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from scipy.spatial.distance import cosine
import argparse
import json
import os
import openai
import pdb
def read_text(input_file):
arr = open(input_file).read().split("\n")
return arr[:-1]
class OpenAIModel:
def __init__(self):
self.debug = False
self.model_name = None
self.skip_key = True
print("In OpenAI API constructor")
def init_model(self,model_name = None):
#print("OpenAI: Init model",model_name)
openai.api_key = os.getenv("OPENAI_API_KEY")
if (openai.api_key == None):
openai.api_key = ""
print("API key not set")
if (len(openai.api_key) == 0 and not self.skip_key):
print("Open API key not set")
if (model_name is None):
self.model_name = "text-similarity-ada-001"
else:
self.model_name = model_name
print("OpenAI: Init model complete",model_name)
def compute_embeddings(self,input_file_name,input_data,is_file):
if (len(openai.api_key) == 0 and not self.skip_key):
print("Open API key not set")
return [],[]
#print("In compute embeddings after key check")
in_file = self.model_name + '.'.join(input_file_name.split('.')[:-1]) + "_embed.json"
cached = False
try:
fp = open(in_file)
cached = True
embeddings = json.load(fp)
print("Using cached embeddings")
except:
pass
texts = read_text(input_data) if is_file == True else input_data
if (not cached):
print(f"Computing embeddings for {input_file_name} and model {self.model_name}")
response = openai.Embedding.create(
input=texts,
model=self.model_name
)
embeddings = []
for i in range(len(response['data'])):
embeddings.append(response['data'][i]['embedding'])
if (not cached):
with open(in_file,"w") as fp:
json.dump(embeddings,fp)
return texts,embeddings
def output_results(self,output_file,texts,embeddings,main_index = 0):
if (len(openai.api_key) == 0 and not self.skip_key):
print("Open API key not set")
return {}
#print("In output results after key check")
# Calculate cosine similarities
# Cosine similarities are in [-1, 1]. Higher means more similar
cosine_dict = {}
#print("Total sentences",len(texts))
for i in range(len(texts)):
cosine_dict[texts[i]] = 1 - cosine(embeddings[main_index], embeddings[i])
#print("Input sentence:",texts[main_index])
sorted_dict = dict(sorted(cosine_dict.items(), key=lambda item: item[1],reverse = True))
if (self.debug):
for key in sorted_dict:
print("Cosine similarity with \"%s\" is: %.3f" % (key, sorted_dict[key]))
if (output_file is not None):
with open(output_file,"w") as fp:
fp.write(json.dumps(sorted_dict,indent=0))
return sorted_dict
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='OpenAI model for sentence embeddings ',formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-input', action="store", dest="input",required=True,help="Input file with sentences")
parser.add_argument('-output', action="store", dest="output",default="output.txt",help="Output file with results")
parser.add_argument('-model', action="store", dest="model",default="text-similarity-ada-001",help="model name")
results = parser.parse_args()
obj = OpenAIModel()
obj.init_model(results.model)
texts, embeddings = obj.compute_embeddings(results.input,is_file = True)
results = obj.output_results(results.output,texts,embeddings)
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