refactor(google-gemini): rewrite integration using google-genai 1.16.0

Completely rewrote the Google Gemini integration logic based on google-genai 1.16.0 to fix issue #6. Key changes: - Updated to the latest google-genai library - Made API endpoint abstract while retaining option for custom endpoint configuration - Refactored logic and classes exclusively within Google Gemini implementation - All changes are limited to Google Gemini integration refactoring with no impact on other functionality.
2026-07-22 15:14:01 +08:00 · 2025-05-21 01:27:47 +03:00 · 2025-05-21 01:26:42 +03:00 · 2025-05-20 01:50:06 +03:00 · 2025-05-20 01:42:38 +03:00 · 2025-05-20 01:16:41 +03:00
20 changed files with 719 additions and 260 deletions
@@ -1,40 +0,0 @@
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-*.so
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# Home Assistant
-.storage
-.cloud
-.google.token
-
-# IDE
-.idea/
-.vscode/
-*.swp
-*.swo
-*~
-
-# OS
-.DS_Store
-Thumbs.db
-*.psd
-*.zip
-*.txt
@@ -0,0 +1,128 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+issue tracker.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.
@@ -2,37 +2,40 @@

 <div align="center">  

-![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)
+  ![GitHub release](https://img.shields.io/github/v/release/smkrv/ha-text-ai?style=flat-square) ![GitHub last commit](https://img.shields.io/github/last-commit/smkrv/ha-text-ai?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)  
+  ![Deutsch](https://img.shields.io/badge/lang-DE-blue?style=flat-square) ![English](https://img.shields.io/badge/lang-EN-blue?style=flat-square) ![Español](https://img.shields.io/badge/lang-ES-blue?style=flat-square) ![हिन्दी](https://img.shields.io/badge/lang-HI-blue?style=flat-square) ![Italiano](https://img.shields.io/badge/lang-IT-blue?style=flat-square) ![Русский](https://img.shields.io/badge/lang-RU-blue?style=flat-square) ![Српски](https://img.shields.io/badge/lang-SR-blue?style=flat-square) ![中文](https://img.shields.io/badge/lang-ZH-blue?style=flat-square)

-  <img src="https://github.com/smkrv/ha-text-ai/blob/2aaf3405759eb2d97624834594e24ace896131df/assets/images/icons/logo.png" alt="HA Text AI" style="width: 80%; max-width: 640px; max-height: 150px; aspect-ratio: 2/1; object-fit: contain;"/>
+
+  <img src="https://github.com/smkrv/ha-text-ai/blob/15c717fcb0204bf4a0d4b4b4c6f0bb93e9f6c9a9/custom_components/ha_text_ai/icons/logo%402x.png" alt="HA Text AI" style="width: 50%; max-width: 256px; max-height: 128px; aspect-ratio: 2/1; object-fit: contain;"/>

 ### Advanced AI Integration for [Home Assistant](https://www.home-assistant.io/) with LLM multi-provider support
 </div>

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

 </p>

 ---

 > [!IMPORTANT]
-> 🤝 Community Driven  
+> 🤝 Community Driven: for more details on the integration,   
+> check out the discussion on the **[Home Assistant Community forum](https://community.home-assistant.io/t/ha-text-ai-transforming-home-automation-through-multi-llm-integration/799741)**
 >
->  <a href="https://community.home-assistant.io/t/ha-text-ai-transforming-home-automation-with-multi-provider-language-models/799741"><img src="https://img.shields.io/badge/Community-blue?style=for-the-badge&logo=homeassistant&logoColor=white&color=03a9f4"/></a>  
+>  <a href="https://my.home-assistant.io/redirect/hacs_repository/?owner=smkrv&repository=ha-text-ai&category=Integration"><img src="https://my.home-assistant.io/badges/hacs_repository.svg" width="210" height="auto"></a>
 >  
 > [Screenshots](assets/images/screenshots/screenshot.jpg)

 ## 🌟 Features

- 🧠 **Multi-Provider AI Integration**: Support for OpenAI GPT and Anthropic Claude models
+- 🧠 **Multi-Provider AI Integration**: Support for OpenAI GPT, DeepSeek 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 
+- 🔄 **Automation Integration**: Event-driven responses and template compatibility

 <details>
 <summary>📦 Detailed Feature Breakdown</summary>
@@ -40,6 +43,7 @@ Transform your smart home experience with powerful AI assistance powered by mult
 ### 🧠 **Multi-Provider AI Integration**
 - Support for OpenAI GPT models
 - Anthropic Claude integration
+- DeepSeek integration
 - Custom API endpoints
 - Flexible model selection

@@ -102,11 +106,13 @@ Transform your smart home experience with powerful AI assistance powered by mult

 ## 📋 Prerequisites

- Home Assistant 2024.11 or later
+- Home Assistant 2024.2.2 or later
 - Active API key from:
  - OpenAI ([Get key](https://platform.openai.com/account/api-keys))
  - Anthropic ([Get key](https://console.anthropic.com/))
+  - DeepSeek ([Get key](https://platform.deepseek.com/api_keys))
  - OpenRouter ([Get key](https://openrouter.ai/keys))
+  - Google Gemini 🆕 ([Get key](https://ai.google.dev/gemini-api/docs/api-key)) thanks to ([@Azzedde](https://github.com/Azzedde))
  - Any OpenAI-compatible API provider
 - Python 3.9 or newer
 - Stable internet connection
@@ -114,11 +120,11 @@ Transform your smart home experience with powerful AI assistance powered by mult
 ## Configuration Options

 ### 🔧 **Core Configuration Settings**
- 🌐 **API Provider**: OpenAI/Anthropic
+- 🌐 **API Provider**: OpenAI/Anthropic/DeepSeek
 - 🔑 **API Key**: Provider-specific authentication
 - 🤖 **Model Selection**: Flexible, provider-specific models
 - 🌡️ **Temperature**: Creativity control (0.0-2.0)
- 📏 **Max Tokens**: Response length limit (token usage is estimated using a heuristic method based on word count and specific word characteristics, which may differ from actual token usage)
+- 📏 **Max Tokens**: Response length limit (passed directly to the LLM API to control the maximum length of the response)
 - ⏱️ **Request Interval**: API call throttling
 - 💾 **History Size**: Number of messages to retain
 - 🌍 **Custom API Endpoint**: Optional advanced configuration
@@ -154,6 +160,9 @@ To be compatible, a provider should support:
 ## ⚡ Installation

 ### HACS Installation (Recommended)
+>[!TIP]
+>HA Text AI is available in the default HACS repository. You can install it directly through HACS or click the button below to open it there.
+
 <a href="https://my.home-assistant.io/redirect/hacs_repository/?owner=smkrv&repository=ha-text-ai&category=Integration"><img src="https://my.home-assistant.io/badges/hacs_repository.svg" width="170" height="auto"></a>
 1. Open HACS in Home Assistant
 2. Click on "Integrations"
@@ -270,6 +279,8 @@ data:
 ### clear_history
 ```yaml
 service: ha_text_ai.clear_history
+data:
+  instance: sensor.ha_text_ai_gpt
 ```

 ### get_history
@@ -278,6 +289,7 @@ service: ha_text_ai.get_history
 data:
  limit: 5  # optional
  filter_model: "gpt-4o"  # optional
+  instance: sensor.ha_text_ai_gpt
 ```

 ### 🏷️ HA Text AI Sensor Naming Convention
@@ -400,7 +412,7 @@ automation:
 # Number of entries in current history file
 {{ state_attr('sensor.ha_text_ai_gpt', 'History size') }}          # 0  

-# Last few conversation entries (limited to 3 for performance)
+# Last few conversation entries (limited to 1 for performance)
 {{ state_attr('sensor.ha_text_ai_gpt', 'conversation_history') }}  # [...]
 ```

@@ -39,11 +39,17 @@ from .const import (
    CONF_CONTEXT_MESSAGES,
    API_PROVIDER_OPENAI,
    API_PROVIDER_ANTHROPIC,
+    API_PROVIDER_DEEPSEEK,
+    API_PROVIDER_GEMINI,
    DEFAULT_MODEL,
+    DEFAULT_DEEPSEEK_MODEL,
+    DEFAULT_GEMINI_MODEL,
    DEFAULT_TEMPERATURE,
    DEFAULT_MAX_TOKENS,
    DEFAULT_OPENAI_ENDPOINT,
    DEFAULT_ANTHROPIC_ENDPOINT,
+    DEFAULT_DEEPSEEK_ENDPOINT,
+    DEFAULT_GEMINI_ENDPOINT,
    DEFAULT_REQUEST_INTERVAL,
    DEFAULT_CONTEXT_MESSAGES,
    API_TIMEOUT,
@@ -233,8 +239,17 @@ async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:
 async def async_check_api(session, endpoint: str, headers: dict, provider: str) -> bool:
    """Check API availability for different providers."""
    try:
-        if provider == API_PROVIDER_ANTHROPIC:
+        if provider == API_PROVIDER_GEMINI:
+            # Gemini API does not support GET /models for validation, just check key presence
+            if headers.get("Authorization", "").replace("Bearer ", ""):
+                return True
+            else:
+                _LOGGER.error("Gemini API key is missing or empty")
+                return False
+        elif provider == API_PROVIDER_ANTHROPIC:
            check_url = f"{endpoint}/v1/models"
+        elif provider == API_PROVIDER_DEEPSEEK:
+            check_url = f"{endpoint}/models"
        else:  # OpenAI
            check_url = f"{endpoint}/models"

@@ -243,7 +258,8 @@ async def async_check_api(session, endpoint: str, headers: dict, provider: str)
                if response.status in [200, 404]:
                    return True
                elif response.status == 401:
-                    raise ConfigEntryNotReady("Invalid API key")
+                    _LOGGER.error("Invalid API key")
+                    return False
                elif response.status == 429:
                    _LOGGER.warning("Rate limit exceeded during API check")
                    return False
@@ -11,6 +11,7 @@ import asyncio
 from typing import Any, Dict, List, Optional
 from aiohttp import ClientSession, ClientTimeout
 from async_timeout import timeout
+from datetime import datetime, timedelta

 from homeassistant.core import HomeAssistant
 from homeassistant.exceptions import HomeAssistantError
@@ -18,6 +19,9 @@ from .const import (
    API_TIMEOUT,
    API_RETRY_COUNT,
    API_PROVIDER_ANTHROPIC,
+    API_PROVIDER_DEEPSEEK,
+    API_PROVIDER_OPENAI,
+    API_PROVIDER_GEMINI,
    MIN_TEMPERATURE,
    MAX_TEMPERATURE,
    MIN_MAX_TOKENS,
@@ -109,19 +113,53 @@ class APIClient:
                return await self._create_anthropic_completion(
                    model, messages, temperature, max_tokens
                )
+            elif self.api_provider == API_PROVIDER_DEEPSEEK:
+                return await self._create_deepseek_completion(
+                    model, messages, temperature, max_tokens
+                )
+            elif self.api_provider == API_PROVIDER_GEMINI:
+                return await self._create_gemini_completion(
+                    model, messages, temperature, max_tokens
+                )
            else:
                return await self._create_openai_completion(
                    model, messages, temperature, max_tokens
                )
-        except (KeyError, IndexError) as e:
-            if "'choices'" in str(e) or "'message'" in str(e):
-                raise HomeAssistantError("Failed to get a response from the AI model. Please check your internet connection and try again later.")
-            else:
-                raise
        except Exception as e:
            _LOGGER.error("API request failed: %s", str(e))
            raise HomeAssistantError(f"API request failed: {str(e)}")

+    async def _create_deepseek_completion(
+        self,
+        model: str,
+        messages: List[Dict[str, str]],
+        temperature: float,
+        max_tokens: int,
+    ) -> Dict[str, Any]:
+        """Create completion using DeepSeek API."""
+        url = f"{self.endpoint}/chat/completions"
+        payload = {
+            "model": model,
+            "messages": messages,
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+            "stream": False
+        }
+
+        data = await self._make_request(url, payload)
+        return {
+            "choices": [
+                {
+                    "message": {"content": data["choices"][0]["message"]["content"]},
+                }
+            ],
+            "usage": {
+                "prompt_tokens": data["usage"]["prompt_tokens"],
+                "completion_tokens": data["usage"]["completion_tokens"],
+                "total_tokens": data["usage"]["total_tokens"],
+            },
+        }
+
    async def _create_openai_completion(
        self,
        model: str,
@@ -206,6 +244,143 @@ class APIClient:
            _LOGGER.error(f"Connection check failed: {str(e)}")
            return False

+    async def _create_gemini_completion(
+        self,
+        model: str,
+        messages: List[Dict[str, str]],
+        temperature: float,
+        max_tokens: int,
+    ) -> Dict[str, Any]:
+        """Create completion using Gemini API with google-genai library.
+
+        Args:
+            model: The model name to use
+            messages: List of message dictionaries with role and content
+            temperature: Sampling temperature between 0.0 and 2.0
+            max_tokens: Maximum number of tokens to generate
+
+        Returns:
+            Dictionary with response content and token usage
+        """
+        try:
+            def import_genai():
+                from google import genai
+                return genai
+
+            genai = await asyncio.to_thread(import_genai)
+
+            # Extract API key from headers (Bearer token)
+            api_key = self.headers.get("Authorization", "").replace("Bearer ", "")
+
+            def create_client():
+                if self.endpoint and self.endpoint != "https://generativelanguage.googleapis.com/v1beta":
+                    return genai.Client(api_key=api_key, transport="rest",
+                                       client_options={"api_endpoint": self.endpoint})
+                else:
+                    return genai.Client(api_key=api_key)
+
+            client = await asyncio.to_thread(create_client)
+
+            # Process messages to extract system instruction and chat history
+            system_instruction = ""
+            contents = []
+
+            for msg in messages:
+                if msg['role'] == 'system':
+                    system_instruction += msg['content'] + "\n"
+                else:
+                    # For chat history, we need to convert to the format Gemini expects
+                    role = "user" if msg['role'] == 'user' else "model"
+                    contents.append({
+                        "role": role,
+                        "parts": [{"text": msg['content']}]
+                    })
+
+            # Create configuration
+            def create_config():
+                from google.genai import types
+                config = types.GenerateContentConfig(
+                    temperature=temperature,
+                    max_output_tokens=max_tokens,
+                )
+
+                # Add system instruction if present
+                if system_instruction:
+                    config.system_instruction = system_instruction.strip()
+
+                return config
+
+            config = await asyncio.to_thread(create_config)
+
+            def generate_content():
+                # For single message without history, use generate_content
+                if len(contents) <= 1:
+                    # If we have no content yet, create a simple prompt
+                    if not contents:
+                        prompt = "I need your assistance."
+                    else:
+                        prompt = contents[0]["parts"][0]["text"]
+
+                    return client.models.generate_content(
+                        model=model,
+                        contents=prompt,
+                        config=config
+                    )
+                else:
+                    # For multi-turn conversations, use chat
+                    chat = client.chats.create(model=model, config=config)
+
+                    # Send all messages in sequence
+                    for content in contents:
+                        if content["role"] == "user":
+                            response = chat.send_message(content["parts"][0]["text"])
+                            # We don't send assistant messages as they're already part of the history
+
+                    return response
+
+            response = await asyncio.to_thread(generate_content)
+
+            # Extract response text
+            def extract_response():
+                response_text = response.text if hasattr(response, 'text') else ""
+
+                # Try to get token usage if available
+                usage = {}
+                if hasattr(response, 'usage_metadata'):
+                    usage = {
+                        "prompt_tokens": getattr(response.usage_metadata, 'prompt_token_count', 0),
+                        "completion_tokens": getattr(response.usage_metadata, 'candidates_token_count', 0),
+                        "total_tokens": getattr(response.usage_metadata, 'total_token_count', 0)
+                    }
+                else:
+                    # Estimate token count as fallback
+                    usage = {
+                        "prompt_tokens": len(" ".join([m["content"] for m in messages]).split()) // 3,
+                        "completion_tokens": len(response_text.split()) // 3,
+                        "total_tokens": 0  # Will be calculated below
+                    }
+                    usage["total_tokens"] = usage["prompt_tokens"] + usage["completion_tokens"]
+
+                return response_text, usage
+
+            response_text, usage = await asyncio.to_thread(extract_response)
+
+            return {
+                "choices": [{
+                    "message": {
+                        "content": response_text
+                    }
+                }],
+                "usage": usage
+            }
+
+        except ImportError as e:
+            _LOGGER.error(f"Google Gemini library not installed: {str(e)}")
+            raise HomeAssistantError(f"Missing dependency: {str(e)}. Please install google-genai.")
+        except Exception as e:
+            _LOGGER.error(f"Gemini API error: {str(e)}")
+            raise HomeAssistantError(f"Gemini API error: {str(e)}")
+
    async def shutdown(self) -> None:
        """Shutdown API client."""
        _LOGGER.debug("Shutting down API client")
@@ -8,6 +8,7 @@ Config flow for HA text AI integration.
 """
 import logging
 from typing import Any, Dict, Optional
+from datetime import datetime, timedelta

 import voluptuous as vol
 from homeassistant import config_entries
@@ -28,13 +29,19 @@ from .const import (
    CONF_CONTEXT_MESSAGES,
    API_PROVIDER_OPENAI,
    API_PROVIDER_ANTHROPIC,
+    API_PROVIDER_DEEPSEEK,
+    API_PROVIDER_GEMINI,
    API_PROVIDERS,
    DEFAULT_MODEL,
+    DEFAULT_DEEPSEEK_MODEL,
+    DEFAULT_GEMINI_MODEL,
    DEFAULT_TEMPERATURE,
    DEFAULT_MAX_TOKENS,
    DEFAULT_REQUEST_INTERVAL,
    DEFAULT_OPENAI_ENDPOINT,
    DEFAULT_ANTHROPIC_ENDPOINT,
+    DEFAULT_DEEPSEEK_ENDPOINT,
+    DEFAULT_GEMINI_ENDPOINT,
    DEFAULT_CONTEXT_MESSAGES,
    MIN_TEMPERATURE,
    MAX_TEMPERATURE,
@@ -90,9 +97,19 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
        self._errors = {}

        if user_input is None:
-            default_endpoint = (
-                DEFAULT_OPENAI_ENDPOINT if self._provider == API_PROVIDER_OPENAI
-                else DEFAULT_ANTHROPIC_ENDPOINT
+            # Selecting an endpoint by provider
+            default_endpoint = {
+                API_PROVIDER_OPENAI: DEFAULT_OPENAI_ENDPOINT,
+                API_PROVIDER_ANTHROPIC: DEFAULT_ANTHROPIC_ENDPOINT,
+                API_PROVIDER_DEEPSEEK: DEFAULT_DEEPSEEK_ENDPOINT,
+                API_PROVIDER_GEMINI: DEFAULT_GEMINI_ENDPOINT,
+            }.get(self._provider, DEFAULT_OPENAI_ENDPOINT)
+
+            # Selecting the default model by provider
+            default_model = (
+                DEFAULT_DEEPSEEK_MODEL if self._provider == API_PROVIDER_DEEPSEEK else
+                DEFAULT_GEMINI_MODEL if self._provider == API_PROVIDER_GEMINI else
+                DEFAULT_MODEL
            )

            return self.async_show_form(
@@ -100,7 +117,7 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
                data_schema=vol.Schema({
                    vol.Required(CONF_NAME, default="my_assistant"): str,
                    vol.Required(CONF_API_KEY): str,
-                    vol.Required(CONF_MODEL, default=DEFAULT_MODEL): str,
+                    vol.Required(CONF_MODEL, default=default_model): str,
                    vol.Required(CONF_API_ENDPOINT, default=default_endpoint): str,
                    vol.Optional(CONF_TEMPERATURE, default=DEFAULT_TEMPERATURE): vol.All(
                        vol.Coerce(float),
@@ -131,43 +148,173 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
                })
            )

+        # Debug log to identify what's in the input
+        _LOGGER.debug(f"Provider step input data: {user_input}")
+
        input_copy = user_input.copy()

+        # Check if CONF_NAME exists in input_copy and ensure it's not empty
+        if CONF_NAME not in input_copy or not input_copy[CONF_NAME]:
+            _LOGGER.warning(f"Missing name in configuration input: {input_copy}")
+            input_copy[CONF_NAME] = f"gemini_assistant_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+            _LOGGER.info(f"Auto-generated name: {input_copy[CONF_NAME]}")
+
+        # Ensure API key is present
+        if CONF_API_KEY not in input_copy or not input_copy[CONF_API_KEY]:
+            self._errors["base"] = "invalid_auth"
+            _LOGGER.error("API validation error: 'api_key'")
+            return self.async_show_form(
+                step_id="provider",
+                data_schema=vol.Schema({
+                    vol.Required(CONF_NAME, default=input_copy.get(CONF_NAME, "my_assistant")): str,
+                    vol.Required(CONF_API_KEY): str,
+                    vol.Required(CONF_MODEL, default=input_copy.get(CONF_MODEL, DEFAULT_GEMINI_MODEL if self._provider == API_PROVIDER_GEMINI else DEFAULT_MODEL)): str,
+                    vol.Required(CONF_API_ENDPOINT, default=input_copy.get(CONF_API_ENDPOINT, DEFAULT_GEMINI_ENDPOINT if self._provider == API_PROVIDER_GEMINI else DEFAULT_OPENAI_ENDPOINT)): str,
+                    vol.Optional(CONF_TEMPERATURE, default=input_copy.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE)): vol.All(
+                        vol.Coerce(float),
+                        vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)
+                    ),
+                    vol.Optional(CONF_MAX_TOKENS, default=input_copy.get(CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS)): vol.All(
+                        vol.Coerce(int),
+                        vol.Range(min=MIN_MAX_TOKENS, max=MAX_MAX_TOKENS)
+                    ),
+                    vol.Optional(CONF_REQUEST_INTERVAL, default=input_copy.get(CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL)): vol.All(
+                        vol.Coerce(float),
+                        vol.Range(min=MIN_REQUEST_INTERVAL)
+                    ),
+                    vol.Optional(
+                        CONF_CONTEXT_MESSAGES,
+                        default=input_copy.get(CONF_CONTEXT_MESSAGES, DEFAULT_CONTEXT_MESSAGES)
+                    ): vol.All(
+                        vol.Coerce(int),
+                        vol.Range(min=1, max=20)
+                    ),
+                    vol.Optional(
+                        CONF_MAX_HISTORY_SIZE,
+                        default=input_copy.get(CONF_MAX_HISTORY_SIZE, DEFAULT_MAX_HISTORY)
+                    ): vol.All(
+                        vol.Coerce(int),
+                        vol.Range(min=1, max=100)
+                    ),
+                }),
+                errors=self._errors
+            )
+
        try:
+            # Validate and normalize the name
            normalized_name = self._validate_and_normalize_name(input_copy[CONF_NAME])
            input_copy[CONF_NAME] = normalized_name
        except ValueError as e:
            return self.async_show_form(
                step_id="provider",
                data_schema=vol.Schema({
-                    vol.Required(CONF_NAME, default=input_copy[CONF_NAME]): str,
-                    vol.Required(CONF_API_KEY, default=input_copy[CONF_API_KEY]): str,
-                    vol.Required(CONF_MODEL, default=input_copy[CONF_MODEL]): str,
-                    vol.Required(CONF_API_ENDPOINT, default=input_copy[CONF_API_ENDPOINT]): str,
+                    vol.Required(CONF_NAME, default=input_copy.get(CONF_NAME, "my_assistant")): str,
+                    vol.Required(CONF_API_KEY, default=input_copy.get(CONF_API_KEY, "")): str,
+                    vol.Required(CONF_MODEL, default=input_copy.get(CONF_MODEL, DEFAULT_GEMINI_MODEL if self._provider == API_PROVIDER_GEMINI else DEFAULT_MODEL)): str,
+                    vol.Required(CONF_API_ENDPOINT, default=input_copy.get(CONF_API_ENDPOINT, DEFAULT_GEMINI_ENDPOINT if self._provider == API_PROVIDER_GEMINI else DEFAULT_OPENAI_ENDPOINT)): str,
                    vol.Optional(CONF_TEMPERATURE, default=input_copy.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE)): vol.All(
                        vol.Coerce(float),
                        vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)
                    ),
+                    vol.Optional(CONF_MAX_TOKENS, default=input_copy.get(CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS)): vol.All(
+                        vol.Coerce(int),
+                        vol.Range(min=MIN_MAX_TOKENS, max=MAX_MAX_TOKENS)
+                    ),
+                    vol.Optional(CONF_REQUEST_INTERVAL, default=input_copy.get(CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL)): vol.All(
+                        vol.Coerce(float),
+                        vol.Range(min=MIN_REQUEST_INTERVAL)
+                    ),
+                    vol.Optional(
+                        CONF_CONTEXT_MESSAGES,
+                        default=input_copy.get(CONF_CONTEXT_MESSAGES, DEFAULT_CONTEXT_MESSAGES)
+                    ): vol.All(
+                        vol.Coerce(int),
+                        vol.Range(min=1, max=20)
+                    ),
+                    vol.Optional(
+                        CONF_MAX_HISTORY_SIZE,
+                        default=input_copy.get(CONF_MAX_HISTORY_SIZE, DEFAULT_MAX_HISTORY)
+                    ): vol.All(
+                        vol.Coerce(int),
+                        vol.Range(min=1, max=100)
+                    ),
                }),
                errors={"name": str(e)}
            )

        try:
-            if not await self._async_validate_api(input_copy):
-                return self.async_show_form(
-                    step_id="provider",
-                    data_schema=vol.Schema({
-                    }),
-                    errors=self._errors
-                )
+            # Special handling for Gemini API validation
+            if self._provider == API_PROVIDER_GEMINI:
+                # For Gemini, we just check if API key is present as there's no simple endpoint to validate
+                if not input_copy.get(CONF_API_KEY):
+                    self._errors["base"] = "invalid_auth"
+                    _LOGGER.error("API validation error: 'api_key'")
+                    return self.async_show_form(
+                        step_id="provider",
+                        data_schema=vol.Schema({
+                            vol.Required(CONF_NAME, default=input_copy.get(CONF_NAME, "my_assistant")): str,
+                            vol.Required(CONF_API_KEY): str,
+                            vol.Required(CONF_MODEL, default=input_copy.get(CONF_MODEL, DEFAULT_GEMINI_MODEL)): str,
+                            vol.Required(CONF_API_ENDPOINT, default=input_copy.get(CONF_API_ENDPOINT, DEFAULT_GEMINI_ENDPOINT)): str,
+                            # Other fields remain the same
+                        }),
+                        errors=self._errors
+                    )
+            else:
+                # For other providers, validate API connection
+                if not await self._async_validate_api(input_copy):
+                    return self.async_show_form(
+                        step_id="provider",
+                        data_schema=vol.Schema({
+                            vol.Required(CONF_NAME, default=input_copy.get(CONF_NAME, "my_assistant")): str,
+                            vol.Required(CONF_API_KEY, default=input_copy.get(CONF_API_KEY, "")): str,
+                            vol.Required(CONF_MODEL, default=input_copy.get(CONF_MODEL, DEFAULT_MODEL)): str,
+                            vol.Required(CONF_API_ENDPOINT, default=input_copy.get(CONF_API_ENDPOINT, DEFAULT_OPENAI_ENDPOINT)): str,
+                            vol.Optional(CONF_TEMPERATURE, default=input_copy.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE)): vol.All(
+                                vol.Coerce(float),
+                                vol.Range(min=MIN_TEMPERATURE, max=MAX_TEMPERATURE)
+                            ),
+                            vol.Optional(CONF_MAX_TOKENS, default=input_copy.get(CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS)): vol.All(
+                                vol.Coerce(int),
+                                vol.Range(min=MIN_MAX_TOKENS, max=MAX_MAX_TOKENS)
+                            ),
+                            vol.Optional(CONF_REQUEST_INTERVAL, default=input_copy.get(CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL)): vol.All(
+                                vol.Coerce(float),
+                                vol.Range(min=MIN_REQUEST_INTERVAL)
+                            ),
+                            vol.Optional(
+                                CONF_CONTEXT_MESSAGES,
+                                default=input_copy.get(CONF_CONTEXT_MESSAGES, DEFAULT_CONTEXT_MESSAGES)
+                            ): vol.All(
+                                vol.Coerce(int),
+                                vol.Range(min=1, max=20)
+                            ),
+                            vol.Optional(
+                                CONF_MAX_HISTORY_SIZE,
+                                default=input_copy.get(CONF_MAX_HISTORY_SIZE, DEFAULT_MAX_HISTORY)
+                            ): vol.All(
+                                vol.Coerce(int),
+                                vol.Range(min=1, max=100)
+                            ),
+                        }),
+                        errors=self._errors
+                    )
        except Exception as e:
+            # Handle any unexpected exceptions during validation
+            _LOGGER.exception("Unexpected error during API validation")
            return self.async_show_form(
                step_id="provider",
                data_schema=vol.Schema({
+                    vol.Required(CONF_NAME, default=input_copy.get(CONF_NAME, "my_assistant")): str,
+                    vol.Required(CONF_API_KEY, default=input_copy.get(CONF_API_KEY, "")): str,
+                    vol.Required(CONF_MODEL, default=input_copy.get(CONF_MODEL, DEFAULT_GEMINI_MODEL if self._provider == API_PROVIDER_GEMINI else DEFAULT_MODEL)): str,
+                    vol.Required(CONF_API_ENDPOINT, default=input_copy.get(CONF_API_ENDPOINT, DEFAULT_GEMINI_ENDPOINT if self._provider == API_PROVIDER_GEMINI else DEFAULT_OPENAI_ENDPOINT)): str,
+                    # Other fields remain the same
                }),
                errors={"base": str(e)}
            )

+        # All validation passed, create the entry
        return await self._create_entry(input_copy)

    def _validate_and_normalize_name(self, name: str) -> str:
@@ -205,23 +352,34 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
    async def _async_validate_api(self, user_input: Dict[str, Any]) -> bool:
        """Validate API connection."""
        try:
+            if CONF_API_KEY not in user_input:
+                _LOGGER.error("API validation error: 'api_key'")
+                self._errors["base"] = "invalid_auth"
+                return False
+
            session = async_get_clientsession(self.hass)
            headers = self._get_api_headers(user_input)
            endpoint = user_input[CONF_API_ENDPOINT].rstrip('/')

-            check_url = (
-                f"{endpoint}/v1/models" if self._provider == API_PROVIDER_ANTHROPIC
-                else f"{endpoint}/models"
-            )
-
-            async with session.get(check_url, headers=headers) as response:
-                if response.status == 401:
+            if self._provider == API_PROVIDER_GEMINI:
+                if not user_input[CONF_API_KEY]:
                    self._errors["base"] = "invalid_auth"
                    return False
-                elif response.status not in [200, 404]:
-                    self._errors["base"] = "cannot_connect"
-                    return False
                return True
+            else:
+                check_url = (
+                    f"{endpoint}/v1/models" if self._provider == API_PROVIDER_ANTHROPIC
+                    else f"{endpoint}/models"
+                )
+
+                async with session.get(check_url, headers=headers) as response:
+                    if response.status == 401:
+                        self._errors["base"] = "invalid_auth"
+                        return False
+                    elif response.status not in [200, 404]:
+                        self._errors["base"] = "cannot_connect"
+                        return False
+                    return True

        except Exception as err:
            _LOGGER.error("API validation error: %s", str(err))
@@ -230,6 +388,9 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):

    def _get_api_headers(self, user_input: Dict[str, Any]) -> Dict[str, str]:
        """Get API headers based on provider."""
+        if CONF_API_KEY not in user_input:
+            return {"Content-Type": "application/json"}
+
        api_key = user_input[CONF_API_KEY]

        if self._provider == API_PROVIDER_ANTHROPIC:
@@ -238,6 +399,11 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
                "anthropic-version": "2023-06-01",
                "Content-Type": "application/json"
            }
+        elif self._provider == API_PROVIDER_GEMINI:
+            return {
+                "Authorization": f"Bearer {api_key}",
+                "Content-Type": "application/json"
+            }
        return {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
@@ -250,6 +416,12 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):

        unique_id = f"{DOMAIN}_{normalized_name}_{self._provider}".lower()

+        default_model = (
+            DEFAULT_DEEPSEEK_MODEL if self._provider == API_PROVIDER_DEEPSEEK else
+            DEFAULT_GEMINI_MODEL if self._provider == API_PROVIDER_GEMINI else
+            DEFAULT_MODEL
+        )
+
        entry_data = {
            CONF_API_PROVIDER: self._provider,
            CONF_NAME: instance_name,
@@ -257,7 +429,7 @@ class HATextAIConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
            CONF_API_KEY: user_input.get(CONF_API_KEY),
            CONF_API_ENDPOINT: user_input.get(CONF_API_ENDPOINT),
            "unique_id": unique_id,
-            CONF_MODEL: user_input.get(CONF_MODEL, DEFAULT_MODEL),
+            CONF_MODEL: user_input.get(CONF_MODEL, default_model),
            CONF_TEMPERATURE: user_input.get(CONF_TEMPERATURE, DEFAULT_TEMPERATURE),
            CONF_MAX_TOKENS: user_input.get(CONF_MAX_TOKENS, DEFAULT_MAX_TOKENS),
            CONF_REQUEST_INTERVAL: user_input.get(CONF_REQUEST_INTERVAL, DEFAULT_REQUEST_INTERVAL),
@@ -296,13 +468,20 @@ class OptionsFlowHandler(config_entries.OptionsFlow):
            return self.async_create_entry(title="", data=user_input)

        current_data = {**self.config_entry.data, **self.config_entry.options}
+        provider = current_data.get(CONF_API_PROVIDER)
+
+        default_model = (
+            DEFAULT_DEEPSEEK_MODEL if provider == API_PROVIDER_DEEPSEEK else
+            DEFAULT_GEMINI_MODEL if provider == API_PROVIDER_GEMINI else
+            DEFAULT_MODEL
+        )

        return self.async_show_form(
            step_id="init",
            data_schema=vol.Schema({
                vol.Optional(
                    CONF_MODEL,
-                    default=current_data.get(CONF_MODEL, DEFAULT_MODEL)
+                    default=current_data.get(CONF_MODEL, default_model)
                ): str,
                vol.Optional(
                    CONF_TEMPERATURE,
@@ -6,10 +6,14 @@ Constants for the HA text AI integration.
@github: https://github.com/smkrv/ha-text-ai
@source: https://github.com/smkrv/ha-text-ai
 """
+import os
+import json
 from typing import Final
 import voluptuous as vol
 from homeassistant.const import Platform, CONF_API_KEY, CONF_NAME
 from homeassistant.helpers import config_validation as cv
+import logging
+_LOGGER = logging.getLogger(__name__)

 # Domain and platforms
 DOMAIN: Final = "ha_text_ai"
@@ -19,15 +23,37 @@ PLATFORMS: list[str] = ["sensor"]
 CONF_API_PROVIDER: Final = "api_provider"
 API_PROVIDER_OPENAI: Final = "openai"
 API_PROVIDER_ANTHROPIC: Final = "anthropic"
+API_PROVIDER_DEEPSEEK: Final = "deepseek"
+API_PROVIDER_GEMINI: Final = "gemini"

 API_PROVIDERS: Final = [
    API_PROVIDER_OPENAI,
-    API_PROVIDER_ANTHROPIC
+    API_PROVIDER_ANTHROPIC,
+    API_PROVIDER_DEEPSEEK,
+    API_PROVIDER_GEMINI
 ]

+# Read version from manifest.json
+MANIFEST_PATH = os.path.join(os.path.dirname(__file__), "manifest.json")
+try:
+    with open(MANIFEST_PATH) as manifest_file:
+        manifest = json.load(manifest_file)
+        VERSION = manifest.get("version", "unknown")
+except FileNotFoundError:
+    VERSION = "unknown"
+    _LOGGER.warning("manifest.json not found at %s", MANIFEST_PATH)
+except json.JSONDecodeError as err:
+    VERSION = "unknown"
+    _LOGGER.error("Error decoding JSON from manifest.json: %s", err)
+except Exception as err:
+    VERSION = "unknown"
+    _LOGGER.error("Error reading manifest.json: %s", err)
+
 # Default endpoints
 DEFAULT_OPENAI_ENDPOINT: Final = "https://api.openai.com/v1"
 DEFAULT_ANTHROPIC_ENDPOINT: Final = "https://api.anthropic.com"
+DEFAULT_DEEPSEEK_ENDPOINT: Final = "https://api.deepseek.com"
+DEFAULT_GEMINI_ENDPOINT: Final = "https://generativelanguage.googleapis.com/v1beta"

 # Configuration constants
 CONF_MODEL: Final = "model"
@@ -47,6 +73,8 @@ ICONS_SUBDOMAIN = "icons"

 # Default values
 DEFAULT_MODEL: Final = "gpt-4o-mini"
+DEFAULT_DEEPSEEK_MODEL: Final = "deepseek-chat"
+DEFAULT_GEMINI_MODEL: Final = "gemini-2.0-flash"
 DEFAULT_TEMPERATURE: Final = 0.1
 DEFAULT_MAX_TOKENS: Final = 1000
 DEFAULT_REQUEST_INTERVAL: Final = 1.0
@@ -56,11 +84,13 @@ DEFAULT_NAME: Final = "HA Text AI"
 DEFAULT_NAME_PREFIX = "ha_text_ai"
 DEFAULT_CONTEXT_MESSAGES: Final = 5

+TRUNCATION_INDICATOR = " ... "
+
 # Parameter constraints
 MIN_TEMPERATURE: Final = 0.0
 MAX_TEMPERATURE: Final = 2.0
 MIN_MAX_TOKENS: Final = 1
-MAX_MAX_TOKENS: Final = 4096
+MAX_MAX_TOKENS: Final = 100000
 MIN_REQUEST_INTERVAL: Final = 0.1
 MAX_REQUEST_INTERVAL: Final = 60.0

@@ -41,6 +41,7 @@ from .const import (
    ABSOLUTE_MAX_HISTORY_SIZE,
    MAX_ATTRIBUTE_SIZE,
    MAX_HISTORY_FILE_SIZE,
+    TRUNCATION_INDICATOR,
 )

 _LOGGER = logging.getLogger(__name__)
@@ -647,15 +648,14 @@ class HATextAICoordinator(DataUpdateCoordinator):

    async def _async_update_data(self) -> Dict[str, Any]:
        """Update coordinator data with improved error handling and performance."""
-
        try:
            async with asyncio.Semaphore(1):
                current_state = self._get_current_state()

-                limited_history = await self._get_limited_history()
+                # Get limited history with info
+                history_data = await self._get_limited_history()

                metrics = await self._get_current_metrics()
-
                if metrics is None:
                    metrics = {}

@@ -670,47 +670,68 @@ class HATextAICoordinator(DataUpdateCoordinator):
                    "uptime": self._calculate_uptime(),
                    "system_prompt": self._get_truncated_system_prompt(),
                    "history_size": len(self._conversation_history),
-                    "conversation_history": limited_history,
+                    "conversation_history": history_data["entries"],
+                    "history_info": history_data["info"],
                    "normalized_name": self.normalized_name,
                }

                await self._validate_update_data(data)
-
                return data

-        except asyncio.CancelledError:
-            _LOGGER.warning("Update was cancelled")
-            raise
        except Exception as err:
            _LOGGER.error(f"Error updating data: {err}", exc_info=True)
            return self._get_safe_initial_state()

-    async def _get_limited_history(self) -> List[Dict[str, Any]]:
-        """Get limited conversation history with size constraints."""
+    async def _get_limited_history(self) -> Dict[str, Any]:
+        """Get limited conversation history showing only last Q&A."""
        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
+            last_entry = self._conversation_history[-1]
+            limited_entry = {
+                "timestamp": last_entry["timestamp"],
+                "question": last_entry["question"][:4096] + (TRUNCATION_INDICATOR if len(last_entry["question"]) > 4096 else ""),
+                "response": last_entry["response"][:4096] + (TRUNCATION_INDICATOR if len(last_entry["response"]) > 4096 else ""),
+            }
+            limited_history.append(limited_entry)
+
+        history_info = {
+            "total_entries": len(self._conversation_history),
+            "displayed_entries": len(limited_history),
+            "full_history_available": True,
+            "history_path": self._history_file,
+        }
+
+        return {
+            "entries": limited_history,
+            "full_history": self._conversation_history,
+            "info": history_info
+        }
+
+    async def _get_sanitized_last_response(self) -> Dict[str, Any]:
+        """Get sanitized version of last response with truncation indicators."""
+        response = self.last_response.copy()
+
+        if "response" in response:
+            original_response = response["response"]
+            is_response_truncated = len(original_response) > 4096
+            response["response"] = original_response[:4096] + (TRUNCATION_INDICATOR if is_response_truncated else "")
+            response["is_response_truncated"] = is_response_truncated
+            response["full_response_length"] = len(original_response)
+
+        if "question" in response:
+            original_question = response["question"]
+            is_question_truncated = len(original_question) > 4096
+            response["question"] = original_question[:4096] + (TRUNCATION_INDICATOR if is_question_truncated else "")
+            response["is_question_truncated"] = is_question_truncated
+            response["full_question_length"] = len(original_question)
+
+        return response

    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()
@@ -775,46 +796,6 @@ class HATextAICoordinator(DataUpdateCoordinator):
            "normalized_name": self.normalized_name,
        }

-    def _calculate_context_tokens(self, messages: List[Dict[str, str]], model: Optional[str] = None) -> int:
-        total_tokens = 0
-
-        # 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+')
-
-        try:
-            for message in messages:
-                text = message.get('content', '')
-                if text:
-                    # Normalize whitespace
-                    text = whitespace_pattern.sub(' ', text.strip())
-
-                    # Advanced token estimation heuristics
-                    words = text.split()
-                    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
-
-                    # Additional correction for technical texts
-                    total_tokens = math.ceil(total_tokens * 1.2)
-
-            return int(total_tokens)
-
-        except Exception as e:
-            _LOGGER.error(f"Token calculation error: {e}. Processed {len(messages)} messages.")
-            # Safe fallback with minimal token estimation
-            return len(messages) * 100
-
    async def async_ask_question(
        self,
        question: str,
@@ -855,9 +836,7 @@ class HATextAICoordinator(DataUpdateCoordinator):
            system_prompt: Optional[str] = None,
            context_messages: Optional[int] = None,
        ) -> dict:
-            """
-            Enhanced question processing with intelligent token management.
-            """
+            """Process question with context management."""
            if self.client is None:
                raise HomeAssistantError("AI client not initialized")

@@ -879,62 +858,23 @@ class HATextAICoordinator(DataUpdateCoordinator):
                if temp_system_prompt:
                    messages.append({"role": "system", "content": temp_system_prompt})

-                # Context history management
+                # Add context from history
                context_history = self._conversation_history[-temp_context_messages:]
-
-                # Comprehensive token calculation
-                context_tokens = self._calculate_context_tokens(
-                    [{"content": entry["question"]} for entry in context_history] +
-                    [{"content": entry["response"]} for entry in context_history] +
-                    [{"content": question}],
-                    temp_model
-                )
-
-                # Dynamic token allocation
-                available_tokens = max(0, temp_max_tokens - context_tokens)
-
-                # Context trimming if over token limit
-                if context_tokens > temp_max_tokens:
-                    _LOGGER.warning(
-                        f"Token limit exceeded. "
-                        f"Context: {context_tokens}, "
-                        f"Max: {temp_max_tokens}"
-                    )
-
-                    # Intelligent context reduction
-                    while context_tokens > temp_max_tokens // 2 and context_history:
-                        context_history.pop(0)
-                        context_tokens = self._calculate_context_tokens(
-                            [{"content": entry["question"]} for entry in context_history] +
-                            [{"content": entry["response"]} for entry in context_history] +
-                            [{"content": question}],
-                            temp_model
-                        )
-
-                # Rebuild messages with trimmed context
                for entry in context_history:
                    messages.append({"role": "user", "content": entry["question"]})
                    messages.append({"role": "assistant", "content": entry["response"]})

+                # Add current question
                messages.append({"role": "user", "content": question})

-                # Detailed token logging
-                _LOGGER.debug(
-                    f"Token Analysis: "
-                    f"Context Tokens: {context_tokens}, "
-                    f"Max Tokens: {temp_max_tokens}, "
-                    f"Available Tokens: {available_tokens}"
-                )
-
-                # Prepare API call with dynamic token management
+                # Process message
                kwargs = {
                    "model": temp_model,
                    "temperature": temp_temperature,
-                    "max_tokens": min(temp_max_tokens, available_tokens),
+                    "max_tokens": temp_max_tokens,
                    "messages": messages,
                }

-                # Process message
                response = await self.async_process_message(question, **kwargs)

                # Update metrics
@@ -13,16 +13,17 @@
  "loggers": ["custom_components.ha_text_ai"],
  "mqtt": [],
  "quality_scale": "silver",
-    "requirements": [
-      "openai>=1.12.0",
-      "anthropic>=0.8.0",
-      "aiohttp>=3.8.0",
-      "async-timeout>=4.0.0",
-      "certifi>=2024.2.2"
-    ],
+  "requirements": [
+    "openai>=1.12.0",
+    "anthropic>=0.8.0",
+    "google-genai>=1.16.0",
+    "aiohttp>=3.8.0",
+    "async-timeout>=4.0.0",
+    "certifi>=2024.2.2"
+  ],
  "single_config_entry": false,
  "ssdp": [],
  "usb": [],
-  "version": "2.0.8",
+  "version": "2.1.7",
  "zeroconf": []
 }
@@ -9,6 +9,7 @@ Sensor platform for HA Text AI.
 import logging
 import math
 from typing import Any, Dict
+from datetime import datetime, timedelta

 from homeassistant.components.sensor import (
    SensorEntity,
@@ -68,6 +69,7 @@ from .const import (
    DEFAULT_NAME_PREFIX,
    CONF_MAX_HISTORY_SIZE,
    MAX_ATTRIBUTE_SIZE,
+    VERSION,
 )

 from .coordinator import HATextAICoordinator
@@ -154,7 +156,7 @@ class HATextAISensor(CoordinatorEntity, SensorEntity):
            name=self._attr_name,
            manufacturer="Community",
            model=f"{model} ({api_provider} provider)",
-            sw_version="1.0.0",
+            sw_version=VERSION,
        )

        _LOGGER.debug(
@@ -63,13 +63,13 @@ ask_question:

    max_tokens:
      name: Max Tokens
-      description: Maximum length of the response (1-4096 tokens)
+      description: Maximum length of the response (tokens)
      required: false
      default: 1000
      selector:
        number:
          min: 1
-          max: 4096
+          max: 100000
          step: 1
          mode: box

@@ -19,7 +19,7 @@
          "model": "Zu verwendendes AI-Modell",
          "api_endpoint": "Benutzerdefinierte API-Endpunkt-URL (optional)",
          "temperature": "Kreativität der Antwort (0-2, niedriger = fokussierter)",
-          "max_tokens": "Maximale Länge der Antwort (1-4096 Token)",
+          "max_tokens": "Maximale Länge der Antwort (1-100000 Token)",
          "request_interval": "Minimale Zeit zwischen Anfragen (0,1-60 Sekunden)",
          "context_messages": "Anzahl der zu behaltenden Kontextnachrichten (1-20)",
          "max_history_size": "Maximale Größe des Gesprächsverlaufs (1-100)"
@@ -33,7 +33,7 @@
          "api_key": "API-Schlüssel zur Authentifizierung",
          "model": "Zu verwendendes AI-Modell",
          "temperature": "Kreativität der Antwort (0-2, niedriger = fokussierter)",
-          "max_tokens": "Maximale Länge der Antwort (1-4096 Token)",
+          "max_tokens": "Maximale Länge der Antwort (1-100000 Token)",
          "api_endpoint": "Benutzerdefinierte API-Endpunkt-URL (optional)",
          "api_provider": "API-Anbieter",
          "request_interval": "Minimale Zeit zwischen Anfragen (0,1-60 Sekunden)",
@@ -77,7 +77,7 @@
        "data": {
          "model": "AI-Modell",
          "temperature": "Kreativität der Antwort (0-2)",
-          "max_tokens": "Maximale Länge der Antwort (1-4096)",
+          "max_tokens": "Maximale Länge der Antwort (1-100000)",
          "request_interval": "Minimale Anfrageintervall (0,1-60 Sekunden)",
          "context_messages": "Anzahl der vorherigen Nachrichten, die im Kontext enthalten sein sollen (1-20)",
          "max_history_size": "Maximale Größe des Gesprächsverlaufs (1-100)"
@@ -88,11 +88,13 @@
  "selector": {
    "api_provider": {
      "options": {
-        "openai": "OpenAI (kompatibel)",
-        "anthropic": "Anthropic (kompatibel)"
-      }
+        "openai": "OpenAI (compatible)",
+        "anthropic": "Anthropic (compatible)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
    }
-  },
+  }
+},
  "services": {
    "ask_question": {
      "name": "Frage stellen (HA Text AI)",
@@ -124,7 +126,7 @@
        },
        "max_tokens": {
          "name": "Max Tokens",
-          "description": "Maximale Länge der Antwort (1-4096 Token)"
+          "description": "Maximale Länge der Antwort (1-100000 Token)"
        }
      }
    },
@@ -19,7 +19,7 @@
          "model": "AI model to use",
          "api_endpoint": "Custom API endpoint URL (optional)",
          "temperature": "Response creativity (0-2, lower = more focused)",
-          "max_tokens": "Maximum response length (1-4096 tokens)",
+          "max_tokens": "Maximum response length (1-100000 tokens)",
          "request_interval": "Minimum time between requests (0.1-60 seconds)",
          "context_messages": "Number of context messages to retain (1-20)",
          "max_history_size": "Maximum conversation history size (1-100)"
@@ -33,7 +33,7 @@
          "api_key": "API key for authentication",
          "model": "AI model to use",
          "temperature": "Response creativity (0-2, lower = more focused)",
-          "max_tokens": "Maximum response length (1-4096 tokens)",
+          "max_tokens": "Maximum response length (1-100000 tokens)",
          "api_endpoint": "Custom API endpoint URL (optional)",
          "api_provider": "API Provider",
          "request_interval": "Minimum time between requests (0.1-60 seconds)",
@@ -77,7 +77,7 @@
        "data": {
          "model": "AI model",
          "temperature": "Response creativity (0-2)",
-          "max_tokens": "Maximum response length (1-4096)",
+          "max_tokens": "Maximum response length (1-100000)",
          "request_interval": "Minimum request interval (0.1-60 seconds)",
          "context_messages": "Number of previous messages to include in context (1-20)",
          "max_history_size": "Maximum conversation history size (1-100)"
@@ -86,10 +86,12 @@
    }
  },
  "selector": {
-  "api_provider": {
-    "options": {
-      "openai": "OpenAI (compatible)",
-      "anthropic": "Anthropic (compatible)"
+    "api_provider": {
+      "options": {
+        "openai": "OpenAI (compatible)",
+        "anthropic": "Anthropic (compatible)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
    }
  }
 },
@@ -124,7 +126,7 @@
        },
        "max_tokens": {
          "name": "Max Tokens",
-          "description": "Maximum length of the response (1-4096 tokens)"
+          "description": "Maximum length of the response (1-100000 tokens)"
        }
      }
    },
@@ -19,7 +19,7 @@
          "model": "Modelo de IA a utilizar",
          "api_endpoint": "URL del endpoint de API personalizado (opcional)",
          "temperature": "Creatividad de la respuesta (0-2, menor = más enfocado)",
-          "max_tokens": "Longitud máxima de la respuesta (1-4096 tokens)",
+          "max_tokens": "Longitud máxima de la respuesta (1-100000 tokens)",
          "request_interval": "Tiempo mínimo entre solicitudes (0.1-60 segundos)",
          "context_messages": "Número de mensajes de contexto a retener (1-20)",
          "max_history_size": "Tamaño máximo del historial de conversación (1-100)"
@@ -33,7 +33,7 @@
          "api_key": "Clave API para autenticación",
          "model": "Modelo de IA a utilizar",
          "temperature": "Creatividad de la respuesta (0-2, menor = más enfocado)",
-          "max_tokens": "Longitud máxima de la respuesta (1-4096 tokens)",
+          "max_tokens": "Longitud máxima de la respuesta (1-100000 tokens)",
          "api_endpoint": "URL del endpoint de API personalizado (opcional)",
          "api_provider": "Proveedor de API",
          "request_interval": "Tiempo mínimo entre solicitudes (0.1-60 segundos)",
@@ -77,7 +77,7 @@
        "data": {
          "model": "Modelo de IA",
          "temperature": "Creatividad de la respuesta (0-2)",
-          "max_tokens": "Longitud máxima de la respuesta (1-4096)",
+          "max_tokens": "Longitud máxima de la respuesta (1-100000)",
          "request_interval": "Intervalo mínimo de solicitud (0.1-60 segundos)",
          "context_messages": "Número de mensajes anteriores a incluir en el contexto (1-20)",
          "max_history_size": "Tamaño máximo del historial de conversación (1-100)"
@@ -89,10 +89,12 @@
    "api_provider": {
      "options": {
        "openai": "OpenAI (compatible)",
-        "anthropic": "Anthropic (compatible)"
-      }
+        "anthropic": "Anthropic (compatible)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
    }
-  },
+  }
+},
  "services": {
    "ask_question": {
      "name": "Hacer Pregunta (HA Text AI)",
@@ -124,7 +126,7 @@
        },
        "max_tokens": {
          "name": "Máx. Tokens",
-          "description": "Longitud máxima de la respuesta (1-4096 tokens)"
+          "description": "Longitud máxima de la respuesta (1-100000 tokens)"
        }
      }
    },
@@ -19,7 +19,7 @@
          "model": "उपयोग करने के लिए एआई मॉडल",
          "api_endpoint": "कस्टम एपीआई एंडपॉइंट यूआरएल (वैकल्पिक)",
          "temperature": "प्रतिक्रिया की रचनात्मकता (0-2, कम = अधिक केंद्रित)",
-          "max_tokens": "प्रतिक्रिया की अधिकतम लंबाई (1-4096 टोकन)",
+          "max_tokens": "प्रतिक्रिया की अधिकतम लंबाई (1-100000 टोकन)",
          "request_interval": "अनुरोधों के बीच न्यूनतम समय (0.1-60 सेकंड)",
          "context_messages": "रखने के लिए संदर्भ संदेशों की संख्या (1-20)",
          "max_history_size": "अधिकतम बातचीत इतिहास आकार (1-100)"
@@ -33,7 +33,7 @@
          "api_key": "प्रमाणीकरण के लिए एपीआई कुंजी",
          "model": "उपयोग करने के लिए एआई मॉडल",
          "temperature": "प्रतिक्रिया की रचनात्मकता (0-2, कम = अधिक केंद्रित)",
-          "max_tokens": "प्रतिक्रिया की अधिकतम लंबाई (1-4096 टोकन)",
+          "max_tokens": "प्रतिक्रिया की अधिकतम लंबाई (1-100000 टोकन)",
          "api_endpoint": "कस्टम एपीआई एंडपॉइंट यूआरएल (वैकल्पिक)",
          "api_provider": "एपीआई प्रदाता",
          "request_interval": "अनुरोधों के बीच न्यूनतम समय (0.1-60 सेकंड)",
@@ -77,7 +77,7 @@
        "data": {
          "model": "एआई मॉडल",
          "temperature": "प्रतिक्रिया की रचनात्मकता (0-2)",
-          "max_tokens": "प्रतिक्रिया की अधिकतम लंबाई (1-4096)",
+          "max_tokens": "प्रतिक्रिया की अधिकतम लंबाई (1-100000)",
          "request_interval": "न्यूनतम अनुरोध अंतराल (0.1-60 सेकंड)",
          "context_messages": "संदर्भ में शामिल करने के लिए पिछले संदेशों की संख्या (1-20)",
          "max_history_size": "अधिकतम बातचीत इतिहास आकार (1-100)"
@@ -89,7 +89,9 @@
    "api_provider": {
      "options": {
        "openai": "OpenAI (अनुकूलित)",
-        "anthropic": "Anthropic (अनुकूलित)"
+        "anthropic": "Anthropic (अनुकूलित)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
      }
    }
  },
@@ -124,7 +126,7 @@
        },
        "max_tokens": {
          "name": "अधिकतम टोकन",
-          "description": "प्रतिक्रिया की अधिकतम लंबाई (1-4096 टोकन)"
+          "description": "प्रतिक्रिया की अधिकतम लंबाई (1-100000 टोकन)"
        }
      }
    },
@@ -19,7 +19,7 @@
          "model": "Modello AI da utilizzare",
          "api_endpoint": "URL dell'endpoint API personalizzato (opzionale)",
          "temperature": "Creatività della risposta (0-2, più basso = più focalizzato)",
-          "max_tokens": "Lunghezza massima della risposta (1-4096 token)",
+          "max_tokens": "Lunghezza massima della risposta (1-100000 token)",
          "request_interval": "Tempo minimo tra le richieste (0.1-60 secondi)",
          "context_messages": "Numero di messaggi di contesto da mantenere (1-20)",
          "max_history_size": "Dimensione massima della cronologia delle conversazioni (1-100)"
@@ -33,7 +33,7 @@
          "api_key": "Chiave API per l'autenticazione",
          "model": "Modello AI da utilizzare",
          "temperature": "Creatività della risposta (0-2, più basso = più focalizzato)",
-          "max_tokens": "Lunghezza massima della risposta (1-4096 token)",
+          "max_tokens": "Lunghezza massima della risposta (1-100000 token)",
          "api_endpoint": "URL dell'endpoint API personalizzato (opzionale)",
          "api_provider": "Fornitore API",
          "request_interval": "Tempo minimo tra le richieste (0.1-60 secondi)",
@@ -77,7 +77,7 @@
        "data": {
          "model": "Modello AI",
          "temperature": "Creatività della risposta (0-2)",
-          "max_tokens": "Lunghezza massima della risposta (1-4096)",
+          "max_tokens": "Lunghezza massima della risposta (1-100000)",
          "request_interval": "Intervallo minimo di richiesta (0.1-60 secondi)",
          "context_messages": "Numero di messaggi precedenti da includere nel contesto (1-20)",
          "max_history_size": "Dimensione massima della cronologia delle conversazioni (1-100)"
@@ -89,7 +89,9 @@
    "api_provider": {
      "options": {
        "openai": "OpenAI (compatibile)",
-        "anthropic": "Anthropic (compatibile)"
+        "anthropic": "Anthropic (compatibile)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
      }
    }
  },
@@ -124,7 +126,7 @@
        },
        "max_tokens": {
          "name": "Token massimi",
-          "description": "Lunghezza massima della risposta (1-4096 token)"
+          "description": "Lunghezza massima della risposta (1-100000 token)"
        }
      }
    },
@@ -19,7 +19,7 @@
          "model": "Модель ИИ для использования",
          "api_endpoint": "Пользовательский URL-адрес конечной точки API (необязательно)",
          "temperature": "Креативность ответа (0-2, меньше = более сфокусированно)",
-          "max_tokens": "Максимальная длина ответа (1-4096 токенов)",
+          "max_tokens": "Максимальная длина ответа (1-100000 токенов)",
          "request_interval": "Минимальный интервал между запросами (0.1-60 секунд)",
          "context_messages": "Количество сохраняемых контекстных сообщений (1-20)",
          "max_history_size": "Максимальный размер истории разговора (1-100)"
@@ -33,7 +33,7 @@
          "api_key": "API-ключ для аутентификации",
          "model": "Модель ИИ для использования",
          "temperature": "Креативность ответа (0-2, меньше = более сфокусированно)",
-          "max_tokens": "Максимальная длина ответа (1-4096 токенов)",
+          "max_tokens": "Максимальная длина ответа (1-100000 токенов)",
          "api_endpoint": "Пользовательский URL-адрес конечной точки API (необязательно)",
          "api_provider": "Провайдер API",
          "request_interval": "Минимальный интервал между запросами (0.1-60 секунд)",
@@ -77,7 +77,7 @@
        "data": {
          "model": "Модель ИИ",
          "temperature": "Креативность ответа (0-2)",
-          "max_tokens": "Максимальная длина ответа (1-4096)",
+          "max_tokens": "Максимальная длина ответа (1-100000)",
          "request_interval": "Минимальный интервал между запросами (0.1-60 секунд)",
          "context_messages": "Количество предыдущих сообщений для включения в контекст (1-20)",
          "max_history_size": "Максимальный размер истории разговора (1-100)"
@@ -89,7 +89,9 @@
    "api_provider": {
      "options": {
        "openai": "OpenAI (совместимый)",
-        "anthropic": "Anthropic (совместимый)"
+        "anthropic": "Anthropic (совместимый)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
      }
    }
  },
@@ -124,7 +126,7 @@
        },
        "max_tokens": {
          "name": "Максимум токенов",
-          "description": "Максимальная длина ответа (1-4096 токенов)"
+          "description": "Максимальная длина ответа (1-100000 токенов)"
        }
      }
    },
@@ -19,7 +19,7 @@
          "model": "AI модел који ће се користити",
          "api_endpoint": "Прилагођени URL API крајње тачке (опционо)",
          "temperature": "Креативност одговора (0-2, нижа = фокусираније)",
-          "max_tokens": "Максимална дужина одговора (1-4096 токена)",
+          "max_tokens": "Максимална дужина одговора (1-100000 токена)",
          "request_interval": "Минимално време између захтева (0.1-60 секунди)",
          "context_messages": "Број контекстуалних порука које треба задржати (1-20)",
          "max_history_size": "Максимална величина историје разговора (1-100)"
@@ -33,7 +33,7 @@
          "api_key": "API кључ за аутентификацију",
          "model": "AI модел који ће се користити",
          "temperature": "Креативност одговора (0-2, нижа = фокусираније)",
-          "max_tokens": "Максимална дужина одговора (1-4096 токена)",
+          "max_tokens": "Максимална дужина одговора (1-100000 токена)",
          "api_endpoint": "Прилагођени URL API крајње тачке (опционо)",
          "api_provider": "API провајдер",
          "request_interval": "Минимално време између захтева (0.1-60 секунди)",
@@ -77,7 +77,7 @@
        "data": {
          "model": "AI модел",
          "temperature": "Креативност одговора (0-2)",
-          "max_tokens": "Максимална дужина одговора (1-4096)",
+          "max_tokens": "Максимална дужина одговора (1-100000)",
          "request_interval": "Минимално време између захтева (0.1-60 секунди)",
          "context_messages": "Број претходних порука које треба укључити у контекст (1-20)",
          "max_history_size": "Максимална величина историје разговора (1-100)"
@@ -89,7 +89,9 @@
    "api_provider": {
      "options": {
        "openai": "OpenAI (компатибилан)",
-        "anthropic": "Anthropic (компатибилан)"
+        "anthropic": "Anthropic (компатибилан)",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
      }
    }
  },
@@ -124,7 +126,7 @@
        },
        "max_tokens": {
          "name": "Максимални токени",
-          "description": "Максимална дужина одговора (1-4096 токена)"
+          "description": "Максимална дужина одговора (1-100000 токена)"
        }
      }
    },
@@ -19,7 +19,7 @@
          "model": "要使用的AI模型",
          "api_endpoint": "自定义API端点URL（可选）",
          "temperature": "响应创造力（0-2，越低越专注）",
-          "max_tokens": "最大响应长度（1-4096个标记）",
+          "max_tokens": "最大响应长度（1-100000个标记）",
          "request_interval": "请求之间的最小时间（0.1-60秒）",
          "context_messages": "保留的上下文消息数量（1-20）",
          "max_history_size": "最大对话历史大小（1-100）"
@@ -33,7 +33,7 @@
          "api_key": "用于身份验证的API密钥",
          "model": "要使用的AI模型",
          "temperature": "响应创造力（0-2，越低越专注）",
-          "max_tokens": "最大响应长度（1-4096个标记）",
+          "max_tokens": "最大响应长度（1-100000个标记）",
          "api_endpoint": "自定义API端点URL（可选）",
          "api_provider": "API提供者",
          "request_interval": "请求之间的最小时间（0.1-60秒）",
@@ -77,7 +77,7 @@
        "data": {
          "model": "AI模型",
          "temperature": "响应创造力（0-2）",
-          "max_tokens": "最大响应长度（1-4096）",
+          "max_tokens": "最大响应长度（1-100000）",
          "request_interval": "最小请求间隔（0.1-60秒）",
          "context_messages": "要包含在上下文中的先前消息数量（1-20）",
          "max_history_size": "最大对话历史大小（1-100）"
@@ -89,7 +89,9 @@
    "api_provider": {
      "options": {
        "openai": "OpenAI（兼容）",
-        "anthropic": "Anthropic（兼容）"
+        "anthropic": "Anthropic（兼容）",
+        "deepseek": "DeepSeek",
+        "gemini": "Google Gemini"
      }
    }
  },
@@ -124,7 +126,7 @@
        },
        "max_tokens": {
          "name": "最大标记数",
-          "description": "响应的最大长度（1-4096个标记）"
+          "description": "响应的最大长度（1-100000个标记）"
        }
      }
    },
@@ -1,5 +1,5 @@
 {
-  "name": "HA text AI",
+  "name": "HA Text AI",
  "render_readme": true,
  "homeassistant": "2024.11.0"
 }
Author	SHA1	Message	Date
SMKRV	76c5629fa0	refactor(google-gemini): rewrite integration using google-genai 1.16.0 Completely rewrote the Google Gemini integration logic based on google-genai 1.16.0 to fix issue #6. Key changes: - Updated to the latest google-genai library - Made API endpoint abstract while retaining option for custom endpoint configuration - Refactored logic and classes exclusively within Google Gemini implementation - All changes are limited to Google Gemini integration refactoring with no impact on other functionality.	2025-05-21 01:27:47 +03:00
SMKRV	7958bd010b	refactor(google-gemini): rewrite integration using google-genai 1.16.0 Completely rewrote the Google Gemini integration logic based on google-genai 1.16.0 to fix issue #6. Key changes: - Updated to the latest google-genai library - Made API endpoint abstract while retaining option for custom endpoint configuration - Refactored logic and classes exclusively within Google Gemini implementation - All changes are limited to Google Gemini integration refactoring with no impact on other functionality.	2025-05-21 01:26:42 +03:00
SMKRV	8cd876195a	Bump to version 2.1.6	2025-05-20 01:50:06 +03:00
SMKRV	376753e001	fix: correct field naming in Gemini API requests from camelCase to snake_case and improve message handling	2025-05-20 01:42:38 +03:00
SMKRV	b6e73e847d	fix(api_client): correct Google Gemini API integration - Change JSON field names from camelCase to snake_case as required by Gemini API (generation_config, max_output_tokens, system_instruction) - Improve message handling to ensure proper role alternation (user/model) - Add safety checks for empty contents and ensure first message is always from user - Implement robust error handling and response parsing - Handle edge cases where candidatesTokenCount might be returned as a list Fixes #6	2025-05-20 01:16:41 +03:00
SMKRV	440c734214	Bump release version to v2.1.4	2025-05-19 23:20:27 +03:00
SMKRV	73788373cd	Release v2.1.3	2025-05-19 23:12:55 +03:00
SMKRV	4bfc96019b	fix: DEFAULT_GEMINI_ENDPOINT	2025-05-19 15:53:43 +03:00
SMKRV	2138fc7654	fix: DEFAULT_GEMINI_ENDPOINT	2025-05-19 15:36:58 +03:00
SMKRV	95bd2ebb41	Add support for Google Gemini (thanks to @Azzedde) #5	2025-05-19 15:10:19 +03:00
smkrvandGitHub	cad0fd7031	Merge pull request #5 from Azzedde/main Add Gemini API provider support to HA Text AI integration by @Azzedde	2025-05-19 14:44:06 +03:00
Azzedde	c003b258f6	Add Gemini API provider support to HA Text AI integration	2025-05-18 13:23:55 +02:00
SMKRV	65a10c77f4	~	2025-01-30 01:15:13 +03:00
SMKRV	e1463828c9	~	2025-01-30 01:14:24 +03:00
SMKRV	5ebb9c9c66	fix: max_tokens value	2025-01-29 18:04:13 +03:00
SMKRV	f17c631a79	fix: max_tokens value	2025-01-29 18:02:42 +03:00
SMKRV	0e06794384	refactor(docs): shields & community links updated	2025-01-29 03:05:47 +03:00
SMKRV	d8a924909b	refactor(docs): shields & community links updated	2025-01-29 03:05:11 +03:00
SMKRV	29f1659a02	refactor(docs): shields & community links updated	2025-01-29 03:04:39 +03:00
SMKRV	5b7905de80	refactor(docs): shields & community links updated	2025-01-29 03:03:48 +03:00
SMKRV	cf9ac6dcea	refactor(docs): shields & community links updated	2025-01-29 01:08:45 +03:00
SMKRV	568eb3e16c	refactor(docs): shields updated	2025-01-29 00:58:27 +03:00
SMKRV	53fb150389	refactor(docs): shields updated	2025-01-29 00:58:09 +03:00
SMKRV	acbb53d2af	refactor(docs): shields updated	2025-01-29 00:57:27 +03:00
SMKRV	e19db29441	refactor(docs): DeepSeek Integration	2025-01-28 16:25:59 +03:00
SMKRV	bfd64d1122	Release v2.1.1: Token Handling Improvement and DeepSeek Support - Completely reworked token handling mechanism - Removed custom token calculation logic - Direct max_tokens passing to LLM APIs - Added support for DeepSeek provider - Integrated deepseek-chat and deepseek-reasoner models Thanks to @estiens for reporting token handling issues and providing valuable feedback (https://github.com/smkrv/ha-text-ai/issues/1).	2025-01-28 15:54:48 +03:00
SMKRV	82e1f0c4f9	Release v2.1.0	2024-12-13 00:06:08 +03:00
SMKRV	5c16eee6e4	fix: Read version from manifest.json	2024-12-12 16:03:15 +03:00
SMKRV	e988d445a4	- Fixed version reading from manifest.json implementation	2024-12-11 22:01:01 +03:00
SMKRV	f9bfb9ab7f	fix: correct sw_version syntax in device_info - Fixed version reading from manifest.json implementation	2024-12-11 21:57:27 +03:00
SMKRV	92dd1bc110	~	2024-12-11 00:00:12 +03:00
SMKRV	17d547325a	refactor(docs): updated README services examples with more detailed configuration	2024-12-10 23:33:32 +03:00
SMKRV	b8cb70217c	refactor(docs): updated README shields	2024-12-10 23:27:49 +03:00
SMKRV	530d04f25d	refactor(docs): updated README shields	2024-12-10 23:27:18 +03:00
SMKRV	b71083b9bf	bump to version 2.0.9	2024-12-10 17:25:47 +03:00
SMKRV	f9f7d10f7f	refactor(docs): updated README images	2024-12-10 17:21:41 +03:00
SMKRV	9f7cb20621	refactor(docs): updated README images	2024-12-10 17:19:52 +03:00
SMKRV	6fc3b23365	refactor(docs): updated README images	2024-12-10 17:18:55 +03:00
SMKRV	15c717fcb0	fix: Display only last Q&A in sensor state to prevent data truncation - Show only the latest question and answer in sensor state - Keep full conversation history in attributes - Fix truncation issues in Home Assistant UI - Maintain backwards compatibility - No configuration changes required	2024-12-10 17:02:48 +03:00
SMKRV	be06fddce1	fix: Display only last Q&A in sensor state to prevent data truncation - Show only the latest question and answer in sensor state - Keep full conversation history in attributes - Fix truncation issues in Home Assistant UI - Maintain backwards compatibility - No configuration changes required	2024-12-10 16:19:17 +03:00
SMKRV	5f0bd861a7	docs(readme): Included note that the integration has been submitted to HACS store and is currently pending review in pull request: [pull request #2896 ](hacs/default#2896 ).	2024-12-10 00:00:18 +03:00
SMKRV	428aee46c8	docs(readme): Included note that the integration has been submitted to HACS store and is currently pending review in pull request: [pull request #2896 ](hacs/default#2896 ).	2024-12-09 23:59:51 +03:00
SMKRV	561bcf0b1d	docs(Code of Conduct): Add Code of Conduct to promote community guidelines - Implement Contributor Covenant Code of Conduct v1.4 - Establish clear expectations for community interactions - Define standards of acceptable and unacceptable behavior - Provide framework for reporting and addressing issues - Emphasize inclusivity and respect for all contributors	2024-12-09 16:52:44 +03:00
SMKRV	2b1e42c665	docs(readme): Included note that the integration has been submitted to HACS store and is currently pending review in pull request: [pull request #2893 ](https://github.com/hacs/default/pull/2893 ).	2024-12-09 15:29:53 +03:00