23 lines
727 B
Python
23 lines
727 B
Python
"""
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BM25模型预下载脚本
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执行后将模型缓存到 ./models/fastembed_cache 目录，打包进Docker镜像
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"""
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import os
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from fastembed.sparse.sparse_text_embedding import SparseTextEmbedding
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if __name__ == "__main__":
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# 指定缓存目录
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cache_dir = "./models/fastembed_cache"
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os.makedirs(cache_dir, exist_ok=True)
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print("正在下载BM25稀疏向量模型...")
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model = SparseTextEmbedding(
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model_name="Qdrant/bm25",
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cache_dir=cache_dir
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)
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# 触发一次推理，确保模型文件完整下载
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list(model.embed(["init trigger"]))
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print(f"✅ BM25模型已成功缓存到: {cache_dir}")
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print("请将该目录提交到项目仓库，打包进Docker镜像")
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