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嵌入向量生成与查询

1 文本嵌入模型

M3E 是 Moka Massive Mixed Embedding 的缩写
  • Moka,此模型由 MokaAI 训练,开源和评测,训练脚本使用 uniem ,评测 BenchMark 使用 MTEB-zh
  • Massive,此模型通过千万级 (2200w+) 的中文句对数据集进行训练
  • Mixed,此模型支持中英双语的同质文本相似度计算,异质文本检索等功能,未来还会支持代码检索
  • Embedding,此模型是文本嵌入模型,可以将自然语言转换成稠密的向量

2 使用M3E

为了方便快速调试,建议直接将相关的内容下载至本地
  
  
  
  
  
  
git clone https://huggingface.co/moka-ai/m3e-base
  
  
  
  
  
  
pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
import numpy as np
model = SentenceTransformer("D:\\gospace\\shua\\m3e-base")
texts = ["老王爱写代码"]
embeddings = model.encode(texts)
for text, embedding in zip(texts, embeddings):
print(f"Text: {text}")
# print(f"Embedding: {embedding}")
print(np.array2string(embedding, separator=', '))
对于m3e-base版本,生成的嵌入向量通常是768维的
  
  
  
  
  
  
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3 Qdrant交互

创建my_collection
  
  
  
  
  
  
http://127.0.0.1:6333/collections/my_collection
{
"vectors": {
"size": 768,
"distance": "Cosine"
}
}
存入四条向量数据,由"老王是领导", "老王是研发", "老王是程序员", "老王是开发者"生成。我们使用的是cosine, 因此结果中score 越接近1, 表明两者越相似。

3.1 使用"老王爱写代码"查询

  
  
  
  
  
  
http://127.0.0.1:6333/collections/my_collection/points/search
{
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-1.50089972e-02, 2.38779679e-01, 1.49614379e-01, 3.62314701e-01, 6.94537103e-01, -3.87004137e-01, 2.10883880e+00, -3.75697196e-01, -1.82452798e-01, -3.22899401e-01, -8.25561047e-01, 1.80501044e+00, 5.33488870e-01, 2.24471204e-02, 6.21750236e-01, 1.03810385e-01, -9.79628026e-01, 9.70385015e-01, -7.47687459e-01, -4.07829493e-01, 1.48922369e-01, -1.19734859e+00, -3.71993333e-01, 4.34708387e-01, -8.65958393e-01, -5.35230339e-01, 1.72286785e+00, 2.20375031e-01, 5.11880279e-01, -7.53360510e-01, 1.08028436e+00, -4.57028896e-01, -1.50861815e-02, 7.17765152e-01, 4.38092917e-01, 3.15289497e-01, 2.67259568e-01, 6.93954825e-02, 3.62798750e-01, 1.36038041e+00, 1.30169702e+00, -4.57468122e-01, -4.37783718e-01, -5.08757949e-01, 8.63207519e-01, -2.89590597e-01, 1.10191441e+00, 1.98435009e-01, 3.19641560e-01, -1.21980719e-01, 1.29272318e+00, 1.45268068e-01, 1.14542401e+00, -4.47698474e-01, 5.11548400e-01, -2.13712007e-01, 7.16760218e-01, 3.56799394e-01, -8.98992419e-01, 2.53364801e-01, 2.46685684e-01, 2.20981494e-01, 2.45370770e+00, -1.58328819e+00, -4.75152791e-01, -6.23002470e-01, 6.19043648e-01, 7.42533743e-01, -4.50964779e-01, 8.05252671e-01, 7.01121390e-02, 1.94267094e-01, 9.58551466e-01, -2.75623322e-01, -4.63303924e-01, -1.89976543e-01, 7.89426208e-01, 5.38144112e-01, 6.55530512e-01, -8.33069980e-01, -7.98664808e-01, 8.72453809e-01, 2.56688178e-01, -1.63096809e+00, -7.62300074e-01, -2.44294450e-01, -1.18391609e+00, -3.18642884e-01, 2.10066810e-01, 1.68145791e-01, -6.57323003e-03, 6.80729449e-01, 1.39025539e-01, 3.32543373e-01, -4.70169991e-01, 2.51329923e+00, -7.84055650e-01, 6.36785984e-01, -2.01801226e-01, -6.74835324e-01, 5.39556563e-01, 6.31205678e-01, 3.46641839e-01, -1.85673684e-02, -2.24657819e-01, 8.87112379e-01, -3.31156075e-01, -6.33936048e-01, -7.78989196e-01, -7.24589527e-01, -8.97557616e-01, -1.19332623e+00, 9.22472417e-01, -7.65920877e-01, 3.12098682e-01, -1.39098316e-02, -8.49792808e-02, 1.01152039e+00, 2.23431200e-01, -1.90913692e-01, 2.77387857e-01, -1.03543140e-02, -7.31578097e-03, 2.77540356e-01, 6.14465535e-01, 1.22704911e+00, -1.18605226e-01, 4.48604554e-01, 3.99403453e-01, 9.76211205e-02, -2.72304147e-01, -6.34450793e-01, -8.57364178e-01, -6.94554448e-01, -3.04330140e-01, 1.05320036e-01, 1.73476115e-01, 1.00599945e+00, 3.46087098e-01, 3.30770224e-01, -2.74857372e-01, -2.06077129e-01, 7.10511386e-01, 1.58540100e-01, -5.68631530e-01, -3.54029030e-01, -1.38866380e-01, -1.23456165e-01, 5.68514943e-01, 2.85906672e-01, -1.28487337e+00, -9.45348591e-02, 4.24727708e-01, 3.99977595e-01, 2.40924731e-01, -4.46219951e-01, 8.51806283e-01, 1.41895026e-01, -5.66927195e-01, -1.45217299e-01, 4.25449193e-01, -3.61947298e-01, 7.06071258e-01, -5.11380136e-01, -6.05433211e-02, -9.70409751e-01, -4.07448590e-01, -1.02107680e+00],
"limit": 4,
"with_payload": true
}
输出
  
  
  
  
  
  
{
"result": [{
"id": 3,
"version": 0,
"score": 0.9361942,
"payload": {
"sentence": "老王是程序员"
}
}, {
"id": 4,
"version": 0,
"score": 0.9035911,
"payload": {
"sentence": "老王是开发者"
}
}, {
"id": 2,
"version": 0,
"score": 0.88393474,
"payload": {
"sentence": "老王是研发"
}
}, {
"id": 1,
"version": 0,
"score": 0.8612121,
"payload": {
"sentence": "老王是领导"
}
}],
"status": "ok",
"time": 0.000412088
}

3.2 使用"老王爱汇报"查询

  
  
  
  
  
  
{
"result": [{
"id": 1,
"version": 0,
"score": 0.8335363,
"payload": {
"sentence": "老王是领导"
}
},
{
"id": 2,
"version": 0,
"score": 0.81425905,
"payload": {
"sentence": "老王是研发"
}
},
{
"id": 4,
"version": 0,
"score": 0.80951333,
"payload": {
"sentence": "老王是开发者"
}
},
{
"id": 3,
"version": 0,
"score": 0.7966002,
"payload": {
"sentence": "老王是程序员"
}
}
],
"status": "ok",
"time": 0.000405555
}
使用上述两个示例, 帮助我们更好的感受文本相似度。
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版权声明:本文为稀土掘金博主「周末去旅行」的原创文章
原文链接:https://juejin.cn/post/7419688489410527242
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