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# 🤖 HA Text AI for Home Assistant
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# HA Text AI for Home Assistant
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<div align="center">
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@@ -8,180 +8,111 @@
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<img src="https://github.com/smkrv/ha-text-ai/blob/main/custom_components/ha_text_ai/icons/logo%402x.png" alt="HA Text AI" style="width: 50%; max-width: 256px; max-height: 128px; aspect-ratio: 2/1; object-fit: contain;"/>
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### Advanced AI Integration for [Home Assistant](https://www.home-assistant.io/) with LLM multi-provider support
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### Multi-provider LLM integration for [Home Assistant](https://www.home-assistant.io/)
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</div>
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<p align="center">
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Transform your smart home experience with powerful AI assistance powered by multiple AI providers including OpenAI GPT, Anthropic Claude, DeepSeek and Google Gemini models. Get intelligent responses, automate complex scenarios, and enhance your home automation with advanced natural language processing.
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Ask OpenAI, Anthropic Claude, DeepSeek and Google Gemini models questions from your automations and scripts. The integration keeps per-instance conversation history, returns full-length responses through response variables, supports structured JSON output, and exposes token, latency and error metrics as sensor attributes.
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</p>
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---
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> [!IMPORTANT]
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> 🤝 Community Driven: for more details on the integration,
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> Community driven: for more details on the integration,
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> check out the discussion on the **[Home Assistant Community forum](https://community.home-assistant.io/t/ha-text-ai-transforming-home-automation-through-multi-llm-integration/799741)**
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>
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> <a href="https://my.home-assistant.io/redirect/hacs_repository/?owner=smkrv&repository=ha-text-ai&category=Integration"><img src="https://my.home-assistant.io/badges/hacs_repository.svg" width="210" height="auto"></a>
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>
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> [Screenshots](assets/images/screenshots/screenshot.jpg)
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## 🌟 Features
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## Features
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- 🧠 **Multi-Provider AI Integration**: Support for OpenAI GPT, Anthropic Claude, DeepSeek and Google Gemini models
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- 💬 **Advanced Language Processing**: Context-aware, multi-turn conversations
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- 📝 **Enhanced Memory Management**: Secure file-based history storage
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- ⚡ **Performance Optimization**: Efficient token usage and smart rate limiting
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- 🎯 **Advanced Customization**: Per-request model and parameter selection
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- 🔒 **Enhanced Security**: Secure API key management and usage monitoring
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- 🎨 **Improved User Experience**: Intuitive configuration and rich interfaces
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- 🔄 **Automation Integration**: Event-driven responses and template compatibility
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- **Multi-provider support**: OpenAI, Anthropic Claude, DeepSeek, Google Gemini, plus any OpenAI-compatible endpoint
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- **Conversation context**: the model sees previous messages; depth is configurable per request (1-20)
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- **Response variables**: `ask_question` returns the full response directly to the calling automation, bypassing the 255-character state limit
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- **Structured output**: JSON responses matching a schema you provide
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- **Per-request overrides**: model, temperature, max_tokens, system prompt, thinking mode
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- **Usage metrics**: token counters, latency and success/error statistics as sensor attributes
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- **File-based history**: per-instance JSON storage with automatic rotation at 1 MB
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<details>
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<summary>📦 Detailed Feature Breakdown</summary>
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### 🧠 **Multi-Provider AI Integration**
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- Support for OpenAI GPT models
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- Anthropic Claude integration
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- DeepSeek integration
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- Google Gemini integration
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- Custom API endpoints
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- Flexible model selection
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### 💬 **Advanced Language Processing**
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- Context-aware responses
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- Multi-turn conversations
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- Custom system instructions
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- Natural conversation flow
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### 📝 **Enhanced Memory Management**
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- File-based conversation history storage
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- Automatic history rotation
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- Configurable history size limits
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- Secure storage in Home Assistant
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### ⚡ **Performance Optimization**
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- Efficient token usage
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- Smart rate limiting
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- Response caching
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- Request interval control
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### 🎯 **Advanced Customization**
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- Per-request model selection
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- Adjustable parameters
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- Custom system prompts
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- Temperature control
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### 🔒 **Enhanced Security**
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- Secure API key storage
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- Rate limiting protection
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- Error handling
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- Usage monitoring
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### 🎨 **Improved User Experience**
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- Intuitive configuration UI
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- Detailed sensor attributes
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- Rich service interface
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- Model selection UI
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### 🔄 **Automation Integration**
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- Event-driven responses
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- Conditional logic support
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- Template compatibility
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- Model-specific automation
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</details>
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#### 🌐 Translations
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#### Translations
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| Code | Language | Status |
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|------|----------|--------|
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| 🇩🇪 de | Deutsch | Full |
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| 🇬🇧 en | English | Primary |
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| 🇪🇸 es | Español | Full |
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| 🇮🇳 hi | हिन्दी | Full |
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| 🇮🇹 it | Italiano | Full |
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| 🇷🇺 ru | Русский | Full |
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| 🇷🇸 sr | Српски | Full |
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| 🇨🇳 zh | 中文 | Full |
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| de | Deutsch | Full |
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| en | English | Primary |
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| es | Español | Full |
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| hi | हिन्दी | Full |
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| it | Italiano | Full |
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| ru | Русский | Full |
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| sr | Српски | Full |
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| zh | 中文 | Full |
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## 📋 Prerequisites
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## Prerequisites
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- Home Assistant 2024.12.0 or later (recommended for best compatibility)
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- Active API key from:
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- Home Assistant 2024.12.0 or later
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- An API key from one of:
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- OpenAI ([Get key](https://platform.openai.com/account/api-keys))
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- Anthropic ([Get key](https://console.anthropic.com/))
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- DeepSeek ([Get key](https://platform.deepseek.com/api_keys))
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- OpenRouter ([Get key](https://openrouter.ai/keys))
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- Google Gemini 🆕 ([Get key](https://ai.google.dev/gemini-api/docs/api-key)) thanks to ([@Azzedde](https://github.com/Azzedde))
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- Google Gemini ([Get key](https://ai.google.dev/gemini-api/docs/api-key)) thanks to ([@Azzedde](https://github.com/Azzedde))
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- Any OpenAI-compatible API provider
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- Python 3.9 or newer
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- Stable internet connection
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## Configuration Options
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### 🔧 **Core Configuration Settings**
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- 🌐 **API Provider**: OpenAI/Anthropic/DeepSeek/Gemini
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- 🔑 **API Key**: Provider-specific authentication
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- 🤖 **Model Selection**: Flexible, provider-specific models
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- 🌡️ **Temperature**: Creativity control (0.0-2.0)
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- 📏 **Max Tokens**: Response length limit (passed directly to the LLM API to control the maximum length of the response)
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- ⏱️ **Request Interval**: API call throttling
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- 💾 **History Size**: Number of messages to retain
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- 🌍 **Custom API Endpoint**: Optional advanced configuration
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### Core Configuration Settings
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- **API Provider**: OpenAI / Anthropic / DeepSeek / Gemini
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- **API Key**: provider-specific authentication
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- **Model**: any model your provider offers
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- **Temperature**: sampling temperature (0.0-2.0)
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- **Max Tokens**: response length cap, passed to the LLM API
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- **Request Interval**: minimum delay between API calls (seconds)
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- **History Size**: number of conversations to retain
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- **Custom API Endpoint**: for OpenRouter, proxies and self-hosted servers
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- **Disable Thinking**: turn off model reasoning where the provider supports it
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- **Allow Local Network**: permit endpoints on private addresses (needed for local servers like Ollama)
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### 🤖 **Recommended Models**
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### Recommended Models
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#### OpenAI Models
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- **GPT-5** - The latest flagship model, best for complex reasoning
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- **GPT-5 mini** - A cost-effective and fast model, suitable for most tasks
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- **GPT-5.6 Sol** - flagship tier for the hardest tasks
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- **GPT-5.6 Terra** - mid-tier for high-volume tasks
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- **GPT-5.6 Luna** - fastest and cheapest, enough for most home automation queries
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#### Anthropic Claude Models
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- **Claude Opus 4.6** - The most capable model for handling complex tasks
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- **Claude Sonnet 4.6** - Offers a balance between performance and cost
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- **Claude Haiku 4.5** - The fastest and most economical option in the series
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- **Claude Fable 5** - the most capable model for complex tasks
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- **Claude Sonnet 5** - balance between quality and cost
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- **Claude Haiku 4.5** - the fastest and cheapest option in the lineup
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#### DeepSeek Models
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- **deepseek-v4-flash** - A fast general-purpose model for a wide range of tasks (default)
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- **deepseek-v4-pro** - A more capable model for reasoning and coding
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- **deepseek-v4-flash** - fast general-purpose model (default)
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- **deepseek-v4-pro** - stronger at reasoning and coding
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> The legacy model names `deepseek-chat` and `deepseek-reasoner` stop working on 2026-07-24. If your instance still uses one of them, switch the model in the integration options.
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#### Google Gemini Models
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- **gemini-3.5-flash** - Fast and cost-efficient, suitable for most tasks (default)
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- **Gemini 3.1 Pro** - The most advanced Gemini model available
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- **gemini-3.5-flash** - default; Google's strongest currently available model
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- **gemini-3.1-pro** - previous flagship, still supported
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> Google shut down `gemini-2.0-flash` on 2026-06-01 and retires the 2.5 family on 2026-10-16. If your instance uses one of those, switch the model in the integration options.
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<details>
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<summary>🌐 Potentially Compatible Providers</summary>
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<summary>Potentially Compatible Providers</summary>
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#### Flexible Provider Ecosystem
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The integration is designed to be flexible and may work with other providers offering OpenAI-compatible APIs:
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Other providers with OpenAI-compatible APIs may work through the custom endpoint option:
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- Groq
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- Together AI
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- Perplexity AI
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- Mistral AI
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- Google AI
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- Local AI servers (like Ollama)
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- Local AI servers (like Ollama - enable **Allow Local Network** in the options)
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- Custom OpenAI-compatible endpoints
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#### 🚨 Compatibility Notes
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- Not all providers guarantee full compatibility
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- Performance may vary between providers
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- Check individual provider's documentation
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- Ensure your API key has sufficient credits/quota
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#### 🔍 Provider Compatibility Requirements
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To be compatible, a provider should support:
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- OpenAI-like REST API structure
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- JSON request/response format
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- Standard authentication method
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- Similar model parameter handling
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Compatibility is not guaranteed. A provider needs an OpenAI-like REST API with JSON request/response format, standard bearer authentication and similar parameter handling. Check the provider's documentation and make sure your API key has sufficient quota.
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</details>
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## ⚡ Installation
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## Installation
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### HACS Installation (Recommended)
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>[!TIP]
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@@ -189,10 +120,9 @@ To be compatible, a provider should support:
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<a href="https://my.home-assistant.io/redirect/hacs_repository/?owner=smkrv&repository=ha-text-ai&category=Integration"><img src="https://my.home-assistant.io/badges/hacs_repository.svg" width="170" height="auto"></a>
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1. Open HACS in Home Assistant
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2. Click on "Integrations"
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3. Search for "HA Text AI"
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4. Click "Download"
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5. Restart Home Assistant
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2. Search for "HA Text AI"
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3. Click "Download"
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4. Restart Home Assistant
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**Alternative Method (Custom Repository):**
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If the integration is not found in the default repository:
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@@ -206,57 +136,99 @@ If the integration is not found in the default repository:
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1. Download `ha_text_ai.zip` from the latest release
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2. Extract the archive and copy the `ha_text_ai` folder into your `custom_components` directory
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3. Restart Home Assistant
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4. Add configuration via UI (Settings → Devices & Services → Add Integration)
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4. Add configuration via UI (Settings > Devices & Services > Add Integration)
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## ⚙️ Configuration
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## Configuration
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### Via UI (Recommended)
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1. Go to Settings → Devices & Services
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1. Go to Settings > Devices & Services
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2. Click "Add Integration"
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3. Search for "HA Text AI"
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4. Follow the configuration steps
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> **Note:** This integration is configured exclusively through the UI (config entries). YAML configuration is not supported.
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## 🛠️ Available Services
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## Quick Start
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### 🔄 Response Variables (New!)
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After configuration you get one entity per instance, named `sensor.ha_text_ai_<name>`. It is a status sensor: it shows the last response and usage metrics, but you don't type questions into it. Questions go through the `ha_text_ai.ask_question` action, called from Developer Tools, automations, or scripts.
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**HA Text AI now supports response variables** - a powerful feature that returns AI responses directly from service calls, eliminating the need for separate text sensors and the 255-character limitation!
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### First question, no YAML
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#### ✨ Key Benefits:
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- **Unlimited response length** - No more 255-character truncation
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- **Direct data access** - Get responses immediately in automations
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- **Race condition prevention** - Eliminates conflicts in parallel automations
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- **Simplified workflows** - No need to read from sensors
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1. Open Developer Tools > Actions (called "Services" in older HA versions).
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2. Search for "HA Text AI: Ask Question".
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3. Pick your instance, type a question, press "Perform action".
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4. The response appears below the form.
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### In an automation
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1. Go to Settings > Automations & scenes > Create automation.
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2. Add a trigger: a button press, a time, a state change.
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3. Add action > search "HA Text AI: Ask Question" > fill in the question and pick your instance.
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4. To use the reply in a follow-up step, the action needs `response_variable: ai_response`. If the visual editor doesn't show a field for it, open the three-dot menu on that action, choose "Edit in YAML", and add the line at the end.
|
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|
|
5. In the next action, `{{ ai_response.response_text }}` holds the full answer, for example as a notification message.
|
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|
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|
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|
|
Complete working automations: [Automation Examples](#automation-examples-with-response-variables).
|
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|
|
|
|
|
|
### On a dashboard
|
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|
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|
|
|
The sensor keeps the last question and answer as attributes, so a Markdown card can show them:
|
|
|
|
|
|
|
|
|
|
```yaml
|
|
|
|
|
type: markdown
|
|
|
|
|
content: >-
|
|
|
|
|
**Q:** {{ state_attr('sensor.ha_text_ai_my_assistant', 'question') }}
|
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|
|
|
|
|
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|
|
**A:** {{ state_attr('sensor.ha_text_ai_my_assistant', 'response') }}
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
The attributes fill in after the first question. They are capped at 2048 characters, so long answers come back complete only via `response_variable`.
|
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|
|
|
|
|
|
## Available Services
|
|
|
|
|
|
|
|
|
|
### Response Variables
|
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|
|
`ask_question` returns its result directly to the calling automation via `response_variable`. The full response text comes back regardless of length (no 255-character truncation), it is available immediately without polling sensor state, and each service call gets its own result, so parallel automations don't overwrite each other.
|
|
|
|
|
|
|
|
|
|
### ask_question
|
|
|
|
|
```yaml
|
|
|
|
|
service: ha_text_ai.ask_question
|
|
|
|
|
data:
|
|
|
|
|
question: "What's the optimal temperature for sleeping?"
|
|
|
|
|
model: "claude-sonnet-4-6-20260217" # optional
|
|
|
|
|
instance: sensor.ha_text_ai_claude
|
|
|
|
|
model: "claude-sonnet-5" # optional, overrides the configured model
|
|
|
|
|
temperature: 0.5 # optional
|
|
|
|
|
max_tokens: 500 # optional
|
|
|
|
|
context_messages: 10 #optional, number of previous messages to include in context, default: 5
|
|
|
|
|
context_messages: 10 # optional, previous messages to include (1-20, default 5)
|
|
|
|
|
system_prompt: "You are a sleep optimization expert" # optional
|
|
|
|
|
instance: sensor.ha_text_ai_gpt
|
|
|
|
|
response_variable: ai_response # NEW! Store response data directly
|
|
|
|
|
disable_thinking: true # optional, disable model reasoning for this request
|
|
|
|
|
response_variable: ai_response
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
#### 📊 Response Data Structure:
|
|
|
|
|
For structured JSON output, add `structured_output` with a schema:
|
|
|
|
|
```yaml
|
|
|
|
|
service: ha_text_ai.ask_question
|
|
|
|
|
data:
|
|
|
|
|
question: "Suggest three energy-saving actions for tonight"
|
|
|
|
|
instance: sensor.ha_text_ai_gpt
|
|
|
|
|
structured_output: true
|
|
|
|
|
json_schema: >-
|
|
|
|
|
{"type": "object", "properties": {"actions": {"type": "array", "items": {"type": "string"}}}}
|
|
|
|
|
response_variable: ai_response
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
#### Response Data Structure
|
|
|
|
|
```yaml
|
|
|
|
|
# The service returns structured data:
|
|
|
|
|
response_text: "The optimal sleeping temperature is 65-68°F (18-20°C)..."
|
|
|
|
|
tokens_used: 150
|
|
|
|
|
prompt_tokens: 50
|
|
|
|
|
completion_tokens: 100
|
|
|
|
|
model_used: "claude-sonnet-4-6-20260217"
|
|
|
|
|
instance: "sensor.ha_text_ai_gpt"
|
|
|
|
|
model_used: "claude-sonnet-5"
|
|
|
|
|
instance: "sensor.ha_text_ai_claude"
|
|
|
|
|
question: "What's the optimal temperature for sleeping?"
|
|
|
|
|
timestamp: "2025-02-09T16:57:00.000Z"
|
|
|
|
|
timestamp: "2026-07-09T16:57:00.000Z"
|
|
|
|
|
success: true
|
|
|
|
|
# error: "Error message" (only present if success: false)
|
|
|
|
|
# error and error_type are present only when success is false
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### set_system_prompt
|
|
|
|
@@ -285,14 +257,14 @@ service: ha_text_ai.get_history
|
|
|
|
|
data:
|
|
|
|
|
limit: 5 # optional, number of conversations to return (values above 200 are clamped); omit to get the full stored history
|
|
|
|
|
filter_model: "gpt-4o" # optional, filter by specific AI model
|
|
|
|
|
start_date: "2025-02-01" # optional, filter conversations from this date
|
|
|
|
|
start_date: "2026-02-01" # optional, filter conversations from this date
|
|
|
|
|
include_metadata: false # optional, include tokens, response time, etc.
|
|
|
|
|
sort_order: "newest" # optional, sort order: "newest" or "oldest"
|
|
|
|
|
instance: sensor.ha_text_ai_gpt
|
|
|
|
|
response_variable: history_result # entries are in history_result.history
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
## 🚀 Advanced Automation Examples with Response Variables
|
|
|
|
|
## Automation Examples with Response Variables
|
|
|
|
|
|
|
|
|
|
### Example 1: Smart Home Advice with Direct Response
|
|
|
|
|
```yaml
|
|
|
|
@@ -309,12 +281,12 @@ automation:
|
|
|
|
|
response_variable: ai_advice
|
|
|
|
|
- service: notify.mobile_app
|
|
|
|
|
data:
|
|
|
|
|
title: "🏠 Smart Home Tip"
|
|
|
|
|
title: "Smart Home Tip"
|
|
|
|
|
message: |
|
|
|
|
|
{{ ai_advice.response_text }}
|
|
|
|
|
|
|
|
|
|
📊 Tokens used: {{ ai_advice.tokens_used }}
|
|
|
|
|
🤖 Model: {{ ai_advice.model_used }}
|
|
|
|
|
Tokens used: {{ ai_advice.tokens_used }}
|
|
|
|
|
Model: {{ ai_advice.model_used }}
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### Example 2: Weather-Based AI Recommendations
|
|
|
|
@@ -340,7 +312,7 @@ automation:
|
|
|
|
|
then:
|
|
|
|
|
- service: persistent_notification.create
|
|
|
|
|
data:
|
|
|
|
|
title: "❄️ Winter Preparation Advice"
|
|
|
|
|
title: "Winter Preparation Advice"
|
|
|
|
|
message: |
|
|
|
|
|
{{ winter_advice.response_text }}
|
|
|
|
|
|
|
|
|
@@ -348,7 +320,7 @@ automation:
|
|
|
|
|
else:
|
|
|
|
|
- service: persistent_notification.create
|
|
|
|
|
data:
|
|
|
|
|
title: "⚠️ AI Service Error"
|
|
|
|
|
title: "AI Service Error"
|
|
|
|
|
message: "Failed to get winter advice: {{ winter_advice.error }}"
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
@@ -384,10 +356,10 @@ automation:
|
|
|
|
|
instance: sensor.ha_text_ai_gpt
|
|
|
|
|
response_variable: recommendations
|
|
|
|
|
|
|
|
|
|
# Step 3: Send comprehensive report
|
|
|
|
|
# Step 3: Send the combined report
|
|
|
|
|
- service: notify.telegram
|
|
|
|
|
data:
|
|
|
|
|
title: "🏠 Home Analysis Report"
|
|
|
|
|
title: "Home Analysis Report"
|
|
|
|
|
message: |
|
|
|
|
|
**Analysis:**
|
|
|
|
|
{{ status_analysis.response_text }}
|
|
|
|
@@ -401,11 +373,11 @@ automation:
|
|
|
|
|
- Generated: {{ recommendations.timestamp }}
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### 💡 Migration from Sensors to Response Variables
|
|
|
|
|
### Migration from Sensors to Response Variables
|
|
|
|
|
|
|
|
|
|
#### Old Method (Limited):
|
|
|
|
|
#### Old Method:
|
|
|
|
|
```yaml
|
|
|
|
|
# ❌ Old way - limited to 255 characters, race conditions
|
|
|
|
|
# Old way: delay-based polling, response truncated by the 255-character state limit
|
|
|
|
|
automation:
|
|
|
|
|
- alias: "Old AI Response Method"
|
|
|
|
|
action:
|
|
|
|
@@ -416,12 +388,12 @@ automation:
|
|
|
|
|
- delay: "00:00:05" # Wait for sensor update
|
|
|
|
|
- service: notify.mobile
|
|
|
|
|
data:
|
|
|
|
|
message: "{{ state_attr('sensor.ha_text_ai_gpt', 'response')[:255] }}..." # Truncated!
|
|
|
|
|
message: "{{ state_attr('sensor.ha_text_ai_gpt', 'response')[:255] }}..." # Truncated
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
#### New Method (Unlimited):
|
|
|
|
|
#### New Method:
|
|
|
|
|
```yaml
|
|
|
|
|
# ✅ New way - unlimited length, immediate access, no race conditions
|
|
|
|
|
# New way: full response, available immediately
|
|
|
|
|
automation:
|
|
|
|
|
- alias: "New AI Response Method"
|
|
|
|
|
action:
|
|
|
|
@@ -429,24 +401,23 @@ automation:
|
|
|
|
|
data:
|
|
|
|
|
question: "Long question here..."
|
|
|
|
|
instance: sensor.ha_text_ai_gpt
|
|
|
|
|
response_variable: ai_response # Direct access!
|
|
|
|
|
response_variable: ai_response
|
|
|
|
|
- service: notify.mobile
|
|
|
|
|
data:
|
|
|
|
|
message: "{{ ai_response.response_text }}" # Full response, no truncation!
|
|
|
|
|
message: "{{ ai_response.response_text }}" # Full response, no truncation
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### 🏷️ HA Text AI Sensor Naming Convention
|
|
|
|
|
### HA Text AI Sensor Naming Convention
|
|
|
|
|
|
|
|
|
|
#### Character Restrictions
|
|
|
|
|
- Only lowercase letters (a-z)
|
|
|
|
|
- Numbers (0-9)
|
|
|
|
|
- Underscore (_)
|
|
|
|
|
- Maximum length: 50 characters (including `ha_text_ai_`)
|
|
|
|
|
#### Naming Rules
|
|
|
|
|
- Only lowercase letters (a-z), numbers (0-9) and underscore (_)
|
|
|
|
|
- The part after the `sensor.ha_text_ai_` prefix is limited to 50 characters
|
|
|
|
|
- No spaces; keep it descriptive but short
|
|
|
|
|
|
|
|
|
|
#### Sensor Name Structure
|
|
|
|
|
```yaml
|
|
|
|
|
# Always starts with 'sensor.ha_text_ai_'
|
|
|
|
|
# You define only the part after the underscore
|
|
|
|
|
# You define only the part after the prefix
|
|
|
|
|
sensor.ha_text_ai_YOUR_UNIQUE_SUFFIX
|
|
|
|
|
|
|
|
|
|
# Examples:
|
|
|
|
@@ -477,107 +448,99 @@ automation:
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'response') }}
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
### 💡 Naming Rules
|
|
|
|
|
- Prefix is always `sensor.ha_text_ai_`
|
|
|
|
|
- Add your unique identifier after the underscore
|
|
|
|
|
- Use lowercase
|
|
|
|
|
- No spaces allowed
|
|
|
|
|
- Keep it descriptive but concise
|
|
|
|
|
### HA Text AI Sensor Attributes
|
|
|
|
|
|
|
|
|
|
### 🔍 HA Text AI Sensor Attributes
|
|
|
|
|
- **Model and provider**: current model, API provider, model used for the last response
|
|
|
|
|
- **System status**: processing, rate-limit and endpoint state
|
|
|
|
|
- **Performance metrics**: request success/failure counters and latency statistics
|
|
|
|
|
- **Token usage**: total, prompt and completion token counters as reported by the provider's API
|
|
|
|
|
- **Last interaction**: most recent question, response and timestamp
|
|
|
|
|
- **System health**: error counter, maintenance flag, uptime
|
|
|
|
|
|
|
|
|
|
- 🤖 **Model and Provider Information**: Tracking current AI model and service provider
|
|
|
|
|
- 🚦 **System Status**: Real-time API and processing readiness
|
|
|
|
|
- 📊 **Performance Metrics**: Request success rates and response times
|
|
|
|
|
- 💬 **Conversation Tracking**: Token usage and interaction history are estimated using a heuristic method based on word count and specific word characteristics, which may differ from actual token usage.
|
|
|
|
|
- 🕒 **Last Interaction Details**: Recent query and response tracking
|
|
|
|
|
- ❤️ **System Health**: Error monitoring and service uptime
|
|
|
|
|
Attributes may be 0 or empty until the first request completes.
|
|
|
|
|
|
|
|
|
|
<details>
|
|
|
|
|
<summary>📦 Detailed Sensor Attributes</summary>
|
|
|
|
|
<summary>Detailed Sensor Attributes</summary>
|
|
|
|
|
|
|
|
|
|
#### Model and Provider Information
|
|
|
|
|
```yaml
|
|
|
|
|
# Name of the AI model currently in use (e.g., latest version of GPT)
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Model') }} # gpt-4o
|
|
|
|
|
# Model currently configured for this instance
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'model') }} # gpt-4o-mini
|
|
|
|
|
|
|
|
|
|
# Service provider for the AI model (determines API endpoint and authentication)
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Api provider') }} # openai
|
|
|
|
|
# Service provider (determines API endpoint and authentication)
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'api_provider') }} # openai
|
|
|
|
|
|
|
|
|
|
# Previous or alternative model configuration
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Last model') }} # gpt-4o
|
|
|
|
|
# Model that produced the last response (may differ after a per-request override)
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'last_model') }} # gpt-4o-mini
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
#### System Status
|
|
|
|
|
```yaml
|
|
|
|
|
# Indicates if a request is currently being processed
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Is processing') }} # false
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'is_processing') }} # false
|
|
|
|
|
|
|
|
|
|
# Shows if the API has hit its request rate limit
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Is rate limited') }} # false
|
|
|
|
|
# Shows if the API has hit its request rate limit
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'is_rate_limited') }} # false
|
|
|
|
|
|
|
|
|
|
# Status of the specific API endpoint being used
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Endpoint status') }} # ready
|
|
|
|
|
# Status of the API endpoint being used
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'endpoint_status') }} # ready
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
#### Performance Metrics
|
|
|
|
|
```yaml
|
|
|
|
|
# Total number of successfully completed API requests
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Successful requests') }} # 0
|
|
|
|
|
# Number of successfully completed API requests
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'successful_requests') }} # 42
|
|
|
|
|
|
|
|
|
|
# Number of API requests that encountered errors
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Failed requests') }} # 0
|
|
|
|
|
# Number of API requests that encountered errors
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'failed_requests') }} # 0
|
|
|
|
|
|
|
|
|
|
# Mean time taken to receive a response from the AI service
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Average latency') }} # 0
|
|
|
|
|
|
|
|
|
|
# Maximum time taken for a single request-response cycle
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Max latency') }} # 0
|
|
|
|
|
# Average / max / min response time, in seconds
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'average_latency') }} # 1.85
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'max_latency') }} # 4.2
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'min_latency') }} # 0.9
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
#### Conversation and Token Usage
|
|
|
|
|
```yaml
|
|
|
|
|
# Number of previous interactions stored in conversation context
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'History size') }} # 0
|
|
|
|
|
# Number of entries in the current history file
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'history_size') }} # 12
|
|
|
|
|
|
|
|
|
|
# Total number of tokens used across all interactions
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'Total tokens') }} # 0
|
|
|
|
|
# Token counters as reported by the provider's API
|
|
|
|
|
{{ state_attr('sensor.ha_text_ai_gpt', 'total_tokens') }} # 4520
|
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{{ state_attr('sensor.ha_text_ai_gpt', 'prompt_tokens') }} # 3100
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{{ state_attr('sensor.ha_text_ai_gpt', 'completion_tokens') }} # 1420
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# Tokens used in the input prompts
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{{ state_attr('sensor.ha_text_ai_gpt', 'Prompt tokens') }} # 0
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# Tokens used in the AI's generated responses
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{{ state_attr('sensor.ha_text_ai_gpt', 'Completion tokens') }} # 0
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# Number of entries in current history file
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{{ state_attr('sensor.ha_text_ai_gpt', 'History size') }} # 0
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# Last few conversation entries (last 5 for performance)
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{{ state_attr('sensor.ha_text_ai_gpt', 'conversation_history') }} # [...]
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# Last 3 conversation entries, each truncated to 256 characters
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# (full history is available via the get_history service)
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{{ state_attr('sensor.ha_text_ai_gpt', 'conversation_history') }} # [...]
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```
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#### Last Interaction Details
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```yaml
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# Most recent complete response generated by the AI service
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{{ state_attr('sensor.ha_text_ai_gpt', 'Response') }} # Last AI response
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# Most recent response, truncated to 2048 characters in the attribute
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# (the response_variable path returns the full text)
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{{ state_attr('sensor.ha_text_ai_gpt', 'response') }} # Last AI response
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# The most recently processed user query or prompt
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{{ state_attr('sensor.ha_text_ai_gpt', 'Question') }} # Last asked question
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# The most recently processed question
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{{ state_attr('sensor.ha_text_ai_gpt', 'question') }} # Last asked question
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# Precise moment when the last interaction occurred (useful for tracking and logging)
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{{ state_attr('sensor.ha_text_ai_gpt', 'Last timestamp') }} # Timestamp
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# When the last interaction occurred
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{{ state_attr('sensor.ha_text_ai_gpt', 'last_timestamp') }} # Timestamp
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|
```
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|
#### System Health
|
|
|
|
|
```yaml
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|
# Cumulative count of all errors encountered during AI service interactions
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{{ state_attr('sensor.ha_text_ai_gpt', 'Total errors') }} # 0
|
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|
# Cumulative count of errors across all requests
|
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{{ state_attr('sensor.ha_text_ai_gpt', 'total_errors') }} # 0
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# Indicates if the AI service is currently undergoing scheduled or emergency maintenance
|
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{{ state_attr('sensor.ha_text_ai_gpt', 'Is maintenance') }} # false
|
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|
# Error message of the last failed request (null after a success)
|
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{{ state_attr('sensor.ha_text_ai_gpt', 'last_error') }} # null
|
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|
# Total continuous operational time of the AI service (in hours or days)
|
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|
{{ state_attr('sensor.ha_text_ai_gpt', 'Uptime') }} # 547,58
|
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|
# Maintenance flag
|
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|
{{ state_attr('sensor.ha_text_ai_gpt', 'is_maintenance') }} # false
|
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|
# Seconds since the integration instance was set up
|
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|
{{ state_attr('sensor.ha_text_ai_gpt', 'uptime') }} # 547.58
|
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|
```
|
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|
|
### History Storage
|
|
|
|
@@ -587,41 +550,36 @@ Conversation history stored in `.storage/ha_text_ai_history/` directory:
|
|
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|
|
- Archived history files are timestamped
|
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|
|
- Default maximum file size: 1MB
|
|
|
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|
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|
|
|
### 💡 Pro Tips
|
|
|
|
|
- Always check attribute existence
|
|
|
|
|
- Use these attributes for monitoring and automation
|
|
|
|
|
- Some values might be 0 or empty initially
|
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</details>
|
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|
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|
## 📘 FAQ
|
|
|
|
|
## FAQ
|
|
|
|
|
|
|
|
|
|
**Q: Which AI providers are supported?**
|
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|
|
A: OpenAI (GPT models), Anthropic (Claude models), DeepSeek, Google Gemini, and OpenRouter are officially supported, with many other OpenAI-compatible providers working as well.
|
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|
|
A: OpenAI, Anthropic, DeepSeek and Google Gemini are built-in providers. OpenRouter and other OpenAI-compatible services work through the OpenAI provider with a custom endpoint.
|
|
|
|
|
|
|
|
|
|
**Q: How can I reduce API costs?**
|
|
|
|
|
A: Use gpt-5-mini or claude-haiku-4-5 for most queries, implement caching, and optimize token usage.
|
|
|
|
|
A: Use a cheap fast model (GPT-5.6 Luna, Claude Haiku 4.5, deepseek-v4-flash, gemini-3.5-flash) for routine queries, lower `context_messages`, and cap `max_tokens`.
|
|
|
|
|
|
|
|
|
|
**Q: Are there limitations on the number of requests?**
|
|
|
|
|
A: Depends on your API provider's plan. We recommend monitoring usage and implementing request throttling via `request_interval` configuration.
|
|
|
|
|
A: Depends on your API provider's plan. Monitor usage via the sensor attributes and throttle calls with the `request_interval` option.
|
|
|
|
|
|
|
|
|
|
**Q: Can I use custom models?**
|
|
|
|
|
A: Yes, you can configure custom endpoints and use any compatible model by specifying it in the configuration.
|
|
|
|
|
|
|
|
|
|
**Q: How do I switch between different AI providers?**
|
|
|
|
|
A: Simply change the model parameter in your configuration or service calls to use the desired provider's model.
|
|
|
|
|
A: Each integration instance is bound to one provider. Add a separate instance per provider and pick the instance in your service calls; within an instance you can override the model per request.
|
|
|
|
|
|
|
|
|
|
**Q: What are the token limits for different models?**
|
|
|
|
|
A: Token limits vary by provider and model. OpenAI's GPT-5 supports up to 1M context tokens, Claude Opus 4.6 supports up to 1M tokens, Gemini 3.1 Pro supports up to 1M tokens, while smaller models typically have 128K-200K limits. Check your provider's documentation for specific limits.
|
|
|
|
|
A: Context window sizes vary by provider and model - check your provider's documentation. The `max_tokens` option caps only the response length, not the context window.
|
|
|
|
|
|
|
|
|
|
**Q: How do I monitor token usage?**
|
|
|
|
|
A: Use the sensor attributes like `Total tokens`, `Prompt tokens`, and `Completion tokens` to track usage. You can also create automations to alert you when usage exceeds certain thresholds.
|
|
|
|
|
A: Use the sensor attributes `total_tokens`, `prompt_tokens` and `completion_tokens`. You can also create automations to alert you when usage exceeds a threshold.
|
|
|
|
|
|
|
|
|
|
**Q: Is my data secure?**
|
|
|
|
|
A: Yes, your data is secure. The system operates entirely on your local machine, keeping your data under your control. API keys are stored securely and all external communications use encrypted connections.
|
|
|
|
|
A: Conversation history and API keys are stored locally in your Home Assistant instance. Questions and context are sent to the provider you configure over HTTPS; nothing is shared with third parties beyond that provider.
|
|
|
|
|
|
|
|
|
|
**Q: How do context messages work?**
|
|
|
|
|
A: Context messages allow the AI to remember and reference previous conversation history. By default, 5 previous messages are included, but you can customize this from 1 to 20 messages to control the conversation depth and token usage.
|
|
|
|
|
A: Context messages let the AI reference previous conversation history. By default 5 previous messages are included; you can set 1 to 20 per request to balance conversation depth against token usage.
|
|
|
|
|
|
|
|
|
|
**Q: Where is conversation history stored?**
|
|
|
|
|
A: History is stored in files under the `.storage/ha_text_ai_history/` directory, with automatic rotation and size management.
|
|
|
|
@@ -630,9 +588,9 @@ A: History is stored in files under the `.storage/ha_text_ai_history/` directory
|
|
|
|
|
A: Yes, archived history files are stored with timestamps and can be accessed manually if needed.
|
|
|
|
|
|
|
|
|
|
**Q: How much history is kept?**
|
|
|
|
|
A: By default, up to 50 conversations are stored (max 200), configurable via UI. Files are automatically rotated when they reach 1MB.
|
|
|
|
|
A: 50 conversations by default, configurable up to 100 in the UI. Files are automatically rotated when they reach 1MB.
|
|
|
|
|
|
|
|
|
|
## 🤝 Contributing
|
|
|
|
|
## Contributing
|
|
|
|
|
|
|
|
|
|
Contributions welcome! Please read our [Contributing Guide](CONTRIBUTING.md).
|
|
|
|
|
|
|
|
|
@@ -655,12 +613,12 @@ DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
|
|
|
|
|
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
|
|
|
|
|
DEALINGS IN THE SOFTWARE.
|
|
|
|
|
|
|
|
|
|
## 📝 License
|
|
|
|
|
## License
|
|
|
|
|
|
|
|
|
|
Author: SMKRV
|
|
|
|
|
[MIT License](https://opensource.org/licenses/MIT) - see [LICENSE](LICENSE) for details.
|
|
|
|
|
|
|
|
|
|
## 💡 Support the Project
|
|
|
|
|
## Support the Project
|
|
|
|
|
|
|
|
|
|
The best support is:
|
|
|
|
|
- Sharing feedback
|
|
|
|
@@ -674,16 +632,13 @@ If you want to say thanks financially, you can send a small token of appreciatio
|
|
|
|
|
**USDT Wallet (TRC10/TRC20):**
|
|
|
|
|
`TXC9zYHYPfWUGi4Sv4R1ctTBGScXXQk5HZ`
|
|
|
|
|
|
|
|
|
|
*Open-source is built by community passion!* 🚀
|
|
|
|
|
|
|
|
|
|
---
|
|
|
|
|
|
|
|
|
|
<div align="center"><img src="https://github.com/smkrv/ha-text-ai/blob/2aaf3405759eb2d97624834594e24ace896131df/assets/images/icons/footer_icon.png" alt="HA Text AI" style="width: 128px; height: auto;"/></div>
|
|
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|
|
<div align="center">
|
|
|
|
|
|
|
|
|
|
Made with ❤️ for the Home Assistant Community
|
|
|
|
|
Made for the Home Assistant Community
|
|
|
|
|
|
|
|
|
|
[Report Bug](https://github.com/smkrv/ha-text-ai/issues) · [Request Feature](https://github.com/smkrv/ha-text-ai/issues)
|
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|
</div>
|
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|
SMKRV
