I Built the SQLite of Vector Search in ~120KB of Pure C & SIMD: 3,000x Faster Cold Starts and Zero Dependencies for AI Agents
⚡ I Built the SQLite of Vector Search in ~120KB of Pure C & SIMD If you're building LLM agents, local RAG systems, or CLI tools in Python today, you've likely faced the vector database dependency nightmare . To store a few thousand embeddings from a conversation history or document chunks, standard tutorials tell you to pip install chromadb or install FAISS. Here is what happens under the hood:…
I recently developed a lightweight vector search library named NanoVector, with a size of just 120KB. This C-based implementation harnesses SIMD (Single Instruction, Multiple Data) technology to significantly outperform traditional solutions in terms of speed and efficiency. In particular, NanoVector boasts an impressive 3,000 times faster cold start time and eliminates the need for external dependencies.
Unlike other vector databases such as ChromaDB and FAISS, NanoVector requires no installation and starts executing within a mere 0.6 milliseconds. This makes it an ideal choice for AI agents, local RAG systems, and CLI tools written in Python, where the overhead of additional libraries and dependencies can be a significant bottleneck.
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