UPDATE
This commit is contained in:
@@ -4,8 +4,15 @@
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"""
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from .irrigation import get_irrigation_recommendation
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from .fertilization import get_fertilization_recommendation
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from .pest_disease import get_pest_disease_detection, get_pest_disease_risk
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from .soil_anomaly import get_soil_anomaly_insight
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from .water_need_prediction import get_water_need_prediction_insight
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__all__ = [
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"get_irrigation_recommendation",
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"get_fertilization_recommendation",
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"get_pest_disease_detection",
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"get_pest_disease_risk",
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"get_soil_anomaly_insight",
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"get_water_need_prediction_insight",
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]
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@@ -54,6 +54,62 @@ def _find_section(sections: list[dict], section_type: str) -> dict | None:
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return None
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def _field_sources(llm_section: dict, fallback_section: dict, merged_section: dict) -> dict[str, str]:
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sources: dict[str, str] = {}
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for key, value in merged_section.items():
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if key == "provenance":
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continue
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llm_value = llm_section.get(key)
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fallback_value = fallback_section.get(key)
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if key in llm_section and value == llm_value and value != fallback_value:
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sources[key] = "llm"
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elif key in fallback_section and value == fallback_value and value != llm_value:
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sources[key] = "fallback"
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elif key in llm_section and key in fallback_section and llm_value == fallback_value == value:
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sources[key] = "shared"
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elif key in llm_section and key in fallback_section:
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sources[key] = "merged"
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else:
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sources[key] = "fallback" if key in fallback_section else "llm"
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return sources
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def _attach_provenance(section_type: str, llm_section: dict, fallback_section: dict, merged_section: dict) -> dict:
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merged = dict(merged_section)
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field_sources = _field_sources(llm_section, fallback_section, merged)
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merged["provenance"] = {
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"sectionType": section_type,
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"llmProvided": bool(llm_section),
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"fallbackUsed": any(source != "llm" for source in field_sources.values()),
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"fieldSources": field_sources,
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}
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return merged
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def _fallback_with_provenance(fallback: dict, reason: str) -> dict:
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sections = []
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for section in fallback.get("sections", []):
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section_with_provenance = dict(section)
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section_with_provenance["provenance"] = {
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"sectionType": section.get("type"),
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"llmProvided": False,
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"fallbackUsed": True,
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"fieldSources": {
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key: "fallback"
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for key in section.keys()
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if key != "provenance"
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},
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}
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sections.append(section_with_provenance)
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return {
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"sections": sections,
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"mergeMetadata": {
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"source": "fallback_only",
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"reason": reason,
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},
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}
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def _build_fertilization_fallback(*, optimized_result: dict | None) -> dict:
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if optimized_result:
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recommended = optimized_result["recommended_strategy"]
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@@ -134,11 +190,11 @@ def _merge_fertilization_response(
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) -> dict:
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fallback = _build_fertilization_fallback(optimized_result=optimized_result)
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if not isinstance(parsed_result, dict):
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return fallback
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return _fallback_with_provenance(fallback, "invalid_llm_payload")
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sections = parsed_result.get("sections")
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if not isinstance(sections, list):
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return fallback
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return _fallback_with_provenance(fallback, "missing_sections")
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recommendation = _find_section(sections, "recommendation") or {}
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list_section = _find_section(sections, "list") or {}
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@@ -169,7 +225,36 @@ def _merge_fertilization_response(
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"content": warning_section.get("content") or fallback_warning["content"],
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}
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return {"sections": [merged_recommendation, merged_list, merged_warning]}
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merged_recommendation = _attach_provenance(
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"recommendation",
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recommendation,
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fallback_recommendation,
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merged_recommendation,
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)
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merged_list = _attach_provenance(
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"list",
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list_section,
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fallback_list,
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merged_list,
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)
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merged_warning = _attach_provenance(
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"warning",
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warning_section,
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fallback_warning,
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merged_warning,
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)
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return {
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"sections": [merged_recommendation, merged_list, merged_warning],
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"mergeMetadata": {
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"source": "llm_with_fallback_merge",
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"llmSectionsDetected": [section.get("type") for section in sections if isinstance(section, dict)],
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"fallbackSectionsApplied": [
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item["type"]
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for item in (fallback_recommendation, fallback_list, fallback_warning)
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],
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},
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}
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def get_fertilization_recommendation(
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@@ -57,6 +57,62 @@ def _find_section(sections: list[dict], section_type: str) -> dict | None:
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return None
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def _field_sources(llm_section: dict, fallback_section: dict, merged_section: dict) -> dict[str, str]:
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sources: dict[str, str] = {}
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for key, value in merged_section.items():
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if key == "provenance":
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continue
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llm_value = llm_section.get(key)
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fallback_value = fallback_section.get(key)
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if key in llm_section and value == llm_value and value != fallback_value:
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sources[key] = "llm"
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elif key in fallback_section and value == fallback_value and value != llm_value:
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sources[key] = "fallback"
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elif key in llm_section and key in fallback_section and llm_value == fallback_value == value:
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sources[key] = "shared"
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elif key in llm_section and key in fallback_section:
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sources[key] = "merged"
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else:
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sources[key] = "fallback" if key in fallback_section else "llm"
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return sources
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def _attach_provenance(section_type: str, llm_section: dict, fallback_section: dict, merged_section: dict) -> dict:
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merged = dict(merged_section)
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field_sources = _field_sources(llm_section, fallback_section, merged)
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merged["provenance"] = {
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"sectionType": section_type,
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"llmProvided": bool(llm_section),
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"fallbackUsed": any(source != "llm" for source in field_sources.values()),
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"fieldSources": field_sources,
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}
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return merged
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def _fallback_with_provenance(fallback: dict, reason: str) -> dict:
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sections = []
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for section in fallback.get("sections", []):
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section_with_provenance = dict(section)
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section_with_provenance["provenance"] = {
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"sectionType": section.get("type"),
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"llmProvided": False,
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"fallbackUsed": True,
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"fieldSources": {
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key: "fallback"
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for key in section.keys()
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if key != "provenance"
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},
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}
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sections.append(section_with_provenance)
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return {
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"sections": sections,
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"mergeMetadata": {
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"source": "fallback_only",
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"reason": reason,
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},
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}
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def _build_irrigation_fallback(
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*,
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optimized_result: dict | None,
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@@ -155,11 +211,11 @@ def _merge_irrigation_response(
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daily_water_needs=daily_water_needs,
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)
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if not isinstance(parsed_result, dict):
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return fallback
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return _fallback_with_provenance(fallback, "invalid_llm_payload")
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sections = parsed_result.get("sections")
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if not isinstance(sections, list):
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return fallback
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return _fallback_with_provenance(fallback, "missing_sections")
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recommendation = _find_section(sections, "recommendation") or {}
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list_section = _find_section(sections, "list") or {}
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@@ -190,7 +246,36 @@ def _merge_irrigation_response(
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"content": warning_section.get("content") or fallback_warning["content"],
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}
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return {"sections": [merged_recommendation, merged_list, merged_warning]}
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merged_recommendation = _attach_provenance(
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"recommendation",
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recommendation,
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fallback_recommendation,
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merged_recommendation,
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)
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merged_list = _attach_provenance(
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"list",
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list_section,
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fallback_list,
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merged_list,
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)
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merged_warning = _attach_provenance(
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"warning",
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warning_section,
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fallback_warning,
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merged_warning,
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)
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return {
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"sections": [merged_recommendation, merged_list, merged_warning],
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"mergeMetadata": {
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"source": "llm_with_fallback_merge",
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"llmSectionsDetected": [section.get("type") for section in sections if isinstance(section, dict)],
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"fallbackSectionsApplied": [
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item["type"]
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for item in (fallback_recommendation, fallback_list, fallback_warning)
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],
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},
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}
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def _resolve_irrigation_method(
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@@ -0,0 +1,415 @@
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"""
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سرویس RAG برای تشخیص تصویری و پیش بینی ریسک آفات و بیماری گیاه.
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"""
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from __future__ import annotations
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import json
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import logging
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from typing import Any
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from farm_data.services import get_farm_details
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from rag.api_provider import get_chat_client
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from rag.chat import (
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_build_content_parts,
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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 RAGConfig, get_service_config, load_rag_config
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from rag.user_data import build_plant_text
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logger = logging.getLogger(__name__)
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KB_NAME = "pest_disease"
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SERVICE_ID = "pest_disease"
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DETECTION_PROMPT = (
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"شما یک دستیار تخصصی تشخیص آفات و بیماری گیاهی هستی. "
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"با استفاده از تصویر، اطلاعات مزرعه، و متن های بازیابی شده از پایگاه دانش تحلیل کن. "
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"پاسخ فقط JSON معتبر باشد و این کلیدها را داشته باشد: "
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"has_issue, category, confidence, severity, summary, detected_signs, possible_causes, immediate_actions, reasoning. "
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"category فقط یکی از no_issue, pest, disease, nutrient_stress, abiotic_stress, unknown باشد. "
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"severity فقط یکی از low, medium, high باشد."
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)
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RISK_PROMPT = (
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"شما یک دستیار تخصصی پیش بینی ریسک آفات و بیماری گیاهی هستی. "
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"با استفاده از داده های مزرعه، آب و هوا، مرحله رشد، و متن های بازیابی شده از پایگاه دانش تحلیل کن. "
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"پاسخ فقط JSON معتبر باشد و این کلیدها را داشته باشد: "
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"summary, forecast_window, overall_risk, disease_risk, pest_risk, key_drivers, recommended_actions. "
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"overall_risk فقط یکی از low, medium, high باشد. "
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"disease_risk و pest_risk باید آبجکت هایی با کلیدهای score, level, likely_conditions, reasoning باشند و level فقط یکی از low, medium, high باشد."
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)
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def _safe_float(value: Any, default: float = 0.0) -> float:
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try:
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if value in (None, ""):
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return default
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return float(value)
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except (TypeError, ValueError):
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return default
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def _normalize_images(images: list[dict[str, str]] | None) -> list[dict[str, str]]:
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output: list[dict[str, str]] = []
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for item in images or []:
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if not isinstance(item, dict):
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continue
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url = item.get("url")
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if not isinstance(url, str) or not url.strip():
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continue
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output.append({"url": url.strip(), "detail": item.get("detail", "auto")})
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return output
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def _clean_json(raw: str) -> dict[str, Any]:
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cleaned = (raw or "").strip()
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if cleaned.startswith("```"):
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cleaned = cleaned.strip("`")
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if cleaned.startswith("json"):
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cleaned = cleaned[4:]
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cleaned = cleaned.strip()
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if not cleaned:
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return {}
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try:
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return json.loads(cleaned)
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except (json.JSONDecodeError, ValueError):
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logger.warning("Invalid JSON returned by pest_disease LLM: %s", cleaned[:500])
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return {}
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def _load_farm_or_error(farm_uuid: str) -> dict[str, Any]:
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farm_details = get_farm_details(farm_uuid)
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if farm_details is None:
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raise ValueError("farm_uuid نامعتبر است یا اطلاعات مزرعه پیدا نشد.")
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return farm_details
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def _build_service_client(cfg: RAGConfig):
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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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return service, client, service.llm.model
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def _weather_risk_summary(farm_details: dict[str, Any]) -> dict[str, Any]:
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weather = farm_details.get("weather") or {}
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soil = (farm_details.get("soil") or {}).get("resolved_metrics") or {}
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humidity = _safe_float(weather.get("humidity_mean"), 55.0)
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temp = _safe_float(weather.get("temperature_mean"), 24.0)
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rain = _safe_float(weather.get("precipitation"), 0.0)
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moisture = _safe_float(soil.get("soil_moisture"), _safe_float(soil.get("wv0033"), 35.0))
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ec = _safe_float(soil.get("electrical_conductivity"), 0.0)
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ph = _safe_float(soil.get("soil_ph") or soil.get("phh2o"), 7.0)
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fungal_score = min(max(round((humidity * 0.45) + (moisture * 0.35) + (rain * 2.5) - 25, 2), 0.0), 100.0)
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pest_score = min(max(round((temp * 2.2) + max(0.0, 45.0 - moisture) + (ec * 3.0) - 20, 2), 0.0), 100.0)
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abiotic_stress = min(max(round((abs(ph - 6.8) * 18.0) + (ec * 8.0), 2), 0.0), 100.0)
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return {
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"humidity_mean": humidity,
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"temperature_mean": temp,
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"precipitation": rain,
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"soil_moisture": moisture,
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"ec": ec,
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"ph": ph,
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"fungal_score": fungal_score,
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"pest_score": pest_score,
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"abiotic_stress_score": abiotic_stress,
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}
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def _risk_level(score: float) -> str:
|
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if score >= 70:
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return "high"
|
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if score >= 40:
|
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return "medium"
|
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return "low"
|
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|
||||
|
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def _build_risk_fallback(farm_details: dict[str, Any], plant_name: str | None, growth_stage: str | None) -> dict[str, Any]:
|
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risk = _weather_risk_summary(farm_details)
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disease_level = _risk_level(risk["fungal_score"])
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pest_level = _risk_level(risk["pest_score"])
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overall_score = max(risk["fungal_score"], risk["pest_score"], risk["abiotic_stress_score"])
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overall_level = _risk_level(overall_score)
|
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drivers = []
|
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if risk["humidity_mean"] >= 70:
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drivers.append("رطوبت بالا")
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if risk["soil_moisture"] >= 60:
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drivers.append("رطوبت خاک بالا")
|
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if risk["temperature_mean"] >= 30:
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drivers.append("دمای بالا")
|
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if risk["precipitation"] > 2:
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drivers.append("بارش موثر")
|
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if risk["ec"] > 2.5:
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drivers.append("EC بالا")
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if abs(risk["ph"] - 6.8) > 0.8:
|
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drivers.append("خروج pH از محدوده مطلوب")
|
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if not drivers:
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drivers.append("شرایط فعلی مزرعه نسبتا پایدار است")
|
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|
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return {
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"summary": "برآورد ریسک آفات و بیماری بر اساس داده های فعلی مزرعه ساخته شد.",
|
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"forecast_window": "24 تا 72 ساعت آینده",
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"overall_risk": overall_level,
|
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"disease_risk": {
|
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"score": risk["fungal_score"],
|
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"level": disease_level,
|
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"likely_conditions": [
|
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"فشار قارچی و بیماری برگی" if disease_level != "low" else "ریسک بیماری فعلا پایین است",
|
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],
|
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"reasoning": [
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f"رطوبت میانگین حدود {risk['humidity_mean']} درصد است.",
|
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f"رطوبت خاک حدود {risk['soil_moisture']} درصد برآورد شده است.",
|
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],
|
||||
},
|
||||
"pest_risk": {
|
||||
"score": risk["pest_score"],
|
||||
"level": pest_level,
|
||||
"likely_conditions": [
|
||||
"فشار آفات مکنده یا تنش زا" if pest_level != "low" else "ریسک آفت فعلا پایین است",
|
||||
],
|
||||
"reasoning": [
|
||||
f"دمای میانگین حدود {risk['temperature_mean']} درجه است.",
|
||||
f"EC فعلی حدود {risk['ec']} و pH حدود {risk['ph']} است.",
|
||||
],
|
||||
},
|
||||
"key_drivers": drivers,
|
||||
"recommended_actions": [
|
||||
"بازدید مزرعه و بررسی برگ ها و پشت برگ انجام شود.",
|
||||
"در صورت مشاهده علائم مشکوک، نمونه برداری تصویری نزدیک تر انجام شود.",
|
||||
"رطوبت ماندگار و یکنواختی آبیاری پایش شود.",
|
||||
],
|
||||
"farm_context": {
|
||||
"plant_name": plant_name,
|
||||
"growth_stage": growth_stage,
|
||||
"risk_summary": risk,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def _build_detection_fallback(images: list[dict[str, str]], plant_name: str | None) -> dict[str, Any]:
|
||||
return {
|
||||
"has_issue": False,
|
||||
"category": "unknown",
|
||||
"confidence": 0.2,
|
||||
"severity": "low",
|
||||
"summary": "تحلیل خودکار تصویر انجام نشد یا برای نتیجه قطعی داده کافی نبود.",
|
||||
"detected_signs": [],
|
||||
"possible_causes": ["کیفیت یا زاویه تصویر برای تشخیص کافی نیست"],
|
||||
"immediate_actions": [
|
||||
"یک تصویر نزدیک تر از برگ و ساقه ارسال شود.",
|
||||
"در صورت مشاهده گسترش علائم، بازدید میدانی انجام شود.",
|
||||
],
|
||||
"reasoning": [
|
||||
f"تعداد تصاویر دریافتی: {len(images)}",
|
||||
f"نام گیاه: {plant_name or 'نامشخص'}",
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def _build_detection_messages(
|
||||
*,
|
||||
service: Any,
|
||||
cfg: RAGConfig,
|
||||
query: str,
|
||||
rag_context: str,
|
||||
plant_text: str,
|
||||
images: list[dict[str, str]],
|
||||
) -> tuple[str, list[dict[str, Any]]]:
|
||||
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(DETECTION_PROMPT)
|
||||
if plant_text:
|
||||
system_parts.append("[اطلاعات گیاه]\n" + plant_text)
|
||||
if rag_context:
|
||||
system_parts.append(rag_context)
|
||||
system_prompt = "\n\n".join(part for part in system_parts if part)
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": _build_content_parts(query, images)},
|
||||
]
|
||||
return system_prompt, messages
|
||||
|
||||
|
||||
def _build_risk_messages(
|
||||
*,
|
||||
service: Any,
|
||||
cfg: RAGConfig,
|
||||
query: str,
|
||||
rag_context: str,
|
||||
structured_context: dict[str, Any],
|
||||
plant_text: str,
|
||||
) -> tuple[str, list[dict[str, str]]]:
|
||||
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(RISK_PROMPT)
|
||||
if plant_text:
|
||||
system_parts.append("[اطلاعات گیاه]\n" + plant_text)
|
||||
system_parts.append("[کانتکست ساختاریافته ریسک]\n" + json.dumps(structured_context, ensure_ascii=False, indent=2, default=str))
|
||||
if rag_context:
|
||||
system_parts.append(rag_context)
|
||||
system_prompt = "\n\n".join(part for part in system_parts if part)
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": query},
|
||||
]
|
||||
return system_prompt, messages
|
||||
|
||||
|
||||
def get_pest_disease_detection(
|
||||
*,
|
||||
farm_uuid: str,
|
||||
plant_name: str | None = None,
|
||||
query: str | None = None,
|
||||
images: list[dict[str, str]] | None = None,
|
||||
) -> dict[str, Any]:
|
||||
normalized_images = _normalize_images(images)
|
||||
if not normalized_images:
|
||||
raise ValueError("حداقل یک تصویر برای تشخیص لازم است.")
|
||||
|
||||
cfg = load_rag_config()
|
||||
service, client, model = _build_service_client(cfg)
|
||||
farm_details = _load_farm_or_error(farm_uuid)
|
||||
resolved_plant_name = plant_name or (farm_details.get("plants") or [{}])[0].get("name")
|
||||
user_query = query or "این تصویر را بررسی کن و بگو آیا گیاه دچار آفت یا بیماری شده است یا نه."
|
||||
plant_text = build_plant_text(resolved_plant_name, "") if resolved_plant_name else ""
|
||||
rag_context = build_rag_context(
|
||||
query=user_query,
|
||||
sensor_uuid=farm_uuid,
|
||||
config=cfg,
|
||||
kb_name=KB_NAME,
|
||||
service_id=SERVICE_ID,
|
||||
farm_details=farm_details,
|
||||
)
|
||||
system_prompt, messages = _build_detection_messages(
|
||||
service=service,
|
||||
cfg=cfg,
|
||||
query=user_query,
|
||||
rag_context=rag_context,
|
||||
plant_text=plant_text or "",
|
||||
images=normalized_images,
|
||||
)
|
||||
audit_log = _create_audit_log(
|
||||
farm_uuid=farm_uuid,
|
||||
service_id=SERVICE_ID,
|
||||
model=model,
|
||||
query=user_query,
|
||||
system_prompt=system_prompt,
|
||||
messages=messages,
|
||||
)
|
||||
try:
|
||||
response = client.chat.completions.create(model=model, messages=messages)
|
||||
raw = response.choices[0].message.content.strip()
|
||||
parsed = _clean_json(raw)
|
||||
_complete_audit_log(audit_log, raw)
|
||||
except Exception as exc:
|
||||
logger.error("Pest disease detection failed for %s: %s", farm_uuid, exc)
|
||||
fallback = _build_detection_fallback(normalized_images, resolved_plant_name)
|
||||
_fail_audit_log(audit_log, str(exc), json.dumps(fallback, ensure_ascii=False))
|
||||
return {
|
||||
**fallback,
|
||||
"farm_uuid": farm_uuid,
|
||||
"knowledge_base": KB_NAME,
|
||||
"tone_file": service.tone_file,
|
||||
"raw_response": None,
|
||||
}
|
||||
|
||||
if not parsed:
|
||||
parsed = _build_detection_fallback(normalized_images, resolved_plant_name)
|
||||
parsed.setdefault("has_issue", parsed.get("category") not in {"no_issue", "unknown"})
|
||||
parsed.setdefault("category", "unknown")
|
||||
parsed.setdefault("confidence", 0.4)
|
||||
parsed.setdefault("severity", "low")
|
||||
parsed.setdefault("detected_signs", [])
|
||||
parsed.setdefault("possible_causes", [])
|
||||
parsed.setdefault("immediate_actions", [])
|
||||
parsed.setdefault("reasoning", [])
|
||||
parsed["farm_uuid"] = farm_uuid
|
||||
parsed["knowledge_base"] = KB_NAME
|
||||
parsed["tone_file"] = service.tone_file
|
||||
parsed["raw_response"] = raw
|
||||
return parsed
|
||||
|
||||
|
||||
def get_pest_disease_risk(
|
||||
*,
|
||||
farm_uuid: str,
|
||||
plant_name: str | None = None,
|
||||
growth_stage: str | None = None,
|
||||
query: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
cfg = load_rag_config()
|
||||
service, client, model = _build_service_client(cfg)
|
||||
farm_details = _load_farm_or_error(farm_uuid)
|
||||
resolved_plant_name = plant_name or (farm_details.get("plants") or [{}])[0].get("name")
|
||||
fallback = _build_risk_fallback(farm_details, resolved_plant_name, growth_stage)
|
||||
user_query = query or "ریسک آفات و بیماری این مزرعه را برای چند روز آینده پیش بینی کن."
|
||||
plant_text = build_plant_text(resolved_plant_name, growth_stage or "") if resolved_plant_name else ""
|
||||
rag_context = build_rag_context(
|
||||
query=user_query,
|
||||
sensor_uuid=farm_uuid,
|
||||
config=cfg,
|
||||
kb_name=KB_NAME,
|
||||
service_id=SERVICE_ID,
|
||||
farm_details=farm_details,
|
||||
)
|
||||
system_prompt, messages = _build_risk_messages(
|
||||
service=service,
|
||||
cfg=cfg,
|
||||
query=user_query,
|
||||
rag_context=rag_context,
|
||||
structured_context=fallback,
|
||||
plant_text=plant_text or "",
|
||||
)
|
||||
audit_log = _create_audit_log(
|
||||
farm_uuid=farm_uuid,
|
||||
service_id=SERVICE_ID,
|
||||
model=model,
|
||||
query=user_query,
|
||||
system_prompt=system_prompt,
|
||||
messages=messages,
|
||||
)
|
||||
try:
|
||||
response = client.chat.completions.create(model=model, messages=messages)
|
||||
raw = response.choices[0].message.content.strip()
|
||||
parsed = _clean_json(raw)
|
||||
_complete_audit_log(audit_log, raw)
|
||||
except Exception as exc:
|
||||
logger.error("Pest disease risk prediction failed for %s: %s", farm_uuid, exc)
|
||||
_fail_audit_log(audit_log, str(exc), json.dumps(fallback, ensure_ascii=False))
|
||||
fallback["farm_uuid"] = farm_uuid
|
||||
fallback["knowledge_base"] = KB_NAME
|
||||
fallback["tone_file"] = service.tone_file
|
||||
fallback["raw_response"] = None
|
||||
return fallback
|
||||
|
||||
if not parsed:
|
||||
parsed = fallback
|
||||
parsed.setdefault("summary", fallback["summary"])
|
||||
parsed.setdefault("forecast_window", fallback["forecast_window"])
|
||||
parsed.setdefault("overall_risk", fallback["overall_risk"])
|
||||
parsed.setdefault("disease_risk", fallback["disease_risk"])
|
||||
parsed.setdefault("pest_risk", fallback["pest_risk"])
|
||||
parsed.setdefault("key_drivers", fallback["key_drivers"])
|
||||
parsed.setdefault("recommended_actions", fallback["recommended_actions"])
|
||||
parsed["farm_uuid"] = farm_uuid
|
||||
parsed["knowledge_base"] = KB_NAME
|
||||
parsed["tone_file"] = service.tone_file
|
||||
parsed["raw_response"] = raw
|
||||
return parsed
|
||||
@@ -0,0 +1,204 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from farm_data.services import get_farm_details
|
||||
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 RAGConfig, get_service_config, load_rag_config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
KB_NAME = "soil_anomaly"
|
||||
SERVICE_ID = "soil_anomaly"
|
||||
|
||||
SOIL_ANOMALY_PROMPT = (
|
||||
"شما یک دستیار تخصصی تحلیل ناهنجاری داده های خاک و سنسور مزرعه هستی. "
|
||||
"ورودی شامل داده های ساختاریافته ناهنجاری، اطلاعات مزرعه، و متن های بازیابی شده از پایگاه دانش است. "
|
||||
"فقط JSON معتبر برگردان و فقط این کلیدها را تولید کن: "
|
||||
"summary, explanation, likely_cause, recommended_action, monitoring_priority, confidence. "
|
||||
"monitoring_priority فقط یکی از low, medium, high, urgent باشد. "
|
||||
"confidence عددی بین 0 و 1 باشد. "
|
||||
"اگر ناهنجاری معناداری وجود ندارد، این موضوع را شفاف و بدون اغراق بیان کن."
|
||||
)
|
||||
|
||||
|
||||
def _clean_json(raw: str) -> dict[str, Any]:
|
||||
cleaned = (raw or "").strip()
|
||||
if cleaned.startswith("```"):
|
||||
cleaned = cleaned.strip("`")
|
||||
if cleaned.startswith("json"):
|
||||
cleaned = cleaned[4:]
|
||||
cleaned = cleaned.strip()
|
||||
if not cleaned:
|
||||
return {}
|
||||
try:
|
||||
return json.loads(cleaned)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
logger.warning("Invalid JSON returned by soil_anomaly LLM: %s", cleaned[:500])
|
||||
return {}
|
||||
|
||||
|
||||
def _load_farm_or_error(farm_uuid: str) -> dict[str, Any]:
|
||||
farm_details = get_farm_details(farm_uuid)
|
||||
if farm_details is None:
|
||||
raise ValueError("farm_uuid نامعتبر است یا اطلاعات مزرعه پیدا نشد.")
|
||||
return farm_details
|
||||
|
||||
|
||||
def _build_service_client(cfg: RAGConfig):
|
||||
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)
|
||||
return service, client, service.llm.model
|
||||
|
||||
|
||||
def _fallback_from_payload(anomaly_payload: dict[str, Any]) -> dict[str, Any]:
|
||||
interpretation = anomaly_payload.get("interpretation") or {}
|
||||
anomalies = anomaly_payload.get("anomalies") or []
|
||||
top_anomaly = anomalies[0] if anomalies else None
|
||||
|
||||
if top_anomaly is None:
|
||||
return {
|
||||
"summary": "در داده های اخیر ناهنجاری معناداری دیده نشد.",
|
||||
"explanation": interpretation.get("explanation")
|
||||
or "داده های فعلی با الگوی معمول مزرعه سازگار هستند و مورد غیرعادی برجسته ای دیده نمی شود.",
|
||||
"likely_cause": interpretation.get("likely_cause")
|
||||
or "شرایط فعلی مزرعه پایدار است یا داده کافی برای تشخیص رخداد غیرعادی وجود ندارد.",
|
||||
"recommended_action": interpretation.get("recommended_action")
|
||||
or "پایش عادی ادامه یابد و روندها در بازه بعدی دوباره بررسی شوند.",
|
||||
"monitoring_priority": "low",
|
||||
"confidence": 0.55,
|
||||
}
|
||||
|
||||
severity = str(top_anomaly.get("severity") or "medium")
|
||||
priority_map = {
|
||||
"low": "medium",
|
||||
"medium": "high",
|
||||
"high": "urgent",
|
||||
"critical": "urgent",
|
||||
}
|
||||
return {
|
||||
"summary": f"ناهنجاري در شاخص {top_anomaly.get('label', 'نامشخص')} شناسايي شد.",
|
||||
"explanation": interpretation.get("explanation")
|
||||
or f"مقدار {top_anomaly.get('label', 'اين شاخص')} از الگوي آماري معمول مزرعه فاصله گرفته است.",
|
||||
"likely_cause": interpretation.get("likely_cause")
|
||||
or "اين الگو مي تواند ناشي از تغيير شرايط محيطي، آبياري، شوري يا خطاي اندازه گيري سنسور باشد.",
|
||||
"recommended_action": interpretation.get("recommended_action")
|
||||
or "روند اين شاخص و شرايط مزرعه در کوتاه مدت بازبيني و در صورت تداوم، اقدام اصلاحي انجام شود.",
|
||||
"monitoring_priority": priority_map.get(severity, "high"),
|
||||
"confidence": 0.7 if severity in {"high", "critical"} else 0.6,
|
||||
}
|
||||
|
||||
|
||||
def _build_messages(
|
||||
*,
|
||||
service: Any,
|
||||
cfg: RAGConfig,
|
||||
query: str,
|
||||
rag_context: str,
|
||||
structured_context: dict[str, Any],
|
||||
) -> tuple[str, list[dict[str, str]]]:
|
||||
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(SOIL_ANOMALY_PROMPT)
|
||||
system_parts.append(
|
||||
"[کانتکست ساختاریافته ناهنجاري خاک]\n"
|
||||
+ json.dumps(structured_context, ensure_ascii=False, indent=2, default=str)
|
||||
)
|
||||
if rag_context:
|
||||
system_parts.append(rag_context)
|
||||
system_prompt = "\n\n".join(part for part in system_parts if part)
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": query},
|
||||
]
|
||||
return system_prompt, messages
|
||||
|
||||
|
||||
def get_soil_anomaly_insight(
|
||||
*,
|
||||
farm_uuid: str,
|
||||
anomaly_payload: dict[str, Any],
|
||||
query: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
cfg = load_rag_config()
|
||||
service, client, model = _build_service_client(cfg)
|
||||
farm_details = _load_farm_or_error(farm_uuid)
|
||||
fallback = _fallback_from_payload(anomaly_payload)
|
||||
user_query = query or "ناهنجاري هاي داده هاي خاک اين مزرعه را تفسير کن و اقدام مناسب پيشنهاد بده."
|
||||
structured_context = {
|
||||
"farm_uuid": farm_uuid,
|
||||
"anomaly_payload": anomaly_payload,
|
||||
"fallback_interpretation": fallback,
|
||||
}
|
||||
rag_context = build_rag_context(
|
||||
query=user_query,
|
||||
sensor_uuid=farm_uuid,
|
||||
config=cfg,
|
||||
kb_name=KB_NAME,
|
||||
service_id=SERVICE_ID,
|
||||
farm_details=farm_details,
|
||||
)
|
||||
system_prompt, messages = _build_messages(
|
||||
service=service,
|
||||
cfg=cfg,
|
||||
query=user_query,
|
||||
rag_context=rag_context,
|
||||
structured_context=structured_context,
|
||||
)
|
||||
audit_log = _create_audit_log(
|
||||
farm_uuid=farm_uuid,
|
||||
service_id=SERVICE_ID,
|
||||
model=model,
|
||||
query=user_query,
|
||||
system_prompt=system_prompt,
|
||||
messages=messages,
|
||||
)
|
||||
try:
|
||||
response = client.chat.completions.create(model=model, messages=messages)
|
||||
raw = response.choices[0].message.content.strip()
|
||||
parsed = _clean_json(raw)
|
||||
_complete_audit_log(audit_log, raw)
|
||||
except Exception as exc:
|
||||
logger.error("Soil anomaly insight failed for %s: %s", farm_uuid, exc)
|
||||
_fail_audit_log(audit_log, str(exc), json.dumps(fallback, ensure_ascii=False))
|
||||
return {
|
||||
**fallback,
|
||||
"farm_uuid": farm_uuid,
|
||||
"knowledge_base": KB_NAME,
|
||||
"tone_file": service.tone_file,
|
||||
"raw_response": None,
|
||||
}
|
||||
|
||||
if not parsed:
|
||||
parsed = fallback
|
||||
parsed.setdefault("summary", fallback["summary"])
|
||||
parsed.setdefault("explanation", fallback["explanation"])
|
||||
parsed.setdefault("likely_cause", fallback["likely_cause"])
|
||||
parsed.setdefault("recommended_action", fallback["recommended_action"])
|
||||
parsed.setdefault("monitoring_priority", fallback["monitoring_priority"])
|
||||
parsed.setdefault("confidence", fallback["confidence"])
|
||||
parsed["farm_uuid"] = farm_uuid
|
||||
parsed["knowledge_base"] = KB_NAME
|
||||
parsed["tone_file"] = service.tone_file
|
||||
parsed["raw_response"] = raw
|
||||
return parsed
|
||||
@@ -0,0 +1,192 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from farm_data.services import get_farm_details
|
||||
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 RAGConfig, get_service_config, load_rag_config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
KB_NAME = "water_need_prediction"
|
||||
SERVICE_ID = "water_need_prediction"
|
||||
|
||||
WATER_NEED_PROMPT = (
|
||||
"شما یک دستیار تخصصی تحليل نياز آبي کوتاه مدت مزرعه هستي. "
|
||||
"ورودي شامل محاسبات ساختاريافته نياز آبي، اطلاعات مزرعه و متن هاي بازيابي شده از پايگاه دانش است. "
|
||||
"فقط JSON معتبر با اين کليدها برگردان: "
|
||||
"summary, irrigation_outlook, recommended_action, risk_note, confidence. "
|
||||
"confidence عددي بين 0 و 1 باشد. "
|
||||
"اعداد اصلي را از داده ورودي بگير و عدد متناقض جديد نساز."
|
||||
)
|
||||
|
||||
|
||||
def _clean_json(raw: str) -> dict[str, Any]:
|
||||
cleaned = (raw or "").strip()
|
||||
if cleaned.startswith("```"):
|
||||
cleaned = cleaned.strip("`")
|
||||
if cleaned.startswith("json"):
|
||||
cleaned = cleaned[4:]
|
||||
cleaned = cleaned.strip()
|
||||
if not cleaned:
|
||||
return {}
|
||||
try:
|
||||
return json.loads(cleaned)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
logger.warning("Invalid JSON returned by water_need_prediction LLM: %s", cleaned[:500])
|
||||
return {}
|
||||
|
||||
|
||||
def _load_farm_or_error(farm_uuid: str) -> dict[str, Any]:
|
||||
farm_details = get_farm_details(farm_uuid)
|
||||
if farm_details is None:
|
||||
raise ValueError("farm_uuid نامعتبر است یا اطلاعات مزرعه پیدا نشد.")
|
||||
return farm_details
|
||||
|
||||
|
||||
def _build_service_client(cfg: RAGConfig):
|
||||
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)
|
||||
return service, client, service.llm.model
|
||||
|
||||
|
||||
def _fallback_from_payload(prediction_payload: dict[str, Any]) -> dict[str, Any]:
|
||||
total = float(prediction_payload.get("totalNext7Days") or 0.0)
|
||||
daily = prediction_payload.get("dailyBreakdown") or []
|
||||
peak_day = max(daily, key=lambda item: float(item.get("gross_irrigation_mm", 0.0) or 0.0), default=None)
|
||||
if total <= 0:
|
||||
return {
|
||||
"summary": "براي چند روز آينده نياز آبي معناداري برآورد نشد.",
|
||||
"irrigation_outlook": "بارش موثر يا شرايط فعلي باعث شده نياز خالص آبياري پايين باشد.",
|
||||
"recommended_action": "پايش رطوبت خاک ادامه يابد و قبل از هر آبياري جديد forecast دوباره بررسي شود.",
|
||||
"risk_note": "اگر forecast تغيير کند يا بارش موثر رخ ندهد، برآورد بايد به روز شود.",
|
||||
"confidence": 0.58,
|
||||
}
|
||||
|
||||
peak_text = ""
|
||||
if peak_day:
|
||||
peak_text = (
|
||||
f" بيشترين فشار آبي در {peak_day.get('forecast_date')} "
|
||||
f"با حدود {peak_day.get('gross_irrigation_mm')} ميلي متر برآورد شده است."
|
||||
)
|
||||
return {
|
||||
"summary": f"جمع نياز آبي 7 روز آينده حدود {round(total, 2)} ميلي متر برآورد شده است.",
|
||||
"irrigation_outlook": "الگوي آبياري بايد در چند روز آينده بر اساس نياز روزانه و بارش موثر تنظيم شود." + peak_text,
|
||||
"recommended_action": "برنامه آبياري کوتاه مدت بر اساس روزهاي اوج نياز تنظيم و صبح زود يا نزديک غروب اجرا شود.",
|
||||
"risk_note": "در صورت تغيير دما، باد يا بارش، مقادير gross irrigation ممکن است تغيير کنند.",
|
||||
"confidence": 0.72,
|
||||
}
|
||||
|
||||
|
||||
def _build_messages(
|
||||
*,
|
||||
service: Any,
|
||||
cfg: RAGConfig,
|
||||
query: str,
|
||||
rag_context: str,
|
||||
structured_context: dict[str, Any],
|
||||
) -> tuple[str, list[dict[str, str]]]:
|
||||
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(WATER_NEED_PROMPT)
|
||||
system_parts.append(
|
||||
"[کانتکست ساختاريافته نياز آبي]\n"
|
||||
+ json.dumps(structured_context, ensure_ascii=False, indent=2, default=str)
|
||||
)
|
||||
if rag_context:
|
||||
system_parts.append(rag_context)
|
||||
system_prompt = "\n\n".join(part for part in system_parts if part)
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": query},
|
||||
]
|
||||
return system_prompt, messages
|
||||
|
||||
|
||||
def get_water_need_prediction_insight(
|
||||
*,
|
||||
farm_uuid: str,
|
||||
prediction_payload: dict[str, Any],
|
||||
query: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
cfg = load_rag_config()
|
||||
service, client, model = _build_service_client(cfg)
|
||||
farm_details = _load_farm_or_error(farm_uuid)
|
||||
fallback = _fallback_from_payload(prediction_payload)
|
||||
user_query = query or "نياز آبي کوتاه مدت اين مزرعه را تفسير کن و اقدام عملياتي پيشنهاد بده."
|
||||
structured_context = {
|
||||
"farm_uuid": farm_uuid,
|
||||
"prediction_payload": prediction_payload,
|
||||
"fallback_summary": fallback,
|
||||
}
|
||||
rag_context = build_rag_context(
|
||||
query=user_query,
|
||||
sensor_uuid=farm_uuid,
|
||||
config=cfg,
|
||||
kb_name=KB_NAME,
|
||||
service_id=SERVICE_ID,
|
||||
farm_details=farm_details,
|
||||
)
|
||||
system_prompt, messages = _build_messages(
|
||||
service=service,
|
||||
cfg=cfg,
|
||||
query=user_query,
|
||||
rag_context=rag_context,
|
||||
structured_context=structured_context,
|
||||
)
|
||||
audit_log = _create_audit_log(
|
||||
farm_uuid=farm_uuid,
|
||||
service_id=SERVICE_ID,
|
||||
model=model,
|
||||
query=user_query,
|
||||
system_prompt=system_prompt,
|
||||
messages=messages,
|
||||
)
|
||||
try:
|
||||
response = client.chat.completions.create(model=model, messages=messages)
|
||||
raw = response.choices[0].message.content.strip()
|
||||
parsed = _clean_json(raw)
|
||||
_complete_audit_log(audit_log, raw)
|
||||
except Exception as exc:
|
||||
logger.error("Water need prediction insight failed for %s: %s", farm_uuid, exc)
|
||||
_fail_audit_log(audit_log, str(exc), json.dumps(fallback, ensure_ascii=False))
|
||||
return {
|
||||
**fallback,
|
||||
"farm_uuid": farm_uuid,
|
||||
"knowledge_base": KB_NAME,
|
||||
"tone_file": service.tone_file,
|
||||
"raw_response": None,
|
||||
}
|
||||
|
||||
if not parsed:
|
||||
parsed = fallback
|
||||
parsed.setdefault("summary", fallback["summary"])
|
||||
parsed.setdefault("irrigation_outlook", fallback["irrigation_outlook"])
|
||||
parsed.setdefault("recommended_action", fallback["recommended_action"])
|
||||
parsed.setdefault("risk_note", fallback["risk_note"])
|
||||
parsed.setdefault("confidence", fallback["confidence"])
|
||||
parsed["farm_uuid"] = farm_uuid
|
||||
parsed["knowledge_base"] = KB_NAME
|
||||
parsed["tone_file"] = service.tone_file
|
||||
parsed["raw_response"] = raw
|
||||
return parsed
|
||||
Reference in New Issue
Block a user