407 lines
16 KiB
Python
407 lines
16 KiB
Python
from __future__ import annotations
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import json
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import logging
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from typing import Any, Literal
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from pydantic import BaseModel, Field, ValidationError
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from farm_data.services import build_ai_farm_snapshot
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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 RAGConfig, get_service_config, load_rag_config
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logger = logging.getLogger(__name__)
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SERVICE_ID = "fertilization_plan_parser"
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KB_NAME = "fertilization_plan_parser"
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CORE_FIELDS = [
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"crop_name",
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"growth_stage",
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"fertilizer_name",
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"formula",
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"amount",
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"application_method",
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"timing",
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"interval_days",
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]
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FERTILIZATION_PLAN_PROMPT = (
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"شما یک تحلیل گر برنامه کودهی هستی. "
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"کاربر ممکن است برنامه کودهی را کامل یا ناقص توضیح دهد. "
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"فقط JSON معتبر برگردان و هرگز متن خارج از JSON، markdown یا کلید اضافه تولید نکن. "
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"اگر اطلاعات کافی بود status را completed بگذار و final_plan را تکمیل کن. "
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"اگر اطلاعات ناقص بود status را needs_clarification بگذار، missing_fields را پر کن و در questions سوال های کوتاه و دقیق برگردان. "
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"اگر چند کود در متن بود، همه را در applications لیست کن. "
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"اگر هرکدام از فیلدهای اصلی خالی، null یا نامشخص بود، حق نداری status را completed بگذاری. "
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"در حالت completed هیچ فیلد null در collected_data و final_plan نباید وجود داشته باشد. "
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"از حدس زدن مقدار، زمان یا روش مصرف خودداری کن. "
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"Schema: "
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"{"
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'"status": "completed" | "needs_clarification", '
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'"summary": string, '
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'"missing_fields": [string], '
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'"questions": [{"id": string, "field": string, "question": string, "rationale": string}], '
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'"collected_data": {'
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'"crop_name": string|null, '
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'"growth_stage": string|null, '
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'"objective": string|null, '
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'"applications": ['
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"{"
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'"fertilizer_name": string|null, '
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'"formula": string|null, '
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'"amount": string|null, '
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'"application_method": string|null, '
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'"timing": string|null, '
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'"interval_days": integer|null, '
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'"purpose": string|null'
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"}"
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"], "
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'"notes": [string]'
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"}, "
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'"final_plan": {same shape as collected_data} | null'
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"}."
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)
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class ClarificationQuestionSchema(BaseModel):
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id: str
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field: str
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question: str
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rationale: str = ""
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class FertilizerApplicationSchema(BaseModel):
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fertilizer_name: str | None = None
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formula: str | None = None
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amount: str | None = None
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application_method: str | None = None
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timing: str | None = None
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interval_days: int | None = None
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purpose: str | None = None
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class FertilizationPlanSchema(BaseModel):
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crop_name: str | None = None
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growth_stage: str | None = None
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objective: str | None = None
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applications: list[FertilizerApplicationSchema] = Field(default_factory=list)
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notes: list[str] = Field(default_factory=list)
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class FertilizationPlanParseResultSchema(BaseModel):
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status: Literal["completed", "needs_clarification"]
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summary: str
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missing_fields: list[str] = Field(default_factory=list)
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questions: list[ClarificationQuestionSchema] = Field(default_factory=list)
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collected_data: FertilizationPlanSchema = Field(default_factory=FertilizationPlanSchema)
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final_plan: FertilizationPlanSchema | None = None
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class FertilizationPlanParserService:
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def parse_plan(
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self,
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*,
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message: str = "",
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answers: dict[str, Any] | None = None,
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partial_plan: dict[str, Any] | None = None,
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farm_uuid: str | None = None,
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) -> dict[str, Any]:
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cfg = load_rag_config()
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service, client, model = self._build_service_client(cfg)
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normalized_message = (message or "").strip()
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normalized_answers = answers if isinstance(answers, dict) else {}
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normalized_partial = partial_plan if isinstance(partial_plan, dict) else {}
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structured_context = {
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"message": normalized_message,
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"answers": normalized_answers,
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"partial_plan": normalized_partial,
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"required_core_fields": CORE_FIELDS,
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"service": "fertilization_plan_parser",
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"endpoint_policy": "parser_first",
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}
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if farm_uuid:
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# Parser-first endpoint: farm context is optional enrichment only.
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structured_context["farm_context_source_metadata"] = {
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"source": "build_ai_farm_snapshot",
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"optional": True,
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}
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rag_query = self._build_retrieval_query(
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message=normalized_message,
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answers=normalized_answers,
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)
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rag_context = build_rag_context(
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query=rag_query,
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sensor_uuid=farm_uuid,
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config=cfg,
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kb_name=KB_NAME,
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service_id=SERVICE_ID,
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)
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system_prompt, messages = self._build_messages(
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service=service,
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cfg=cfg,
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structured_context=structured_context,
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rag_context=rag_context,
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)
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audit_log = None
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if farm_uuid:
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try:
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audit_log = _create_audit_log(
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farm_uuid=farm_uuid,
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service_id=SERVICE_ID,
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model=model,
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query=rag_query,
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system_prompt=system_prompt,
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messages=messages,
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)
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except Exception as exc:
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logger.warning("Fertilization plan parser audit log creation failed for %s: %s", farm_uuid, exc)
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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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response_format={"type": "json_object"},
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)
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raw = (response.choices[0].message.content or "").strip()
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parsed = self._clean_json(raw)
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validated = FertilizationPlanParseResultSchema.model_validate(parsed)
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normalized = self._normalize_result(validated)
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if audit_log is not None:
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_complete_audit_log(audit_log, raw)
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return normalized
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except (ValidationError, ValueError, KeyError, IndexError) as exc:
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logger.warning("Fertilization plan parser parsing failed: %s", exc)
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if audit_log is not None:
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_fail_audit_log(audit_log, str(exc))
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return self._fallback_result(
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message=normalized_message,
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answers=normalized_answers,
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partial_plan=normalized_partial,
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)
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except Exception as exc:
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logger.error("Fertilization plan parser failed: %s", exc)
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if audit_log is not None:
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_fail_audit_log(audit_log, str(exc))
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return self._fallback_result(
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message=normalized_message,
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answers=normalized_answers,
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partial_plan=normalized_partial,
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)
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def _build_service_client(self, 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 _build_messages(
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self,
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*,
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service: Any,
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cfg: RAGConfig,
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structured_context: dict[str, Any],
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rag_context: str,
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) -> tuple[str, list[dict[str, str]]]:
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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(FERTILIZATION_PLAN_PROMPT)
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system_parts.append(
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"[structured_context]\n"
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+ json.dumps(structured_context, ensure_ascii=False, indent=2, default=str)
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)
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if rag_context:
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system_parts.append(rag_context)
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system_prompt = "\n\n".join(part for part in system_parts if part)
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": "برنامه کودهی را استخراج یا برای تکمیل آن سوال بپرس."},
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]
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return system_prompt, messages
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def _build_retrieval_query(
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self,
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*,
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message: str,
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answers: dict[str, Any],
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) -> str:
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answer_lines = [f"{key}: {value}" for key, value in answers.items()]
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parts = [part for part in [message, "\n".join(answer_lines)] if part]
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return "\n".join(parts) or "استخراج برنامه کودهی از متن کاربر"
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def _normalize_result(self, validated: FertilizationPlanParseResultSchema) -> dict[str, Any]:
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collected = validated.collected_data.model_dump()
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final_plan = validated.final_plan.model_dump() if validated.final_plan is not None else None
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missing_fields = list(dict.fromkeys(validated.missing_fields))
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computed_missing = self._find_missing_fields(final_plan or collected)
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for field in computed_missing:
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if field not in missing_fields:
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missing_fields.append(field)
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can_complete = validated.status == "completed" and not missing_fields
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if can_complete:
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final_plan = final_plan or collected
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questions: list[dict[str, Any]] = []
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status_fa = "تکمیل شد"
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else:
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questions = [item.model_dump() for item in validated.questions]
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if not questions and missing_fields:
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questions = self._build_generic_questions(missing_fields)
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final_plan = None
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validated.status = "needs_clarification"
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status_fa = "نیازمند پرسش تکمیلی"
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return {
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"status": "completed" if can_complete else "needs_clarification",
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"status_fa": status_fa,
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"summary": validated.summary,
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"missing_fields": missing_fields,
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"questions": questions,
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"collected_data": collected,
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"final_plan": final_plan,
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}
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def _fallback_result(
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self,
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*,
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message: str,
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answers: dict[str, Any],
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partial_plan: dict[str, Any],
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) -> dict[str, Any]:
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applications = partial_plan.get("applications")
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if not isinstance(applications, list):
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applications = []
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notes = list(partial_plan.get("notes") or [])
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if message:
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notes.append(f"متن اولیه کاربر: {message}")
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if answers:
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notes.append("پاسخ های تکمیلی کاربر دریافت شده است.")
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return {
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"status": "needs_clarification",
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"status_fa": "نیازمند پرسش تکمیلی",
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"summary": "اطلاعات برنامه کودهی برای ساخت JSON نهایی کافی نیست و به چند پاسخ تکمیلی نیاز است.",
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"missing_fields": CORE_FIELDS,
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"questions": self._build_generic_questions(CORE_FIELDS),
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"collected_data": {
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"crop_name": partial_plan.get("crop_name"),
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"growth_stage": partial_plan.get("growth_stage"),
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"objective": partial_plan.get("objective"),
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"applications": applications,
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"notes": notes,
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},
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"final_plan": None,
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}
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def _build_generic_questions(self, missing_fields: list[str]) -> list[dict[str, str]]:
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catalog = {
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"crop_name": {
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"id": "crop_name",
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"field": "crop_name",
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"question": "این برنامه کودهی برای کدام محصول است؟",
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"rationale": "نام محصول برای ثبت برنامه لازم است.",
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},
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"growth_stage": {
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"id": "growth_stage",
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"field": "growth_stage",
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"question": "محصول الان در چه مرحله رشدی قرار دارد؟",
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"rationale": "مرحله رشد برای تکمیل برنامه لازم است.",
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},
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"fertilizer_name": {
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"id": "fertilizer_name",
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"field": "fertilizer_name",
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"question": "نام کود یا ترکیب کودی چیست؟",
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"rationale": "بدون نام کود نمی توان برنامه را نهایی کرد.",
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},
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"formula": {
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"id": "formula",
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"field": "formula",
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"question": "فرمول یا آنالیز کود چیست؟ مثلا 20-20-20.",
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"rationale": "ترکیب دقیق کود هنوز مشخص نشده است.",
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},
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"amount": {
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"id": "amount",
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"field": "amount",
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"question": "مقدار مصرف هر نوبت کود چقدر است؟",
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"rationale": "دوز مصرف در متن مشخص نشده است.",
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},
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"application_method": {
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"id": "application_method",
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"field": "application_method",
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"question": "روش مصرف کود چیست؟ مثلا کودآبیاری، سرک یا محلول پاشی.",
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"rationale": "روش اجرا هنوز معلوم نیست.",
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},
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"timing": {
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"id": "timing",
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"field": "timing",
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"question": "زمان مصرف کود چه موقع است؟ مثلا هر 10 روز یا بعد از آبیاری.",
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"rationale": "زمان بندی برنامه نیاز به شفاف سازی دارد.",
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},
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"interval_days": {
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"id": "interval_days",
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"field": "interval_days",
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"question": "فاصله بین نوبت های مصرف کود چند روز است؟",
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"rationale": "عدد فاصله بین نوبت ها برای JSON نهایی لازم است.",
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},
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}
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return [catalog[field] for field in missing_fields if field in catalog][:5]
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def _find_missing_fields(self, plan: dict[str, Any]) -> list[str]:
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missing: list[str] = []
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if not isinstance(plan, dict):
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return CORE_FIELDS[:]
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if plan.get("crop_name") in (None, ""):
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missing.append("crop_name")
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if plan.get("growth_stage") in (None, ""):
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missing.append("growth_stage")
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applications = plan.get("applications")
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if not isinstance(applications, list) or not applications:
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return missing + [
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field
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for field in ["fertilizer_name", "formula", "amount", "application_method", "timing", "interval_days"]
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if field not in missing
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]
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first_application = applications[0] if isinstance(applications[0], dict) else {}
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for field in ["fertilizer_name", "formula", "amount", "application_method", "timing", "interval_days"]:
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value = first_application.get(field)
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if value is None or (isinstance(value, str) and not value.strip()):
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missing.append(field)
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return missing
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def _clean_json(self, 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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raise ValueError("Fertilization plan parser response was empty.")
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parsed = json.loads(cleaned)
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if not isinstance(parsed, dict):
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raise ValueError("Fertilization plan parser response root must be an object.")
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return parsed
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