Edge AI Tutorial with LLM

说明与用途

在Raspberry Pi上构建一个边缘代理，监视遥测文件夹，将数据发送到Oxlo.ai，接收结构化JSON异常报告，并归档已处理文件。

问题

Ops teams on lightweight gateways need reasoning on IoT telemetry without managing GPU infrastructure and without cost inflation from token-based billing.

结果

The agent processes telemetry files every 5 seconds, outputs structured JSON alerts, and archives processed files, enabling lightweight edge reasoning without GPU costs.

工作流

• Create project scaffold with agent.py and directories
• Generate mock telemetry files with mock_sensor.py
• Watch the folder and build plain-text prompts
• Define a system prompt that enforces a strict JSON schema
• Wire the Oxlo.ai inference call using llama-3.3-70b
• Run a continuous loop that processes files and archives them
• Start mock sensor and agent in separate terminals
• Observe real-time anomaly reports
• Plan next steps: replace polling with MQTT/inotify, add function calling, batch requests, or upgrade plan

证据

We are building an edge telemetry agent that watches a local folder for IoT sensor dumps, formats the payloads, and calls an LLM via Oxlo.ai to flag anomalies and suggest fixes.

来源

The five-second sleep keeps CPU usage negligible on a Raspberry Pi.

来源