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| from db.vector_store import VectorStore | |
| from src.modelling.embed import DalaEmbedder | |
| from typing import List | |
| class SemanticSearcher: | |
| """ | |
| Perform semantic search over embedded Kazakh text. | |
| """ | |
| def __init__(self): | |
| self.embedder = DalaEmbedder() | |
| self.vector_store = VectorStore() | |
| def search(self, query: str, top_k: int = 5) -> List[dict]: | |
| """ | |
| Embed the query and retrieve the most relevant chunks. | |
| """ | |
| query_embedding = self.embedder.embed_text(query) | |
| results = self.vector_store.search(query_embedding, top_k = top_k) | |
| return results | |