29 lines
796 B
Python
29 lines
796 B
Python
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"""
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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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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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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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)
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