embedding是什么

from dotenv import load_dotenv
  import numpy as np
  from numpy import dot
  from numpy.linalg import norm
  from langchain.embeddings import OpenAIEmbeddings
  import warnings
  warnings.filterwarnings("ignore")
  
  load_dotenv()
  
  
  def cos_sim(a, b):
      return dot(a, b)/(norm(a)*norm(b))
  
  
  def l2(a, b):
      x = np.asarray(a)-np.asarray(b)
      return norm(x)
  
  
  model = OpenAIEmbeddings(model='text-embedding-ada-002')
  
  query = "业绩增长"
  documents = [
      "小明，我们公司三季度接了个大单",
      "小明，我们公司今天三季度同比增长60%",
      "小明，我们公司第二季度，有一笔交易被认定的死账",
      "小明，公司CEO信心满满，下一季度再创新高",
      "小明，公司计划要扩招300人",
  ]
  
  query_vec = model.embed_query(query)
  doc_vecs = model.embed_documents(documents)
  
  print("Cosine distance:")  # 越大越相似
  print(cos_sim(query_vec, query_vec))
  for vec in doc_vecs:
      print(cos_sim(query_vec, vec))
  
  print("\nEuclidean distance:")  # 越小越相似
  print(l2(query_vec, query_vec))
  for vec in doc_vecs:
      print(l2(query_vec, vec))
  

from langchain.embeddings import OpenAIEmbeddings
  from sklearn.cluster import KMeans, DBSCAN
  
  texts = [
      "这个接口返回的参数不对",
      "帮我添加一个下载按钮",
      "我需要在window上也能用",
      "接口报500错误",
      "接口反应超时了",
      "服务挂了",
      "我需要新增一个新的配置",
      "这个可以取消不用做了",
      "我们做一个适配的接口吧",
      "系统上报一个监控到的错误日志"
  ]
  model = OpenAIEmbeddings(model='text-embedding-ada-002')
  X = []
  for t in texts:
      embedding = model.embed_query(t)
      X.append(embedding)
  
  # clusters = KMeans(n_clusters=3, random_state=42, n_init="auto").fit(X)
  clusters = DBSCAN(eps=0.55, min_samples=2).fit(X)
  for i, t in enumerate(texts):
      print("{}\t{}".format(clusters.labels_[i], t))
  

from langchain.embeddings import HuggingFaceBgeEmbeddings
  
  model_name = "BAAI/bge-large-zh-v1.5"
  model_kwargs = {'device': 'cpu'}
  encode_kwargs = {'normalize_embeddings': True}
  model = HuggingFaceBgeEmbeddings(
      model_name=model_name,
      model_kwargs=model_kwargs,
      encode_kwargs=encode_kwargs
  )
  

query = "前瞻性"
  documents = [
      "我们需要借助最近流行的起来的GPT帮我们提升工作效率",
      "你按照这个guide page一步一步走就可以了",
      "我们一起来探索下，看看这个新的开源框架能不能帮助到我们",
      "Hugging Face上有很多新的开源模型，我们可以基于我们环境来尝试试验下",
      "Langchain上的开源工具也很多，我们的选择性很大，加油",
  ]
  
  query_vec = model.embed_query(query)
  doc_vecs = model.embed_documents(documents)
  
  print("Cosine distance:")  # 越大越相似
  print(cos_sim(query_vec, query_vec))
  for vec in doc_vecs:
      print(cos_sim(query_vec, vec))