remove redundant memory layer

cookbook/pocketflow-chat-memory/vector_index.py

@@ -1,92 +0,0 @@
-import numpy as np
-import faiss
-
-def create_index(dimension=128):
-    """Create a new vector index for fast similarity search
-    
-    Args:
-        dimension: The dimensionality of the vectors to be indexed
-        
-    Returns:
-        tuple: (index, items_list) where:
-            - index is the FAISS index for searching
-            - items_list is an empty list for storing the items
-    """
-    # Create a flat (exact, brute-force) index for storing vectors
-    index = faiss.IndexFlatL2(dimension)
-    # Initialize an empty list to store the actual items
-    items_list = []
-    return index, items_list
-
-def add_to_index(index, items_list, embedding, item):
-    """Add an item and its vector representation to the index
-    
-    Args:
-        index: The FAISS index
-        items_list: The list of items corresponding to vectors in the index
-        embedding: The vector representation of the item (numpy array)
-        item: The actual item to store
-        
-    Returns:
-        int: The position where the item was added
-    """
-    # Make sure the embedding is a numpy array with the right shape for FAISS
-    vector = np.array(embedding).reshape(1, -1).astype(np.float32)
-    
-    # Add the vector to the index
-    index.add(vector)
-    
-    # Store the item and return its position
-    items_list.append(item)
-    return len(items_list) - 1
-
-def search_index(index, items_list, query_embedding, k=1):
-    """Search for the k most similar items to the query vector
-    
-    Args:
-        index: The FAISS index
-        items_list: The list of items corresponding to vectors in the index
-        query_embedding: The query vector (numpy array)
-        k: Number of results to return (default: 1)
-        
-    Returns:
-        tuple: (found_items, distances) where:
-            - found_items is a list of the k most similar items
-            - distances is a list of the corresponding distances
-    """
-    # Make sure we don't try to retrieve more items than exist in the index
-    k = min(k, len(items_list))
-    if k == 0:
-        return [], []
-        
-    # Make sure the query is a numpy array with the right shape for FAISS
-    query_vector = np.array(query_embedding).reshape(1, -1).astype(np.float32)
-    
-    # Search the index
-    D, I = index.search(query_vector, k)
-    
-    # Get the items corresponding to the found indices
-    found_items = [items_list[i] for i in I[0]]
-    distances = D[0].tolist()
-    
-    return found_items, distances
-
-# Example usage
-if __name__ == "__main__":
-    # Create a new index
-    index, items = create_index(dimension=3)
-    
-    # Add some random vectors and items
-    for i in range(5):
-        vector = np.random.random(3)
-        add_to_index(index, items, vector, f"Item {i}")
-        
-    print(f"Added {len(items)} items to the index")
-    
-    # Search for a similar vector
-    query = np.random.random(3)
-    found_items, distances = search_index(index, items, query, k=2)
-    
-    print("Query:", query)
-    print("Found items:", found_items)
-    print("Distances:", distances)