Embedding

embedding直译是：嵌入。和实际意思有些差距。其实就是把文本转换为向量表示的过程。用“向量化”更直接，但这又和tensor有点儿混。它是变成向量的一个过程。

embedding 的应用领域：

文本分类： 将文本嵌入转换为向量后，可以用于训练文本分类模型。这在垃圾邮件检测、情感分析等任务中非常有用。

语义相似度： ChatGPT embedding 可用于衡量文本之间的语义相似度。这对于搜索引擎、信息检索以及推荐系统等领域非常重要。

对话系统： ChatGPT embedding 可以用于对话系统，将用户的输入和系统生成的响应嵌入为向量，从而更好地理解上下文并生成更有针对性的回复。

实体识别： 嵌入可以用于实体识别任务，帮助模型理解文本中提到的具体实体（如人名、地点、组织等）。

命名实体识别（NER）： ChatGPT embedding 在 NER 任务中有广泛应用，用于从文本中识别和分类命名实体。

信息检索： ChatGPT embedding 可以用于改进信息检索系统，使其更能理解用户查询，并找到相关的文档或信息。

聚类分析： 将文本嵌入用于聚类分析，可以帮助发现文本数据中的模式和关联。

语言生成任务： ChatGPT embedding 可以作为输入，帮助生成连贯、相关性更强的文本，适用于自然语言生成任务。

文本摘要： 在文本摘要任务中，将输入文本嵌入为向量有助于模型理解文本的重要信息，从而更好地生成摘要。

语境理解： ChatGPT embedding 可以用于改进模型对上下文的理解，这对于在对话中保持一致性和上下文敏感性非常重要。

实际运行一个embedding如下：

import openai

response = openai.embeddings.create(
    input = "Quantum entanglement impact on future computing.",
    model="text-embedding-ada-002"
)

print(response.data[0].embedding)

运行结果：

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-0.026304317638278008, -0.015230960212647915, 0.017173893749713898, 0.026141274720430374, 
0.013240473344922066, 0.0149728087708354, 0.008688848465681076, 0.0032761511392891407, 
0.014021722599864006, -0.01936138980090618, 0.016385849565267563, -0.013845092616975307, 
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这才一句话而已。
向量出来了，各种比对，就有戏唱了。