229 lines
9.0 KiB
Python
229 lines
9.0 KiB
Python
"""
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چت RAG با استریم — استفاده از دیتای embed شده کاربر و Adapter API (GapGPT / Avalai)
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"""
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import logging
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from pathlib import Path
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from .config import load_rag_config, RAGConfig, get_service_config, ServiceConfig
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from .api_provider import get_chat_client
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from .retrieve import search_with_query
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from .user_data import build_user_soil_text, build_user_weather_text
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logger = logging.getLogger(__name__)
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def _load_tone(config: RAGConfig | None) -> str:
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"""بارگذاری فایل لحن پیشفرض (chat KB)."""
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cfg = config or load_rag_config()
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base = Path(__file__).resolve().parent.parent
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chat_kb = cfg.knowledge_bases.get("chat")
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if chat_kb:
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tone_path = base / chat_kb.tone_file
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logger.debug("Loading default tone from path=%s", tone_path)
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if tone_path.exists():
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logger.debug("Default tone file found: %s", tone_path)
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return tone_path.read_text(encoding="utf-8").strip()
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logger.warning("Default tone file not found: %s", tone_path)
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return ""
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def _load_kb_tone(kb_name: str, config: RAGConfig | None = None) -> str:
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"""بارگذاری فایل لحن مخصوص یک پایگاه دانش."""
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cfg = config or load_rag_config()
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kb_cfg = cfg.knowledge_bases.get(kb_name)
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if not kb_cfg:
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return ""
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base = Path(__file__).resolve().parent.parent
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tone_path = base / kb_cfg.tone_file
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logger.debug("Loading kb tone for kb=%s path=%s", kb_name, tone_path)
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if tone_path.exists():
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logger.debug("KB tone file found for kb=%s", kb_name)
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return tone_path.read_text(encoding="utf-8").strip()
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logger.warning("KB tone file not found for kb=%s path=%s", kb_name, tone_path)
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return ""
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def _load_service_tone(service: ServiceConfig, config: RAGConfig | None = None) -> str:
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cfg = config or load_rag_config()
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if service.tone_file:
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base = Path(__file__).resolve().parent.parent
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tone_path = base / service.tone_file
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if tone_path.exists():
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return tone_path.read_text(encoding="utf-8").strip()
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return _load_kb_tone(service.knowledge_base, cfg)
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def _detect_kb_intent(query: str) -> str:
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"""تشخیص ساده نوع پایگاه دانش مورد نیاز از روی متن سوال."""
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q = query.lower()
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irrigation_keywords = {"آبیاری", "آب", "رطوبت", "irrigation", "water", "et0", "بارش", "خشکی"}
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fertilization_keywords = {"کود", "کودهی", "fertiliz", "npk", "ازت", "فسفر", "پتاسیم", "nitrogen", "phosphorus", "potassium"}
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if any(kw in q for kw in irrigation_keywords):
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return "irrigation"
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if any(kw in q for kw in fertilization_keywords):
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logger.info("Detected KB intent=fertilization")
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return "fertilization"
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logger.info("Detected KB intent=chat")
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return "chat"
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def build_rag_context(
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query: str,
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sensor_uuid: str | None = None,
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config: RAGConfig | None = None,
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limit: int = 8,
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kb_name: str | None = None,
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service_id: str | None = None,
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) -> str:
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"""
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ساخت context برای LLM: دیتای فعلی خاک کاربر + متنهای مرتبط از RAG.
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دیتای کاربر همیشه اول میآید تا LLM مقادیر واقعی (مثل pH) را ببیند.
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"""
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logger.info(
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"Building RAG context sensor_uuid=%s kb_name=%s limit=%s query_len=%s",
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sensor_uuid,
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kb_name,
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limit,
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len(query or ""),
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)
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parts: list[str] = []
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cfg = config or load_rag_config()
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service = get_service_config(service_id, cfg) if service_id else None
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include_user_embeddings = service.use_user_embeddings if service else True
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resolved_kb_name = kb_name or (service.knowledge_base if service else None)
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if include_user_embeddings and sensor_uuid:
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user_soil = build_user_soil_text(sensor_uuid)
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if user_soil and user_soil.strip():
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parts.append("[دادههای فعلی خاک شما]\n" + user_soil.strip())
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logger.debug("Included user soil section sensor_uuid=%s", sensor_uuid)
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else:
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logger.info("No user soil data found sensor_uuid=%s", sensor_uuid)
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weather_text = build_user_weather_text(sensor_uuid)
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if weather_text and weather_text.strip():
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parts.append("[پیشبینی هواشناسی]\n" + weather_text.strip())
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logger.debug("Included weather section sensor_uuid=%s", sensor_uuid)
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else:
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logger.info("No weather data found sensor_uuid=%s", sensor_uuid)
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results = search_with_query(
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query,
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sensor_uuid=sensor_uuid,
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limit=limit,
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config=cfg,
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kb_name=resolved_kb_name,
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service_id=service_id,
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use_user_embeddings=include_user_embeddings,
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)
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if results:
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logger.info("Retrieved RAG results count=%s sensor_uuid=%s", len(results), sensor_uuid)
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rag_texts = [r.get("text", "").strip() for r in results if r.get("text")]
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if rag_texts:
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parts.append("[متنهای مرجع]\n" + "\n\n---\n\n".join(rag_texts))
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logger.debug("Included RAG reference texts count=%s", len(rag_texts))
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else:
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logger.info("No RAG results found sensor_uuid=%s kb_name=%s", sensor_uuid, kb_name)
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return "\n\n---\n\n".join(parts) if parts else ""
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def chat_rag_stream(
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query: str,
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sensor_uuid: str | None = None,
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config: RAGConfig | None = None,
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limit: int = 5,
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system_override: str | None = None,
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kb_name: str | None = None,
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service_id: str | None = None,
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):
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logger.info(
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"chat_rag_stream started sensor_uuid=%s kb_name=%s limit=%s query_len=%s",
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sensor_uuid,
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kb_name,
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limit,
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len(query or ""),
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)
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"""
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چت RAG با استریم: دیتای embed شده را بازیابی میکند و با LLM جواب میدهد.
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فقط دیتای همان کاربر (sensor_uuid) قابل دسترسی است.
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Args:
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query: پیام کاربر
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sensor_uuid: شناسه سنسور کاربر — اجباری
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config: تنظیمات RAG
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limit: تعداد چانکهای بازیابیشده
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system_override: جایگزین system prompt (اختیاری)
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Yields:
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تکتک deltaهای content بهصورت رشته
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"""
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cfg = config or load_rag_config()
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resolved_service_id = service_id or kb_name or _detect_kb_intent(query)
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service = get_service_config(resolved_service_id, cfg)
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service_llm_config = service.llm
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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_config,
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knowledge_bases=cfg.knowledge_bases,
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services=cfg.services,
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chromadb=cfg.chromadb,
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)
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client = get_chat_client(service_cfg)
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model = service_llm_config.model
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logger.debug("Loaded service config service_id=%s model=%s", resolved_service_id, model)
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detected_kb = kb_name or service.knowledge_base
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logger.info("Using knowledge base=%s for service_id=%s", detected_kb, resolved_service_id)
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context = build_rag_context(
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query,
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sensor_uuid,
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config=cfg,
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limit=limit,
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kb_name=detected_kb,
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service_id=resolved_service_id,
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)
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logger.debug("Built context length=%s", len(context))
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if system_override is not None:
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system_content = system_override
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else:
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tone = _load_service_tone(service, cfg)
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if not tone:
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tone = _load_tone(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(
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"با استفاده از بخش «دادههای فعلی خاک شما» و «متنهای مرجع» زیر به سوال کاربر پاسخ بده. "
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"برای سوالاتی درباره خاک کاربر (مثل pH، رطوبت، NPK) حتماً از دادههای فعلی استفاده کن. "
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"اطلاعات هواشناسی در بخش «پیشبینی هواشناسی» آمده. "
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"پاسخ را به زبان کاربر بنویس."
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)
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if context:
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system_parts.append("\n\n" + context)
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system_content = "\n".join(system_parts)
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messages = [
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{"role": "system", "content": system_content},
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{"role": "user", "content": query},
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]
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logger.info("Prepared messages for model=%s service_id=%s", model, resolved_service_id)
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stream = client.chat.completions.create(
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model=model,
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messages=messages,
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stream=True,
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)
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logger.info("Started streaming response from model=%s", model)
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for chunk in stream:
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delta = chunk.choices[0].delta if chunk.choices else None
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content = delta.content if delta else ""
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if content:
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logger.debug("Streaming chunk len=%s", len(content))
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yield content
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logger.info("chat_rag_stream completed sensor_uuid=%s", sensor_uuid)
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