29 docs tagged with "NeoMind"

Get NeoMind running in 5 minutes: install → configure LLM → connect first device → see data on dashboard → ask AI Chat. Every step has a checkpoint, tips, and troubleshooting.

NeoMind AI Agent guide: autonomous agent concepts, execution modes (Focused/Free), scheduling (cron/event/interval), resource binding, memory system, execution history, and status management.

A complete solution for person and object detection with notification push using the NeoMind platform, offering two implementation paths — YOLO Inference extension (edge inference) and AI Agent (LLM-powered analysis) — supporting NE101/NE301 smart cameras.

NeoMind AI Chat guide: query and control devices in natural language, create dashboards and rules, upload images for visual analysis, tool call mechanism, Chat vs Agent, and multi-session management.

Build NeoMind extensions, dashboard components, and contribute to the main project efficiently using Claude Code and AI-assisted development: project context setup, extension workflow, component workflow, prompt patterns, and real-world examples.

NeoMind automation rules guide: rule structure, conditions (comparison/range/logical), actions (notify/execute/trigger_agent), triggers (data_change/schedule/manual), with UI walkthrough, CLI, API examples, and import/export.

Configure LLM backends in NeoMind: local Ollama (recommended qwen3.5:4b) and cloud models (OpenAI/Anthropic/Qwen/DeepSeek/GLM etc.) — setup steps, CLI commands, model selection, and multimodal capability.

Contributing to the NeoMind main project: development setup, code standards (Rust clippy / frontend ESLint), commit conventions, CI pipeline, PR workflow, and testing requirements.

NeoMind system architecture overview: process model, data lifecycle, data transforms, extension mechanism, agent model, and notification system. Based on actual codebase, user-facing.

NeoMind dashboard component development guide: ZIP package structure, complete manifest.json reference, bundle.js IIFE format, component Props API, CSS variable theming, data source binding, complete Temperature Gauge example, installation and debugging.

NeoMind Data Push guide: push device telemetry to external Webhook or MQTT Broker in real time or on a schedule, with target configuration, data filtering, retry strategy, batch delivery, delivery logs and stats.

NeoMind data transforms guide: use JavaScript code to transform device telemetry in real time, generate derived metrics, invoke extension commands, with transform builder UI, scopes, testing, CLI/API management, and import/export.

NeoMind developer guide overview: start from one of four repo dimensions (device types / extensions / dashboard components / main project), with tech stack, crate layout, and entry points to deeper docs.

NeoMind device type development guide: device data model, DeviceTypeTemplate, ConnectionConfig, MQTT topics and webhook data formats, auto-discovery flow, CLI device management, ESP32/Python hands-on examples.

End-to-end guide for building a NeoMind extension from scratch with neomind-extension-sdk: scaffolding, Cargo.toml, Extension trait implementation, neomind_export!, cross-platform compile, .nep packaging, install & debug.

NeoMind extension management guide: install/uninstall extensions (.nep), official marketplace, extension details (overview/configuration/commands/metrics/logs), extension capabilities (metrics/commands/components), process isolation and crash protection, CLI and REST API.

neomind-extension-sdk reference: Extension trait, ExtensionMetadata, MetricDescriptor, neomind_export! FFI macro, capability declaration, ML model lifecycle (lazy-load + keep-loaded), cross-platform packaging (cdylib + panic=unwind).

A face recognition solution based on the NeoMind platform, using the Face Recognition extension to detect faces and identify individuals, with real-time dashboard display, history review, and AI Chat natural language queries for NE101/NE301 smart cameras.

NeoMind core glossary: definitions, relationships, and examples for Device, Device Type, Extension, Capability, Metric, DataSourceId, Agent, AI Chat, Memory, Transform, Rule, Cooldown, Dashboard, Widget, LLM Backend, MQTT Broker, Telemetry, SSE, and more.

Complete install flow for NeoMind on desktop (macOS/Windows/Linux) or server, covering one-line script, Docker, manual install, nginx reverse proxy, and development setup.

NeoMind notifications and messages complete guide: configure 9 message channels (Webhook, Email, Telegram, WeCom, DingTalk, Slack, Feishu), channel filters, message lifecycle, CLI and REST API.

A NeoMind-based OCR solution for general text recognition, using the OCR extension to extract text from images with dashboard display, history viewing, and AI Chat natural language queries, supporting NE101/NE301 smart cameras.

Complete guide to onboarding devices into NeoMind via MQTT (embedded broker, auto-discovery), HTTP webhook, manual registration, or an external broker. Includes ESP32/Python examples and the draft approval flow.

NeoMind technical architecture deep dive: crate layout and dependencies, main process + extension process isolation, event bus, extension FFI ABI, redb storage layer, Tokio concurrency and semaphores.

NeoMind REST API reference: base URL, auth (JWT + API Key), unified response format, main endpoint groups (devices / dashboards / rules / agents / messages / extensions / data-push / LLM backends), Swagger entry, error format.

Hardware, operating system, network port, and runtime requirements for NeoMind desktop and server deployment, including recommended local LLM (Ollama) configurations.

NeoMind troubleshooting: service startup failures, port conflicts, Ollama connection, MQTT issues, multimodal 400 errors, extension crashes, data directory permissions, and log locations.

NeoMind dashboard guide: create and edit dashboards, component library (value card / charts / toggle / image / video / map), real-time data, data source binding, public sharing links, and mobile responsiveness.

NeoMind is an edge-deployed AI platform for IoT that runs LLM-powered agents directly on your hardware, connects to devices via MQTT/BLE/Webhook, automates responses through a rule engine, and visualizes everything on real-time dashboards.