VecEngine is a lightweight and efficient vector search engine designed to perform high-speed semantic retrieval and similarity matching using embeddings. It aims to provide a simple and extensible architecture for developers building local or small-scale retrieval-augmented generation (RAG) systems without relying on heavy external dependencies or cloud-based vector databases.
The engine supports operations such as embedding storage, normalization, similarity scoring, and batch or top-k retrieval. By integrating quantization and clustering strategies, VecEngine balances accuracy and performance for real-time applications like chatbots, search systems, and contextual AI assistants.
VecEngine is built to be modular, making it easy to plug in different embedding models (such as Qwen or OpenAI embeddings) and customize retrieval strategies. Its minimal design focuses on clarity, control, and extensibility, allowing developers to understand and optimize every step of the retrieval process.