新增了调用llm做语义方向判断

file_load.py

@@ -9,9 +9,7 @@ from sklearn.metrics.pairwise import cosine_similarity
 
 from openai import OpenAI
 
-os.environ["OPENAI_API_KEY"]= "sk-PRJ811XeKzEy20Ug3dA98a34Af8b40B5816dE15503D33599"
-os.environ["OPENAI_BASE_URL"]= "http://154.9.28.247:3000/v1/"
-client = OpenAI()
+client = OpenAI(api_key="sk-PRJ811XeKzEy20Ug3dA98a34Af8b40B5816dE15503D33599",base_url="http://154.9.28.247:3000/v1/")
 
 def batch():
 	scales = os.listdir("Scales")
@@ -44,6 +42,17 @@ def old_type(str):
 	with open(str,"w") as file:
 		file.write(json.dumps(new))
 
+def liguistic(a:str,b:str):
+	completion = client.chat.completions.create(
+	  model="large",
+	  messages=[
+	    {"role": "system", "content": "请严格按照要求回答，不要有多余解释。\n判断这两句话的语意是否相反，相反返回True，否则返回False。"},
+	    {"role": "user", "content": a+'\n'+b}
+	  ]
+	)
+	print(completion.choices[0].message.content)
+	return 'True' in completion.choices[0].message.content
+
 def calc_similarity(scale):
 	item=[]
 	vec=[]
@@ -56,6 +65,9 @@ def calc_similarity(scale):
 	que=[]
 	for i,v in enumerate(simi):
 		for j in range(0,i):
+			if liguistic(scale[item[i]],scale[item[j]]):
+				simi[i][j]*=-1
+				print("Opposite:",scale[item[i]],scale[item[j]])
 			que.append({"from":item[j], "to":item[i], "similarity":simi[i][j]})
 	return 	que
 
@@ -96,14 +108,13 @@ def correlation():
 			if i!=j:
 				try:
 					que[i,j]=data[i].corr(data[j])
-					if i in rev:
-						que[i,j]*=-1
-					if j in rev:
-						que[i,j]*=-1
+					# if i in rev:
+					# 	que[i,j]*=-1
+					# if j in rev:
+					# 	que[i,j]*=-1
 					que[j,i]=que[i,j]
 				except:
 					pass
 			else:
 				break
 	return que
-

main.py

@@ -20,8 +20,6 @@ for i in similarity:
 		s+=str(i["similarity"])+','+str(correlation[i["from"],i["to"]])+'\n'
 		x.append(i["similarity"])
 		y.append(correlation[i["from"],i["to"]])
-		if(i['similarity']>0.5 and correlation[i['from'],i['to']]<-0.5):
-			print(i,correlation[i['from'],i['to']])
 	except:
 		pass