sajad-dev
2c42ebe01c
- Removed deprecated user_info files and paths from configuration. - Added user soil data integration in chat context to improve response accuracy. - Updated build_rag_context and chat_rag_stream functions to include sensor_uuid for user-specific data retrieval. - Enhanced load_sources function to load user data from the database. - Implemented filtering in search_with_query and QdrantVectorStore to isolate user data based on sensor_uuid. - Introduced Celery Beat schedule for periodic user data ingestion.
35 lines
1.0 KiB
Python
35 lines
1.0 KiB
Python
"""
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بازیابی RAG: embed کوئری و جستجو در vector store
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"""
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from .config import load_rag_config, RAGConfig
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from .embedding import embed_single
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from .vector_store import QdrantVectorStore
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def search_with_query(
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query: str,
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sensor_uuid: str,
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limit: int = 5,
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score_threshold: float | None = None,
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config: RAGConfig | None = None,
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) -> list[dict]:
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"""
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کوئری را embed میکند و در vector store جستجو میکند.
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فقط chunks مربوط به sensor_uuid یا __global__ برمیگردد (ایزولهسازی کاربر).
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Args:
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sensor_uuid: شناسه سنسور کاربر — اجباری برای امنیت
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Returns:
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لیست نتایج با id, score, text, metadata
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"""
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cfg = config or load_rag_config()
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query_vector = embed_single(query, config=cfg)
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store = QdrantVectorStore(config=cfg)
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return store.search(
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query_vector=query_vector,
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limit=limit,
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score_threshold=score_threshold,
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sensor_uuid=sensor_uuid,
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)
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