Local Knowledge Graph and AI Tools Using Ollama

说明与用途

作者建议使用 Ollama 在本地硬件上运行 AI 模型（如 Llama 3.2）。这解决了与云服务提供商相关的令牌成本和数据隐私问题。该解决方案允许免费构建 RAG 系统和其他工具，而无需将数据发送到外部服务器。

问题

Cloud AI incurs costs per token and API call, and requires sending data to external servers, raising privacy and cost concerns.

结果

The author successfully built 45 free, local, open-source AI tools using this stack, including RAG systems and code reviewers, eliminating cloud costs and data privacy risks.

工作流

• Install Ollama using Homebrew (brew install ollama).
• Pull a local model, such as llama3.2 (ollama pull llama3.2).
• Start the local server (ollama serve).
• Use Python requests to send prompts to the local API endpoint (http://localhost:11434/api/generate).
• Build applications such as RAG systems, offline voice assistants, or code reviewers using the local model.

证据

Every token costs money. Every API call adds up. And your data goes to their servers.

来源

I built 45 tools using this stack. All free. All local. All open source.

来源