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@@ -5,6 +5,7 @@ from service.trunks_service import TrunksService
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from utils.text_splitter import TextSplitter
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from utils.vector_distance import VectorDistance
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from model.response import StandardResponse
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+from utils.vectorizer import Vectorizer
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import logging
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logger = logging.getLogger(__name__)
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@@ -36,10 +37,10 @@ async def search_text(request: TextSearchRequest):
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# 如果有缓存结果,计算向量距离
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min_distance = float('inf')
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best_result = None
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- sentence_vector = trunks_service.get_vector(sentence)
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+ sentence_vector = Vectorizer.get_embedding(sentence)
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for cached_result in cached_results:
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- content_vector = trunks_service.get_vector(cached_result['content']);
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+ content_vector = Vectorizer.get_embedding(cached_result['content'])
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distance = VectorDistance.calculate_distance(sentence_vector, content_vector)
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if distance < min_distance:
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min_distance = distance
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