feat(performance): Optimize system resources and token estimation

- Improve JSON history file processing
- Add memory and disk space validation
- Enhance parallel request handling
- Refine token counting heuristics

README.md

@@ -4,7 +4,7 @@
 
 ![GitHub release](https://img.shields.io/github/release/smkrv/ha-text-ai.svg?style=flat-square) ![GitHub last commit](https://img.shields.io/github/last-commit/smkrv/ha-text-ai.svg?style=flat-square) [![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg?style=flat-square)](https://creativecommons.org/licenses/by-nc-sa/4.0/) [![hacs_badge](https://img.shields.io/badge/HACS-Custom-41BDF5.svg?style=flat-square)](https://github.com/hacs/integration) ![/README.md](https://img.shields.io/badge/language-English-green?style=flat-square) ![/README_RU.md](https://img.shields.io/badge/language-Russian-green?style=flat-square) ![](https://img.shields.io/badge/language-Deutch-green?style=flat-square)
 
-  <img src="https://github.com/smkrv/ha-text-ai/blob/524849f6a945ec62c2cf6a6b7ecd9a28b37bf0fa/misc/icons/logo.jpg" alt="HA Text AI" height="160"/>
+  <img src="https://github.com/smkrv/ha-text-ai/blob/e03b5315c2cc7b489a6e1175da728075275ed614/misc/icons/logo.png" alt="HA Text AI" style="width: 80%; max-width: 640px; max-height: 150px; aspect-ratio: 2/1; object-fit: contain;"/>
 
 ### Advanced AI Integration for [Home Assistant](https://www.home-assistant.io/) with LLM multi-provider support
 </div>
@@ -27,54 +27,68 @@ Transform your smart home experience with powerful AI assistance powered by mult
 
 ## 🌟 Features
 
-- 🧠 **Multi-Provider AI Integration**:
+- 🧠 **Multi-Provider AI Integration**: Support for OpenAI GPT and Anthropic Claude models
+- 💬 **Advanced Language Processing**: Context-aware, multi-turn conversations
+- 📝 **Enhanced Memory Management**: Secure file-based history storage
+- ⚡ **Performance Optimization**: Efficient token usage and smart rate limiting
+- 🎯 **Advanced Customization**: Per-request model and parameter selection
+- 🔒 **Enhanced Security**: Secure API key management and usage monitoring
+- 🎨 **Improved User Experience**: Intuitive configuration and rich interfaces
+- 🔄 **Automation Integration**: Event-driven responses and template compatibility
+
+<details>
+<summary>📦 Detailed Feature Breakdown</summary>
+
+### 🧠 **Multi-Provider AI Integration**
 - Support for OpenAI GPT models
 - Anthropic Claude integration
 - Custom API endpoints
 - Flexible model selection
 
-- 💬 **Advanced Language Processing**:
+### 💬 **Advanced Language Processing**
 - Context-aware responses
 - Multi-turn conversations
 - Custom system instructions
 - Natural conversation flow
 
-- 📝 **Enhanced Memory Management**:
+### 📝 **Enhanced Memory Management**
 - File-based conversation history storage
 - Automatic history rotation
 - Configurable history size limits
 - Secure storage in Home Assistant
 
-- ⚡ **Performance Optimization**:
+### ⚡ **Performance Optimization**
 - Efficient token usage
 - Smart rate limiting
 - Response caching
 - Request interval control
 
-- 🎯 **Advanced Customization**:
+### 🎯 **Advanced Customization**
 - Per-request model selection
 - Adjustable parameters
 - Custom system prompts
 - Temperature control
 
-- 🔒 **Enhanced Security**:
+### 🔒 **Enhanced Security**
 - Secure API key storage
 - Rate limiting protection
 - Error handling
 - Usage monitoring
 
-- 🎨 **Improved User Experience**:
+### 🎨 **Improved User Experience**
 - Intuitive configuration UI
 - Detailed sensor attributes
 - Rich service interface
 - Model selection UI
 
-- 🔄 **Automation Integration**:
+### 🔄 **Automation Integration**
 - Event-driven responses
 - Conditional logic support
 - Template compatibility
 - Model-specific automation
 
+</details>
+
 ## 📋 Prerequisites
 
 - Home Assistant 2024.11 or later
@@ -85,16 +99,22 @@ Transform your smart home experience with powerful AI assistance powered by mult
 - Python 3.9 or newer
 - Stable internet connection
 
-### Configuration Options
+## Configuration Options
-- API Provider (OpenAI/Anthropic)
-- API Key (provider-specific)
-- Model Selection (flexible, provider-specific models)
-- Temperature (Creativity control, 0.0-2.0)
-- Max Tokens (Response length limit)
-- Request Interval (API call throttling)
-- Custom API Endpoint (optional)
 
-#### ⓘ Potentially Compatible Providers  
+### 🔧 **Core Configuration Settings**
+- 🌐 **API Provider**: OpenAI/Anthropic
+- 🔑 **API Key**: Provider-specific authentication
+- 🤖 **Model Selection**: Flexible, provider-specific models
+- 🌡️ **Temperature**: Creativity control (0.0-2.0)
+- 📏 **Max Tokens**: Response length limit
+- ⏱️ **Request Interval**: API call throttling
+- 💾 **History Size**: Number of messages to retain
+- 🌍 **Custom API Endpoint**: Optional advanced configuration
+
+<details>
+<summary>🌐 Potentially Compatible Providers</summary>
+
+#### Flexible Provider Ecosystem
 The integration is designed to be flexible and may work with other providers offering OpenAI-compatible APIs:
 - Groq
 - Together AI
@@ -104,18 +124,19 @@ The integration is designed to be flexible and may work with other providers off
 - Local AI servers (like Ollama)
 - Custom OpenAI-compatible endpoints
 
-#### Additional Notes  
+#### 🚨 Compatibility Notes
 - Not all providers guarantee full compatibility
 - Performance may vary between providers
 - Check individual provider's documentation
 - Ensure your API key has sufficient credits/quota
 
-#### Provider Compatibility Requirements  
+#### 🔍 Provider Compatibility Requirements
 To be compatible, a provider should support:
 - OpenAI-like REST API structure
 - JSON request/response format
 - Standard authentication method
 - Similar model parameter handling
+</details>
 
 ## ⚡ Installation
 
@@ -144,7 +165,8 @@ To be compatible, a provider should support:
 3. Search for "HA Text AI"
 4. Follow the configuration steps
 
-### Via YAML
+<details>
+<summary>📦 Via YAML (Advanced)</summary>
 
 ### Platform Configuration (Global Settings)
 
@@ -202,6 +224,8 @@ sensor:
 | `temperature` | Float | ❌ | Platform setting | Override global temperature |
 | `max_tokens` | Integer | ❌ | Platform setting | Override global max tokens |
 
+</details>
+
 ## 🛠️ Available Services
 
 ### ask_question
@@ -258,7 +282,7 @@ sensor.ha_text_ai_YOUR_UNIQUE_SUFFIX
 # Examples:
 sensor.ha_text_ai_gpt      # GPT-based sensor
 sensor.ha_text_ai_claude   # Claude-based sensor
-sensor.ha_text_ai_gpt   # Custom suffix
+sensor.ha_text_ai_abc      # Custom suffix
 ```
 
 #### Response Retrieval
@@ -291,6 +315,16 @@ automation:
 
 ### 🔍 HA Text AI Sensor Attributes
 
+- 🤖 **Model and Provider Information**: Tracking current AI model and service provider
+- 🚦 **System Status**: Real-time API and processing readiness
+- 📊 **Performance Metrics**: Request success rates and response times
+- 💬 **Conversation Tracking**: Token usage and interaction history (for more precise token counting, install `tiktoken`; otherwise, a fallback estimation method is automatically used)
+- 🕒 **Last Interaction Details**: Recent query and response tracking
+- ❤️ **System Health**: Error monitoring and service uptime
+
+<details>
+<summary>📦 Detailed Sensor Attributes</summary>
+
 #### Model and Provider Information
 ```yaml
 # Name of the AI model currently in use (e.g., latest version of GPT)  
@@ -352,7 +386,6 @@ automation:
 
 # Last few conversation entries (limited to 3 for performance)
 {{ state_attr('sensor.ha_text_ai_gpt', 'conversation_history') }}  # [...]
-
 ```
 
 #### Last Interaction Details
@@ -391,6 +424,7 @@ Conversation history stored in `.storage/ha_text_ai_history/` directory:
 - Use these attributes for monitoring and automation
 - Some values might be 0 or empty initially
 
+</details>
 
 ## 📘 FAQ
 
@@ -427,7 +461,6 @@ A: Yes, archived history files are stored with timestamps and can be accessed ma
 **Q: How much history is kept?**  
 A: By default, up to 100 conversations are stored, but this can be configured. Files are automatically rotated when they reach 1MB.
 
-
 ## 🤝 Contributing
 
 Contributions welcome! Please read our [Contributing Guide](CONTRIBUTING.md).
@@ -474,6 +507,7 @@ If you want to say thanks financially, you can send a small token of appreciatio
 
 ---
 
+<div align="center"><img src="https://github.com/smkrv/ha-text-ai/blob/f2a41aaa2cd88adaf6929ba5f228205a315efdfa/custom_components/ha_text_ai/icons/dark_icon%402x.png" alt="HA Text AI" style="width: 128px; height: auto;"/></div>  
 <div align="center">
 
 Made with ❤️ and Claude 3.5 Sonnet for the Home Assistant Community

custom_components/ha_text_ai/__init__.py

@@ -11,8 +11,9 @@ from __future__ import annotations
 import logging
 import os
 import shutil
+import hashlib
 from datetime import datetime, timedelta
-from typing import Any, Dict
+from typing import Any, Dict, TypeVar
 
 import voluptuous as vol
 from async_timeout import timeout
@@ -52,11 +53,13 @@ from .const import (
     SERVICE_SET_SYSTEM_PROMPT,
     DEFAULT_MAX_HISTORY,
     CONF_MAX_HISTORY_SIZE,
+    ICONS_SUBDOMAIN,
 )
 
 _LOGGER = logging.getLogger(__name__)
 
 CONFIG_SCHEMA = cv.config_entry_only_config_schema(DOMAIN)
+ConfigType = TypeVar("ConfigType", bound=Dict[str, Any])
 
 SERVICE_SCHEMA_ASK_QUESTION = vol.Schema({
     vol.Required("instance"): cv.string,
@@ -90,19 +93,18 @@ def get_coordinator_by_instance(hass: HomeAssistant, instance: str) -> HATextAIC
 
     raise HomeAssistantError(f"Instance {instance} not found")
 
-async def async_setup(hass: HomeAssistant, config: Dict[str, Any]) -> bool:
+def get_file_hash(file_path: str) -> str:
-    """Set up the HA Text AI component."""
+    """Calculate SHA256 hash of file."""
-    hass.data.setdefault(DOMAIN, {})
+    sha256_hash = hashlib.sha256()
+    with open(file_path, "rb") as f:
+        for byte_block in iter(lambda: f.read(4096), b""):
+            sha256_hash.update(byte_block)
+    return sha256_hash.hexdigest()
 
-    try:
+async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:
-        source = os.path.join(os.path.dirname(__file__), 'icons', 'icon.svg')
+    """Set up the Home Assistant Text AI component."""
-        dest_dir = os.path.join(hass.config.path('www'), 'icons')
+    # Initialize domain data storage
-        os.makedirs(dest_dir, exist_ok=True)
+    hass.data.setdefault(DOMAIN, {})
-        dest = os.path.join(dest_dir, 'icon.png')
-        if not os.path.exists(dest):
-            shutil.copyfile(source, dest)
-    except Exception as ex:
-        _LOGGER.warning("Failed to copy custom icon: %s", str(ex))
 
     async def async_ask_question(call: ServiceCall) -> None:
         """Handle ask_question service."""
@@ -150,6 +152,7 @@ async def async_setup(hass: HomeAssistant, config: Dict[str, Any]) -> bool:
             _LOGGER.error("Error setting system prompt: %s", str(err))
             raise HomeAssistantError(f"Failed to set system prompt: {str(err)}")
 
+    # Register services
     hass.services.async_register(
         DOMAIN,
         SERVICE_ASK_QUESTION,
@@ -178,6 +181,53 @@ async def async_setup(hass: HomeAssistant, config: Dict[str, Any]) -> bool:
         schema=SERVICE_SCHEMA_SET_SYSTEM_PROMPT
     )
 
+    # Handle icons
+    try:
+        source_icon_path = os.path.join(
+            os.path.dirname(__file__),
+            ICONS_SUBDOMAIN,
+            'icon@2x.png'
+        )
+
+        destination_directory = os.path.join(
+            hass.config.path('www'),
+            DOMAIN,
+            ICONS_SUBDOMAIN
+        )
+
+        destination_icon_path = os.path.join(
+            destination_directory,
+            'icon.png'
+        )
+
+        if not os.path.exists(source_icon_path):
+            _LOGGER.error("Source icon not found: %s", source_icon_path)
+            return True
+
+        def create_directory():
+            os.makedirs(destination_directory, exist_ok=True)
+
+        await hass.async_add_executor_job(create_directory)
+
+        should_copy = True
+
+        if os.path.exists(destination_icon_path):
+            source_hash = await hass.async_add_executor_job(get_file_hash, source_icon_path)
+            dest_hash = await hass.async_add_executor_job(get_file_hash, destination_icon_path)
+            should_copy = source_hash != dest_hash
+
+        if should_copy:
+            def copy_file():
+                shutil.copyfile(source_icon_path, destination_icon_path)
+
+            await hass.async_add_executor_job(copy_file)
+            _LOGGER.debug("Icon updated: %s", destination_icon_path)
+
+    except PermissionError as e:
+        _LOGGER.error("Permission denied when managing icons: %s", str(e))
+    except Exception as e:
+        _LOGGER.error("Failed to manage icons: %s", str(e))
+
     return True
 
 async def async_check_api(session, endpoint: str, headers: dict, provider: str) -> bool:

custom_components/ha_text_ai/const.py

@@ -41,7 +41,9 @@ CONF_IS_ANTHROPIC: Final = "is_anthropic"
 CONF_CONTEXT_MESSAGES: Final = "context_messages"
 
 ABSOLUTE_MAX_HISTORY_SIZE = 500
-MAX_ENTRY_SIZE = 1 * 1024 * 1024
+MAX_ATTRIBUTE_SIZE = 4 * 1024
+MAX_HISTORY_FILE_SIZE = 1 * 1024 * 1024
+ICONS_SUBDOMAIN = "icons"
 
 # Default values
 DEFAULT_MODEL: Final = "gpt-4o-mini"

custom_components/ha_text_ai/coordinator.py

@@ -12,9 +12,13 @@ import logging
 import traceback
 import aiofiles
 import os
+import json
 import asyncio
+import psutil
+import re
+import math
 from datetime import datetime, timedelta
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 from homeassistant.core import HomeAssistant
 from homeassistant.helpers.update_coordinator import DataUpdateCoordinator
@@ -35,11 +39,26 @@ from .const import (
     DEFAULT_MAX_HISTORY,
     DEFAULT_CONTEXT_MESSAGES,
     ABSOLUTE_MAX_HISTORY_SIZE,
-    MAX_ENTRY_SIZE,
+    MAX_ATTRIBUTE_SIZE,
+    MAX_HISTORY_FILE_SIZE,
 )
 
 _LOGGER = logging.getLogger(__name__)
 
+def _check_memory_available(self):
+    """Check if enough memory is available."""
+    memory = psutil.virtual_memory()
+
+    # Log the total and available memory
+    _LOGGER.debug("Total memory: %s, Available memory: %s", memory.total, memory.available)
+
+    if memory.available > 1024 * 1024 * 100:  # 100MB
+        _LOGGER.debug("Sufficient memory available: %s bytes", memory.available)
+        return True
+    else:
+        _LOGGER.warning("Insufficient memory available: %s bytes", memory.available)
+        return False
+
 class AsyncFileHandler:
     """Async context manager for file operations."""
 
@@ -62,7 +81,7 @@ class HATextAICoordinator(DataUpdateCoordinator):
         hass: HomeAssistant,
         client: Any,
         model: str,
-        update_interval: int,  # Moved up
+        update_interval: int,
         instance_name: str,
         max_tokens: int = DEFAULT_MAX_TOKENS,
         temperature: float = DEFAULT_TEMPERATURE,
@@ -78,26 +97,13 @@ class HATextAICoordinator(DataUpdateCoordinator):
         from .config_flow import normalize_name
 
         self.normalized_name = normalize_name(instance_name)
-
+        self._metrics_file = os.path.join(
-        self.hass = hass
+            hass.config.path(".storage"),
-        self.client = client
+            "ha_text_ai_history",
-        self.model = model
+            f"ha_text_ai_metrics_{normalize_name(instance_name)}.json"
-        self.temperature = temperature
-        self.max_tokens = max_tokens
-        self.max_history_size = min(
-            max(1, max_history_size),
-            ABSOLUTE_MAX_HISTORY_SIZE
         )
-        self.is_anthropic = is_anthropic
-
-        # Initialize essential attributes first
-        self._is_processing = False
-        self._is_rate_limited = False
-        self._is_maintenance = False
-        self.endpoint_status = "ready"
-        self._system_prompt = None
-        self._conversation_history = []
 
+        # Initialize performance metrics first
         self._performance_metrics = {
             "total_tokens": 0,
             "prompt_tokens": 0,
@@ -110,6 +116,26 @@ class HATextAICoordinator(DataUpdateCoordinator):
             "min_latency": float("inf"),
         }
 
+        # Continue with other initializations
+        self.hass = hass
+        self.client = client
+        self.model = model
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+        self.max_history_size = min(
+            max(1, max_history_size),
+            ABSOLUTE_MAX_HISTORY_SIZE
+        )
+        self.is_anthropic = is_anthropic
+
+        # Initialize essential attributes
+        self._is_processing = False
+        self._is_rate_limited = False
+        self._is_maintenance = False
+        self.endpoint_status = "ready"
+        self._system_prompt = None
+        self._conversation_history = []
+
         self._last_response = {
             "timestamp": dt_util.utcnow().isoformat(),
             "question": "",
@@ -122,7 +148,7 @@ class HATextAICoordinator(DataUpdateCoordinator):
 
         update_interval_td = timedelta(seconds=update_interval)
 
-        # Call super().__init__ BEFORE other property access
+        # Call super().__init__
         super().__init__(
             hass,
             _LOGGER,
@@ -130,11 +156,11 @@ class HATextAICoordinator(DataUpdateCoordinator):
             update_interval=update_interval_td,
         )
 
-        # Now initialize _initial_state (after super().__init__)
+        # Now initialize _initial_state
         self._initial_state = {
             "state": STATE_READY,
             "metrics": self._performance_metrics.copy(),
-            "last_response": self.last_response.copy(),  # Accessing here
+            "last_response": self.last_response.copy(),
             "is_processing": self._is_processing,
             "is_rate_limited": self._is_rate_limited,
             "is_maintenance": self._is_maintenance,
@@ -156,17 +182,22 @@ class HATextAICoordinator(DataUpdateCoordinator):
             hass.config.path(".storage"),
             "ha_text_ai_history"
         )
-        os.makedirs(self._history_dir, exist_ok=True)
+        hass.async_create_task(self._create_history_dir())
 
         hass.async_create_task(self._check_history_directory())
 
+        hass.async_create_task(self._migrate_history_from_txt_to_json())
+
+        hass.async_create_task(self._initialize_metrics())
+
         # History file path using instance name
         self._history_file = os.path.join(
             self._history_dir,
-            f"{self.normalized_name}_history.txt"
+            f"{self.normalized_name}_history.json"
         )
-        # Maximum history file size (10 MB)
+
-        self._max_history_file_size = 10 * 1024 * 1024
+        # Maximum history file size (1 MB) from const.py
+        self._max_history_file_size = MAX_HISTORY_FILE_SIZE
 
         # Asynchronous file initialization
         hass.async_create_task(self.async_initialize_history_file())
@@ -221,46 +252,206 @@ class HATextAICoordinator(DataUpdateCoordinator):
         """
         self._last_response = value
 
+    async def _file_exists(self, path: str) -> bool:
+        """
+        Check if file exists asynchronously.
+
+        Args:
+            path (str): Full path to the file to check
+
+        Returns:
+            bool: True if file exists, False otherwise
+        """
+        try:
+            result = await self.hass.async_add_executor_job(os.path.exists, path)
+
+            _LOGGER.debug(f"File existence check: {path} - {'Exists' if result else 'Not Found'}")
+
+            return result
+
+        except Exception as e:
+            _LOGGER.error(f"Error checking file existence for {path}: {e}")
+            return False
+
+    async def _create_history_dir(self):
+        """
+        Asynchronously create history directory.
+
+        Creates the directory for storing history files
+        without blocking the event loop.
+        """
+        try:
+            await self.hass.async_add_executor_job(
+                lambda: os.makedirs(self._history_dir, exist_ok=True)
+            )
+            _LOGGER.debug(f"Directory creation details: exist_ok=True")
+        except PermissionError:
+            _LOGGER.error(f"Permission denied when creating history directory: {self._history_dir}")
+            raise
+        except OSError as e:
+            _LOGGER.error(f"Error creating history directory {self._history_dir}: {e}")
+            raise
+
+    async def _initialize_metrics(self) -> None:
+        self._performance_metrics = await self._load_metrics() or {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+            "failed_requests": 0,
+            "total_errors": 0,
+            "average_latency": 0,
+            "max_latency": 0,
+            "min_latency": float("inf"),
+        }
+
+    async def _load_metrics(self) -> Dict[str, Any]:
+        try:
+            if await self._file_exists(self._metrics_file):
+                def read_metrics():
+                    with open(self._metrics_file, 'r') as f:
+                        try:
+                            return json.load(f)
+                        except json.JSONDecodeError:
+                            _LOGGER.warning("Metrics file corrupted, creating new")
+                            return None
+                return await self.hass.async_add_executor_job(read_metrics)
+        except Exception as e:
+            _LOGGER.warning(f"Failed to load metrics: {e}")
+        return None
+
+    async def _save_metrics(self) -> None:
+        try:
+            def write_metrics():
+                with open(self._metrics_file, 'w') as f:
+                    json.dump(self._performance_metrics, f)
+            await self.hass.async_add_executor_job(write_metrics)
+        except Exception as e:
+            _LOGGER.warning(f"Failed to save metrics: {e}")
+
+    async def _update_metrics(self, latency: float, response: dict) -> None:
+        """Update performance metrics and save to storage."""
+        metrics = self._performance_metrics
+        tokens = response.get("tokens", {})
+
+        metrics["total_tokens"] += tokens.get("total", 0)
+        metrics["prompt_tokens"] += tokens.get("prompt", 0)
+        metrics["completion_tokens"] += tokens.get("completion", 0)
+        metrics["successful_requests"] += 1
+
+        metrics["average_latency"] = (
+            (metrics["average_latency"] * (metrics["successful_requests"] - 1) + latency)
+            / metrics["successful_requests"]
+        )
+        metrics["max_latency"] = max(metrics["max_latency"], latency)
+        metrics["min_latency"] = min(metrics["min_latency"], latency)
+
+        # Save metrics after update
+        await self._save_metrics()
+
+    async def _get_current_metrics(self) -> Dict[str, Any]:
+        """Get current performance metrics."""
+        metrics = self._performance_metrics.copy()
+        _LOGGER.debug(f"Current performance metrics: {metrics}")
+        return metrics
+
+    async def _handle_error(self, error: Exception) -> None:
+        """Enhanced error handling with metric persistence."""
+        self._performance_metrics["total_errors"] += 1
+        self._performance_metrics["failed_requests"] += 1
+
+        # Save metrics after error
+        await self._save_metrics()
+
+        error_details = {
+            "timestamp": dt_util.utcnow().isoformat(),
+            "model": self.model,
+            "instance": self.instance_name,
+            "error_message": str(error),
+            "error_type": type(error).__name__,
+            "traceback": traceback.format_exc() if _LOGGER.isEnabledFor(logging.DEBUG) else None,
+        }
+
+        # Specific error type handling
+        error_mapping = {
+            HomeAssistantError: {"is_ha_error": True},
+            ConnectionError: {
+                "is_connection_error": True,
+                "is_rate_limited": True
+            },
+            TimeoutError: {"is_timeout": True},
+            PermissionError: {"is_permission_denied": True},
+            ValueError: {"is_validation_error": True}
+        }
+
+        for error_type, error_flags in error_mapping.items():
+            if isinstance(error, error_type):
+                error_details.update(error_flags)
+                break
+
+        # Update system state based on error type
+        if error_details.get("is_rate_limited"):
+            self._is_rate_limited = True
+            _LOGGER.warning(f"Rate limit detected for {self.instance_name}")
+
+        if error_details.get("is_connection_error"):
+            self.endpoint_status = "unavailable"
+
+        self.last_response = error_details
+        _LOGGER.error(f"AI Processing Error: {error_details}")
+
+        if _LOGGER.isEnabledFor(logging.DEBUG):
+            _LOGGER.debug(f"Full Error Traceback: {error_details['traceback']}")
+
     async def async_initialize_history_file(self) -> None:
         """
         Asynchronously initialize history file.
 
         Creates the file and writes an initialization timestamp
-        without blocking the event loop.
+        without blocking the event loop using Home Assistant's executor.
         """
         try:
-            # Use asyncio.to_thread to perform file I/O in a separate thread
+            await self.hass.async_add_executor_job(self._sync_initialize_history_file)
-            await asyncio.to_thread(self._sync_initialize_history_file)
         except Exception as e:
             _LOGGER.error(f"Could not initialize history file: {e}")
             _LOGGER.debug(traceback.format_exc())
 
-    def _sync_initialize_history_file(self) -> None:
+    async def _sync_initialize_history_file(self) -> None:
-        """
-        Synchronous method to create and initialize history file.
-
-        Runs in a separate thread to avoid blocking the event loop.
-        """
         try:
-            with open(self._history_file, 'a') as f:
+            if not await self._file_exists(self._history_file):
-                f.write(f"History initialized at: {dt_util.utcnow().isoformat()}\n")
+                def write_empty_history():
+                    with open(self._history_file, 'w') as f:
+                        json.dump([], f)
+                await self.hass.async_add_executor_job(write_empty_history)
         except Exception as e:
-            _LOGGER.error(f"Synchronous history file initialization failed: {e}")
+            _LOGGER.error(f"History file initialization failed: {e}")
 
-    # Add size check to _update_history method
+    # Size check to _update_history method
     async def _update_history(self, question: str, response: dict) -> None:
-        """Update conversation history with size limits."""
+        """Update conversation history with size validation."""
         try:
-            # Limit entry size
-            question = question[:MAX_ENTRY_SIZE]
-            response_content = response.get("content", "")[:MAX_ENTRY_SIZE]
-
             history_entry = {
                 "timestamp": dt_util.utcnow().isoformat(),
-                "question": question,
+                "question": self._truncate_text(question, MAX_ATTRIBUTE_SIZE),
-                "response": response_content,
+                "response": self._truncate_text(
+                    response.get("content", ""),
+                    MAX_ATTRIBUTE_SIZE
+                ),
             }
 
+            # Check approximate entry size
+            entry_size = len(json.dumps(history_entry).encode('utf-8'))
+            current_size = await self._check_file_size(self._history_file)
+
+            if current_size + entry_size > MAX_HISTORY_FILE_SIZE:
+                _LOGGER.warning(
+                    f"History size limit approaching. "
+                    f"Current: {current_size}, "
+                    f"Entry: {entry_size}, "
+                    f"Max: {MAX_HISTORY_FILE_SIZE}"
+                )
+                await self._rotate_history()
+
             self._conversation_history.append(history_entry)
 
             # Enforce size limit
@@ -269,48 +460,93 @@ class HATextAICoordinator(DataUpdateCoordinator):
 
             await self._write_history_entry(history_entry)
 
-            await self._rotate_history()
-
         except Exception as e:
             _LOGGER.error(f"Error updating history: {e}")
             _LOGGER.debug(traceback.format_exc())
 
-    # Update _write_history_entry method
     async def _write_history_entry(self, entry: dict) -> None:
-        """Write a single history entry to file asynchronously."""
+        """Write history entry with file size checks."""
         try:
-            if not os.path.exists(self._history_dir):
+            if not await self._file_exists(self._history_dir):
-                os.makedirs(self._history_dir, exist_ok=True)
+                await self._create_history_dir()
 
-            _LOGGER.debug(f"Writing history entry to {self._history_file}")
+            # Check current file size
-
+            current_size = 0
-            async with AsyncFileHandler(self._history_file) as f:
+            if await self._file_exists(self._history_file):
-                await f.write(
+                current_size = await self.hass.async_add_executor_job(
-                    f"{entry['timestamp']}: "
+                    os.path.getsize,
-                    f"Question: {entry['question']} - "
+                    self._history_file
-                    f"Response: {entry['response']}\n"
                 )
-                await f.flush()
 
-            _LOGGER.debug(f"Successfully wrote history entry")
+            # Calculate approximate entry size
+            entry_size = len(json.dumps(entry).encode('utf-8'))
+
+            if current_size + entry_size > MAX_HISTORY_FILE_SIZE:
+                _LOGGER.warning(
+                    f"History file size limit reached. Current: {current_size}, "
+                    f"Entry size: {entry_size}, Max: {MAX_HISTORY_FILE_SIZE}"
+                )
+                # Trigger rotation before writing
+                await self._rotate_history()
+
+            # Continue with writing
+            history = []
+            if await self._file_exists(self._history_file):
+                async with AsyncFileHandler(self._history_file, 'r') as f:
+                    content = await f.read()
+                    if content:
+                        history = json.loads(content)
+
+            history.append(entry)
+
+            if len(history) > self.max_history_size:
+                history = history[-self.max_history_size:]
+
+            async with AsyncFileHandler(self._history_file, 'w') as f:
+                await f.write(json.dumps(history, indent=2))
 
         except Exception as e:
             _LOGGER.error(f"Error writing history entry: {e}")
             _LOGGER.debug(traceback.format_exc())
 
-    def _sync_write_history_entry(self, entry: dict) -> None:
+    async def _check_file_size(self, file_path: str) -> int:
         """
-        Synchronous method to write history entry.
+        Check file size asynchronously.
 
-        Runs in a separate thread to avoid blocking the event loop.
+        Args:
+            file_path: Path to file to check
+
+        Returns:
+            int: File size in bytes
         """
         try:
-            with open(self._history_file, 'a') as f:
+            if await self._file_exists(file_path):
-                f.write(
+                size = await self.hass.async_add_executor_job(os.path.getsize, file_path)
-                    f"{entry['timestamp']}: "
+                _LOGGER.debug(f"File size check for {file_path}: {size} bytes")
-                    f"Question: {entry['question']} - "
+                return size
-                    f"Response: {entry['response']}\n"
+            return 0
-                )
+        except Exception as e:
+            _LOGGER.error(f"Error checking file size for {file_path}: {e}")
+            return 0
+
+    def _sync_write_history_entry(self, entry: dict) -> None:
+        """Synchronous method to write history entry."""
+        try:
+            history = []
+            if os.path.exists(self._history_file):
+                with open(self._history_file, 'r') as f:
+                    content = f.read()
+                    if content:
+                        history = json.loads(content)
+
+            history.append(entry)
+
+            if len(history) > self.max_history_size:
+                history = history[-self.max_history_size:]
+
+            with open(self._history_file, 'w') as f:
+                json.dump(history, f, indent=2)
+
         except Exception as e:
             _LOGGER.error(f"Synchronous history entry writing failed: {e}")
 
@@ -318,9 +554,8 @@ class HATextAICoordinator(DataUpdateCoordinator):
         """Rotate conversation history with file management."""
         try:
             _LOGGER.debug(f"Starting history rotation for {self._history_file}")
-            await asyncio.to_thread(self._sync_rotate_history)
+            await self.hass.async_add_executor_job(self._rotate_history_files)
             _LOGGER.debug(f"Completed history rotation")
-
         except Exception as e:
             _LOGGER.error(f"Error rotating history: {e}")
             _LOGGER.debug(traceback.format_exc())
@@ -332,7 +567,7 @@ class HATextAICoordinator(DataUpdateCoordinator):
         try:
             # Test write permission in a separate thread
             test_file_path = os.path.join(self._history_dir, ".write_test")
-            await asyncio.to_thread(self._sync_test_directory_write, test_file_path)
+            await self.hass.async_add_executor_job(self._sync_test_directory_write, test_file_path)
 
         except PermissionError:
             _LOGGER.error(f"No write permissions for history directory: {self._history_dir}")
@@ -350,92 +585,128 @@ class HATextAICoordinator(DataUpdateCoordinator):
         except Exception as e:
             _LOGGER.error(f"Directory write test failed: {e}")
 
-    def _sync_rotate_history(self) -> None:
+    async def _rotate_history_files(self) -> None:
-        """
+        """Rotate history files with size validation."""
-        Synchronous method to rotate history files.
-
-        Runs in a separate thread to avoid blocking the event loop.
-        """
         try:
-            # Check and manage file size
+            if await self._file_exists(self._history_file):
-            if os.path.exists(self._history_file):
+                current_size = await self._check_file_size(self._history_file)
-                file_size = os.path.getsize(self._history_file)
+
-                if file_size > self._max_history_file_size:
+                if current_size > MAX_HISTORY_FILE_SIZE:
-                    # Create timestamped archive
+                    _LOGGER.info(
+                        f"Rotating history file. "
+                        f"Current size: {current_size}, "
+                        f"Max allowed: {MAX_HISTORY_FILE_SIZE}"
+                    )
+
+                    # Create archive filename with timestamp
                     archive_file = os.path.join(
                         self._history_dir,
-                        f"{self.normalized_name}_history_{dt_util.utcnow().strftime('%Y%m%d_%H%M%S')}.txt"
+                        f"{self.normalized_name}_history_{dt_util.utcnow().strftime('%Y%m%d_%H%M%S')}.json"
                     )
-                    os.rename(self._history_file, archive_file)
 
-                    # Optional: Log rotation event
+                    # Rotate files
-                    _LOGGER.info(f"History file rotated: {archive_file}")
+                    await self.hass.async_add_executor_job(
-
+                        os.rename,
-            # Ensure history doesn't exceed max size
+                        self._history_file,
-            if len(self._conversation_history) > self.max_history_size:
+                        archive_file
-                # Trim in-memory history
-                self._conversation_history = self._conversation_history[-self.max_history_size:]
-
-            # Write history entries
-            with open(self._history_file, 'a') as f:
-                for entry in self._conversation_history:
-                    f.write(
-                        f"{entry['timestamp']}: {entry['question']} - {entry['response']}\n"
                     )
 
+                    # Create new history file with recent entries
+                    async with AsyncFileHandler(self._history_file, 'w') as f:
+                        await f.write(json.dumps(
+                            self._conversation_history[-self.max_history_size:],
+                            indent=2
+                        ))
+
+                    _LOGGER.info(f"History file rotated to: {archive_file}")
+
         except Exception as e:
-            _LOGGER.error(f"Synchronous history rotation failed: {e}")
+            _LOGGER.error(f"History rotation failed: {e}")
             _LOGGER.debug(traceback.format_exc())
 
     async def _async_update_data(self) -> Dict[str, Any]:
-        """Update data via library."""
+        """Update coordinator data with improved error handling and performance."""
+
         try:
+            async with asyncio.Semaphore(1):
                 current_state = self._get_current_state()
-            _LOGGER.debug(
-                f"Updating data for {self.instance_name}, current state: {current_state}"
-            )
 
-            limited_history = []
+                limited_history = await self._get_limited_history()
-            if self._conversation_history:
-                for entry in self._conversation_history[-3:]:
-                    limited_entry = {
-                        "timestamp": entry["timestamp"],
-                        "question": entry["question"][:4096],
-                        "response": entry["response"][:4096],
-                    }
-                    limited_history.append(limited_entry)
 
-            limited_last_response = self.last_response.copy()
+                metrics = await self._get_current_metrics()
-            if "response" in limited_last_response:
+
-                limited_last_response["response"] = limited_last_response["response"][:4096]
+                if metrics is None:
-            if "question" in limited_last_response:
+                    metrics = {}
-                limited_last_response["question"] = limited_last_response["question"][:4096]
 
                 data = {
                     "state": current_state,
-                "metrics": self._performance_metrics,
+                    "metrics": metrics,
-                "last_response": limited_last_response,
+                    "last_response": await self._get_sanitized_last_response(),
                     "is_processing": self._is_processing,
                     "is_rate_limited": self._is_rate_limited,
                     "is_maintenance": self._is_maintenance,
                     "endpoint_status": self.endpoint_status,
-                "uptime": (dt_util.utcnow() - self._start_time).total_seconds(),
+                    "uptime": self._calculate_uptime(),
-                "system_prompt": self._system_prompt[:4096] if self._system_prompt else None,
+                    "system_prompt": self._get_truncated_system_prompt(),
                     "history_size": len(self._conversation_history),
                     "conversation_history": limited_history,
                     "normalized_name": self.normalized_name,
                 }
 
-            # Validate data
+                await self._validate_update_data(data)
-            if not isinstance(data, dict):
-                raise ValueError("Invalid data format")
 
-            _LOGGER.debug(f"Updated data for {self.instance_name}: {data}")
                 return data
 
+        except asyncio.CancelledError:
+            _LOGGER.warning("Update was cancelled")
+            raise
         except Exception as err:
-            _LOGGER.error(f"Error updating data for {self.instance_name}: {err}")
+            _LOGGER.error(f"Error updating data: {err}", exc_info=True)
-            return self._initial_state
+            return self._get_safe_initial_state()
+
+    async def _get_limited_history(self) -> List[Dict[str, Any]]:
+        """Get limited conversation history with size constraints."""
+        limited_history = []
+        if self._conversation_history:
+            for entry in self._conversation_history[-3:]:
+                limited_entry = {
+                    "timestamp": entry["timestamp"],
+                    "question": self._truncate_text(entry["question"]),
+                    "response": self._truncate_text(entry["response"]),
+                }
+                limited_history.append(limited_entry)
+        return limited_history
+
+    def _truncate_text(self, text: str, max_length: int = MAX_ATTRIBUTE_SIZE) -> str:
+        """Safely truncate text to maximum length."""
+        return text[:max_length] if text else ""
+
+    async def _get_sanitized_last_response(self) -> Dict[str, Any]:
+        """Get sanitized version of last response."""
+        response = self.last_response.copy()
+        if "response" in response:
+            response["response"] = self._truncate_text(response["response"])
+        if "question" in response:
+            response["question"] = self._truncate_text(response["question"])
+        return response
+
+    def _calculate_uptime(self) -> float:
+        """Calculate current uptime in seconds."""
+        return (dt_util.utcnow() - self._start_time).total_seconds()
+
+    def _get_truncated_system_prompt(self) -> Optional[str]:
+        """Get truncated system prompt."""
+        return self._truncate_text(self._system_prompt, 4096) if self._system_prompt else None
+
+    async def _validate_update_data(self, data: Dict[str, Any]) -> None:
+        """Validate update data structure and content."""
+        required_keys = ["state", "metrics", "last_response"]
+        for key in required_keys:
+            if key not in data:
+                raise ValueError(f"Missing required key: {key}")
+
+        if not isinstance(data["metrics"], dict):
+            raise ValueError("Invalid metrics format")
 
     async def async_update_ha_state(self) -> None:
         """Update Home Assistant state."""
@@ -466,66 +737,61 @@ class HATextAICoordinator(DataUpdateCoordinator):
             return STATE_ERROR
         return STATE_READY
 
-    def _calculate_context_tokens(self, messages: List[Dict[str, str]], model: str = None) -> int:
+    def _get_safe_initial_state(self) -> Dict[str, Any]:
-        """
+        """Return safe initial state when update fails."""
-        Estimate tokens for conversation context.
+        return {
+            "state": STATE_ERROR,
+            "metrics": {},
+            "last_response": self.last_response,
+            "is_processing": False,
+            "is_rate_limited": False,
+            "is_maintenance": False,
+            "endpoint_status": "error",
+            "uptime": self._calculate_uptime(),
+            "system_prompt": None,
+            "history_size": 0,
+            "conversation_history": [],
+            "normalized_name": self.normalized_name,
+        }
 
-        Args:
+    def _calculate_context_tokens(self, messages: List[Dict[str, str]]) -> int:
-            messages: List of message dictionaries
+        total_tokens = 0
-            model: Optional model name for provider-specific estimation
+
+        # Compile regular expressions for performance
+        number_pattern = re.compile(r'[0-9]')
+        special_char_pattern = re.compile(r'[^\w\s]')
+        whitespace_pattern = re.compile(r'\s+')
 
-        Returns:
-            Estimated number of tokens
-        """
         try:
-            # Anthropic specific token counting
+            for message in messages:
-            if self.is_anthropic and hasattr(self.client, 'count_tokens'):
+                text = message.get('content', '')
-                return sum(self.client.count_tokens(msg['content']) for msg in messages)
+                if text:
+                    # Normalize whitespace
+                    text = whitespace_pattern.sub(' ', text.strip())
 
-            def estimate_tokens(text: str) -> int:
+                    # Advanced token estimation heuristics
-                """
+                    words = text.split()
-                Flexible token estimation algorithm.
+                    for word in words:
+                        # Complexity-based token calculation
+                        if len(word) > 8:  # Long words
+                            total_tokens += 2
+                        elif number_pattern.search(word):  # Words with numbers
+                            total_tokens += 1.5
+                        elif special_char_pattern.search(word):  # Words with special characters
+                            total_tokens += 1.5
+                        elif word.isupper():  # Acronyms and technical terms
+                            total_tokens += 1.5
+                        else:  # Regular words
+                            total_tokens += 1
 
-                Heuristics:
+                    # Additional correction for technical texts
-                - Count words
+                    total_tokens = math.ceil(total_tokens * 1.2)
-                - Estimate special characters
-                - Fallback to character-based estimation
-                """
-                # Word-based estimation
-                words = len(text.split())
 
-                # Special character handling
+            return int(total_tokens)
-                special_chars = sum(1 for char in text if not char.isalnum())
-
-                # Character-based fallback
-                char_tokens = len(text) // 4
-
-                # Combine estimations with bias towards words
-                total_tokens = (words * 1.5) + (special_chars * 0.5) + char_tokens
-
-                return max(int(total_tokens), words)
-
-            # Calculate total tokens across all messages
-            total_tokens = sum(estimate_tokens(msg['content']) for msg in messages)
-
-            # Logging for debugging
-            _LOGGER.debug(
-                f"Token Estimation: "
-                f"Messages: {len(messages)}, "
-                f"Estimated Tokens: {total_tokens}"
-            )
-
-            return total_tokens
 
         except Exception as e:
-            # Safe fallback with detailed logging
+            _LOGGER.error(f"Token calculation error: {e}. Processed {len(messages)} messages.")
-            _LOGGER.warning(
+            # Safe fallback with minimal token estimation
-                f"Token estimation failed. "
-                f"Error: {e}. "
-                f"Using conservative estimation."
-            )
-
-            # Conservative token estimation
             return len(messages) * 100
 
     async def async_ask_question(
@@ -571,6 +837,9 @@ class HATextAICoordinator(DataUpdateCoordinator):
             """
             Enhanced question processing with intelligent token management.
             """
+            if self.client is None:
+                raise HomeAssistantError("AI client not initialized")
+
             try:
                 self._is_processing = True
                 await self.async_update_ha_state()
@@ -650,7 +919,7 @@ class HATextAICoordinator(DataUpdateCoordinator):
                 # Update metrics
                 end_time = dt_util.utcnow()
                 latency = (end_time - start_time).total_seconds()
-                self._update_metrics(latency, response)
+                await self._update_metrics(latency, response)
 
                 await self._update_history(question, response)
 
@@ -667,6 +936,7 @@ class HATextAICoordinator(DataUpdateCoordinator):
     async def async_process_message(self, question: str, **kwargs) -> dict:
         """Process message using the AI client."""
         try:
+            async with asyncio.timeout(60):  # 60 second timeout
                 if self.is_anthropic:
                     response = await self._process_anthropic_message(question, **kwargs)
                 else:
@@ -678,11 +948,15 @@ class HATextAICoordinator(DataUpdateCoordinator):
                 "response": response["content"],
                 "model": kwargs.get("model", self.model),
                 "instance": self.instance_name,
+                "normalized_name": self.normalized_name,
                 "error": None,
             }
 
             return response
 
+        except asyncio.TimeoutError:
+            raise HomeAssistantError("Request timed out")
+
         except Exception as err:
             self._handle_error(err)
             raise
@@ -732,73 +1006,73 @@ class HATextAICoordinator(DataUpdateCoordinator):
             _LOGGER.error(f"Error in OpenAI API call: {str(e)}")
             raise
 
-    def _update_metrics(self, latency: float, response: dict) -> None:
+    async def _migrate_history_from_txt_to_json(self) -> None:
-        """Update performance metrics."""
+        try:
-        metrics = self._performance_metrics
+            old_history_file = os.path.join(
-        tokens = response.get("tokens", {})
+                self._history_dir,
-
+                f"{self.normalized_name}_history.txt"
-        metrics["total_tokens"] += tokens.get("total", 0)
-        metrics["prompt_tokens"] += tokens.get("prompt", 0)
-        metrics["completion_tokens"] += tokens.get("completion", 0)
-        metrics["successful_requests"] += 1
-
-        metrics["average_latency"] = (
-            (metrics["average_latency"] * (metrics["successful_requests"] - 1) + latency)
-            / metrics["successful_requests"]
             )
-        metrics["max_latency"] = max(metrics["max_latency"], latency)
-        metrics["min_latency"] = min(metrics["min_latency"], latency)
 
-    def _handle_error(self, error: Exception) -> None:
+            if not await self._file_exists(old_history_file):
-        """
+                return
-        Enhanced error handling with comprehensive diagnostics.
 
-        Captures detailed error information, tracks error metrics,
+            _LOGGER.info(f"Found old history file for {self.instance_name}, starting migration to JSON format")
-        and provides context for troubleshooting AI processing issues.
-        """
-        self._performance_metrics["total_errors"] += 1
-        self._performance_metrics["failed_requests"] += 1
 
-        error_details = {
+            # Read old history
-            "timestamp": dt_util.utcnow().isoformat(),
+            history_entries = []
-            "model": self.model,
+            async with AsyncFileHandler(old_history_file, 'r') as f:
-            "instance": self.instance_name,
+                content = await f.read()
-            "error_message": str(error),
-            "error_type": type(error).__name__,
-            "traceback": traceback.format_exc() if _LOGGER.isEnabledFor(logging.DEBUG) else None,
-        }
 
-        # Specific error type handling
+            # Parse txt content
-        error_mapping = {
+            for line in content.split('\n'):
-            HomeAssistantError: {"is_ha_error": True},
+                if not line or line.startswith("History initialized at:"):
-            ConnectionError: {
+                    continue
-                "is_connection_error": True,
-                "is_rate_limited": True
-            },
-            TimeoutError: {"is_timeout": True},
-            PermissionError: {"is_permission_denied": True},
-            ValueError: {"is_validation_error": True}
-        }
 
-        for error_type, error_flags in error_mapping.items():
+                try:
-            if isinstance(error, error_type):
+                    # Parse the old format: "timestamp: Question: question - Response: response"
-                error_details.update(error_flags)
+                    parts = line.split(': ', 1)
-                break
+                    if len(parts) != 2:
+                        continue
 
-        # Update system state based on error type
+                    timestamp = parts[0]
-        if error_details.get("is_rate_limited"):
+                    content_parts = parts[1].split(' - ')
-            self._is_rate_limited = True
+                    if len(content_parts) != 2:
-            _LOGGER.warning(f"Rate limit detected for {self.instance_name}")
+                        continue
 
-        if error_details.get("is_connection_error"):
+                    question = content_parts[0].replace('Question: ', '')
-            self.endpoint_status = "unavailable"
+                    response = content_parts[1].replace('Response: ', '')
 
-        self.last_response = error_details
+                    history_entries.append({
-        _LOGGER.error(f"AI Processing Error: {error_details}")
+                        "timestamp": timestamp,
+                        "question": question,
+                        "response": response
+                    })
 
-        # Optional: Add more sophisticated error tracking or notification logic
+                except Exception as e:
-        if _LOGGER.isEnabledFor(logging.DEBUG):
+                    _LOGGER.warning(f"Error parsing history line: {line}. Error: {e}")
-            _LOGGER.debug(f"Full Error Traceback: {error_details['traceback']}")
+                    continue
+
+            if history_entries:
+                # Write to new JSON file
+                async with AsyncFileHandler(self._history_file, 'w') as f:
+                    await f.write(json.dumps(history_entries, indent=2))
+
+                # Backup old file
+                backup_file = old_history_file + '.backup'
+                os.rename(old_history_file, backup_file)
+
+                _LOGGER.info(
+                    f"Successfully migrated {len(history_entries)} entries "
+                    f"from txt to JSON for {self.instance_name}. "
+                    f"Old file backed up as {backup_file}"
+                )
+
+                # Update in-memory history
+                self._conversation_history = history_entries
+
+        except Exception as e:
+            _LOGGER.error(f"Error during history migration for {self.instance_name}: {e}")
+            _LOGGER.debug(traceback.format_exc())
 
     async def async_clear_history(self) -> None:
         """
@@ -807,16 +1081,11 @@ class HATextAICoordinator(DataUpdateCoordinator):
         Removes in-memory history and deletes history file.
         """
         try:
-            # Clear in-memory history
             self._conversation_history = []
-
+            if await self._file_exists(self._history_file):
-            # Safely remove history file
+                await self.hass.async_add_executor_job(os.remove, self._history_file)
-            if os.path.exists(self._history_file):
-                await asyncio.to_thread(os.remove, self._history_file)
-
             await self.async_update_ha_state()
             _LOGGER.info(f"History for {self.instance_name} cleared successfully")
-
         except Exception as e:
             _LOGGER.error(f"Error clearing history: {e}")
             _LOGGER.debug(traceback.format_exc())

custom_components/ha_text_ai/manifest.json

@@ -23,6 +23,6 @@
   "single_config_entry": false,
   "ssdp": [],
   "usb": [],
-  "version": "2.0.5-beta",
+  "version": "2.0.6-beta",
   "zeroconf": []
 }

custom_components/ha_text_ai/sensor.py

@@ -67,6 +67,7 @@ from .const import (
     ENTITY_ICON_PROCESSING,
     DEFAULT_NAME_PREFIX,
     CONF_MAX_HISTORY_SIZE,
+    MAX_ATTRIBUTE_SIZE,
 )
 
 from .coordinator import HATextAICoordinator
@@ -178,12 +179,29 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
 
     def _sanitize_attributes(self, attributes: Dict[str, Any]) -> Dict[str, Any]:
         """Sanitize all attributes for JSON serialization."""
-        return {
+        sanitized = {
             key: self._sanitize_value(value)
             for key, value in attributes.items()
             if value is not None
         }
 
+        # Log metrics for debugging
+        metrics_keys = [
+            METRIC_TOTAL_TOKENS,
+            METRIC_PROMPT_TOKENS,
+            METRIC_COMPLETION_TOKENS,
+            METRIC_SUCCESSFUL_REQUESTS,
+            METRIC_FAILED_REQUESTS,
+            METRIC_AVERAGE_LATENCY,
+            METRIC_MAX_LATENCY,
+            METRIC_MIN_LATENCY,
+        ]
+
+        metrics_values = {k: sanitized.get(k) for k in metrics_keys if k in sanitized}
+        _LOGGER.debug(f"Metrics for {self.entity_id}: {metrics_values}")
+
+        return sanitized
+
     @property
     def native_value(self) -> StateType:
         """Return the native value of the sensor."""
@@ -212,68 +230,65 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
 
         try:
             data = self.coordinator.data
+            metrics = data.get("metrics", {})
+
+            # Base attributes
             attributes = {
                 ATTR_MODEL: self._config_entry.data.get(CONF_MODEL, "Unknown"),
-                ATTR_API_PROVIDER: self._config_entry.data.get(
+                ATTR_API_PROVIDER: self._config_entry.data.get(CONF_API_PROVIDER, "Unknown"),
-                    CONF_API_PROVIDER, "Unknown"
-                ),
                 ATTR_API_STATUS: self._current_state,
-                ATTR_TOTAL_ERRORS: self._error_count,
+                ATTR_TOTAL_ERRORS: metrics.get("total_errors", 0),
-                ATTR_LAST_ERROR: self._last_error,
                 "instance_name": self._instance_name,
                 "normalized_name": self._normalized_name,
-                ATTR_SYSTEM_PROMPT: data.get("system_prompt"),
+                ATTR_SYSTEM_PROMPT: (data.get("system_prompt", "")[:MAX_ATTRIBUTE_SIZE]
+                                   if data.get("system_prompt") else None),
                 ATTR_IS_PROCESSING: data.get("is_processing", False),
                 ATTR_IS_RATE_LIMITED: data.get("is_rate_limited", False),
                 ATTR_IS_MAINTENANCE: data.get("is_maintenance", False),
                 ATTR_ENDPOINT_STATUS: data.get("endpoint_status", "unknown"),
-                ATTR_UPTIME: data.get("uptime", 0),
+                ATTR_UPTIME: round(data.get("uptime", 0), 2),
                 ATTR_HISTORY_SIZE: data.get("history_size", 0),
-                ATTR_CONVERSATION_HISTORY: data.get("conversation_history", []),
             }
 
-            # Add metrics
+            # History limit
-            metrics = data.get("metrics", {})
+            conversation_history = data.get("conversation_history", [])
+            if conversation_history:
+                limited_history = []
+                for entry in conversation_history:
+                    limited_entry = {
+                        "timestamp": entry["timestamp"],
+                        "question": entry["question"][:MAX_ATTRIBUTE_SIZE],
+                        "response": entry["response"][:MAX_ATTRIBUTE_SIZE]
+                    }
+                    limited_history.append(limited_entry)
+                attributes[ATTR_CONVERSATION_HISTORY] = limited_history
+
+            # Metrics
             if isinstance(metrics, dict):
-                self._metrics = metrics
+                attributes.update({
-                attributes.update(
-                    {
                     METRIC_TOTAL_TOKENS: metrics.get("total_tokens", 0),
                     METRIC_PROMPT_TOKENS: metrics.get("prompt_tokens", 0),
                     METRIC_COMPLETION_TOKENS: metrics.get("completion_tokens", 0),
-                        METRIC_SUCCESSFUL_REQUESTS: metrics.get(
+                    METRIC_SUCCESSFUL_REQUESTS: metrics.get("successful_requests", 0),
-                            "successful_requests", 0
-                        ),
                     METRIC_FAILED_REQUESTS: metrics.get("failed_requests", 0),
-                        METRIC_AVERAGE_LATENCY: metrics.get("average_latency", 0),
+                    METRIC_AVERAGE_LATENCY: round(metrics.get("average_latency", 0), 2),
-                        METRIC_MAX_LATENCY: metrics.get("max_latency", 0),
+                    METRIC_MAX_LATENCY: round(metrics.get("max_latency", 0), 2),
-                        METRIC_MIN_LATENCY: metrics.get("min_latency", float("inf")),
+                    METRIC_MIN_LATENCY: (metrics.get("min_latency")
-                    }
+                                       if metrics.get("min_latency") != float("inf")
-                )
+                                       else None),
+                })
 
-            # Add last response
+            # Last response handling
             last_response = data.get("last_response", {})
             if isinstance(last_response, dict):
-                self._last_response = last_response
+                attributes.update({
-                attributes.update(
+                    ATTR_RESPONSE: last_response.get("response", "")[:MAX_ATTRIBUTE_SIZE],
-                    {
+                    ATTR_QUESTION: last_response.get("question", "")[:MAX_ATTRIBUTE_SIZE],
-                        ATTR_RESPONSE: last_response.get("response", ""),
-                        ATTR_QUESTION: last_response.get("question", ""),
                     "last_model": last_response.get("model", ""),
                     "last_timestamp": last_response.get("timestamp", ""),
-                        "last_error": last_response.get("error"),
+                    "last_error": (last_response.get("error", "")[:MAX_ATTRIBUTE_SIZE]
-                    }
+                                 if last_response.get("error") else None),
-                )
+                })
-
-            # Add performance metrics if available
-            if ATTR_PERFORMANCE_METRICS in data:
-                attributes[ATTR_PERFORMANCE_METRICS] = data[
-                    ATTR_PERFORMANCE_METRICS
-                ]
-
-            # Add API version if available
-            if ATTR_API_VERSION in data:
-                attributes[ATTR_API_VERSION] = data[ATTR_API_VERSION]
 
             return self._sanitize_attributes(attributes)
 
@@ -299,6 +314,12 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
 
             self._is_processing = data.get("is_processing", False)
 
+            # Update metrics
+            metrics = data.get("metrics", {})
+            if isinstance(metrics, dict):
+                self._metrics.update(metrics)
+                _LOGGER.debug(f"Updated metrics for {self.entity_id}: {self._metrics}")
+
             # Update conversation history and system prompt
             self._conversation_history = data.get("conversation_history", [])
             self._system_prompt = data.get("system_prompt")

structure.md

@@ -1,25 +1,32 @@
 ```
 ha-text-ai/
-│
+├── custom_components
-├── custom_components/
+│   └── ha_text_ai
-    ├── ha_text_ai/
+│       ├── __init__.py
-    │   ├── __init__.py
+│       ├── api_client.py
-    │   ├── config_flow.py
+│       ├── config_flow.py
-    │   ├── coordinator.py
+│       ├── const.py
-    │   ├── manifest.json
+│       ├── coordinator.py
-    │   ├── sensor.py
+│       ├── icons
-    │   ├── services.yaml
+│       │   ├── dark_icon.png
-    │   ├── const.py
+│       │   ├── dark_icon@2x.png
-    │   └── api_client.py
+│       │   ├── dark_logo.png
-    │
+│       │   ├── dark_logo@2x.png
-    ├── translations/
+│       │   ├── icon.png
-    │   ├── en.json
+│       │   ├── icon@2x.png
-    │   ├── de.json
+│       │   ├── logo.png
-    │   └── ru.json
+│       │   └── logo@2x.png
-    │
+│       ├── manifest.json
-    └── icons/
+│       ├── sensor.py
-        ├── icon.png
+│       ├── services.yaml
-        ├── icon@2x.png
+│       └── translations
-        ├── logo.png
+│           ├── de.json
-        └── logo@2x.png
+│           ├── en.json
+│           ├── es.json
+│           ├── hi.json
+│           ├── it.json
+│           ├── ru.json
+│           ├── sr.json
+│           └── zh.json
+
 ```