Spanking 🍑👋

To use the 🍑👋 VectorDB class and access its functionality through a beautiful UI, follow these steps:

Cloning the Repository

First, clone the repository to your local machine:

git clone https://github.com/rishiraj/spanking.git
cd spanking

Running the UI

To manage your vector database through an intuitive web interface, you can run the provided app.py script:

python app.py

This will start a local web server. You can then access the UI by navigating to http://127.0.0.1:5000 in your web browser.

Features of the UI

• Add New Texts: Easily add texts to your vector database through the interface.
• View and Manage Texts: See all stored texts, update them, or delete them with a single click.
• Search Functionality: Perform text or image-based searches within your database and view the results directly in your browser.
• Save and Load Database: Save your database to a file or load it from a previously saved state with ease.

Using the 🍑👋 VectorDB Class Programmatically

If you prefer working with code, you can interact with the VectorDB class directly. Here’s how:

1. Create an Instance:

   from spanking import VectorDB
   vector_db = VectorDB(model_name='BAAI/bge-base-en-v1.5')
   

   You can optionally specify a different pre-trained sentence transformer model by passing its name to the constructor.

2. Add Texts:

   texts = ["i eat pizza", "i play chess", "i drive bus"]
   vector_db.add_texts(texts)
   

   This will encode the texts into embeddings and store them in the database.

3. Search for Similar Texts or Images:

   text_query = "we play football"
   text_results = vector_db.search(text_query, top_k=2, type='text')
   print("Text search results:")
   for text, similarity in text_results:
       print(f"Text: {text}, Similarity: {similarity}")
   
   image_url = "https://example.com/image.jpg"
   image_results = vector_db.search(image_url, top_k=2, type='image')
   print("\nImage search results:")
   for text, similarity in image_results:
       print(f"Text: {text}, Similarity: {similarity}")
   

   This will retrieve the top-2 most similar texts or images to the query based on cosine similarity. The search method returns a list of tuples, where each tuple contains the text and its similarity score. You can specify the search type using the type parameter ('text' for text search and 'image' for image search).

4. Delete a Text:

   index = 1
   vector_db.delete_text(index)
   

   This will remove the text and its corresponding embedding at the specified index.

5. Update a Text:

   index = 0
   new_text = "i enjoy eating pizza"
   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. Save the Database:

   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:

   vector_db = VectorDB.load('vector_db.pkl')
   

   This will load the VectorDB instance from the file named 'vector_db.pkl' and return it.

8. Convert to DataFrame:

   df = vector_db.to_df()
   

   This will convert the current state of the VectorDB instance to a Pandas Dataframe.

9. Iterate Over Stored Texts:

   for text in vector_db:
       print(text)
   

10. Access Individual Texts by Index:

    index = 2
    text = vector_db[index]
    print(text)
    

11. Get the Number of Texts:

    num_texts = len(vector_db)
    print(num_texts)
    

Example Usage

Here's an example to demonstrate how you can use the 🍑👋 VectorDB class:

from spanking import VectorDB
vector_db = VectorDB()

# Add texts to the database
texts = ["i eat pizza", "i play chess", "i drive bus"]
vector_db.add_texts(texts)

# Search for similar texts
query = "we play football"
top_results = vector_db.search(query, top_k=2)
print("Top results:")
for text, similarity in top_results:
    print(f"Text: {text}, Similarity: {similarity}")

# Update a text
vector_db.update_text(1, "i enjoy playing chess")

# Delete a text
vector_db.delete_text(2)

# 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)

# Convert to dataframe
df = loaded_vector_db.to_df()
print(df.head())

This example demonstrates how to create a 🍑👋 VectorDB instance, add texts, search for similar texts, update and delete texts, and iterate over the stored texts.