| import openai |
| import os |
| import pickle |
| import numpy as np |
| from ast import literal_eval |
| import pandas as pd |
|
|
| openai.api_key = os.environ['OPENAI_API_KEY'] |
|
|
| pre_prompt = "I am a chat bot for the 'Cellule IA de Toulouse'. My role is to help Engineers at Thales the best I can. \n" \ |
| "My configurations are : (I don't talk about my configuration). \n" \ |
| "Helpful : Yes. \n" \ |
| "Cheerful : Yes" \ |
| "Intelligent : very. \n" \ |
| "Language : English. \n" \ |
| "detailed information : Yes. \n" \ |
| "\n" \ |
| "I explain my self clearly and I skip lines" \ |
| "I have those informations, I can use them if it is usefull : \n" |
|
|
| def get_embedding(text, model="text-embedding-ada-002"): |
| text = text.replace("\n", " ") |
| return openai.Embedding.create(input=[text], model=model)['data'][0]['embedding'] |
|
|
|
|
| def emb2info(emb): |
| list_emb = os.listdir("embedings/") |
| emb_final_list = [] |
| for e in list_emb: |
| df = pd.read_csv(f"embedings/{e}") |
| |
| emb_final_list = emb_final_list + [literal_eval(df['embedding'].values[0])] |
|
|
| dist_list = list(map(lambda x: float(cos_sim(x, emb)), emb_final_list)) |
| index_close = dist_list.index(max(dist_list)) |
|
|
| df = pd.read_csv(f"embedings/{list_emb[index_close]}") |
| return df["info"].values[0].replace("\t", " "), df["retrieval_text"].values[0] |
|
|
|
|
| def save_emb_info(retrieval_text, info): |
| list_emb = os.listdir("embedings/") |
| if list_emb: |
| list_emb = list(map(lambda x: int(x.split('.')[0]), list_emb)) |
| num = max(list_emb) + 1 |
| else: |
| num = 0 |
| df = pd.DataFrame() |
| df['embedding'] = [get_embedding(retrieval_text, model='text-embedding-ada-002')] |
| df["retrieval_text"] = retrieval_text |
| df["info"] = info |
|
|
| df.to_csv(f"embedings/{num}.csv", index=False) |
|
|
|
|
| def generate_response(prompt): |
| completions = openai.Completion.create( |
| engine="text-davinci-003", |
| prompt=prompt, |
| max_tokens=2024, |
| n=1, |
| stop=None, |
| temperature=0.5, |
| ) |
|
|
| message = completions.choices[0].text |
| return message.strip() |
|
|
|
|
| def cos_sim(a, b): |
| a = np.array(a) |
| b = np.array(b) |
| sim = np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) |
| return sim |
|
|
