50 lines
970 B
Python
50 lines
970 B
Python
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import os
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os.environ["OPENAI_API_KEY"]= "sk-PRJ811XeKzEy20Ug3dA98a34Af8b40B5816dE15503D33599"
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os.environ["OPENAI_BASE_URL"]= "http://154.9.28.247:3000/v1/"
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from openai import OpenAI
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client = OpenAI()
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from sklearn.metrics.pairwise import cosine_similarity
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import json
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def embedding(s:str):
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if len(s)==0:
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return
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else:
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return client.embeddings.create(
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input=s, model="text-embedding-3-small" # nomic-embed-text text-embedding-3-small
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).data[0].embedding
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class edge:
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a:str
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b:str
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sim:float
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def __init__(self, a:str, b:str, sim:float):
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self.a=a
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self.b=b
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self.sim=sim
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def main(scale:json):
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item=[]
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vec=[]
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for i in scale["item"]:
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item.append(i)
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vec.append(embedding(scale["item"][i]))
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simi=cosine_similarity(vec)
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que=[]
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for i,v in enumerate(simi):
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for j in range(0,i):
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# print(simi[i][j],',',item[i],',',item[j])
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que.append(edge(item[i], item[j], simi[i][j]))
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# print("\n")
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sorted(que,key= lambda t : t.sim)
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return que
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