Update agent.py

agent.py

@@ -397,6 +397,43 @@ def create_documents(data_source: str, data: List[dict]) -> List[Document]:
 
 import faiss
 
+import os
+import numpy as np
+import faiss
+
+# Step 1: Define the path where the index will be saved
+index_dir = "/home/wendy/Downloads/faiss_index"
+os.makedirs(index_dir, exist_ok=True)  # Create the directory if it doesn't exist
+index_file_path = os.path.join(index_dir, "index.faiss")
+
+# Step 2: Generate random data (e.g., 1000 vectors of dimension 128)
+d = 128  # Vector dimensionality
+n = 1000  # Number of vectors
+np.random.seed(42)  # For reproducibility
+
+# Generate random vectors (uniform distribution)
+xb = np.random.random((n, d)).astype('float32')
+
+# Step 3: Create the FAISS index
+# Using the L2 distance metric (Euclidean distance)
+index = faiss.IndexFlatL2(d)
+
+# Step 4: Add the vectors to the index
+index.add(xb)
+
+# Step 5: Save the index to the specified path
+faiss.write_index(index, index_file_path)
+
+print(f"FAISS index saved to: {index_file_path}")
+
+# Step 6: Load the FAISS index from the file
+loaded_index = faiss.read_index(index_file_path)
+
+print("FAISS index loaded successfully from:", index_file_path)
+
+
+
+
 # Custom FAISS wrapper (optional, if you still want it)
 class MyVector_Store:
     def __init__(self, index: faiss.Index):