", "content": user_query},
    ]
    audit_log = _create_audit_log(
        farm_uuid=resolved_farm_uuid,
        service_id=SERVICE_ID,
        model=model,
        query=user_query,
        system_prompt=system_content,
        messages=messages,
    )

    try:
        response = client.chat.completions.create(
            model=model,
            messages=messages,
        )
        raw = response.choices[0].message.content.strip()
    except Exception as exc:
        logger.error("Fertilization recommendation error for %s: %s", resolved_farm_uuid, exc)
        _fail_audit_log(audit_log, str(exc))
        raise RuntimeError(
            f"Fertilization recommendation failed for farm {resolved_farm_uuid}."
        ) from exc

    try:
        cleaned = raw
        if cleaned.startswith("```"):
            cleaned = cleaned.strip("`").removeprefix("json").strip()
        result = json.loads(cleaned)
    except (json.JSONDecodeError, ValueError):
        result = {}

    result = _validate_fertilization_response(result)
    result["raw_response"] = raw
    result["simulation_optimizer"] = optimized_result
    _complete_audit_log(
        audit_log,
        json.dumps(result, ensure_ascii=False, default=str),
    )
    return result