嵌入向量生成与查询

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

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-transformersfrom 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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Qdrant交互

创建m3e_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。

使用"老王爱写代码"查询

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}

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
}

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
}