lollmsvectordb 0.1.0

A modular text-based database manager for retrieval-augmented generation (RAG), seamlessly integrating with the LoLLMs ecosystem.

LoLLMsVectorDB

LoLLMsVectorDB: A modular text-based database manager for retrieval-augmented generation (RAG), seamlessly integrating with the LoLLMs ecosystem. Supports various vectorization methods and directory bindings for efficient text data management.

Features

• Flexible Vectorization: Supports multiple vectorization methods including TF-IDF and Word2Vec.
• Directory Binding: Automatically updates the vector store with text data from a specified directory.
• Efficient Search: Provides fast and accurate search results with metadata to locate the original text chunks.
• Modular Design: Easily extendable to support new vectorization methods and functionalities.

Installation

pip install lollmsvectordb

Usage

Example with TFIDFVectorizer

from lollmsvectordb import TFIDFVectorizer, VectorDatabase, DirectoryBinding

# Initialize the vectorizer
tfidf_vectorizer = TFIDFVectorizer()
tfidf_vectorizer.fit(["This is a sample text.", "Another sample text."])

# Create the vector database
db = VectorDatabase("vector_db.sqlite", tfidf_vectorizer)

# Bind a directory to the vector database
directory_binding = DirectoryBinding("path_to_your_directory", db)

# Update the vector store with text data from the directory
directory_binding.update_vector_store()

# Search for a query in the vector database
results = directory_binding.search("This is a sample text.")
print(results)

Adding New Vectorization Methods

To add a new vectorization method, create a subclass of the Vectorizer class and implement the vectorize method.

from lollmsvectordb import Vectorizer

class CustomVectorizer(Vectorizer):
    def vectorize(self, data):
        # Implement your custom vectorization logic here
        pass

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

License

This project is licensed under the MIT License.

Contact

For any questions or suggestions, feel free to reach out to the author:

• Email: [e-mail]
• Twitter: @ParisNeo_AI
• Discord: Join our Discord
• Sub-Reddit: r/lollms
• Instagram: spacenerduino

Acknowledgements

Special thanks to the LoLLMs community for their continuous support and contributions.