feat: add page for Gemini model provider documentation

docs/api-reference/models.md

@@ -11,6 +11,9 @@
 ::: strands.models.anthropic
     options:
       heading_level: 2
+::: strands.models.gemini
+    options:
+      heading_level: 2
 ::: strands.models.litellm
     options:
       heading_level: 2

docs/user-guide/concepts/model-providers/gemini.md

@@ -0,0 +1,225 @@
+# Gemini
+
+[Google Gemini](https://ai.google.dev/api) is Google's family of multimodal large language models designed for advanced reasoning, code generation, and creative tasks. The Strands Agents SDK implements a Gemini provider, allowing you to run agents against the Gemini models available through Google's AI API.
+
+## Installation
+
+Gemini is configured as an optional dependency in Strands Agents. 
+
+To install it, run:
+
+```bash
+pip install 'strands-agents[gemini]' strands-agents-tools
+```
+
+## Usage
+
+After installing `strands-agents[gemini]`, you can import and initialize the Strands Agents' Gemini provider as follows:
+
+```python
+from strands import Agent
+from strands.models.gemini import GeminiModel
+from strands_tools import calculator
+
+model = GeminiModel(
+    client_args={
+        "api_key": "<KEY>",
+    },
+    # **model_config
+    model_id="gemini-2.5-flash",
+    params={
+        # some sample model parameters 
+        "temperature": 0.7,
+        "max_output_tokens": 2048,
+        "top_p": 0.9,
+        "top_k": 40
+    }
+)
+
+agent = Agent(model=model, tools=[calculator])
+response = agent("What is 2+2")
+print(response)
+```
+
+## Configuration
+
+### Client Configuration
+
+The `client_args` configure the underlying Google GenAI client. For a complete list of available arguments, please refer to the [Google GenAI documentation](https://googleapis.github.io/python-genai/).
+
+### Model Configuration
+
+The `model_config` configures the underlying model selected for inference. The supported configurations are:
+
+|  Parameter | Description | Example | Options |
+|------------|-------------|---------|---------|
+| `model_id` | ID of a Gemini model to use | `"gemini-2.5-flash"` | [Available models](#available-models) |
+| `params` | Model specific parameters | `{"temperature": 0.7, "maxOutputTokens": 2048}` | [Parameter reference](#model-parameters) |
+
+### Model Parameters
+
+For a complete list of supported parameters, see the [Gemini API documentation](https://ai.google.dev/api/generate-content#generationconfig).
+
+**Common Parameters:**
+| Parameter | Description | Type |
+|-----------|-------------|------|
+| `temperature` | Controls randomness in responses | `float` |
+| `max_output_tokens` | Maximum tokens to generate | `int` |
+| `top_p` | Nucleus sampling parameter | `float` |
+| `top_k` | Top-k sampling parameter | `int` |
+| `candidate_count` | Number of response candidates | `int` |
+| `stop_sequences` | Custom stopping sequences | `list[str]` |
+
+**Example:**
+```python
+params = {
+    "temperature": 0.8,
+    "max_output_tokens": 4096,
+    "top_p": 0.95,
+    "top_k": 40,
+    "candidate_count": 1,
+    "stop_sequences": ['STOP!']
+}
+```
+
+### Available Models
+
+For a complete list of supported models, see the [Gemini API documentation](https://ai.google.dev/gemini-api/docs/models).
+
+**Popular Models:**
+- `gemini-2.5-pro` - Most advanced model for complex reasoning and thinking
+- `gemini-2.5-flash` - Best balance of performance and cost
+- `gemini-2.5-flash-lite` - Most cost-efficient option
+- `gemini-2.0-flash` - Next-gen features with improved speed
+- `gemini-2.0-flash-lite` - Cost-optimized version of 2.0
+
+## Troubleshooting
+
+### Module Not Found
+
+If you encounter the error `ModuleNotFoundError: No module named 'google.genai'`, this means the `google-genai` dependency hasn't been properly installed in your environment. To fix this, run `pip install 'strands-agents[gemini]'`.
+
+### API Key Issues
+
+Make sure your Google AI API key is properly set in `client_args` or as the `GOOGLE_API_KEY` environment variable. You can obtain an API key from the [Google AI Studio](https://aistudio.google.com/app/apikey).
+
+### Rate Limiting and Safety Issues
+
+The Gemini provider handles several types of errors automatically:
+
+- **Safety/Content Policy**: When content is blocked due to safety concerns, the model will return a safety message
+- **Rate Limiting**: When quota limits are exceeded, a `ModelThrottledException` is raised
+- **Server Errors**: Temporary server issues are handled with appropriate error messages
+
+```python
+from strands.types.exceptions import ModelThrottledException
+
+try:
+    response = agent("Your query here")
+except ModelThrottledException as e:
+    print(f"Rate limit exceeded: {e}")
+    # Implement backoff strategy
+```
+
+## Advanced Features
+
+### Structured Output
+
+Gemini models support structured output through their native JSON schema capabilities. When you use [`Agent.structured_output()`](../../../api-reference/agent.md#strands.agent.agent.Agent.structured_output), the Strands SDK automatically converts your Pydantic models to Gemini's JSON schema format.
+
+```python
+from pydantic import BaseModel, Field
+from strands import Agent
+from strands.models.gemini import GeminiModel
+
+class MovieReview(BaseModel):
+    """Analyze a movie review."""
+    title: str = Field(description="Movie title")
+    rating: int = Field(description="Rating from 1-10", ge=1, le=10)
+    genre: str = Field(description="Primary genre")
+    sentiment: str = Field(description="Overall sentiment: positive, negative, or neutral")
+    summary: str = Field(description="Brief summary of the review")
+
+model = GeminiModel(
+    client_args={"api_key": "<KEY>"},
+    model_id="gemini-2.5-flash",
+    params={
+        "temperature": 0.3,
+        "max_output_tokens": 1024,
+        "top_p": 0.85
+    }
+)
+
+agent = Agent(model=model)
+
+result = agent.structured_output(
+    MovieReview,
+    """
+    Just watched "The Matrix" - what an incredible sci-fi masterpiece! 
+    The groundbreaking visual effects and philosophical themes make this 
+    a must-watch. Keanu Reeves delivers a solid performance. 9/10!
+    """
+)
+
+print(f"Movie: {result.title}")
+print(f"Rating: {result.rating}/10")
+print(f"Genre: {result.genre}")
+print(f"Sentiment: {result.sentiment}")
+```
+
+### Multimodal Capabilities
+
+Gemini models support text, image, and document inputs, making them ideal for multimodal applications:
+
+```python
+from strands import Agent
+from strands.models.gemini import GeminiModel
+
+model = GeminiModel(
+    client_args={"api_key": "<KEY>"},
+    model_id="gemini-2.5-flash",
+    params={
+        "temperature": 0.5,
+        "max_output_tokens": 2048,
+        "top_p": 0.9
+    }
+)
+
+agent = Agent(model=model)
+
+# Process image with text
+response = agent([
+    {
+        "role": "user",
+        "content": [
+            {"text": "What do you see in this image?"},
+            {"image": {"format": "png", "source": {"bytes": image_bytes}}}
+        ]
+    }
+])
+```
+
+The implementation also supports document inputs:
+
+```python
+response = agent([
+    {
+        "role": "user", 
+        "content": [
+            {"text": "Summarize this document"},
+            {"document": {"format": "pdf", "source": {"bytes": document_bytes}}}
+        ]
+    }
+])
+```
+
+**Supported formats:**
+- **Images**: PNG, JPEG, GIF, WebP (automatically detected via MIME type)
+- **Documents**: PDF and other binary formats (automatically detected via MIME type)
+
+## References
+
+- [API](../../../api-reference/models.md)
+- [Google Gemini](https://ai.google.dev/api)
+- [Google GenAI SDK documentation](https://googleapis.github.io/python-genai/)
+- [Google AI Studio](https://aistudio.google.com/)

mkdocs.yml

@@ -90,6 +90,7 @@ nav:
       - Model Providers:
         - Amazon Bedrock: user-guide/concepts/model-providers/amazon-bedrock.md
         - Anthropic: user-guide/concepts/model-providers/anthropic.md
+        - Gemini: user-guide/concepts/model-providers/gemini.md
         - LiteLLM: user-guide/concepts/model-providers/litellm.md
         - llama.cpp: user-guide/concepts/model-providers/llamacpp.md
         - LlamaAPI: user-guide/concepts/model-providers/llamaapi.md