UPDATE

rag/chat.py

@@ -1,12 +1,14 @@
 """
-چت RAG برای API چت عمومی — استفاده مستقیم از داده مزرعه بدون retrieval/embedding.
+چت RAG برای API چت عمومی — با ارسال کامل داده مزرعه و retrieval تکمیلی از KB.
 """
 import json
 import logging
 from pathlib import Path
 
 from .api_provider import get_chat_client
+from .chunker import chunk_text
 from .config import RAGConfig, ServiceConfig, get_service_config, load_rag_config
+from .retrieve import search_with_texts
 
 logger = logging.getLogger(__name__)
 
@@ -61,6 +63,17 @@ def _format_farm_context_from_details(farm_details: dict) -> str:
     return "[اطلاعات کامل مزرعه]\n" + serialized
 
 
+def _load_farm_details_context(
+    sensor_uuid: str | None,
+    farm_details: dict | None = None,
+) -> str:
+    if not sensor_uuid:
+        return ""
+    if farm_details is not None:
+        return _format_farm_context_from_details(farm_details)
+    return _format_farm_context(sensor_uuid)
+
+
 def _build_system_prompt(
     service: ServiceConfig,
     query: str,
@@ -72,8 +85,10 @@ def _build_system_prompt(
     if service.system_prompt:
         system_parts.append(service.system_prompt)
     system_parts.append(
-        "با استفاده از اطلاعات کامل مزرعه که در ادامه آمده به سوال کاربر پاسخ بده. "
+        "با استفاده از اطلاعات کامل مزرعه و اطلاعات بازیابی‌شده از پایگاه دانش که در ادامه آمده "
+        "به سوال کاربر پاسخ بده. "
         "اگر داده‌ای در اطلاعات مزرعه وجود دارد، همان را مبنای پاسخ قرار بده و چیزی حدس نزن. "
+        "نتایج بازیابی‌شده از پایگاه دانش را برای تکمیل یا توضیح پاسخ استفاده کن. "
         "اگر داده کافی نبود، این کمبود را شفاف بگو. "
         "پاسخ را به زبان کاربر بنویس."
     )
@@ -141,13 +156,15 @@ def build_rag_context(
     limit: int = 8,
     kb_name: str | None = None,
     service_id: str | None = None,
+    farm_details: dict | None = None,
 ) -> str:
     """
-    ساخت context برای سرویس‌های توصیه با استفاده از RAG قدیمی.
-    این تابع برای سازگاری با irrigation/fertilization حفظ شده است.
+    ساخت context مشترک برای همه سرویس‌های RAG.
+    شامل:
+    - اطلاعات کامل مزرعه از farm_data/services.py
+    - جستجوی KB بر اساس پیام کاربر
+    - جستجوی KB بر اساس chunk های کامل داده مزرعه
     """
-    from .retrieve import search_with_query
-    from .user_data import build_user_soil_text, build_user_weather_text
 
     logger.info(
         "Building RAG context sensor_uuid=%s kb_name=%s limit=%s query_len=%s",
@@ -161,20 +178,23 @@ def build_rag_context(
     service = get_service_config(service_id, cfg) if service_id else None
     include_user_embeddings = service.use_user_embeddings if service else True
     resolved_kb_name = kb_name or (service.knowledge_base if service else None)
+    farm_context = _load_farm_details_context(
+        sensor_uuid=sensor_uuid,
+        farm_details=farm_details,
+    )
 
-    if include_user_embeddings and sensor_uuid:
-        user_soil = build_user_soil_text(sensor_uuid)
-        if user_soil and user_soil.strip():
-            parts.append("[داده‌های فعلی خاک شما]\n" + user_soil.strip())
+    if farm_context:
+        parts.append(farm_context)
 
-        weather_text = build_user_weather_text(sensor_uuid)
-        if weather_text and weather_text.strip():
-            parts.append("[پیش‌بینی هواشناسی]\n" + weather_text.strip())
+    search_texts = [query]
+    if farm_context:
+        search_texts.extend(chunk_text(farm_context, config=cfg))
 
-    results = search_with_query(
-        query,
+    results = search_with_texts(
+        search_texts,
         sensor_uuid=sensor_uuid,
         limit=limit,
+        per_text_limit=3,
         config=cfg,
         kb_name=resolved_kb_name,
         service_id=service_id,
@@ -230,20 +250,23 @@ def chat_rag_stream(
         len(query or ""),
     )
 
-    if farm_details is None:
-        farm_context = _format_farm_context(farm_uuid)
-    else:
-        farm_context = _format_farm_context_from_details(farm_details)
+    context = build_rag_context(
+        query=query,
+        sensor_uuid=farm_uuid,
+        config=cfg,
+        service_id=service_id,
+        farm_details=farm_details,
+    )
     logger.info(
-        "Loaded farm context for farm_uuid=%s context_len=%s",
+        "Loaded augmented context for farm_uuid=%s context_len=%s",
         farm_uuid,
-        len(farm_context),
+        len(context),
     )
 
     if system_override is not None:
         system_prompt = system_override
     else:
-        system_prompt = _build_system_prompt(service, query, farm_context, cfg)
+        system_prompt = _build_system_prompt(service, query, context, cfg)
 
     messages = [
         {"role": "system", "content": system_prompt},

rag/retrieve.py

@@ -2,10 +2,37 @@
 بازیابی RAG: embed کوئری و جستجو در vector store
 """
 from .config import load_rag_config, RAGConfig, get_service_config
-from .embedding import embed_single
+from .embedding import embed_single, embed_texts
 from .vector_store import QdrantVectorStore
 
 
+def _resolve_search_options(
+    sensor_uuid: str | None = None,
+    config: RAGConfig | None = None,
+    kb_name: str | None = None,
+    service_id: str | None = None,
+    use_user_embeddings: bool | None = None,
+) -> tuple[RAGConfig, list[str], list[str]]:
+    cfg = config or load_rag_config()
+    service = get_service_config(service_id, cfg) if service_id else None
+    resolved_kb_name = kb_name or (service.knowledge_base if service else None)
+    include_user_embeddings = (
+        use_user_embeddings
+        if use_user_embeddings is not None
+        else (service.use_user_embeddings if service else True)
+    )
+
+    sensor_filters = ["__global__"]
+    if include_user_embeddings and sensor_uuid:
+        sensor_filters.insert(0, sensor_uuid)
+
+    kb_filters = [resolved_kb_name] if resolved_kb_name else []
+    if include_user_embeddings:
+        kb_filters.append("__all__")
+
+    return cfg, sensor_filters, kb_filters
+
+
 def search_with_query(
     query: str,
     sensor_uuid: str | None = None,
@@ -28,23 +55,14 @@ def search_with_query(
     Returns:
         لیست نتایج با id, score, text, metadata
     """
-    cfg = config or load_rag_config()
-    service = get_service_config(service_id, cfg) if service_id else None
-    resolved_kb_name = kb_name or (service.knowledge_base if service else None)
-    include_user_embeddings = (
-        use_user_embeddings
-        if use_user_embeddings is not None
-        else (service.use_user_embeddings if service else True)
+    cfg, sensor_filters, kb_filters = _resolve_search_options(
+        sensor_uuid=sensor_uuid,
+        config=config,
+        kb_name=kb_name,
+        service_id=service_id,
+        use_user_embeddings=use_user_embeddings,
     )
 
-    sensor_filters = ["__global__"]
-    if include_user_embeddings and sensor_uuid:
-        sensor_filters.insert(0, sensor_uuid)
-
-    kb_filters = [resolved_kb_name] if resolved_kb_name else []
-    if include_user_embeddings:
-        kb_filters.append("__all__")
-
     query_vector = embed_single(query, config=cfg)
     store = QdrantVectorStore(config=cfg)
     return store.search(
@@ -54,3 +72,54 @@ def search_with_query(
         sensor_uuids=sensor_filters,
         kb_names=kb_filters,
     )
+
+
+def search_with_texts(
+    texts: list[str],
+    sensor_uuid: str | None = None,
+    limit: int = 8,
+    per_text_limit: int = 3,
+    score_threshold: float | None = None,
+    config: RAGConfig | None = None,
+    kb_name: str | None = None,
+    service_id: str | None = None,
+    use_user_embeddings: bool | None = None,
+) -> list[dict]:
+    """
+    چند متن را embed می‌کند و نتیجه جستجوها را به صورت dedupe شده برمی‌گرداند.
+    برای حالتی مناسب است که هم پیام کاربر و هم داده‌های مزرعه را علیه KB جستجو کنیم.
+    """
+    normalized_texts = [text.strip() for text in texts if text and text.strip()]
+    if not normalized_texts:
+        return []
+
+    cfg, sensor_filters, kb_filters = _resolve_search_options(
+        sensor_uuid=sensor_uuid,
+        config=config,
+        kb_name=kb_name,
+        service_id=service_id,
+        use_user_embeddings=use_user_embeddings,
+    )
+
+    store = QdrantVectorStore(config=cfg)
+    vectors = embed_texts(normalized_texts, config=cfg)
+    merged_results: dict[str, dict] = {}
+
+    for vector in vectors:
+        results = store.search(
+            query_vector=vector,
+            limit=per_text_limit,
+            score_threshold=score_threshold,
+            sensor_uuids=sensor_filters,
+            kb_names=kb_filters,
+        )
+        for item in results:
+            current = merged_results.get(item["id"])
+            if current is None or item["score"] > current["score"]:
+                merged_results[item["id"]] = item
+
+    return sorted(
+        merged_results.values(),
+        key=lambda item: item["score"],
+        reverse=True,
+    )[:limit]

rag/services/irrigation.py

@@ -5,6 +5,8 @@
 import json
 import logging
 
+from django.db import transaction
+from irrigation.models import IrrigationMethod
 from irrigation.evapotranspiration import calculate_forecast_water_needs, resolve_crop_profile, resolve_kc
 from farm_data.models import SensorData
 from rag.api_provider import get_chat_client
@@ -42,6 +44,31 @@ DEFAULT_IRRIGATION_PROMPT = (
 )
 
 
+def _resolve_irrigation_method(
+    sensor: SensorData | None,
+    irrigation_method_name: str | None,
+) -> IrrigationMethod | None:
+    if irrigation_method_name:
+        return IrrigationMethod.objects.filter(name=irrigation_method_name).first()
+    if sensor is not None:
+        return sensor.irrigation_method
+    return None
+
+
+def _persist_irrigation_method_on_farm(
+    sensor: SensorData | None,
+    irrigation_method: IrrigationMethod | None,
+) -> None:
+    if sensor is None or irrigation_method is None:
+        return
+    if sensor.irrigation_method_id == irrigation_method.id:
+        return
+
+    with transaction.atomic():
+        sensor.irrigation_method = irrigation_method
+        sensor.save(update_fields=["irrigation_method", "updated_at"])
+
+
 def get_irrigation_recommendation(
     sensor_uuid: str,
     plant_name: str | None = None,
@@ -89,6 +116,9 @@ def get_irrigation_recommendation(
         .filter(farm_uuid=sensor_uuid)
         .first()
     )
+    irrigation_method = _resolve_irrigation_method(sensor, irrigation_method_name)
+    _persist_irrigation_method_on_farm(sensor, irrigation_method)
+
     plant = None
     resolved_plant_name = plant_name
     if sensor is not None and plant_name:
@@ -106,19 +136,11 @@ def get_irrigation_recommendation(
             WeatherForecast.objects.filter(location=sensor.center_location, forecast_date__isnull=False)
             .order_by("forecast_date")[:7]
         )
-        efficiency_percent = None
-        resolved_irrigation_method_name = irrigation_method_name
-        method = None
-        if irrigation_method_name:
-            from irrigation.models import IrrigationMethod
-
-            method = IrrigationMethod.objects.filter(name=irrigation_method_name).first()
-        elif sensor is not None:
-            method = sensor.irrigation_method
-            if method is not None:
-                resolved_irrigation_method_name = method.name
-
-        efficiency_percent = getattr(method, "water_efficiency_percent", None) if method else None
+        efficiency_percent = (
+            getattr(irrigation_method, "water_efficiency_percent", None)
+            if irrigation_method
+            else None
+        )
         daily_water_needs = calculate_forecast_water_needs(
             forecasts=forecasts,
             latitude_deg=float(sensor.center_location.latitude),
@@ -132,8 +154,8 @@ def get_irrigation_recommendation(
     )
 
     extra_parts: list[str] = []
-    resolved_irrigation_method_name = irrigation_method_name or (
-        sensor.irrigation_method.name if sensor is not None and sensor.irrigation_method else None
+    resolved_irrigation_method_name = (
+        irrigation_method.name if irrigation_method is not None else None
     )
     if resolved_plant_name and growth_stage:
         plant_text = build_plant_text(resolved_plant_name, growth_stage)
@@ -222,6 +244,16 @@ def get_irrigation_recommendation(
         "crop_profile": crop_profile,
         "active_kc": active_kc,
     }
+    result["selected_irrigation_method"] = (
+        {
+            "id": irrigation_method.id,
+            "name": irrigation_method.name,
+            "category": irrigation_method.category,
+            "water_efficiency_percent": irrigation_method.water_efficiency_percent,
+        }
+        if irrigation_method is not None
+        else None
+    )
     _complete_audit_log(
         audit_log,
         json.dumps(result, ensure_ascii=False, default=str),

rag/tests/test_chat_context.py

@@ -2,51 +2,54 @@ from unittest.mock import patch
 
 from django.test import SimpleTestCase
 
-from rag.chat import build_chat_context
+from rag.chat import build_rag_context
 
 
 class ChatContextTests(SimpleTestCase):
-    @patch("rag.chat.search_with_query")
-    @patch("rag.chat._rank_text_chunks_by_query")
+    @patch("rag.chat.search_with_texts")
     @patch("rag.chat.chunk_text")
-    def test_build_chat_context_combines_farm_and_kb_context(
+    def test_build_rag_context_includes_full_farm_and_kb_results(
         self,
         mock_chunk_text,
-        mock_rank_text_chunks_by_query,
-        mock_search_with_query,
+        mock_search_with_texts,
     ):
-        mock_chunk_text.return_value = ["chunk-a", "chunk-b"]
-        mock_rank_text_chunks_by_query.return_value = ["chunk-b"]
-        mock_search_with_query.return_value = [
-            {"text": "kb text 1"},
-            {"text": "kb text 2"},
+        mock_chunk_text.return_value = ["farm chunk 1", "farm chunk 2"]
+        mock_search_with_texts.return_value = [
+            {"id": "kb-1", "score": 0.8, "text": "kb text 1", "metadata": {}},
+            {"id": "kb-2", "score": 0.7, "text": "kb text 2", "metadata": {}},
         ]
 
-        context = build_chat_context(
+        context = build_rag_context(
             query="وضعیت مزرعه چطور است؟",
-            farm_uuid="farm-123",
+            sensor_uuid="farm-123",
+            service_id="chat",
             farm_details={"sensor_payload": {"sensor-7-1": {"soil_moisture": 30}}},
         )
 
-        self.assertIn("[بخش‌های مرتبط بازیابی‌شده از اطلاعات مزرعه]", context)
-        self.assertIn("chunk-b", context)
-        self.assertIn("[اطلاعات بازیابی‌شده از پایگاه دانش]", context)
+        self.assertIn("[اطلاعات کامل مزرعه]", context)
+        self.assertIn("soil_moisture", context)
+        self.assertIn("[متن‌های مرجع]", context)
         self.assertIn("kb text 1", context)
         self.assertIn("kb text 2", context)
+        mock_search_with_texts.assert_called_once()
+        sent_texts = mock_search_with_texts.call_args.kwargs["texts"]
+        self.assertEqual(sent_texts[0], "وضعیت مزرعه چطور است؟")
+        self.assertIn("farm chunk 1", sent_texts)
+        self.assertIn("farm chunk 2", sent_texts)
 
-    @patch("rag.chat.search_with_query", return_value=[])
-    @patch("rag.chat._rank_text_chunks_by_query", return_value=[])
+    @patch("rag.chat.search_with_texts", return_value=[])
     @patch("rag.chat.chunk_text", return_value=["farm chunk"])
-    def test_build_chat_context_falls_back_to_full_farm_context(
+    def test_build_rag_context_returns_full_farm_when_kb_empty(
         self,
         _mock_chunk_text,
-        _mock_rank_text_chunks_by_query,
-        _mock_search_with_query,
+        _mock_search_with_texts,
     ):
-        context = build_chat_context(
+        context = build_rag_context(
             query="رطوبت چقدر است؟",
-            farm_uuid="farm-123",
+            sensor_uuid="farm-123",
+            service_id="chat",
             farm_details={"sensor_payload": {"sensor-7-1": {"soil_moisture": 30}}},
         )
 
-        self.assertEqual(context, "")
+        self.assertIn("[اطلاعات کامل مزرعه]", context)
+        self.assertIn("soil_moisture", context)

rag/tests/test_recommendation_services.py

@@ -68,6 +68,46 @@ class RecommendationServiceDefaultsTests(TestCase):
         mock_build_rag_context.assert_called_once()
         mock_build_plant_text.assert_called_once_with("گوجه‌فرنگی", "میوه‌دهی")
         mock_build_irrigation_method_text.assert_called_once_with("آبیاری قطره‌ای")
+        self.assertEqual(
+            result["selected_irrigation_method"]["name"],
+            "آبیاری قطره‌ای",
+        )
+
+    @patch("rag.services.irrigation.calculate_forecast_water_needs", return_value=[])
+    @patch("rag.services.irrigation.resolve_kc", return_value=0.9)
+    @patch("rag.services.irrigation.resolve_crop_profile", return_value={})
+    @patch("rag.services.irrigation.build_irrigation_method_text", return_value="method text")
+    @patch("rag.services.irrigation.build_plant_text", return_value="plant text")
+    @patch("rag.services.irrigation.build_rag_context", return_value="")
+    @patch("rag.services.irrigation.get_chat_client")
+    def test_irrigation_recommendation_persists_selected_method_on_farm(
+        self,
+        mock_get_chat_client,
+        _mock_build_rag_context,
+        _mock_build_plant_text,
+        mock_build_irrigation_method_text,
+        _mock_resolve_crop_profile,
+        _mock_resolve_kc,
+        _mock_calculate_forecast_water_needs,
+    ):
+        sprinkler = IrrigationMethod.objects.create(name="بارانی")
+        self.farm.irrigation_method = None
+        self.farm.save(update_fields=["irrigation_method", "updated_at"])
+
+        mock_response = Mock()
+        mock_response.choices = [Mock(message=Mock(content='{"plan": {"frequencyPerWeek": 4}}'))]
+        mock_get_chat_client.return_value.chat.completions.create.return_value = mock_response
+
+        result = get_irrigation_recommendation(
+            sensor_uuid=str(self.farm_uuid),
+            growth_stage="میوه‌دهی",
+            irrigation_method_name="بارانی",
+        )
+
+        self.farm.refresh_from_db()
+        self.assertEqual(self.farm.irrigation_method_id, sprinkler.id)
+        self.assertEqual(result["selected_irrigation_method"]["id"], sprinkler.id)
+        mock_build_irrigation_method_text.assert_called_once_with("بارانی")
 
     @patch("rag.services.fertilization.build_plant_text", return_value="plant text")
     @patch("rag.services.fertilization.build_rag_context", return_value="")

rag/urls.py

@@ -8,6 +8,6 @@ from .views import (
 
 urlpatterns = [
     path("chat/", ChatView.as_view()),
-    path("recommend/irrigation/", IrrigationRecommendationView.as_view(), name="recommend-irrigation"),
-    path("recommend/fertilization/", FertilizationRecommendationView.as_view(), name="recommend-fertilization"),
+    # path("recommend/irrigation/", IrrigationRecommendationView.as_view(), name="recommend-irrigation"),
+    # path("recommend/fertilization/", FertilizationRecommendationView.as_view(), name="recommend-fertilization"),
 ]