Delete create_faiss.py

create_faiss.py

@@ -1,26 +0,0 @@
-from datasets import load_dataset
-import faiss
-from sentence_transformers import SentenceTransformer
-import numpy as np
-
-# Load the US-LegalKit dataset
-dataset = load_dataset("macadeliccc/US-LegalKit", split="train")
-
-# Extract legal text documents
-law_data = [item['text'] for item in dataset if 'text' in item]
-
-# Load embedding model
-model = SentenceTransformer("all-MiniLM-L6-v2")
-
-# Generate embeddings
-embeddings = model.encode(law_data, convert_to_numpy=True)
-
-# Create FAISS index
-dimension = embeddings.shape[1]
-index = faiss.IndexFlatL2(dimension)  # L2 Distance Index
-index.add(embeddings)  # Add vectors to FAISS index
-
-# Save FAISS index
-faiss.write_index(index, "faiss_index.bin")
-
-print("✅ FAISS index saved successfully as 'faiss_index.bin'!")