NanoVector v0.1.2 — Interactive Google Colab Support & Python 3.9 Compatibility

说明与用途

NanoVector 是一款轻量级 Python 向量搜索库，提供即时导入、精确搜索和单文件持久化，简化 LLM 代理和 RAG 开发。

问题

Python developers building LLM agents or local RAG systems face heavy vector database dependencies (e.g., chromadb, FAISS) that inflate package size, slow cold starts, and add complex transitive dependencies.

结果

NanoVector achieves 3,000× faster import, 0.13 ms search latency for 2,000 vectors, 1,414,000 vectors/sec ingestion, and single‑file persistence, outperforming ChromaDB and FAISS on small‑to‑medium workloads.

工作流

• Install NanoVector via pip: pip install nanovector
• Create an Index: index = nanovector.Index(dim=384, metric="cosine")
• Add embeddings with metadata: index.add("doc_1", vec, metadata="{\"title\": \"NanoVector Launch\", \"author\": \"eminsk\"}")
• Search top‑k results: results = index.search(query, top_k=5)
• Print results: iterate over results and access r.id, r.score, r.metadata
• Persist index to disk: index.save("memory.nvec")
• Reload index: loaded = nanovector.load("memory.nvec")

证据

Wheel Download Size: NanoVector 38 KB (~120 KB unpacked) vs ChromaDB ~120 MB+

来源

Python Cold Import Time: NanoVector 0.6 ms (3,000× faster) vs ChromaDB 1,850 ms

来源