delete rag.py

rag.py

@@ -1,69 +0,0 @@
-from sentence_transformers import SentenceTransformer
-from wikipediaapi import Wikipedia
-import textwrap
-import numpy as np
-import openai
-from openai import OpenAI
-
-matryoshka_dim = 128
-model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka", trust_remote_code=True , truncate_dim = matryoshka_dim)
-
-
-wiki = Wikipedia('RAGBot/0.0', 'ar')
-doc = wiki.page('جابر بن حيان').text
-paragraphs = doc.split('\n\n') # chunking
-
-for i, p in enumerate(paragraphs):
-  wrapped_text = textwrap.fill(p, width=100)
-
-  print("-----------------------------------------------------------------")
-  print(wrapped_text)
-  print("-----------------------------------------------------------------")
-
-  docs_embed = model.encode(paragraphs, normalize_embeddings=True)
-
-  docs_embed.shape
-  docs_embed[0]
-
-
-query = "من هو جابر بن حيان؟"
-query_embed = model.encode(query, normalize_embeddings=True)
-
-
-similarities = np.dot(docs_embed, query_embed.T)
-
-similarities.shape
-similarities
-
-top_3_idx = np.argsort(similarities, axis=0)[-3:][::-1].tolist()
-top_3_idx
-
-most_similar_documents = [paragraphs[idx] for idx in top_3_idx]
-
-CONTEXT = ""
-for i, p in enumerate(most_similar_documents):
-  wrapped_text = textwrap.fill(p, width=100)
-
-  print("-----------------------------------------------------------------")
-  print(wrapped_text)
-  print("-----------------------------------------------------------------")
-  CONTEXT += wrapped_text + "\n\n"
-
-prompt = f"""
-    use the following CONTEXT to answer the QUESTION at the end.
-    If you don't know the answer, just say that you don't know, don't try to make up an answer.
-
-    CONTEXT: {CONTEXT}
-    QUESTION: {query}
-"""
-
-client = OpenAI(api_key=userdata.get('OPENAI_API_KEY'))
-
-response = client.chat.completions.create(
-  model="gpt-4o",
-  messages=[
-    {"role": "user", "content": prompt},
-  ]
-)
-
-print(response.choices[0].message.content)