嵌入向量生成与查询
文本嵌入模型
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M3E 是 Moka Massive Mixed Embedding 的缩写
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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
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使用M3E
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为了方便快速调试，建议直接将相关的内容下载至本地
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git clone https://huggingface.co/moka-ai/m3e-base -
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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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Qdrant交互
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创建m3e_collection
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{ "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 } -
使用上述两个示例， 帮助我们更好的感受文本相似度。
