Staging

README.md

@@ -39,22 +39,34 @@ vector_db.update_text(index, new_text)
 ```
 This will update the text and its corresponding embedding at the specified index with the new text.
 
-6. Iterate over the stored texts:
+6. Save the database to a file:
+```python
+vector_db.save('vector_db.pkl')
+```
+This will save the current state of the `VectorDB` instance to a file named 'vector_db.pkl'.
+
+7. Load the database from a file:
+```python
+vector_db = VectorDB.load('vector_db.pkl')
+```
+This will load the `VectorDB` instance from the file named 'vector_db.pkl' and return it.
+
+8. Iterate over the stored texts:
 ```python
 for text in vector_db:
     print(text)
 ```
 This will iterate over all the texts stored in the database.
 
-7. Access individual texts by index:
+9. Access individual texts by index:
 ```python
 index = 2
 text = vector_db[index]
 print(text)
 ```
 This will retrieve the text at the specified index.
 
-8. Get the number of texts in the database:
+10. Get the number of texts in the database:
 ```python
 num_texts = len(vector_db)
 print(num_texts)
@@ -84,9 +96,15 @@ vector_db.update_text(1, "i enjoy playing chess")
 # Delete a text
 vector_db.delete_text(2)
 
-# Iterate over the stored texts
-print("\nStored texts:")
-for text in vector_db:
+# Save the database
+vector_db.save('vector_db.pkl')
+
+# Load the database
+loaded_vector_db = VectorDB.load('vector_db.pkl')
+
+# Iterate over the stored texts in the loaded database
+print("\nStored texts in the loaded database:")
+for text in loaded_vector_db:
     print(text)
 ```
 

spanking/main.py

@@ -8,36 +8,36 @@ def __init__(self, model_name='BAAI/bge-base-en-v1.5'):
         self.model = SentenceTransformer(model_name)
         self.texts = []
         self.embeddings = None
-    
+
     def add_texts(self, texts):
-        new_embeddings = self.model.encode(texts, normalize_embeddings=True)
+        new_embeddings = jnp.array(self.model.encode(texts, normalize_embeddings=True))
         if self.embeddings is None:
             self.embeddings = new_embeddings
         else:
             self.embeddings = jnp.concatenate((self.embeddings, new_embeddings), axis=0)
         self.texts.extend(texts)
-    
+
     def delete_text(self, index):
         if 0 <= index < len(self.texts):
             self.texts.pop(index)
-            self.embeddings = jnp.delete(self.embeddings, index, axis=0)
+            self.embeddings = self.embeddings.at[index].delete()
         else:
             raise IndexError("Invalid index")
-    
+
     def update_text(self, index, new_text):
         if 0 <= index < len(self.texts):
             self.texts[index] = new_text
-            new_embedding = self.model.encode([new_text], normalize_embeddings=True).squeeze()
-            self.embeddings = (self.embeddings).at[index].set(new_embedding)
+            new_embedding = jnp.array(self.model.encode([new_text], normalize_embeddings=True)).squeeze()
+            self.embeddings = self.embeddings.at[index].set(new_embedding)
         else:
             raise IndexError("Invalid index")
-    
+
     def search(self, query, top_k=5):
-        query_embedding = self.model.encode([query], normalize_embeddings=True)
+        query_embedding = jnp.array(self.model.encode([query], normalize_embeddings=True))
         similarities = jnp.dot(self.embeddings, query_embedding.T).squeeze()
         top_indices = jnp.argsort(similarities)[-top_k:][::-1]
         return [(self.texts[i], similarities[i]) for i in top_indices]
-    
+
     def save(self, file_path):
         with open(file_path, 'wb') as file:
             pickle.dump(self, file)
@@ -46,13 +46,13 @@ def save(self, file_path):
     def load(file_path):
         with open(file_path, 'rb') as file:
             return pickle.load(file)
-    
+
     def __len__(self):
         return len(self.texts)
-    
+
     def __getitem__(self, index):
         return self.texts[index]
-    
+
     def __iter__(self):
         return iter(self.texts)