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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("OpanAI: Init model",model_name) | |
| try: | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| except: | |
| 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 | |
| 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) | |