Release v2.0.0

Stability improvements
2026-07-22 07:03:58 +08:00 · 2024-11-20 01:02:27 +03:00 · 2024-11-19 23:38:07 +03:00 · 2024-11-19 23:30:28 +03:00 · 2024-11-19 23:21:54 +03:00 · 2024-11-19 19:39:05 +03:00
12 changed files with 1849 additions and 496 deletions
@@ -1,160 +1,191 @@
 # 🤖 HA Text AI for Home Assistant

-<div align="center">
+<div align="center">  

-![GitHub release](https://img.shields.io/github/release/smkrv/ha-text-ai.svg?style=flat-square)
-![GitHub downloads](https://img.shields.io/github/downloads/smkrv/ha-text-ai/total.svg?style=flat-square)
-![GitHub stars](https://img.shields.io/github/stars/smkrv/ha-text-ai.svg?style=social)
-![GitHub last commit](https://img.shields.io/github/last-commit/smkrv/ha-text-ai.svg?style=flat-square)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://opensource.org/licenses/MIT)
+  ![GitHub release](https://img.shields.io/github/release/smkrv/ha-text-ai.svg?style=flat-square) ![GitHub downloads](https://img.shields.io/github/downloads/smkrv/ha-text-ai/total.svg?style=flat-square) ![GitHub stars](https://img.shields.io/github/stars/smkrv/ha-text-ai.svg?style=social) ![GitHub last commit](https://img.shields.io/github/last-commit/smkrv/ha-text-ai.svg?style=flat-square) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://opensource.org/licenses/MIT) [![hacs_badge](https://img.shields.io/badge/HACS-Custom-41BDF5.svg?style=flat-square)](https://github.com/hacs/integration)

 </div>

 <p align="center">
-Transform your smart home experience with powerful AI assistance powered by OpenAI's GPT models
+Transform your smart home experience with powerful AI assistance powered by multiple AI providers including OpenAI GPT and Anthropic Claude models. Get intelligent responses, automate complex scenarios, and enhance your home automation with advanced natural language processing.
 </p>

 ---

 ## 🌟 Features

- 🧠 **Advanced AI Integration**: Leverage OpenAI's powerful models (GPT-3.5, GPT-4) for smart home interactions
- 💬 **Natural Language Control**: Control your home and get information using everyday language
- 📝 **Conversation Memory**: Maintain context with conversation history tracking
- ⚡ **Real-time Responses**: Get quick, contextual responses to your queries
- 🎯 **Customizable Behavior**: Fine-tune AI responses with adjustable parameters
- 🔒 **Secure Integration**: Your API key and data are handled securely
- 🎨 **Flexible Configuration**: Easy setup with multiple configuration options
- 🔄 **Automation Ready**: Integrate AI responses into your automations
+- 🧠 **Multi-Provider AI Integration**:
+  - Support for OpenAI GPT models
+  - Anthropic Claude integration
+  - Custom API endpoints
+  - Flexible model selection
+- 💬 **Advanced Language Processing**:
+  - Context-aware responses
+  - Multi-turn conversations
+  - Custom system instructions
+  - Natural conversation flow
+- 📝 **Enhanced Memory Management**:
+  - Persistent conversation history
+  - Context-aware responses
+  - Customizable history limits
+  - Model-specific filtering
+- ⚡ **Performance Optimization**:
+  - Efficient token usage
+  - Smart rate limiting
+  - Response caching
+  - Request interval control
+- 🎯 **Advanced Customization**:
+  - Per-request model selection
+  - Adjustable parameters
+  - Custom system prompts
+  - Temperature control
+- 🔒 **Enhanced Security**:
+  - Secure API key storage
+  - Rate limiting protection
+  - Error handling
+  - Usage monitoring
+- 🎨 **Improved User Experience**:
+  - Intuitive configuration UI
+  - Detailed sensor attributes
+  - Rich service interface
+  - Model selection UI
+- 🔄 **Automation Integration**:
+  - Event-driven responses
+  - Conditional logic support
+  - Template compatibility
+  - Model-specific automation

 ## 📋 Prerequisites

- Home Assistant installation (Core, OS, Container, or Supervised)
- OpenAI API key ([Get one here](https://platform.openai.com/account/api-keys))
+- Home Assistant 2023.8.0 or newer
+- API key from supported providers:
+  - OpenAI ([Get key](https://platform.openai.com/account/api-keys))
+  - Anthropic ([Get key](https://console.anthropic.com/))
 - Python 3.9 or newer
+- Stable internet connection

-## ⚡ Quick Start
+## ⚡ Installation
+
+### HACS Installation (Recommended)  
+1. Open HACS in Home Assistant  
+2. Click on the three dots in the top right corner  
+3. Select "Custom repositories"  
+4. Add `https://github.com/smkrv/ha-text-ai` as Integration  
+5. Click "Add"  
+6. Click on "Integrations" in HACS  
+7. Search for "HA Text AI"  
+8. Click "Download"  
+9. Restart Home Assistant  

 ### Manual Installation
-1. Download the repository
-2. Copy `custom_components/ha_text_ai` to your `custom_components` directory
+1. Download the latest release
+2. Extract and copy `custom_components/ha_text_ai` to your `custom_components` directory
 3. Restart Home Assistant
-4. Add configuration to `configuration.yaml`:
+4. Add configuration via UI or YAML
+
+## ⚙️ Configuration
+
+### Via UI (Recommended)
+1. Go to Settings → Devices & Services
+2. Click "Add Integration"
+3. Search for "HA Text AI"
+4. Follow the configuration steps
+
+### Via YAML
 ```yaml
 ha_text_ai:
-  api_key: !secret openai_api_key
+  api_key: !secret ai_api_key
+  model: gpt-3.5-turbo  # or claude-3-sonnet
+  temperature: 0.7
+  max_tokens: 1000
+  request_interval: 1.0
+  api_endpoint: https://api.openai.com/v1  # optional, for custom endpoints
+  system_prompt: |
+    You are a home automation expert assistant.
+    Focus on practical and efficient solutions.
 ```

-## ⚙️ Configuration Options
-
-| Option | Type | Default | Description |
-|--------|------|---------|-------------|
-| `api_key` | string | Required | Your OpenAI API key |
-| `model` | string | `gpt-3.5-turbo` | AI model to use |
-| `temperature` | float | `0.7` | Response creativity (0-2) |
-| `max_tokens` | integer | `1000` | Maximum response length |
-| `request_interval` | float | `1.0` | Minimum seconds between requests |
-| `api_endpoint` | string | OpenAI default | Custom API endpoint URL |
-
 ## 🛠️ Available Services

 ### ask_question
-Ask the AI assistant a question:
 ```yaml
 service: ha_text_ai.ask_question
 data:
  question: "What's the optimal temperature for sleeping?"
-  model: "gpt-4"  # optional
+  model: "claude-3-sonnet"  # optional
  temperature: 0.5  # optional
  max_tokens: 500  # optional
+  system_prompt: "You are a sleep optimization expert"  # optional
 ```

 ### set_system_prompt
-Configure AI behavior:
 ```yaml
 service: ha_text_ai.set_system_prompt
 data:
-  prompt: "You are a home automation expert focused on energy efficiency"
+  prompt: |
+    You are a home automation expert focused on:
+    1. Energy efficiency
+    2. Comfort optimization
+    3. Security considerations
+    Provide practical, actionable advice.
 ```

 ### clear_history
-Reset conversation history:
 ```yaml
 service: ha_text_ai.clear_history
 ```

 ### get_history
-Retrieve conversation history:
 ```yaml
 service: ha_text_ai.get_history
 data:
  limit: 5  # optional
+  filter_model: "gpt-4"  # optional
 ```

-## 🔧 Practical Examples
+## 📘 FAQ

-### Smart Temperature Management
-```yaml
-automation:
-  trigger:
-    platform: time_pattern
-    hours: "/1"
-  action:
-    service: ha_text_ai.ask_question
-    data:
-      question: >
-        Current temperature is {{ states('sensor.living_room_temperature') }}°C.
-        Should I adjust the thermostat for optimal comfort and energy savings?
-```
+**Q: Which AI providers are supported?**

-### Smart Lighting Assistant
-```yaml
-automation:
-  trigger:
-    platform: state
-    entity_id: binary_sensor.living_room_motion
-    to: 'on'
-  condition:
-    condition: template
-    value_template: "{{ states('sensor.illuminance') | float < 10 }}"
-  action:
-    service: ha_text_ai.ask_question
-    data:
-      question: >
-        Motion detected in living room with low light levels.
-        What's the best lighting scene to set based on the time of day?
-```
+A: Currently OpenAI (GPT models) and Anthropic (Claude models) are supported, with more providers planned.

-## ❗ Common Issues
+**Q: How can I reduce API costs?**

-### API Rate Limits
- Increase `request_interval` if hitting rate limits
- Consider upgrading your OpenAI plan
- Use caching for frequent queries
+A: Use GPT-3.5-Turbo or Claude-3-Sonnet for most queries, implement caching, and optimize token usage.

-### High Token Usage
- Reduce `max_tokens` parameter
- Clear conversation history regularly
- Use focused system prompts
+**Q: Can I use custom models?**

-### Connection Issues
- Check internet connectivity
- Verify API key validity
- Ensure endpoint accessibility
+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.
+
+**Q: How can I reduce API costs?**
+
+A: Use GPT-3.5-Turbo for most queries, implement caching, and optimize token usage.
+
+**Q: Is my data secure?**
+
+A: Yes, API keys are stored securely and data is transmitted via encrypted connections.
+
+**Q: Can I use custom models?**
+
+A: Yes, configure custom endpoints and models via configuration options.

 ## 🤝 Contributing

-Contributions are welcome! Please feel free to submit a Pull Request.
+Contributions welcome! Please read our [Contributing Guide](CONTRIBUTING.md).

 1. Fork the repository
-2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
-3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
-4. Push to the branch (`git push origin feature/AmazingFeature`)
-5. Open a Pull Request
+2. Create feature branch (`git checkout -b feature/Enhancement`)
+3. Commit changes (`git commit -m 'Add Enhancement'`)
+4. Push branch (`git push origin feature/Enhancement`)
+5. Open Pull Request

 ## 📝 License

-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+MIT License - see [LICENSE](LICENSE) for details.

 ---

@@ -162,4 +193,6 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file

 Made with ❤️ 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)
+
 </div>
@@ -1,205 +1,375 @@
-"""The HA text AI integration."""
+"""The HA Text AI integration."""
 import logging
-from typing import Any
-
+from typing import Any, Dict, Optional
+import asyncio
 import voluptuous as vol
+import json
+from datetime import datetime

 from homeassistant.config_entries import ConfigEntry
 from homeassistant.const import CONF_API_KEY
-from homeassistant.core import HomeAssistant, ServiceCall
-import homeassistant.helpers.config_validation as cv
-from homeassistant.exceptions import HomeAssistantError, ConfigEntryNotReady
+from homeassistant.core import HomeAssistant, ServiceCall, callback
+from homeassistant.exceptions import ConfigEntryNotReady, HomeAssistantError
+from homeassistant.helpers import aiohttp_client
+from homeassistant.helpers import config_validation as cv
+from async_timeout import timeout

+from .coordinator import HATextAICoordinator
 from .const import (
    DOMAIN,
    PLATFORMS,
-    SERVICE_ASK_QUESTION,
-    SERVICE_CLEAR_HISTORY,
-    SERVICE_GET_HISTORY,
-    SERVICE_SET_SYSTEM_PROMPT,
    CONF_MODEL,
    CONF_TEMPERATURE,
    CONF_MAX_TOKENS,
    CONF_API_ENDPOINT,
    CONF_REQUEST_INTERVAL,
+    DEFAULT_MODEL,
+    DEFAULT_TEMPERATURE,
+    DEFAULT_MAX_TOKENS,
+    DEFAULT_API_ENDPOINT,
+    DEFAULT_REQUEST_INTERVAL,
+    API_VERSION,
+    API_MODELS_PATH,
+    API_CHAT_PATH,
+    API_TIMEOUT,
+    API_RETRY_COUNT,
+    API_BACKOFF_FACTOR,
+    LOGGER_NAME,
+    STATE_ERROR,
+    STATE_READY,
+    STATE_PROCESSING,
+    STATE_RATE_LIMITED,
+    STATE_MAINTENANCE,
+    STATE_DISCONNECTED,
+    STATE_RETRYING,
+    STATE_QUEUED,
+    STATE_UPDATING,
+    SUPPORTED_MODELS,
+    EVENT_RESPONSE_RECEIVED,
+    EVENT_ERROR_OCCURRED,
+    EVENT_STATE_CHANGED,
 )
-from .coordinator import HATextAICoordinator

-_LOGGER = logging.getLogger(__name__)
+_LOGGER = logging.getLogger(LOGGER_NAME)

+CONFIG_SCHEMA = cv.config_entry_only_config_schema(DOMAIN)

-CONFIG_SCHEMA = vol.Schema(
-    {
-        DOMAIN: vol.Schema(
-            {
-                vol.Required(CONF_API_KEY): cv.string,
-                vol.Optional(CONF_MODEL, default="gpt-3.5-turbo"): cv.string,
-                vol.Optional(CONF_TEMPERATURE, default=0.7): vol.Coerce(float),
-                vol.Optional(CONF_MAX_TOKENS, default=1000): vol.Coerce(int),
-                vol.Optional(CONF_REQUEST_INTERVAL, default=1.0): vol.Coerce(float),
-                vol.Optional(CONF_API_ENDPOINT): cv.string,
-            }
-        )
-    },
-    extra=vol.ALLOW_EXTRA,
-)
+# Service validation schemas
+SERVICE_SCHEMA_ASK_QUESTION = vol.Schema({
+    vol.Required("question"): cv.string,
+    vol.Optional("system_prompt"): cv.string,
+    vol.Optional("model"): vol.In(SUPPORTED_MODELS),
+    vol.Optional("temperature"): vol.All(
+        vol.Coerce(float), vol.Range(min=0, max=2)
+    ),
+    vol.Optional("max_tokens"): vol.All(
+        vol.Coerce(int), vol.Range(min=1, max=4096)
+    ),
+    vol.Optional("priority"): vol.Boolean,
+})
+
+SERVICE_SCHEMA_GET_HISTORY = vol.Schema({
+    vol.Optional("limit", default=10): vol.All(
+        vol.Coerce(int), vol.Range(min=1, max=100)
+    ),
+    vol.Optional("filter_model"): vol.In(SUPPORTED_MODELS),
+    vol.Optional("start_date"): cv.datetime,
+    vol.Optional("include_metadata"): vol.Boolean,
+})
+
+SERVICE_SCHEMA_SET_SYSTEM_PROMPT = vol.Schema({
+    vol.Required("prompt"): cv.string,
+})
+
+async def async_check_api(session, endpoint: str, headers: dict, is_anthropic: bool = False) -> bool:
+    """Check API availability for different providers."""
+    try:
+        if is_anthropic:
+            check_url = f"{endpoint}/v1/models"
+        else:
+            check_url = f"{endpoint}/{API_VERSION}/{API_MODELS_PATH}"
+
+        async with timeout(API_TIMEOUT):
+            async with session.get(check_url, headers=headers) as response:
+                if response.status == 200:
+                    return True
+                elif response.status == 401:
+                    raise ConfigEntryNotReady("Invalid API key")
+                elif response.status == 429:
+                    _LOGGER.warning("Rate limit exceeded during API check")
+                    return False
+                else:
+                    _LOGGER.error("API check failed with status: %d", response.status)
+                    return False
+    except Exception as ex:
+        _LOGGER.error("API check error: %s", str(ex))
+        return False

 async def async_setup(hass: HomeAssistant, config: dict[str, Any]) -> bool:
-    """Set up the HA text AI component."""
-    hass.data.setdefault(DOMAIN, {})
-
-    async def async_ask_question(call: ServiceCall) -> None:
-        """Handle the ask_question service call."""
-        if not hass.data[DOMAIN]:
-            raise HomeAssistantError("No AI Text integration configured")
-
-        coordinator = next(iter(hass.data[DOMAIN].values()))
-        question = call.data["question"]
-
-
-        original_params = {
-            "model": coordinator.model,
-            "temperature": coordinator.temperature,
-            "max_tokens": coordinator.max_tokens
+    """Set up the HA Text AI component."""
+    hass.data.setdefault(DOMAIN, {
+        "coordinators": {},
+        "metrics": {
+            "total_requests": 0,
+            "total_tokens": 0,
+            "errors": {},
+            "model_usage": {},
        }
-
-        try:
-
-            if "model" in call.data:
-                coordinator.model = call.data["model"]
-            if "temperature" in call.data:
-                coordinator.temperature = call.data["temperature"]
-            if "max_tokens" in call.data:
-                coordinator.max_tokens = call.data["max_tokens"]
-
-            await coordinator.async_ask_question(question)
-        except Exception as ex:
-            _LOGGER.error("Error asking question: %s", str(ex))
-            raise HomeAssistantError(f"Failed to ask question: {str(ex)}") from ex
-        finally:
-
-            coordinator.model = original_params["model"]
-            coordinator.temperature = original_params["temperature"]
-            coordinator.max_tokens = original_params["max_tokens"]
-
-    async def async_clear_history(call: ServiceCall) -> None:
-        """Handle the clear_history service call."""
-        if not hass.data[DOMAIN]:
-            raise HomeAssistantError("No AI Text integration configured")
-
-        coordinator = next(iter(hass.data[DOMAIN].values()))
-        coordinator._responses.clear()
-        await coordinator.async_refresh()
-
-    async def async_get_history(call: ServiceCall) -> dict[str, list]:
-        """Handle the get_history service call."""
-        if not hass.data[DOMAIN]:
-            raise HomeAssistantError("No AI Text integration configured")
-
-        coordinator = next(iter(hass.data[DOMAIN].values()))
-        if not coordinator._responses:
-            return {"history": []}
-
-        limit = call.data.get("limit", 10)
-        history = list(coordinator._responses.items())
-        limited_history = history[-limit:] if len(history) > limit else history
-
-        return {
-            "history": [
-                {"question": q, "response": r} for q, r in limited_history
-            ]
-        }
-
-    async def async_set_system_prompt(call: ServiceCall) -> None:
-        """Handle the set_system_prompt service call."""
-        if not hass.data[DOMAIN]:
-            raise HomeAssistantError("No AI Text integration configured")
-
-        coordinator = next(iter(hass.data[DOMAIN].values()))
-        coordinator.system_prompt = call.data["prompt"]
-
-
-    hass.services.async_register(
-        DOMAIN,
-        SERVICE_ASK_QUESTION,
-        async_ask_question,
-        schema=vol.Schema({
-            vol.Required("question"): cv.string,
-            vol.Optional("model"): cv.string,
-            vol.Optional("temperature"): vol.All(
-                vol.Coerce(float), vol.Range(min=0, max=2)
-            ),
-            vol.Optional("max_tokens"): vol.All(
-                vol.Coerce(int), vol.Range(min=1, max=4096)
-            ),
-        })
-    )
-
-    hass.services.async_register(
-        DOMAIN,
-        SERVICE_CLEAR_HISTORY,
-        async_clear_history,
-        schema=vol.Schema({})
-    )
-
-    hass.services.async_register(
-        DOMAIN,
-        SERVICE_GET_HISTORY,
-        async_get_history,
-        schema=vol.Schema({
-            vol.Optional("limit", default=10): vol.All(
-                vol.Coerce(int), vol.Range(min=1)
-            ),
-        })
-    )
-
-    hass.services.async_register(
-        DOMAIN,
-        SERVICE_SET_SYSTEM_PROMPT,
-        async_set_system_prompt,
-        schema=vol.Schema({
-            vol.Required("prompt"): cv.string,
-        })
-    )
-
+    })
    return True

 async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
-    """Set up HA text AI from a config entry."""
+    """Set up HA Text AI from a config entry."""
    try:
+        session = aiohttp_client.async_get_clientsession(hass)
+
+        # Determine API type based on model
+        model = entry.data.get(CONF_MODEL, DEFAULT_MODEL)
+        is_anthropic = any(model.startswith("claude") for model in SUPPORTED_MODELS)
+
+        api_key = entry.data[CONF_API_KEY]
+        endpoint = entry.data.get(CONF_API_ENDPOINT, DEFAULT_API_ENDPOINT).rstrip('/')
+
+        # Configure headers based on API type
+        headers = {
+            "Content-Type": "application/json",
+            "Accept": "application/json"
+        }
+
+        if is_anthropic:
+            headers["x-api-key"] = api_key
+            headers["anthropic-version"] = "2023-06-01"
+        else:
+            headers["Authorization"] = f"Bearer {api_key}"
+
+        # Check API with retries
+        for attempt in range(API_RETRY_COUNT):
+            if await async_check_api(session, endpoint, headers, is_anthropic):
+                break
+            if attempt < API_RETRY_COUNT - 1:
+                delay = API_BACKOFF_FACTOR * (2 ** attempt)
+                await asyncio.sleep(delay)
+            else:
+                raise ConfigEntryNotReady("Failed to connect to API")
+
+        # Create and initialize coordinator
        coordinator = HATextAICoordinator(
            hass,
-            api_key=entry.data[CONF_API_KEY],
-            endpoint=entry.data.get(CONF_API_ENDPOINT),
-            model=entry.data.get(CONF_MODEL),
-            temperature=entry.data.get(CONF_TEMPERATURE),
-            max_tokens=entry.data.get(CONF_MAX_TOKENS),
-            request_interval=entry.data.get(CONF_REQUEST_INTERVAL),
+            api_key=api_key,
+            endpoint=endpoint,
+            model=model,
+            temperature=entry.data.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE),
+            max_tokens=entry.data.get(CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS),
+            request_interval=entry.data.get(CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL),
+            session=session,
+            is_anthropic=is_anthropic
        )

+        # Initialize coordinator
        await coordinator.async_config_entry_first_refresh()
+
+        # Check coordinator status
+        if coordinator.endpoint_status == "auth_error":
+            raise ConfigEntryNotReady("Authentication failed")
+        elif coordinator.endpoint_status == "rate_limited":
+            _LOGGER.warning("API rate limited during setup")
+        elif coordinator.endpoint_status == "maintenance":
+            raise ConfigEntryNotReady("API is in maintenance mode")
+        elif coordinator.endpoint_status == "error":
+            raise ConfigEntryNotReady("API error during setup")
+        elif not coordinator.last_update_success:
+            raise ConfigEntryNotReady("Failed to initialize coordinator")
+
        hass.data[DOMAIN][entry.entry_id] = coordinator

-        return await hass.config_entries.async_forward_entry_setups(entry, PLATFORMS)
+        await hass.config_entries.async_forward_entry_setups(entry, PLATFORMS)
+
+        # Register event handlers
+        @callback
+        def handle_state_change(event):
+            """Handle state changes."""
+            if event.data.get("entity_id").startswith(f"{DOMAIN}."):
+                _LOGGER.debug("State changed: %s", event.data)
+
+        hass.bus.async_listen(EVENT_STATE_CHANGED, handle_state_change)
+
+        # Register services
+        async def async_ask_question(call: ServiceCall) -> None:
+            """Handle the ask_question service call."""
+            question = call.data.get("question", "")
+            if not question:
+                _LOGGER.error("No question provided in service call")
+                return
+
+            request_params = {}
+            for param in ["system_prompt", "model", "temperature", "max_tokens"]:
+                if param in call.data:
+                    request_params[param] = call.data[param]
+
+            try:
+                await coordinator.async_ask_question(question, **request_params)
+            except Exception as err:
+                _LOGGER.error("Error asking question: %s", str(err))
+
+        async def async_clear_history(call: ServiceCall) -> None:
+            """Handle the clear_history service call."""
+            try:
+                coordinator._responses.clear()
+                await coordinator.async_refresh()
+                _LOGGER.info("History cleared successfully")
+            except Exception as err:
+                _LOGGER.error("Error clearing history: %s", str(err))
+
+        async def async_get_history(call: ServiceCall) -> dict:
+            """Handle the get_history service call."""
+            try:
+                limit = min(int(call.data.get("limit", 10)), 100)
+                filter_model = str(call.data.get("filter_model", ""))
+                start_date = call.data.get("start_date")
+                include_metadata = call.data.get("include_metadata", False)
+
+                responses = coordinator._responses
+                metrics = {
+                    "total_requests": coordinator.request_count,
+                    "total_tokens": coordinator.tokens_used,
+                    "api_version": coordinator.api_version,
+                    "endpoint_status": coordinator.endpoint_status,
+                    "error_count": coordinator.error_count
+                }
+
+                filtered_responses = responses.copy()
+
+                if filter_model:
+                    filtered_responses = {
+                        k: v for k, v in filtered_responses.items()
+                        if v.get("model") == filter_model
+                    }
+
+                if start_date:
+                    filtered_responses = {
+                        k: v for k, v in filtered_responses.items()
+                        if v.get("timestamp") >= start_date
+                    }
+
+                if not include_metadata:
+                    filtered_responses = {
+                        k: {
+                            "question": v["question"],
+                            "response": v["response"],
+                            "timestamp": v["timestamp"]
+                        } for k, v in filtered_responses.items()
+                    }
+
+                sorted_responses = dict(
+                    sorted(
+                        filtered_responses.items(),
+                        key=lambda x: x[1]["timestamp"],
+                        reverse=True
+                    )[:limit]
+                )
+
+                return {
+                    "metrics": metrics,
+                    "responses": sorted_responses
+                }
+            except Exception as err:
+                _LOGGER.error("Error getting history: %s", str(err))
+                return {}
+
+        async def async_set_system_prompt(call: ServiceCall) -> None:
+            """Handle the set_system_prompt service call."""
+            try:
+                prompt = str(call.data.get("prompt", "")).strip()
+                if prompt:
+                    coordinator.system_prompt = prompt
+                    _LOGGER.info("System prompt updated successfully")
+                else:
+                    _LOGGER.error("Empty prompt provided")
+            except Exception as err:
+                _LOGGER.error("Error setting system prompt: %s", str(err))
+
+        # Register services with validation
+        hass.services.async_register(
+            DOMAIN,
+            "ask_question",
+            async_ask_question,
+            schema=SERVICE_SCHEMA_ASK_QUESTION
+        )
+
+        hass.services.async_register(
+            DOMAIN,
+            "clear_history",
+            async_clear_history
+        )
+
+        hass.services.async_register(
+            DOMAIN,
+            "get_history",
+            async_get_history,
+            schema=SERVICE_SCHEMA_GET_HISTORY
+        )
+
+        hass.services.async_register(
+            DOMAIN,
+            "set_system_prompt",
+            async_set_system_prompt,
+            schema=SERVICE_SCHEMA_SET_SYSTEM_PROMPT
+        )
+
+        _LOGGER.info(
+            "Successfully set up HA Text AI with model: %s",
+            entry.data.get(CONF_MODEL, DEFAULT_MODEL)
+        )
+
+        return True
+
    except Exception as ex:
-        raise ConfigEntryNotReady(f"Failed to setup entry: {str(ex)}") from ex
+        _LOGGER.exception("Setup error: %s", str(ex))
+        raise ConfigEntryNotReady from ex

 async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
    """Unload a config entry."""
-    unload_ok = await hass.config_entries.async_unload_platforms(entry, PLATFORMS)
-    if unload_ok:
-        hass.data[DOMAIN].pop(entry.entry_id)
+    try:
+        coordinator = hass.data[DOMAIN].get(entry.entry_id)
+        if coordinator:
+            # Clear queue and history
+            coordinator._responses.clear()
+            while not coordinator._question_queue.empty():
+                try:
+                    coordinator._question_queue.get_nowait()
+                    coordinator._question_queue.task_done()
+                except Exception:
+                    pass

+            # Close connection
+            await coordinator.async_shutdown()

-        if not hass.data[DOMAIN]:
-            services = [
-                SERVICE_ASK_QUESTION,
-                SERVICE_CLEAR_HISTORY,
-                SERVICE_GET_HISTORY,
-                SERVICE_SET_SYSTEM_PROMPT
-            ]
-            for service in services:
-                if service in hass.services.async_services().get(DOMAIN, {}):
-                    hass.services.async_remove(DOMAIN, service)
+        # Remove services
+        for service in ["ask_question", "clear_history", "get_history", "set_system_prompt"]:
+            hass.services.async_remove(DOMAIN, service)

-    return unload_ok
+        unload_ok = await hass.config_entries.async_unload_platforms(entry, PLATFORMS)
+        if unload_ok:
+            hass.data[DOMAIN].pop(entry.entry_id)
+
+        return unload_ok
+
+    except Exception as ex:
+        _LOGGER.exception("Error unloading entry: %s", str(ex))
+        return False
+
+async def async_migrate_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
+    """Migrate old entry."""
+    _LOGGER.debug("Migrating from version %s", entry.version)
+
+    if entry.version == 1:
+        new = {**entry.data}
+
+        # Migrate settings
+        if CONF_MODEL in new and new[CONF_MODEL] not in SUPPORTED_MODELS:
+            new[CONF_MODEL] = DEFAULT_MODEL
+
+        entry.version = 2
+        hass.config_entries.async_update_entry(entry, data=new)
+
+    return True
@@ -1,12 +1,17 @@
 """Config flow for HA text AI integration."""
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Tuple
 import voluptuous as vol
+import asyncio
+import aiohttp
+from async_timeout import timeout
+from urllib.parse import urlparse, urljoin

 from homeassistant import config_entries
 from homeassistant.const import CONF_API_KEY
 import homeassistant.helpers.config_validation as cv
 from homeassistant.core import callback
-import openai
+from homeassistant.data_entry_flow import FlowResult
+from homeassistant.helpers.aiohttp_client import async_get_clientsession

 from .const import (
    DOMAIN,
@@ -20,25 +25,136 @@ from .const import (
    DEFAULT_MAX_TOKENS,
    DEFAULT_API_ENDPOINT,
    DEFAULT_REQUEST_INTERVAL,
+    MIN_TEMPERATURE,
+    MAX_TEMPERATURE,
+    MIN_MAX_TOKENS,
+    MAX_MAX_TOKENS,
+    MIN_REQUEST_INTERVAL,
+    API_VERSION,
+    API_MODELS_PATH,
+    ERROR_INVALID_API_KEY,
+    ERROR_CANNOT_CONNECT,
+    ERROR_UNKNOWN,
+    ERROR_INVALID_MODEL,
+    ERROR_RATE_LIMIT,
+    ERROR_API_ERROR,
+    ERROR_TIMEOUT,
 )

-STEP_USER_DATA_SCHEMA = vol.Schema({
-    vol.Required(CONF_API_KEY): str,
-    vol.Optional(CONF_MODEL, default=DEFAULT_MODEL): str,
-    vol.Optional(
-        CONF_TEMPERATURE,
-        default=DEFAULT_TEMPERATURE
-    ): vol.All(vol.Coerce(float), vol.Range(min=0, max=2)),
-    vol.Optional(
-        CONF_MAX_TOKENS,
-        default=DEFAULT_MAX_TOKENS
-    ): vol.All(vol.Coerce(int), vol.Range(min=1, max=4096)),
-    vol.Optional(CONF_API_ENDPOINT, default=DEFAULT_API_ENDPOINT): str,
-    vol.Optional(
-        CONF_REQUEST_INTERVAL,
-        default=DEFAULT_REQUEST_INTERVAL
-    ): vol.All(vol.Coerce(float), vol.Range(min=0.1)),
-})
+import logging
+_LOGGER = logging.getLogger(__name__)
+
+STEP_USER_DATA_SCHEMA = vol.Schema(
+    {
+        vol.Required(CONF_API_KEY): cv.string,
+        vol.Optional(CONF_MODEL, default=DEFAULT_MODEL): cv.string,
+        vol.Optional(
+            CONF_TEMPERATURE,
+            default=DEFAULT_TEMPERATURE
+        ): vol.All(
+            vol.Coerce(float),
+            vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)
+        ),
+        vol.Optional(
+            CONF_MAX_TOKENS,
+            default=DEFAULT_MAX_TOKENS
+        ): vol.All(
+            vol.Coerce(int),
+            vol.Range(min=MIN_MAX_TOKENS, max=MAX_MAX_TOKENS)
+        ),
+        vol.Optional(
+            CONF_API_ENDPOINT,
+            default=DEFAULT_API_ENDPOINT
+        ): cv.string,
+        vol.Optional(
+            CONF_REQUEST_INTERVAL,
+            default=DEFAULT_REQUEST_INTERVAL
+        ): vol.All(
+            vol.Coerce(float),
+            vol.Range(min=MIN_REQUEST_INTERVAL)
+        ),
+    }
+)
+
+async def validate_api_connection(
+    hass,
+    api_key: str,
+    endpoint: str,
+    model: str,
+    retry_count: int = 3,
+    retry_delay: float = 1.0
+) -> Tuple[bool, str, list]:
+    """Validate API connection with retry logic."""
+    session = async_get_clientsession(hass)
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json; charset=utf-8",
+        "Accept": "application/json; charset=utf-8",
+        "Accept-Charset": "utf-8"
+    }
+
+    base_url = endpoint.rstrip('/')
+    if not base_url.endswith(f"/{API_VERSION}"):
+        base_url = f"{base_url}/{API_VERSION}"
+
+    models_url = f"{base_url}/{API_MODELS_PATH}"
+    _LOGGER.debug("Attempting to connect to: %s", models_url)
+
+    for attempt in range(retry_count):
+        try:
+            async with timeout(10):
+                async with session.get(models_url, headers=headers) as response:
+                    if response.status == 200:
+                        data = await response.json()
+                        model_ids = [m["id"] for m in data.get("data", [])]
+
+                        _LOGGER.debug("Available models: %s", ", ".join(model_ids))
+
+                        if model not in model_ids:
+                            _LOGGER.warning(
+                                "Model %s not found in available models: %s",
+                                model,
+                                ", ".join(model_ids)
+                            )
+                            return False, ERROR_INVALID_MODEL, model_ids
+                        return True, "", model_ids
+
+                    elif response.status == 401:
+                        _LOGGER.error("Authentication failed")
+                        return False, ERROR_INVALID_API_KEY, []
+
+                    elif response.status == 429:
+                        _LOGGER.error("Rate limit exceeded")
+                        return False, ERROR_RATE_LIMIT, []
+
+                    else:
+                        response_text = await response.text()
+                        _LOGGER.error(
+                            "API error: %s - %s",
+                            response.status,
+                            response_text
+                        )
+                        return False, ERROR_API_ERROR, []
+
+        except asyncio.TimeoutError:
+            _LOGGER.warning(
+                "Timeout during API validation (attempt %d/%d)",
+                attempt + 1,
+                retry_count
+            )
+            if attempt == retry_count - 1:
+                return False, ERROR_TIMEOUT, []
+            await asyncio.sleep(retry_delay)
+
+        except aiohttp.ClientError as err:
+            _LOGGER.error("Connection error: %s", str(err))
+            return False, ERROR_CANNOT_CONNECT, []
+
+        except Exception as err:
+            _LOGGER.exception("Unexpected error during validation: %s", str(err))
+            return False, ERROR_UNKNOWN, []
+
+    return False, ERROR_UNKNOWN, []

 class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
    """Handle a config flow for HA text AI."""
@@ -48,41 +164,69 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
    async def async_step_user(
        self,
        user_input: Optional[Dict[str, Any]] = None
-    ) -> Dict[str, Any]:
+    ) -> FlowResult:
        """Handle the initial step."""
        errors: Dict[str, str] = {}

        if user_input is not None:
            try:
- 
-                client = openai.OpenAI(
-                    api_key=user_input[CONF_API_KEY],
-                    base_url=user_input.get(CONF_API_ENDPOINT, DEFAULT_API_ENDPOINT)
-                )
-                await self.hass.async_add_executor_job(
-                    client.models.list
+                endpoint = user_input.get(CONF_API_ENDPOINT, DEFAULT_API_ENDPOINT)
+                try:
+                    result = urlparse(endpoint)
+                    if not all([result.scheme, result.netloc]):
+                        errors["base"] = "invalid_url_format"
+                        return self.async_show_form(
+                            step_id="user",
+                            data_schema=STEP_USER_DATA_SCHEMA,
+                            errors=errors
+                        )
+                except Exception as e:
+                    _LOGGER.error("URL parsing error: %s", str(e))
+                    errors["base"] = "invalid_url_format"
+                    return self.async_show_form(
+                        step_id="user",
+                        data_schema=STEP_USER_DATA_SCHEMA,
+                        errors=errors
+                    )
+
+                validated_input = STEP_USER_DATA_SCHEMA(user_input)
+
+                is_valid, error_code, available_models = await validate_api_connection(
+                    self.hass,
+                    validated_input[CONF_API_KEY],
+                    endpoint,
+                    validated_input[CONF_MODEL]
                )

+                if is_valid:
+                    await self.async_set_unique_id(validated_input[CONF_API_KEY])
+                    self._abort_if_unique_id_configured()

-                await self.async_set_unique_id(user_input[CONF_API_KEY])
-                self._abort_if_unique_id_configured()
+                    return self.async_create_entry(
+                        title="HA Text AI",
+                        data=validated_input
+                    )

-                return self.async_create_entry(
-                    title="HA text AI",
-                    data=user_input
-                )
+                errors["base"] = error_code
+                if error_code == ERROR_INVALID_MODEL:
+                    _LOGGER.warning(
+                        "Selected model %s not found in available models: %s",
+                        validated_input[CONF_MODEL],
+                        ", ".join(available_models)
+                    )

-            except openai.AuthenticationError:
-                errors["base"] = "invalid_auth"
-            except openai.APIError:
-                errors["base"] = "cannot_connect"
-            except Exception:  # pylint: disable=broad-except
-                errors["base"] = "unknown"
+            except vol.Invalid as err:
+                _LOGGER.error("Validation error: %s", str(err))
+                errors["base"] = "invalid_input"

        return self.async_show_form(
            step_id="user",
            data_schema=STEP_USER_DATA_SCHEMA,
            errors=errors,
+            description_placeholders={
+                "default_model": DEFAULT_MODEL,
+                "default_endpoint": DEFAULT_API_ENDPOINT,
+            }
        )

    @staticmethod
@@ -103,7 +247,7 @@ class OptionsFlowHandler(config_entries.OptionsFlow):
    async def async_step_init(
        self,
        user_input: Optional[Dict[str, Any]] = None
-    ) -> Dict[str, Any]:
+    ) -> FlowResult:
        """Handle options flow."""
        if user_input is not None:
            return self.async_create_entry(title="", data=user_input)
@@ -114,22 +258,28 @@ class OptionsFlowHandler(config_entries.OptionsFlow):
                default=self.config_entry.options.get(
                    CONF_TEMPERATURE, DEFAULT_TEMPERATURE
                ),
-                description="Temperature for response generation (0-2)",
-            ): vol.All(vol.Coerce(float), vol.Range(min=0, max=2)),
+            ): vol.All(
+                vol.Coerce(float),
+                vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)
+            ),
            vol.Optional(
                CONF_MAX_TOKENS,
                default=self.config_entry.options.get(
                    CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS
                ),
-                description="Maximum tokens in response (1-4096)",
-            ): vol.All(vol.Coerce(int), vol.Range(min=1, max=4096)),
+            ): vol.All(
+                vol.Coerce(int),
+                vol.Range(min=MIN_MAX_TOKENS, max=MAX_MAX_TOKENS)
+            ),
            vol.Optional(
                CONF_REQUEST_INTERVAL,
                default=self.config_entry.options.get(
                    CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL
                ),
-                description="Minimum time between API requests (seconds)",
-            ): vol.All(vol.Coerce(float), vol.Range(min=0.1)),
+            ): vol.All(
+                vol.Coerce(float),
+                vol.Range(min=MIN_REQUEST_INTERVAL)
+            ),
        })

        return self.async_show_form(
@@ -2,7 +2,7 @@
 from typing import Final
 from homeassistant.const import Platform

-# Domain
+# Domain and platforms
 DOMAIN: Final = "ha_text_ai"
 PLATFORMS: Final = [Platform.SENSOR]

@@ -13,12 +13,27 @@ CONF_MAX_TOKENS: Final = "max_tokens"
 CONF_API_ENDPOINT: Final = "api_endpoint"
 CONF_REQUEST_INTERVAL: Final = "request_interval"

+# Model constants
+SUPPORTED_MODELS: Final = [
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-16k",
+    "gpt-4",
+    "gpt-4-32k",
+    "gpt-4-1106-preview",
+    "claude-3-sonnet",
+    "claude-3-opus"
+]
+
 # Default values
 DEFAULT_MODEL: Final = "gpt-3.5-turbo"
-DEFAULT_TEMPERATURE: Final = 0.7
+DEFAULT_TEMPERATURE: Final = 0.1
 DEFAULT_MAX_TOKENS: Final = 1000
-DEFAULT_API_ENDPOINT: Final = "https://api.openai.com/v1"
+DEFAULT_API_ENDPOINT: Final = "https://api.openai.com"
 DEFAULT_REQUEST_INTERVAL: Final = 1.0
+DEFAULT_TIMEOUT: Final = 30
+DEFAULT_QUEUE_SIZE: Final = 100
+DEFAULT_HISTORY_LIMIT: Final = 50
+DEFAULT_RETRY_COUNT: Final = 3

 # Parameter constraints
 MIN_TEMPERATURE: Final = 0.0
@@ -26,6 +41,25 @@ MAX_TEMPERATURE: Final = 2.0
 MIN_MAX_TOKENS: Final = 1
 MAX_MAX_TOKENS: Final = 4096
 MIN_REQUEST_INTERVAL: Final = 0.1
+MIN_TIMEOUT: Final = 5
+MAX_TIMEOUT: Final = 120
+MAX_PROMPT_LENGTH: Final = 1000
+MAX_HISTORY_LIMIT: Final = 100
+
+# API constants
+API_VERSION: Final = "v1"
+API_MODELS_PATH: Final = "models"
+API_CHAT_PATH: Final = "chat/completions"
+API_TIMEOUT: Final = 30
+API_RETRY_COUNT: Final = 3
+API_BACKOFF_FACTOR: Final = 1.5
+API_MAX_RETRIES: Final = 3
+
+# History constants
+HISTORY_FILTER_MODEL: Final = "filter_model"
+HISTORY_FILTER_DATE: Final = "start_date"
+HISTORY_SORT_ORDER: Final = "sort_order"
+HISTORY_INCLUDE_METADATA: Final = "include_metadata"

 # Service names
 SERVICE_ASK_QUESTION: Final = "ask_question"
@@ -43,11 +77,37 @@ SERVICE_SET_SYSTEM_PROMPT_DESCRIPTION: Final = "Set system prompt for AI model"
 ATTR_QUESTION: Final = "question"
 ATTR_RESPONSE: Final = "response"
 ATTR_LAST_UPDATED: Final = "last_updated"
+ATTR_MODEL: Final = "model"
+ATTR_TEMPERATURE: Final = "temperature"
+ATTR_MAX_TOKENS: Final = "max_tokens"
+ATTR_TOTAL_RESPONSES: Final = "total_responses"
+ATTR_SYSTEM_PROMPT: Final = "system_prompt"
+ATTR_RESPONSE_TIME: Final = "response_time"
+ATTR_QUEUE_SIZE: Final = "queue_size"
+ATTR_API_STATUS: Final = "api_status"
+ATTR_ERROR_COUNT: Final = "error_count"
+ATTR_LAST_ERROR: Final = "last_error"
+ATTR_API_VERSION: Final = "api_version"
+ATTR_ENDPOINT_STATUS: Final = "endpoint_status"
+ATTR_REQUEST_COUNT: Final = "request_count"
+ATTR_TOKENS_USED: Final = "tokens_used"
+ATTR_RETRY_COUNT: Final = "retry_count"
+ATTR_QUEUE_POSITION: Final = "queue_position"
+ATTR_ESTIMATED_WAIT: Final = "estimated_wait"

 # Error messages
 ERROR_INVALID_API_KEY: Final = "invalid_api_key"
 ERROR_CANNOT_CONNECT: Final = "cannot_connect"
 ERROR_UNKNOWN: Final = "unknown_error"
+ERROR_INVALID_MODEL: Final = "invalid_model"
+ERROR_RATE_LIMIT: Final = "rate_limit_exceeded"
+ERROR_CONTEXT_LENGTH: Final = "context_length_exceeded"
+ERROR_API_ERROR: Final = "api_error"
+ERROR_TIMEOUT: Final = "timeout_error"
+ERROR_QUEUE_FULL: Final = "queue_full"
+ERROR_INVALID_PROMPT: Final = "invalid_prompt"
+ERROR_INVALID_PARAMETERS: Final = "invalid_parameters"
+ERROR_SERVICE_UNAVAILABLE: Final = "service_unavailable"

 # Configuration descriptions
 CONF_MODEL_DESCRIPTION: Final = "AI model to use for responses"
@@ -56,6 +116,69 @@ CONF_MAX_TOKENS_DESCRIPTION: Final = "Maximum tokens in response (1-4096)"
 CONF_API_ENDPOINT_DESCRIPTION: Final = "API endpoint URL"
 CONF_REQUEST_INTERVAL_DESCRIPTION: Final = "Minimum time between API requests (seconds)"

+# Entity attributes descriptions
+ATTR_QUESTION_DESCRIPTION: Final = "Last question asked"
+ATTR_RESPONSE_DESCRIPTION: Final = "Last response received"
+ATTR_LAST_UPDATED_DESCRIPTION: Final = "Time of last update"
+ATTR_MODEL_DESCRIPTION: Final = "Current AI model in use"
+ATTR_TEMPERATURE_DESCRIPTION: Final = "Current temperature setting"
+ATTR_MAX_TOKENS_DESCRIPTION: Final = "Current max tokens setting"
+ATTR_TOTAL_RESPONSES_DESCRIPTION: Final = "Total number of responses"
+ATTR_SYSTEM_PROMPT_DESCRIPTION: Final = "Current system prompt"
+ATTR_RESPONSE_TIME_DESCRIPTION: Final = "Time taken for last response"
+ATTR_QUEUE_SIZE_DESCRIPTION: Final = "Current size of question queue"
+ATTR_API_STATUS_DESCRIPTION: Final = "Current API connection status"
+ATTR_ERROR_COUNT_DESCRIPTION: Final = "Total number of errors"
+ATTR_LAST_ERROR_DESCRIPTION: Final = "Last error message"
+ATTR_API_VERSION_DESCRIPTION: Final = "Current API version"
+ATTR_ENDPOINT_STATUS_DESCRIPTION: Final = "Current endpoint status"
+ATTR_REQUEST_COUNT_DESCRIPTION: Final = "Total number of API requests"
+ATTR_TOKENS_USED_DESCRIPTION: Final = "Total tokens used"
+
 # Entity attributes
 ENTITY_NAME: Final = "HA Text AI"
 ENTITY_ICON: Final = "mdi:robot"
+ENTITY_ICON_ERROR: Final = "mdi:robot-dead"
+ENTITY_ICON_PROCESSING: Final = "mdi:robot-excited"
+ENTITY_ICON_OFFLINE: Final = "mdi:robot-off"
+ENTITY_ICON_QUEUE: Final = "mdi:robot-confused"
+
+# Translation keys
+TRANSLATION_KEY_CONFIG: Final = "config"
+TRANSLATION_KEY_OPTIONS: Final = "options"
+TRANSLATION_KEY_ERROR: Final = "error"
+TRANSLATION_KEY_STATE: Final = "state"
+TRANSLATION_KEY_SERVICES: Final = "services"
+
+# State attributes
+STATE_READY: Final = "ready"
+STATE_PROCESSING: Final = "processing"
+STATE_ERROR: Final = "error"
+STATE_DISCONNECTED: Final = "disconnected"
+STATE_RATE_LIMITED: Final = "rate_limited"
+STATE_INITIALIZING: Final = "initializing"
+STATE_MAINTENANCE: Final = "maintenance"
+STATE_RETRYING: Final = "retrying"
+STATE_QUEUED: Final = "queued"
+STATE_UPDATING: Final = "updating"
+
+# Logging
+LOGGER_NAME: Final = "custom_components.ha_text_ai"
+LOG_LEVEL_DEFAULT: Final = "INFO"
+
+# Queue constants
+QUEUE_TIMEOUT: Final = 5
+QUEUE_MAX_SIZE: Final = 100
+
+# Service schema constants
+SCHEMA_QUESTION: Final = "question"
+SCHEMA_MODEL: Final = "model"
+SCHEMA_TEMPERATURE: Final = "temperature"
+SCHEMA_MAX_TOKENS: Final = "max_tokens"
+SCHEMA_PROMPT: Final = "prompt"
+SCHEMA_LIMIT: Final = "limit"
+
+# Event names
+EVENT_RESPONSE_RECEIVED: Final = f"{DOMAIN}_response_received"
+EVENT_ERROR_OCCURRED: Final = f"{DOMAIN}_error_occurred"
+EVENT_STATE_CHANGED: Final = f"{DOMAIN}_state_changed"
@@ -1,51 +1,25 @@
-"""The HA Text AI integration."""
-from homeassistant.config_entries import ConfigEntry
-from homeassistant.core import HomeAssistant
-from homeassistant.exceptions import ConfigEntryNotReady
-from .const import DOMAIN, PLATFORMS
-from .coordinator import HATextAICoordinator
-
-async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
-    """Set up HA Text AI from a config entry."""
-    try:
-        coordinator = HATextAICoordinator(
-            hass,
-            api_key=entry.data["api_key"],
-            endpoint=entry.data.get("api_endpoint", "https://api.openai.com/v1"),
-            model=entry.data.get("model", "gpt-3.5-turbo"),
-            temperature=entry.data.get("temperature", 0.7),
-            max_tokens=entry.data.get("max_tokens", 1000),
-            request_interval=entry.data.get("request_interval", 1.0),
-        )
-
-        await coordinator.async_config_entry_first_refresh()
-
-        hass.data.setdefault(DOMAIN, {})
-        hass.data[DOMAIN][entry.entry_id] = coordinator
-
-        return await hass.config_entries.async_forward_entry_setups(entry, PLATFORMS)
-    except Exception as ex:
-        raise ConfigEntryNotReady(f"Failed to setup entry: {str(ex)}") from ex
-
-async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
-    """Unload a config entry."""
-    unload_ok = await hass.config_entries.async_unload_platforms(entry, PLATFORMS)
-    if unload_ok:
-        hass.data[DOMAIN].pop(entry.entry_id)
-    return unload_ok
-
 """Data coordinator for HA text AI."""
 import asyncio
 import logging
 from datetime import timedelta
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, List
+import time

-import openai
+from openai import AsyncOpenAI, APIError, AuthenticationError, RateLimitError
+from anthropic import AsyncAnthropic
 from homeassistant.core import HomeAssistant
 from homeassistant.helpers.update_coordinator import DataUpdateCoordinator
-from homeassistant.exceptions import ConfigEntryAuthFailed
+from homeassistant.util import dt as dt_util
+import async_timeout

-from .const import DOMAIN
+from .const import (
+    DOMAIN,
+    DEFAULT_TIMEOUT,
+    MAX_QUEUE_SIZE,
+    MAX_RETRIES,
+    RETRY_DELAY,
+    SUPPORTED_MODELS,
+)

 _LOGGER = logging.getLogger(__name__)

@@ -61,8 +35,22 @@ class HATextAICoordinator(DataUpdateCoordinator):
        temperature: float,
        max_tokens: int,
        request_interval: float,
+        session: Optional[Any] = None,
+        is_anthropic: bool = False,
    ) -> None:
-        """Initialize."""
+        """Initialize coordinator.
+
+        Args:
+            hass: HomeAssistant instance
+            api_key: API key for the service
+            endpoint: API endpoint URL
+            model: Model name to use
+            temperature: Temperature parameter for generation
+            max_tokens: Maximum tokens to generate
+            request_interval: Interval between requests
+            session: Optional session object
+            is_anthropic: Whether to use Anthropic API
+        """
        super().__init__(
            hass,
            _LOGGER,
@@ -70,6 +58,46 @@ class HATextAICoordinator(DataUpdateCoordinator):
            update_interval=timedelta(seconds=request_interval),
        )

+        self._validate_params(api_key, temperature, max_tokens)
+
+        self.api_key = api_key
+        self.endpoint = endpoint
+        self.model = model
+        self.temperature = float(temperature)
+        self.max_tokens = int(max_tokens)
+        self._question_queue = asyncio.PriorityQueue(maxsize=MAX_QUEUE_SIZE)
+        self._responses: Dict[str, Any] = {}
+        self.system_prompt: Optional[str] = None
+        self._is_ready = False
+        self._is_processing = False
+        self._is_rate_limited = False
+        self._is_maintenance = False
+        self._error_count = 0
+        self._MAX_ERRORS = 3
+        self._request_count = 0
+        self._tokens_used = 0
+        self._api_version = "v1"
+        self._endpoint_status = "disconnected"
+        self._performance_metrics: Dict[str, Any] = {
+            "avg_response_time": 0,
+            "total_errors": 0,
+            "success_rate": 100,
+            "requests_per_minute": 0,
+        }
+        self._last_request_time = 0
+        self._is_anthropic = is_anthropic
+
+        if is_anthropic:
+            self.client = AsyncAnthropic(api_key=self.api_key)
+        else:
+            self.client = AsyncOpenAI(
+                api_key=self.api_key,
+                base_url=self.endpoint,
+                http_client=session,
+            )
+
+    def _validate_params(self, api_key: str, temperature: float, max_tokens: int) -> None:
+        """Validate initialization parameters."""
        if not api_key:
            raise ValueError("API key is required")
        if not isinstance(temperature, (int, float)) or not 0 <= temperature <= 2:
@@ -77,58 +105,514 @@ class HATextAICoordinator(DataUpdateCoordinator):
        if not isinstance(max_tokens, int) or max_tokens < 1:
            raise ValueError("Max tokens must be a positive integer")

-        self.api_key = api_key
-        self.endpoint = endpoint or "https://api.openai.com/v1"
-        self.model = model or "gpt-3.5-turbo"
-        self.temperature = float(temperature)
-        self.max_tokens = int(max_tokens)
-        self._question_queue = asyncio.Queue()
-        self._responses: Dict[str, Any] = {}
-        self.system_prompt: Optional[str] = None
-
-        self.client = openai.OpenAI(
-            api_key=self.api_key,
-            base_url=self.endpoint
-        )
-
    async def _async_update_data(self) -> Dict[str, Any]:
-        """Update data via OpenAI API."""
+        """Update data via API."""
        if self._question_queue.empty():
            return self._responses

        try:
-            question = await self._question_queue.get()
-            response_content = await self.hass.async_add_executor_job(
-                self._make_api_call, question
+            async with async_timeout.timeout(DEFAULT_TIMEOUT):
+                self._is_processing = True
+                priority, question_data = await self._question_queue.get()
+                question = question_data["question"]
+                params = question_data["params"]
+
+                try:
+                    response_data = await self._make_api_call(
+                        question,
+                        model=params.get("model"),
+                        temperature=params.get("temperature"),
+                        max_tokens=params.get("max_tokens"),
+                        system_prompt=params.get("system_prompt")
+                    )
+
+                    self._update_metrics(response_data)
+                    self._responses[question] = {
+                        "question": question,
+                        "response": response_data["response"],
+                        "error": None,
+                        "timestamp": dt_util.utcnow(),
+                        "model": response_data["model"],
+                        "temperature": params.get("temperature", self.temperature),
+                        "max_tokens": params.get("max_tokens", self.max_tokens),
+                        "response_time": response_data.get("response_time"),
+                        "tokens": response_data.get("tokens", 0),
+                        "priority": priority
+                    }
+                    self._error_count = 0
+                    self._is_ready = True
+                    self._endpoint_status = "connected"
+                    self._request_count += 1
+                    self._tokens_used += response_data.get("tokens", 0)
+                    self._last_request_time = time.time()
+
+                    # Fire event for successful response
+                    self.hass.bus.async_fire(f"{DOMAIN}_response_received", {
+                        "question": question,
+                        "model": response_data["model"],
+                        "tokens": response_data.get("tokens", 0)
+                    })
+
+                    _LOGGER.debug("Response received for question: %s", question)
+
+                except Exception as err:
+                    await self._handle_api_error(question, err)
+                finally:
+                    self._is_processing = False
+                    self._question_queue.task_done()
+
+                return self._responses
+
+        except asyncio.TimeoutError as err:
+            _LOGGER.error("Timeout while processing question")
+            await self._handle_timeout_error()
+            return self._responses
+
+    async def _handle_api_error(self, question: str, error: Exception) -> None:
+        """Handle API errors with retry logic."""
+        self._error_count += 1
+        self._performance_metrics["total_errors"] += 1
+        error_msg = str(error)
+
+        if isinstance(error, AuthenticationError):
+            error_msg = "Authentication failed - invalid API key"
+            self._is_ready = False
+            self._endpoint_status = "auth_error"
+        elif isinstance(error, RateLimitError):
+            error_msg = "Rate limit exceeded"
+            self._is_rate_limited = True
+            self._endpoint_status = "rate_limited"
+            # Implement exponential backoff
+            await asyncio.sleep(RETRY_DELAY * (2 ** (self._error_count - 1)))
+        elif isinstance(error, APIError):
+            if "maintenance" in str(error).lower():
+                self._is_maintenance = True
+                self._endpoint_status = "maintenance"
+            error_msg = f"API error: {error}"
+        else:
+            self._endpoint_status = "error"
+
+        self._responses[question] = {
+            "question": question,
+            "response": None,
+            "error": error_msg,
+            "timestamp": dt_util.utcnow(),
+            "model": self.model,
+            "temperature": self.temperature,
+            "max_tokens": self.max_tokens
+        }
+
+        # Fire error event
+        self.hass.bus.async_fire(f"{DOMAIN}_error_occurred", {
+            "error_type": type(error).__name__,
+            "error_message": error_msg,
+            "question": question
+        })
+
+        _LOGGER.error("API error (%s): %s", type(error).__name__, error_msg)
+
+        if self._error_count >= self._MAX_ERRORS:
+            _LOGGER.warning(
+                "Multiple errors occurred (%d). Coordinator needs attention.",
+                self._error_count
            )
-            self._responses[question] = {
-                "question": question,
-                "response": response_content
-            }
-            _LOGGER.debug("Response from API: %s", response_content)
-            return self._responses

-        except openai.AuthenticationError as err:
-            raise ConfigEntryAuthFailed from err
-        except Exception as err:
-            _LOGGER.error("Error communicating with API: %s", err)
-            return self._responses
+    def _update_metrics(self, response_data: Dict[str, Any]) -> None:
+        """Update performance metrics."""
+        response_time = response_data.get("response_time", 0)
+        current_avg = self._performance_metrics["avg_response_time"]
+        self._performance_metrics["avg_response_time"] = (
+            (current_avg * self._request_count + response_time) /
+            (self._request_count + 1)
+        )

-    def _make_api_call(self, question: str) -> str:
-        """Make API call to OpenAI."""
+        total_requests = self._request_count + 1
+        self._performance_metrics["success_rate"] = (
+            (total_requests - self._performance_metrics["total_errors"]) /
+            total_requests * 100
+        )
+
+        # Calculate requests per minute
+        if self._last_request_time:
+            time_diff = time.time() - self._last_request_time
+            if time_diff > 0:
+                self._performance_metrics["requests_per_minute"] = 60 / time_diff
+
+    async def _make_api_call(
+        self,
+        question: str,
+        model: Optional[str] = None,
+        temperature: Optional[float] = None,
+        max_tokens: Optional[int] = None,
+        system_prompt: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """Make API call to the selected service."""
        try:
-            messages = []
-            if self.system_prompt:
-                messages.append({"role": "system", "content": self.system_prompt})
-            messages.append({"role": "user", "content": question})
+            start_time = dt_util.utcnow()
+
+            if self._is_anthropic:
+                response = await self._make_anthropic_call(
+                    question, model, temperature, max_tokens, system_prompt
+                )
+            else:
+                response = await self._make_openai_call(
+                    question, model, temperature, max_tokens, system_prompt
+                )
+
+            response_time = (dt_util.utcnow() - start_time).total_seconds()
+
+            return {
+                **response,
+                "response_time": response_time
+            }

-            completion = self.client.chat.completions.create(
-                model=self.model,
-                messages=messages,
-                temperature=self.temperature,
-                max_tokens=self.max_tokens,
-            )
-            return completion.choices[0].message.content
        except Exception as err:
            _LOGGER.error("Error in API call: %s", err)
            raise
+
+    async def _make_anthropic_call(
+        self,
+        question: str,
+        model: Optional[str],
+        temperature: Optional[float],
+        max_tokens: Optional[int],
+        system_prompt: Optional[str]
+    ) -> Dict[str, Any]:
+        """Make API call to Anthropic."""
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.append({"role": "user", "content": question})
+
+        completion = await self.client.messages.create(
+            model=model or self.model,
+            messages=messages,
+            temperature=temperature if temperature is not None else self.temperature,
+            max_tokens=max_tokens if max_tokens is not None else self.max_tokens,
+        )
+
+        return {
+            "response": completion.content[0].text,
+            "model": completion.model,
+            "tokens": completion.usage.total_tokens if hasattr(completion, 'usage') else 0
+        }
+
+    async def _make_openai_call(
+        self,
+        question: str,
+        model: Optional[str],
+        temperature: Optional[float],
+        max_tokens: Optional[int],
+        system_prompt: Optional[str]
+    ) -> Dict[str, Any]:
+        """Make API call to OpenAI."""
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.append({"role": "user", "content": question})
+
+        completion = await self.client.chat.completions.create(
+            model=model or self.model,
+            messages=messages,
+            temperature=temperature if temperature is not None else self.temperature,
+            max_tokens=max_tokens if max_tokens is not None else self.max_tokens,
+        )
+
+        return {
+            "response": completion.choices[0].message.content,
+            "model": completion.model,
+            "tokens": completion.usage.total_tokens if hasattr(completion, 'usage') else 0
+        }
+
+    async def async_ask_question(
+        self,
+        question: str,
+        system_prompt: Optional[str] = None,
+        model: Optional[str] = None,
+        temperature: Optional[float] = None,
+        max_tokens: Optional[int] = None,
+        priority: bool = False
+    ) -> None:
+        """Add question to queue with priority support."""
+        if not self._is_ready and self._error_count >= self._MAX_ERRORS:
+            _LOGGER.warning("Coordinator is not ready due to previous errors")
+            return
+
+        question_data = {
+            "question": question,
+            "params": {
+                "system_prompt": system_prompt,
+                "model": model,
+                "temperature": temperature,
+                "max_tokens": max_tokens
+            }
+        }
+
+        # Priority: 0 for high priority, 1 for normal
+        priority_level = 0 if priority else 1
+
+        try:
+            await self._question_queue.put((priority_level, question_data))
+            await self.async_refresh()
+        except asyncio.QueueFull:
+            _LOGGER.error("Question queue is full. Try again later.")
+            raise RuntimeError("Queue is full")
+
+    async def async_shutdown(self) -> None:
+        """Shutdown the coordinator."""
+        try:
+            while not self._question_queue.empty():
+                try:
+                    self._question_queue.get_nowait()
+                    self._question_queue.task_done()
+                except asyncio.QueueEmpty:
+                    break
+
+            if hasattr(self.client, 'close'):
+                await self.client.close()
+
+            self._is_ready = False
+            self._endpoint_status = "disconnected"
+
+            # Final metrics update
+            self._update_final_metrics()
+
+        except Exception as err:
+            _LOGGER.error("Error during shutdown: %s", err)
+
+    def _update_final_metrics(self) -> None:
+        """Update final metrics before shutdown."""
+        if self._request_count > 0:
+            self._performance_metrics["final_success_rate"] = (
+                (self._request_count - self._performance_metrics["total_errors"]) /
+                self._request_count * 100
+            )
+            self._performance_metrics["total_requests"] = self._request_count
+            self._performance_metrics["total_tokens"] = self._tokens_used
+
+    @property
+    def performance_metrics(self) -> Dict[str, Any]:
+        """Return current performance metrics."""
+        return self._performance_metrics
+
+    @property
+    def queue_size(self) -> int:
+        """Return current queue size."""
+        return self._question_queue.qsize()
+
+    @property
+    def is_queue_full(self) -> bool:
+        """Return whether queue is full."""
+        return self._question_queue.full()
+
+    @property
+    def is_ready(self) -> bool:
+        """Return if coordinator is ready."""
+        return self._is_ready and self._error_count < self._MAX_ERRORS
+
+    @property
+    def is_processing(self) -> bool:
+        """Return if coordinator is processing."""
+        return self._is_processing
+
+    @property
+    def is_rate_limited(self) -> bool:
+        """Return if coordinator is rate limited."""
+        return self._is_rate_limited
+
+    @property
+    def is_maintenance(self) -> bool:
+        """Return if API is in maintenance."""
+        return self._is_maintenance
+
+    @property
+    def error_count(self) -> int:
+        """Return current error count."""
+        return self._error_count
+
+    @property
+    def request_count(self) -> int:
+        """Return total request count."""
+        return self._request_count
+
+    @property
+    def tokens_used(self) -> int:
+        """Return total tokens used."""
+        return self._tokens_used
+
+    @property
+    def api_version(self) -> str:
+        """Return API version."""
+        return self._api_version
+
+    @property
+    def endpoint_status(self) -> str:
+        """Return endpoint status."""
+        return self._endpoint_status
+
+    @property
+    def responses(self) -> Dict[str, Any]:
+        """Return all responses."""
+        return self._responses
+
+    @property
+    def last_response(self) -> Optional[Dict[str, Any]]:
+        """Return the last response."""
+        if not self._responses:
+            return None
+        return next(iter(self._responses.values()))
+
+    def reset_error_count(self) -> None:
+        """Reset error counter."""
+        self._error_count = 0
+        self._is_rate_limited = False
+        self._is_maintenance = False
+        if not self._is_ready:
+            self._is_ready = True
+            self._endpoint_status = "connected"
+
+    async def clear_queue(self) -> None:
+        """Clear the question queue."""
+        try:
+            while not self._question_queue.empty():
+                try:
+                    self._question_queue.get_nowait()
+                    self._question_queue.task_done()
+                except asyncio.QueueEmpty:
+                    break
+        except Exception as err:
+            _LOGGER.error("Error clearing queue: %s", err)
+
+    async def clear_history(self) -> None:
+        """Clear response history."""
+        self._responses.clear()
+        await self.async_refresh()
+
+    def get_response(self, question: str) -> Optional[Dict[str, Any]]:
+        """Get specific response by question."""
+        return self._responses.get(question)
+
+    def get_recent_responses(self, limit: int = 10) -> List[Dict[str, Any]]:
+        """Get most recent responses."""
+        return list(sorted(
+            self._responses.values(),
+            key=lambda x: x["timestamp"],
+            reverse=True
+        ))[:limit]
+
+    async def retry_failed_requests(self) -> None:
+        """Retry failed requests."""
+        failed_requests = [
+            (q, r) for q, r in self._responses.items()
+            if r.get("error") is not None
+        ]
+
+        for question, response in failed_requests:
+            await self.async_ask_question(
+                question,
+                system_prompt=response.get("system_prompt"),
+                model=response.get("model"),
+                temperature=response.get("temperature"),
+                max_tokens=response.get("max_tokens"),
+                priority=True
+            )
+
+    def update_system_prompt(self, new_prompt: str) -> None:
+        """Update system prompt."""
+        self.system_prompt = new_prompt
+        _LOGGER.info("System prompt updated")
+
+    async def health_check(self) -> Dict[str, Any]:
+        """Perform health check."""
+        health_status = {
+            "is_ready": self.is_ready,
+            "is_processing": self.is_processing,
+            "is_rate_limited": self.is_rate_limited,
+            "is_maintenance": self.is_maintenance,
+            "error_count": self.error_count,
+            "endpoint_status": self.endpoint_status,
+            "queue_size": self.queue_size,
+            "request_count": self.request_count,
+            "tokens_used": self.tokens_used,
+            "performance_metrics": self.performance_metrics
+        }
+
+        return health_status
+
+    async def _handle_timeout_error(self) -> None:
+        """Handle timeout errors."""
+        self._error_count += 1
+        self._endpoint_status = "timeout"
+        self._performance_metrics["total_errors"] += 1
+
+        if not self._question_queue.empty():
+            await self.clear_queue()
+
+        # Fire timeout event
+        self.hass.bus.async_fire(f"{DOMAIN}_timeout_error", {
+            "error_count": self._error_count,
+            "endpoint_status": self._endpoint_status
+        })
+
+    def export_metrics(self) -> Dict[str, Any]:
+        """Export all metrics and statistics."""
+        return {
+            "performance": self._performance_metrics,
+            "requests": {
+                "total": self._request_count,
+                "successful": self._request_count - self._performance_metrics["total_errors"],
+                "failed": self._performance_metrics["total_errors"]
+            },
+            "tokens": {
+                "total_used": self._tokens_used,
+                "average_per_request": self._tokens_used / self._request_count if self._request_count > 0 else 0
+            },
+            "status": {
+                "is_ready": self.is_ready,
+                "endpoint_status": self._endpoint_status,
+                "error_count": self._error_count
+            },
+            "queue": {
+                "size": self.queue_size,
+                "is_full": self.is_queue_full
+            }
+        }
+
+    async def validate_model(self, model: str) -> bool:
+        """Validate if model is supported."""
+        return model in SUPPORTED_MODELS
+
+    async def estimate_tokens(self, text: str) -> int:
+        """Estimate token count for text."""
+        # Простая оценка: примерно 4 символа на токен
+        return len(text) // 4
+
+    def get_rate_limit_info(self) -> Dict[str, Any]:
+        """Get rate limit information."""
+        return {
+            "is_rate_limited": self._is_rate_limited,
+            "retry_after": RETRY_DELAY * (2 ** (self._error_count - 1)) if self._is_rate_limited else 0
+        }
+
+    async def optimize_queue(self) -> None:
+        """Optimize queue by removing duplicate requests."""
+        if self._question_queue.empty():
+            return
+
+        seen_questions = set()
+        optimized_queue = asyncio.PriorityQueue(maxsize=MAX_QUEUE_SIZE)
+
+        while not self._question_queue.empty():
+            try:
+                priority, question_data = self._question_queue.get_nowait()
+                question = question_data["question"]
+
+                if question not in seen_questions:
+                    seen_questions.add(question)
+                    await optimized_queue.put((priority, question_data))
+
+                self._question_queue.task_done()
+            except asyncio.QueueEmpty:
+                break
+
+        self._question_queue = optimized_queue
@@ -1,14 +1,42 @@
 {
  "domain": "ha_text_ai",
  "name": "HA Text AI",
+  "codeowners": ["@smkrv"],
  "config_flow": true,
-  "documentation": "https://github.com/smkrv/ha-text-ai/wiki",
-  "issue_tracker": "https://github.com/smkrv/ha-text-ai/issues",
-  "requirements": ["openai>=1.0.0"],
-  "ssdp": [],
-  "zeroconf": [],
  "dependencies": [],
-  "version": "1.0.1c",
+  "documentation": "https://github.com/smkrv/ha-text-ai",
+  "homepage": "https://github.com/smkrv/ha-text-ai",
  "iot_class": "cloud_polling",
-  "codeowners": ["@smkrv"]
+  "issue_tracker": "https://github.com/smkrv/ha-text-ai/issues",
+  "requirements": [
+    "openai>=1.12.0",
+    "anthropic>=0.8.0",
+    "aiohttp>=3.8.0",
+    "async-timeout>=4.0.0"
+  ],
+  "version": "2.0.0",
+  "homeassistant": "2024.1.0",
+  "quality_scale": "silver",
+  "integration_type": "service",
+  "translations": [
+    "en"
+  ],
+  "zeroconf": [],
+  "ssdp": [],
+  "usb": [],
+  "bluetooth": [],
+  "mqtt": [],
+  "loggers": ["custom_components.ha_text_ai"],
+  "after_dependencies": ["http"],
+  "title": "Text AI Assistant",
+  "description": "AI-powered text assistant supporting multiple models including GPT and Claude",
+  "render_readme": true,
+  "tags": [
+    "ai",
+    "automation",
+    "chat",
+    "assistant",
+    "openai",
+    "claude"
+  ]
 }
@@ -1,7 +1,7 @@
 """Sensor platform for HA text AI."""
 from datetime import datetime
 import logging
-from typing import Any, Callable, Dict, Optional
+from typing import Any, Dict, Optional

 from homeassistant.components.sensor import (
    SensorEntity,
@@ -13,8 +13,38 @@ from homeassistant.core import HomeAssistant
 from homeassistant.helpers.entity_platform import AddEntitiesCallback
 from homeassistant.helpers.typing import StateType
 from homeassistant.helpers.update_coordinator import CoordinatorEntity
+from homeassistant.util import dt as dt_util

-from .const import DOMAIN, ATTR_QUESTION, ATTR_RESPONSE, ATTR_LAST_UPDATED
+from .const import (
+    DOMAIN,
+    ATTR_QUESTION,
+    ATTR_RESPONSE,
+    ATTR_LAST_UPDATED,
+    ATTR_MODEL,
+    ATTR_TEMPERATURE,
+    ATTR_MAX_TOKENS,
+    ATTR_TOTAL_RESPONSES,
+    ATTR_SYSTEM_PROMPT,
+    ATTR_QUEUE_SIZE,
+    ATTR_API_STATUS,
+    ATTR_ERROR_COUNT,
+    ATTR_LAST_ERROR,
+    ATTR_RESPONSE_TIME,
+    ATTR_API_VERSION,
+    ATTR_ENDPOINT_STATUS,
+    ATTR_REQUEST_COUNT,
+    ATTR_TOKENS_USED,
+    ENTITY_ICON,
+    ENTITY_ICON_ERROR,
+    ENTITY_ICON_PROCESSING,
+    STATE_READY,
+    STATE_PROCESSING,
+    STATE_ERROR,
+    STATE_DISCONNECTED,
+    STATE_RATE_LIMITED,
+    STATE_INITIALIZING,
+    STATE_MAINTENANCE,
+)
 from .coordinator import HATextAICoordinator

 _LOGGER = logging.getLogger(__name__)
@@ -28,13 +58,13 @@ async def async_setup_entry(
    coordinator = hass.data[DOMAIN][entry.entry_id]
    async_add_entities([HATextAISensor(coordinator, entry)], True)

+
 class HATextAISensor(CoordinatorEntity, SensorEntity):
    """HA text AI Sensor."""

    _attr_has_entity_name = True
    _attr_state_class = SensorStateClass.MEASUREMENT
    _attr_device_class = SensorDeviceClass.TIMESTAMP
-    _attr_icon = "mdi:robot"

    def __init__(
        self,
@@ -46,49 +76,160 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
        self._config_entry = config_entry
        self._attr_unique_id = f"{config_entry.entry_id}"
        self._attr_name = "Last Response"
+        self._attr_suggested_display_precision = 0
+        self._error_count = 0
+        self._last_error = None
+        self._state = STATE_INITIALIZING
+        self._attr_device_info = {
+            "identifiers": {(DOMAIN, self._attr_unique_id)},
+            "name": "HA Text AI",
+            "manufacturer": "Community",
+            "model": coordinator.model,
+            "sw_version": coordinator.api_version,
+        }
+
+    @property
+    def icon(self) -> str:
+        """Return the icon based on the current state."""
+        if self._state == STATE_PROCESSING:
+            return ENTITY_ICON_PROCESSING
+        elif self._state in [STATE_ERROR, STATE_DISCONNECTED, STATE_RATE_LIMITED]:
+            return ENTITY_ICON_ERROR
+        return ENTITY_ICON

    @property
    def state(self) -> StateType:
        """Return the state of the sensor."""
-        if not self.coordinator.data:
-            return None
-        return self.coordinator.last_update_success_time
-
-    @property
-    def extra_state_attributes(self) -> Optional[Dict[str, Any]]:
-        """Return entity specific state attributes."""
-        if not self.coordinator.data:
+        if not self.coordinator.data or not self.coordinator.last_update_success:
            return None

        try:
-
-            history = list(self.coordinator.data.items())
-            if not history:
-                return None
-
-            last_question, last_data = history[-1]
-
-
-            if isinstance(last_data, dict):
-                last_response = last_data.get("response", "")
-            else:
-                last_response = str(last_data)
-
-            return {
-                ATTR_QUESTION: last_question,
-                ATTR_RESPONSE: last_response,
-                ATTR_LAST_UPDATED: self.coordinator.last_update_success_time,
-            }
-        except (IndexError, KeyError, AttributeError) as err:
-            _LOGGER.warning("Error getting attributes: %s", err)
+            if self.coordinator.data and isinstance(self.coordinator.data, dict):
+                last_update = self.coordinator.data.get("last_update")
+                if isinstance(last_update, datetime):
+                    return dt_util.as_local(last_update)
+                return last_update
            return None
+        except Exception as err:
+            _LOGGER.error("Error getting state: %s", err, exc_info=True)
+            return None
+
+    @property
+    def extra_state_attributes(self) -> Dict[str, Any]:
+        """Return entity specific state attributes."""
+        attributes = {
+            ATTR_TOTAL_RESPONSES: 0,
+            ATTR_MODEL: self.coordinator.model,
+            ATTR_TEMPERATURE: self.coordinator.temperature,
+            ATTR_MAX_TOKENS: self.coordinator.max_tokens,
+            ATTR_SYSTEM_PROMPT: self.coordinator.system_prompt,
+            ATTR_QUEUE_SIZE: self.coordinator._question_queue.qsize(),
+            ATTR_API_STATUS: self._state,
+            ATTR_ERROR_COUNT: self._error_count,
+            ATTR_LAST_ERROR: self._last_error,
+            ATTR_API_VERSION: self.coordinator.api_version,
+            ATTR_ENDPOINT_STATUS: self.coordinator.endpoint_status,
+            ATTR_REQUEST_COUNT: self.coordinator.request_count,
+            ATTR_TOKENS_USED: self.coordinator.tokens_used,
+        }
+
+        if not self.coordinator.data:
+            return attributes
+
+        try:
+            history = list(self.coordinator._responses.items())
+            if history:
+                last_question, last_data = history[-1]
+
+                if isinstance(last_data, dict):
+                    last_response = last_data.get("response", "")
+                    last_updated = last_data.get("timestamp") or self.coordinator.data.get("last_update")
+                    response_time = last_data.get("response_time")
+
+                    model = last_data.get("model", self.coordinator.model)
+                    temperature = last_data.get("temperature", self.coordinator.temperature)
+                    max_tokens = last_data.get("max_tokens", self.coordinator.max_tokens)
+                    error = last_data.get("error")
+
+                    if error:
+                        self._last_error = error
+                        self._state = STATE_ERROR
+                else:
+                    last_response = str(last_data)
+                    last_updated = self.coordinator.data.get("last_update")
+                    response_time = None
+                    model = self.coordinator.model
+                    temperature = self.coordinator.temperature
+                    max_tokens = self.coordinator.max_tokens
+
+                if isinstance(last_updated, datetime):
+                    last_updated = dt_util.as_local(last_updated)
+
+                attributes.update({
+                    ATTR_QUESTION: last_question,
+                    ATTR_RESPONSE: last_response,
+                    ATTR_LAST_UPDATED: last_updated,
+                    ATTR_TOTAL_RESPONSES: len(history),
+                    ATTR_MODEL: model,
+                    ATTR_TEMPERATURE: temperature,
+                    ATTR_MAX_TOKENS: max_tokens,
+                })
+
+                if response_time is not None:
+                    attributes[ATTR_RESPONSE_TIME] = response_time
+
+            return attributes
+
+        except Exception as err:
+            _LOGGER.error("Error getting attributes: %s", err, exc_info=True)
+            self._error_count += 1
+            self._last_error = str(err)
+            self._state = STATE_ERROR
+            return attributes

    @property
    def available(self) -> bool:
        """Return if entity is available."""
        return self.coordinator.last_update_success

-    @property
-    def should_poll(self) -> bool:
-        """No need to poll. Coordinator notifies entity of updates."""
-        return False
+    async def async_added_to_hass(self) -> None:
+        """When entity is added to hass."""
+        await super().async_added_to_hass()
+        self._handle_coordinator_update()
+        self._state = STATE_READY
+
+    def _handle_coordinator_update(self) -> None:
+        """Handle updated data from the coordinator."""
+        try:
+            if self.coordinator.data:
+                if self.coordinator._is_ready:
+                    if self.coordinator._is_processing:
+                        self._state = STATE_PROCESSING
+                    elif self.coordinator._is_rate_limited:
+                        self._state = STATE_RATE_LIMITED
+                    elif self.coordinator._is_maintenance:
+                        self._state = STATE_MAINTENANCE
+                    else:
+                        self._state = STATE_READY
+                else:
+                    self._state = STATE_DISCONNECTED
+            else:
+                self._state = STATE_DISCONNECTED
+
+            # Обновляем счетчик ошибок только если статус изменился на ошибку
+            if self._state == STATE_ERROR:
+                self._error_count += 1
+
+        except Exception as err:
+            _LOGGER.error("Error handling update: %s", err, exc_info=True)
+            self._error_count += 1
+            self._last_error = str(err)
+            self._state = STATE_ERROR
+
+        self.async_write_ha_state()
+
+    async def async_reset_error_count(self) -> None:
+        """Reset the error counter."""
+        self._error_count = 0
+        self._last_error = None
+        self.async_write_ha_state()
@@ -1,35 +1,71 @@
 ask_question:
  name: Ask Question
-  description: Send a question to the AI model and receive a detailed response
+  description: >-
+    Send a question to the AI model and receive a detailed response.
+    The response will be stored in the conversation history and can be retrieved later.
+    Response time may vary based on model selection and server load.
+    Supports various AI models with different capabilities and pricing.
  fields:
    question:
      name: Question
-      description: Your question or prompt for the AI assistant
+      description: >-
+        Your question or prompt for the AI assistant. Be specific and clear for better results.
+        You can ask about home automation, technical advice, or general questions.
+        For complex queries, consider breaking them into smaller parts.
+        The system will maintain conversation context for follow-up questions.
      required: true
-      example: "What automations would you recommend for a smart kitchen?"
+      example: |
+        What automations would you recommend for a smart kitchen?
+        Consider energy efficiency, convenience, and integration with:
+        - Smart lighting
+        - Appliance control
+        - Temperature monitoring
+        - Voice commands
      selector:
        text:
          multiline: true
+          type: text

    model:
      name: Model
-      description: Select an AI model to use (optional, overrides default setting)
+      description: >-
+        Select an AI model to use (optional, overrides default setting).
+        Different models have different capabilities, context limits, and response characteristics:
+        - GPT-3.5 Turbo: Fast, efficient, good for most tasks
+        - GPT-4: More capable, better reasoning, slower
+        - Claude-3: Advanced capabilities, longer context
+        Note: More capable models may have longer response times and higher API costs.
      required: false
      example: "gpt-3.5-turbo"
      default: "gpt-3.5-turbo"
      selector:
        select:
+          custom_value: true
          options:
-            - label: "GPT-3.5 Turbo"
+            - label: "GPT-3.5 Turbo (Fast & Efficient)"
              value: "gpt-3.5-turbo"
-            - label: "GPT-4"
+            - label: "GPT-3.5 Turbo 16K (Extended)"
+              value: "gpt-3.5-turbo-16k"
+            - label: "GPT-4 (Most Capable)"
              value: "gpt-4"
-            - label: "GPT-4 32K"
+            - label: "GPT-4 32K (Extended Context)"
              value: "gpt-4-32k"
+            - label: "GPT-4 Turbo (Latest)"
+              value: "gpt-4-1106-preview"
+            - label: "Claude-3 Sonnet (Balanced)"
+              value: "claude-3-sonnet"
+            - label: "Claude-3 Opus (Most Advanced)"
+              value: "claude-3-opus"
+          mode: dropdown

    temperature:
      name: Temperature
-      description: "Controls response creativity (0-2): Lower values for focused responses, higher for creative ones"
+      description: >-
+        Controls response creativity (0.0-2.0):
+        0.0-0.3: Focused, consistent responses (best for technical/factual queries)
+        0.4-0.7: Balanced responses (recommended for most uses)
+        0.8-2.0: More creative, varied responses (best for brainstorming)
+        Note: Higher values may produce less predictable results.
      required: false
      default: 0.7
      selector:
@@ -38,10 +74,17 @@ ask_question:
          max: 2.0
          step: 0.1
          mode: slider
+          unit_of_measurement: ""

    max_tokens:
      name: Max Tokens
-      description: Maximum length of the response
+      description: >-
+        Maximum length of the response (1-4096 tokens).
+        Recommended ranges:
+        - Short (256-512): Quick answers, status updates
+        - Medium (512-1024): Detailed explanations
+        - Long (1024-4096): Complex analysis
+        Note: Token limits vary by model. Actual length may be shorter.
      required: false
      default: 1000
      selector:
@@ -51,18 +94,42 @@ ask_question:
          step: 1
          mode: box

+    system_prompt:
+      name: System Prompt
+      description: >-
+        Optional system prompt to set context for this specific question.
+        This will temporarily override the default system prompt.
+        Use this to specify expertise areas, response format, or special instructions.
+      required: false
+      example: "You are a home automation expert focused on energy efficiency and security"
+      selector:
+        text:
+          multiline: true
+
 clear_history:
  name: Clear History
-  description: Delete all stored questions and responses
+  description: >-
+    Delete all stored questions and responses from the conversation history.
+    This action cannot be undone. Consider using 'get_history' first if you need to backup the data.
+    System prompt settings and configuration will be preserved.
  fields: {}

 get_history:
  name: Get History
-  description: Retrieve recent conversation history
+  description: >-
+    Retrieve recent conversation history, including:
+    - Questions and responses
+    - Timestamps and response times
+    - Models used and token counts
+    - Temperature and other settings
+    Results are ordered from newest to oldest.
  fields:
    limit:
      name: Limit
-      description: Number of most recent conversations to return
+      description: >-
+        Number of most recent conversations to return (1-100).
+        Higher values return more history but may take longer to process.
+        Default: 10 conversations
      required: false
      default: 10
      selector:
@@ -72,15 +139,67 @@ get_history:
          step: 1
          mode: box

+    filter_model:
+      name: Filter by Model
+      description: >-
+        Only return conversations using a specific AI model.
+        Leave empty to show all models.
+      required: false
+      selector:
+        select:
+          custom_value: true
+          options:
+            - label: "All Models"
+              value: ""
+            - label: "GPT-3.5 Turbo"
+              value: "gpt-3.5-turbo"
+            - label: "GPT-4"
+              value: "gpt-4"
+            - label: "GPT-4 Turbo"
+              value: "gpt-4-1106-preview"
+            - label: "Claude-3 Sonnet"
+              value: "claude-3-sonnet"
+            - label: "Claude-3 Opus"
+              value: "claude-3-opus"
+          mode: dropdown
+
+    start_date:
+      name: Start Date
+      description: >-
+        Optional start date for filtering history.
+        Format: YYYY-MM-DD
+      required: false
+      selector:
+        datetime:
+          type: date
+
 set_system_prompt:
  name: Set System Prompt
-  description: Configure the AI's behavior by setting a system prompt
+  description: >-
+    Set default system behavior instructions for all future conversations.
+    This defines how the AI should behave and respond to questions.
+    The prompt will be used for all models unless overridden per question.
  fields:
    prompt:
      name: System Prompt
-      description: Instructions that define how the AI should behave and respond
+      description: >-
+        Instructions that define how the AI should behave and respond.
+        Be specific about:
+        - Desired expertise and knowledge areas
+        - Response tone and style
+        - Output format preferences
+        - Special handling instructions
+        Maximum length: 1000 characters
      required: true
-      example: "You are a home automation expert assistant. Provide practical advice focused on smart home technology."
+      example: |
+        You are a home automation expert assistant. Focus on:
+        1. Practical and efficient solutions
+        2. Energy-saving recommendations
+        3. Integration with popular smart home platforms
+        4. Security and privacy considerations
+        Provide detailed but concise responses with clear steps when applicable.
+        Format complex responses with bullet points or numbered lists.
+        Include warnings about potential risks or limitations.
      selector:
        text:
          multiline: true
@@ -1,59 +1,147 @@
 {
-  "config": {
-    "step": {
-      "user": {
-        "title": "Set up HA text AI",
-        "description": "Configure your OpenAI integration for smart home interactions",
-        "data": {
-          "api_key": "OpenAI API Key",
-          "model": "AI Model",
-          "temperature": "Temperature",
-          "max_tokens": "Max Tokens",
-          "api_endpoint": "API Endpoint",
-          "request_interval": "Request Interval"
+    "config": {
+        "step": {
+            "user": {
+                "title": "Set up HA Text AI",
+                "description": "Configure your AI integration for various providers (OpenAI, Anthropic, etc.). Different models have different capabilities and pricing.",
+                "data": {
+                    "api_key": "API key for authentication (required)",
+                    "model": "AI model to use (e.g., gpt-3.5-turbo, gpt-4, claude-3-sonnet, claude-3-opus)",
+                    "temperature": "Response creativity (0-2, lower = more focused and consistent)",
+                    "max_tokens": "Maximum response length (1-4096 tokens)",
+                    "api_endpoint": "Custom API endpoint URL (default varies by provider)",
+                    "request_interval": "Minimum time between requests in seconds (min: 0.1)",
+                    "system_prompt": "Default instructions for AI behavior and expertise"
+                }
+            }
+        },
+        "error": {
+            "invalid_auth": "Authentication failed - check your API key",
+            "invalid_api_key": "Invalid API key - please verify your credentials",
+            "cannot_connect": "Connection failed - check endpoint and network status",
+            "invalid_model": "Model unavailable or not supported by the API",
+            "rate_limit": "Rate limit exceeded - please reduce request frequency",
+            "context_length": "Input exceeds maximum context length for model",
+            "api_error": "API service error - check provider status",
+            "timeout": "Request timeout - server not responding",
+            "queue_full": "Request queue full - try again later",
+            "invalid_prompt": "Invalid system prompt format or length",
+            "invalid_url_format": "Invalid API endpoint URL format",
+            "invalid_input": "Invalid configuration parameters",
+            "unknown": "Unexpected error - check logs for details"
        }
-      }
    },
-    "error": {
-      "invalid_auth": "Invalid API key. Please check your OpenAI API key and try again.",
-      "cannot_connect": "Failed to connect to API. Please check your internet connection and API endpoint.",
-      "unknown": "Unexpected error occurred. Please check the logs for more details.",
-      "already_exists": "This API key is already configured in another integration."
+    "options": {
+        "step": {
+            "init": {
+                "title": "HA Text AI Settings",
+                "description": "Adjust your AI integration parameters and behavior",
+                "data": {
+                    "model": "Select AI model for responses (capabilities vary)",
+                    "temperature": "Response creativity (0-2, affects variation)",
+                    "max_tokens": "Maximum response length in tokens",
+                    "request_interval": "Minimum seconds between requests (rate limiting)",
+                    "system_prompt": "Default AI behavior and expertise instructions",
+                    "timeout": "Request timeout in seconds (default: 30)",
+                    "retry_count": "Number of retry attempts for failed requests",
+                    "queue_size": "Maximum pending requests in queue"
+                }
+            }
+        }
    },
-    "abort": {
-      "already_configured": "This OpenAI integration is already configured",
-      "auth_failed": "Authentication failed. Please verify your API key."
-    }
-  },
-  "options": {
-    "step": {
-      "init": {
-        "title": "HA text AI Options",
-        "description": "Adjust your OpenAI integration settings",
-        "data": {
-          "temperature": "Temperature",
-          "max_tokens": "Max Tokens",
-          "request_interval": "Request Interval"
+    "services": {
+        "ask_question": {
+            "name": "Ask Question",
+            "description": "Send a question to the AI model and get a detailed response. Supports context awareness for follow-up questions.",
+            "fields": {
+                "question": {
+                    "name": "Question",
+                    "description": "Your question or prompt for the AI model. Be specific for better results."
+                },
+                "system_prompt": {
+                    "name": "System Prompt",
+                    "description": "Optional behavior instructions for this specific question"
+                },
+                "model": {
+                    "name": "Model",
+                    "description": "Optional specific AI model for this request (overrides default)"
+                },
+                "temperature": {
+                    "name": "Temperature",
+                    "description": "Optional creativity setting (0-2, affects response variation)"
+                },
+                "max_tokens": {
+                    "name": "Max Tokens",
+                    "description": "Optional maximum response length (1-4096 tokens)"
+                }
+            }
+        },
+        "clear_history": {
+            "name": "Clear History",
+            "description": "Delete all stored conversation history, responses, and metadata"
+        },
+        "get_history": {
+            "name": "Get History",
+            "description": "Retrieve conversation history with detailed metadata including timestamps",
+            "fields": {
+                "limit": {
+                    "name": "Limit",
+                    "description": "Maximum number of conversations to return (1-100)"
+                },
+                "filter_model": {
+                    "name": "Filter Model",
+                    "description": "Show only responses from a specific model"
+                },
+                "start_date": {
+                    "name": "Start Date",
+                    "description": "Filter conversations from this date (YYYY-MM-DD)"
+                }
+            }
+        },
+        "set_system_prompt": {
+            "name": "Set System Prompt",
+            "description": "Update default AI behavior and expertise instructions",
+            "fields": {
+                "prompt": {
+                    "name": "System Prompt",
+                    "description": "Instructions for AI behavior, expertise, and response style"
+                }
+            }
        }
-      }
-    }
-  },
-  "entity": {
-    "sensor": {
-      "last_response": {
-        "name": "Last Response",
-        "state_attributes": {
-          "last_updated": {
-            "name": "Last Updated"
-          },
-          "question": {
-            "name": "Last Question"
-          },
-          "response": {
-            "name": "AI Response"
-          }
+    },
+    "state": {
+        "ready": "Ready for requests",
+        "processing": "Processing request",
+        "error": "Error occurred",
+        "disconnected": "API disconnected",
+        "rate_limited": "Rate limit reached",
+        "initializing": "Starting up",
+        "retrying": "Retrying request",
+        "queued": "Request queued"
+    },
+    "entity": {
+        "sensor": {
+            "status": {
+                "name": "AI Status",
+                "state": {
+                    "ready": "Ready",
+                    "processing": "Processing",
+                    "error": "Error",
+                    "disconnected": "Disconnected",
+                    "rate_limited": "Rate Limited",
+                    "initializing": "Initializing",
+                    "retrying": "Retrying",
+                    "queued": "Queued"
+                }
+            },
+            "last_response": {
+                "name": "Last Response",
+                "state": {
+                    "success": "Success",
+                    "error": "Error",
+                    "timeout": "Timeout"
+                }
+            }
        }
-      }
    }
-  }
 }
@@ -4,6 +4,6 @@
  "domains": ["sensor"],
  "homeassistant": "2024.11.0",
  "icon": "mdi:brain",
-  "version": "1.0.2",
+  "version": "2.0.0",
  "documentation": "https://github.com/smkrv/ha-text-ai"
 }
@@ -0,0 +1,17 @@
+```
+ha-text-ai/
+│
+├── custom_components/
+│   └── ha_text_ai/
+│       ├── __init__.py
+│       ├── config_flow.py
+│       ├── coordinator.py
+│       ├── manifest.json
+│       ├── sensor.py
+│       ├── services.yaml
+│       └── const.py
+│
+└── strings/
+    ├── en.json
+    └── ru.json
+```
Author	SHA1	Message	Date
SMKRV	5037622fb6	Release v2.0.0	2024-11-20 01:02:27 +03:00
SMKRV	962a089bf4	Stability improvements	2024-11-19 23:38:07 +03:00
SMKRV	4c56565b66	Stability improvements	2024-11-19 23:30:28 +03:00
SMKRV	8432038f09	Stability improvements	2024-11-19 23:21:54 +03:00
SMKRV	929d916d41	Bugfixes	2024-11-19 19:39:05 +03:00
SMKRV	675975d951	Minor changes	2024-11-19 19:31:26 +03:00
SMKRV	524ec87395	Minor changes	2024-11-19 19:30:42 +03:00
SMKRV	94f8193996	last_update_success_time > last_update_success	2024-11-19 19:28:01 +03:00
SMKRV	7c3fcf73c3	Text edits	2024-11-19 19:23:03 +03:00
SMKRV	20bbf89679	Changes	2024-11-19 19:20:52 +03:00
SMKRV	86dca52d07	Small changes	2024-11-19 19:18:16 +03:00
SMKRV	78d1561e74	Markdown	2024-11-19 19:17:04 +03:00
SMKRV	054e4af258	Quick bugfix	2024-11-19 19:15:57 +03:00
SMKRV	dc03faa97e	docs: update HACS installation for custom repository - Change HACS badge from Default to Custom - Add custom repository installation steps - Update installation instructions - Revise documentation format	2024-11-19 19:03:51 +03:00
SMKRV	9a7635c2ae	Release v1.1.0	2024-11-19 18:55:14 +03:00
SMKRV	df3d79c20c	feat: add multi-provider support, config improvements	2024-11-19 18:53:51 +03:00
SMKRV	d6144be7ed	v.1.0.10	2024-11-19 17:49:48 +03:00
SMKRV	9afbb904b3	__init__.py: added: from .coordinator import HATextAICoordinator	2024-11-19 17:48:00 +03:00
SMKRV	93558b2444	Version update	2024-11-19 17:34:27 +03:00
SMKRV	9f93f1ee18	Main changes: Removed the validate_endpoint function Optimized the validate_api_connection function Simplified API connection verification Preserved all error handling and retry logic Improved exception handling The integration should now correctly verify the OpenAI API connection without false endpoint_not_available errors.	2024-11-19 17:33:27 +03:00
SMKRV	30a9b53ba1	Markdown changes	2024-11-19 17:18:23 +03:00
SMKRV	5175970d55	structure.md added	2024-11-19 17:16:30 +03:00
SMKRV	f6bfbd4a07	Release v1.0.8	2024-11-19 17:02:38 +03:00
SMKRV	4ddb0dc977	Translation fixes	2024-11-19 16:59:42 +03:00
SMKRV	24dc4ac4d4	Hotfix	2024-11-19 16:53:27 +03:00
SMKRV	976f4f16a3	translation fixes	2024-11-19 16:40:56 +03:00
SMKRV	2bdef2b494	Main changes: Created global SSL_CONTEXT at module level Removed blocking create_default_context calls from async functions Optimized aiohttp.ClientSession handling: Using single connector with SSL context Session is created once for all requests in validate_api_connection Improved resource management: Automatic session closure using context managers More efficient connection handling These changes should eliminate the blocking call warning and improve overall code performance.	2024-11-19 16:33:30 +03:00
SMKRV	42324a793b	bufix	2024-11-19 16:25:44 +03:00
SMKRV	30aa894634	Release v1.0.6	2024-11-19 15:10:06 +03:00
SMKRV	398b2550a9	Release v1.0.5	2024-11-19 14:46:12 +03:00
SMKRV	f1deaa2014	Release v1.0.4	2024-11-19 14:35:45 +03:00
SMKRV	f4b0ce902e	Release v1.0.3	2024-11-19 14:06:32 +03:00
SMKRV	d899144149	Release v1.0.3	2024-11-19 14:00:33 +03:00
SMKRV	5d49b4a40b	Release v1.0.2	2024-11-19 13:16:14 +03:00
SMKRV	1a84727cf1	Release v1.0.2	2024-11-19 13:14:00 +03:00
SMKRV	d3c7e25202	Release v1.0.2	2024-11-19 13:12:41 +03:00