我在 Python 中看到了一段代码，它生成一个包含嵌入表示（表示字符串的向量）的文件。
“all-MiniLM-L6-v2”模型中生成的文件格式为：
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我正在尝试让 ChatGPT 理解它是什么，并用 C# 生成一个 conde，它使用像参数一样的字符串来通过这些向量搜索附近的字符串。
示例：
var drive = searchInEmbeddings(“sena”);
并返回艾尔顿·塞纳。
这里有人刚刚做了类似的东西，可以帮助我吗？
我提到的Python代码是：
import json
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
with open('embeddings.json', 'r') as f:
data = json.load(f)
model = SentenceTransformer('all-MiniLM-L6-v2')
def find_most_similar(descriptions, query, top_n=3):
query_embedding = model.encode([query])
description_embeddings = np.array([desc['embedding'] for desc in descriptions])
similarities = cosine_similarity(query_embedding, description_embeddings)[0]
top_indices = np.argsort(similarities)[-top_n:][::-1]
return [{"codigo": descriptions[i]['codigo'], "descricao": descriptions[i]['descricao']} for i in top_indices]
query = "sena"
top_matches = find_most_similar(data, query, top_n=3)
print("Question:", query , "\Answers: \nNearest occurrences:\n", top_matches)
并返回：
$ python search.py
Question: sena
Answers:
Nearest occurrences:
[{'codigo': 3, 'descricao': 'Ayrton Senna'}, {'codigo': 31, 'descricao': 'Niki Lauda'}, {'codigo': 21, 'descricao': 'Kimi Räikkönen'}]
感谢您的帮助。
如果您的问题是，“我怎样才能让 ChatGPT 帮我为此编写 C# 代码...”您可以问它：
请用 C# 编写一个小函数，将字符串到单词嵌入的映射和字符串“a”作为输入，并返回字符串“a”的最近邻居。
接下来你还可以问：
好的，请编写一个 C# 函数，从输入文件中读取字符串到单词嵌入的映射。
对于第一个问题，ChatGPT3.5 将生成不完全正确的代码，但很容易纠正。