For developers and engineers building AI solutions, we have built a flexible and modular Python framework designed to streamline data processing and ETL from multiple data sources to all major vector databases. Using just a configuration file and a few lines of code, VectorETL allows developers to integrate quick and efficient data processing into any AI application.

Key Features:

1). Modular Architecture: Easily integrate new data sources, embedding models, and vector databases.

2). Batch Processing & Configurable Chunking: Efficiently handle large datasets with configurable batch sizes while maintaining semantic context.

3). Multiple Data Source Support: From PostgreSQL to Amazon S3, VectorETL covers a wide range of data sources.

4). Plug-and-Play Embedding Models: Integrate with leading models from OpenAI, Cohere, and Google Gemini.