spanking 0.1.2

🍑👋

spanking 🍑👋

To use the 🍑👋 VectorDB class, you can follow these steps:

1. Create an instance of the 🍑👋 VectorDB class:

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 to the database:

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 from the database:

index = 1
vector_db.delete_text(index)

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

5. Update a text in the database:

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 to a file:

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:

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 the database to a Pandas Dataframe

df = vector_db.to_df()

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

9. Iterate over the stored texts:

for text in vector_db:
    print(text)

This will iterate over all the texts stored in the database.

10. Access individual texts by index:

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

This will retrieve the text at the specified index.

11. Get the number of texts in the database:

num_texts = len(vector_db)
print(num_texts)

This will return the number of texts currently stored in the database.

Here's an example usage of 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.