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简介

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文本对相似度算子，两段文本各过 CLIP 文本塔后取余弦相似度。算子只产出分数，不按 min/max 过滤，过滤交给 pipeline 的 where。CLIP 封装复用 daft/aihc/functions/multimodal/_clip_dual_tower.py。

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功能描述

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算子参数

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输入

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输入含义
texts_a文本 A 列
texts_b文本 B 列，与 A 按行配对
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输出

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输出含义
simfloat64，两段文本 CLIP 文本向量的余弦相似度；任一侧为空或该批推理失败返回 None
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参数

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参数名称类型默认值描述
model_pathstr\"/opt/aihc/model\"权重根目录
model_namestr\"openai/clip-vit-base-patch32\"相对 model_path 的 CLIP 权重子目录
dtypestr\"float32\"权重精度，可选 float16 / float32 / bfloat16
batch_sizeint32算子内部推理的微批大小，与 aihc_udf 的 batch_size 相互独立
rankint0多卡场景 worker 序号，实际设备取 cuda:{rank % 卡数}
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注意事项

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调用示例

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\n Python\n \n
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1from __future__ import annotations\n2\n3import os\n4\n5import daft\n6from daft import col\n7\n8from daft.aihc.common.udf import aihc_udf\n9from daft.aihc.functions.text.text_pair_similarity import TextPairSimilarity\n10\n11if __name__ == \"__main__\":\n12    if os.getenv(\"DAFT_RUNNER\", \"native\") == \"ray\":\n13        import ray\n14        ray.init(dashboard_host=\"0.0.0.0\", ignore_reinit_error=True)\n15        daft.set_runner_ray()\n16    daft.set_execution_config(actor_udf_ready_timeout=6000, min_cpu_per_task=0)\n17\n18ds = daft.from_pydict(\n19        {\n20            \"a\": [\"a dog on grass\", \"a dog on grass\", \"a dog on grass\"],\n21            \"b\": [\"a dog on grass\", \"quantum physics lecture notes\", \"\"],\n22        }\n23    )\n24    ds = ds.with_column(\n25        \"sim\",\n26        aihc_udf(\n27            TextPairSimilarity,\n28            construct_args={\n29                \"model_path\": \"/path/to/models\",\n30                \"model_name\": \"openai/clip-vit-base-patch32\",\n31            },\n32            num_cpus=1,\n33            num_gpus=1,\n34            concurrency=1,\n35            batch_size=32,\n36        )(col(\"a\"), col(\"b\")),\n37    )\n38    ds.show()
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