| import openai |
| import faiss |
| import numpy as np |
| import os |
| import gradio as gr |
|
|
| openai.api_key = os.environ['api_top'] |
| text_file_path = 'risale.txt' |
|
|
| |
| with open(text_file_path, 'r', encoding='utf-8') as file: |
| text_content = file.read() |
|
|
| |
| allText = [sentence.strip() for sentence in text_content.split('<br>') if sentence.strip()] |
|
|
| def encode_open(input): |
| |
| MODEL = "text-embedding-ada-002" |
| res = openai.Embedding.create( |
| input=input, engine=MODEL |
| ) |
| embeds = [record['embedding'] for record in res['data']] |
| import torch |
| embeds=torch.FloatTensor(embeds) |
| return embeds |
|
|
| encoded_data=encode_open(allText) |
| d=encoded_data.shape[1] |
| index = faiss.IndexIDMap(faiss.IndexFlatIP(d)) |
| index.add_with_ids(encoded_data, np.arange(encoded_data.shape[0])) |
|
|
| def search_openai_pdf(query, k=10): |
| try: |
| query_vector = encode_open([query]) |
| top_k = index.search(query_vector, k) |
| |
| return [ |
| allText[_id] for _id in top_k[1][0] |
| ] |
| except Exception as e: |
| return ["Hata oluştu", e] |
| |
|
|
|
|
| iface = gr.Interface( |
| fn=search_openai_pdf, |
| inputs=gr.Textbox(placeholder="Fihristte aramak için bir şeyler yaz.",label='query'), |
| outputs=gr.Textbox(label="Sonuçlar"), |
| live=False, |
| title="Sözler Fihristi Anlamsal Arama Motoru" |
| ) |
|
|
| iface.launch() |
|
|
|
|
