local-vector-memory

Zero-cloud, local-first vector memory CLI. Powered by Ollama embeddings + Qdrant.

100% local, 100% free, supports Chinese out of the box.

Why?

Most vector memory solutions require cloud APIs (OpenAI, Pinecone, etc.). This one runs entirely on your machine — perfect for privacy-first setups, air-gapped environments, or just saving money.

Features

• 🔒 100% local — Ollama embeddings, local Qdrant file storage
• 🇨🇳 Chinese-first — defaults to qwen3-embedding:4b (2560d, best Chinese accuracy)
• ⚡ Fast — ~230ms/query on M1 Mac
• 📦 Zero cloud deps — no API keys, no Docker, no signup
• 🔄 Auto reindex — point at your markdown files, rebuild index in seconds
• 🎯 Accurate — 100% Top-3 hit rate in real-world tests

Quick Start

Prerequisites

# Install Ollama (https://ollama.com)
curl -fsSL https://ollama.com/install.sh | sh

# Pull embedding model
ollama pull qwen3-embedding:4b

# Install qdrant-client
pip install qdrant-client requests

Install

pip install local-vector-memory

Usage

# Initialize (first time)
lvm init

# Add a memory
lvm add "OpenClaw baseUrl must be http://localhost:11434 without /v1"

# Search
lvm search "how to fix baseUrl"
lvm search "baseUrl配置" --limit 3

# Reindex markdown files
lvm reindex --dir ~/notes --glob "**/*.md"

# List stats
lvm stats

Configuration

Environment variables (or .env file):

Embedding Model Comparison

Tested on Chinese memory queries (M1 Mac, 16GB):

Recommendation: qwen3-embedding:4b for Chinese/English mixed content.

Architecture

Your .md files → chunking → Ollama embed → Qdrant (local file) → cosine search

No Docker. No cloud. No API keys. Just local files + Ollama.

License

MIT