---
title: "Google GenAI"
id: integrations-google-genai
description: "Google GenAI integration for Haystack"
slug: "/integrations-google-genai"
---


## haystack_integrations.components.embedders.google_genai.document_embedder

### GoogleGenAIDocumentEmbedder

Computes document embeddings using Google AI models.

### Authentication examples

**1. Gemini Developer API (API Key Authentication)**

````python
from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder

# export the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
document_embedder = GoogleGenAIDocumentEmbedder(model="gemini-embedding-001")

**2. Vertex AI (Application Default Credentials)**
```python
from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder

# Using Application Default Credentials (requires gcloud auth setup)
document_embedder = GoogleGenAIDocumentEmbedder(
    api="vertex",
    vertex_ai_project="my-project",
    vertex_ai_location="us-central1",
    model="gemini-embedding-001"
)
````

**3. Vertex AI (API Key Authentication)**

```python
from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder

# export the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
document_embedder = GoogleGenAIDocumentEmbedder(
    api="vertex",
    model="gemini-embedding-001"
)
```

### Usage example

```python
from haystack import Document
from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = GoogleGenAIDocumentEmbedder()

result = document_embedder.run([doc])
print(result['documents'][0].embedding)

# [0.017020374536514282, -0.023255806416273117, ...]
```

#### __init__

```python
__init__(
    *,
    api_key: Secret = Secret.from_env_var(
        ["GOOGLE_API_KEY", "GEMINI_API_KEY"], strict=False
    ),
    api: Literal["gemini", "vertex"] = "gemini",
    vertex_ai_project: str | None = None,
    vertex_ai_location: str | None = None,
    model: str = "gemini-embedding-001",
    prefix: str = "",
    suffix: str = "",
    batch_size: int = 32,
    progress_bar: bool = True,
    meta_fields_to_embed: list[str] | None = None,
    embedding_separator: str = "\n",
    config: dict[str, Any] | None = None
) -> None
```

Creates an GoogleGenAIDocumentEmbedder component.

**Parameters:**

- **api_key** (<code>Secret</code>) – Google API key, defaults to the `GOOGLE_API_KEY` and `GEMINI_API_KEY` environment variables.
  Not needed if using Vertex AI with Application Default Credentials.
  Go to https://aistudio.google.com/app/apikey for a Gemini API key.
  Go to https://cloud.google.com/vertex-ai/generative-ai/docs/start/api-keys for a Vertex AI API key.
- **api** (<code>Literal['gemini', 'vertex']</code>) – Which API to use. Either "gemini" for the Gemini Developer API or "vertex" for Vertex AI.
- **vertex_ai_project** (<code>str | None</code>) – Google Cloud project ID for Vertex AI. Required when using Vertex AI with
  Application Default Credentials.
- **vertex_ai_location** (<code>str | None</code>) – Google Cloud location for Vertex AI (e.g., "us-central1", "europe-west1").
  Required when using Vertex AI with Application Default Credentials.
- **model** (<code>str</code>) – The name of the model to use for calculating embeddings.
  The default model is `gemini-embedding-001`.
- **prefix** (<code>str</code>) – A string to add at the beginning of each text. It can be used to specify a task type for
  `gemini-embedding-2`. For available task types, see
  [Gemini documentation](https://ai.google.dev/gemini-api/docs/embeddings#task-types).
- **suffix** (<code>str</code>) – A string to add at the end of each text.
- **batch_size** (<code>int</code>) – Number of documents to embed at once.
- **progress_bar** (<code>bool</code>) – If `True`, shows a progress bar when running.
- **meta_fields_to_embed** (<code>list\[str\] | None</code>) – List of metadata fields to embed along with the document text.
- **embedding_separator** (<code>str</code>) – Separator used to concatenate the metadata fields to the document text.
- **config** (<code>dict\[str, Any\] | None</code>) – A dictionary of keyword arguments to configure embedding content configuration.
  See [Google API documentation](https://googleapis.github.io/python-genai/genai.html#genai.types.EmbedContentConfig)
  for the available options.
  Specifying task types in `config` does not take effect for `gemini-embedding-2`.
  See [Gemini documentation](https://ai.google.dev/gemini-api/docs/embeddings#task-types) for more
  information.

#### to_dict

```python
to_dict() -> dict[str, Any]
```

Serializes the component to a dictionary.

**Returns:**

- <code>dict\[str, Any\]</code> – Dictionary with serialized data.

#### from_dict

```python
from_dict(data: dict[str, Any]) -> GoogleGenAIDocumentEmbedder
```

Deserializes the component from a dictionary.

**Parameters:**

- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.

**Returns:**

- <code>GoogleGenAIDocumentEmbedder</code> – Deserialized component.

#### run

```python
run(documents: list[Document]) -> dict[str, list[Document]] | dict[str, Any]
```

Embeds a list of documents.

**Parameters:**

- **documents** (<code>list\[Document\]</code>) – A list of documents to embed.

**Returns:**

- <code>dict\[str, list\[Document\]\] | dict\[str, Any\]</code> – A dictionary with the following keys:
- `documents`: A list of documents with embeddings.
- `meta`: Information about the usage of the model.

#### run_async

```python
run_async(
    documents: list[Document],
) -> dict[str, list[Document]] | dict[str, Any]
```

Embeds a list of documents asynchronously.

**Parameters:**

- **documents** (<code>list\[Document\]</code>) – A list of documents to embed.

**Returns:**

- <code>dict\[str, list\[Document\]\] | dict\[str, Any\]</code> – A dictionary with the following keys:
- `documents`: A list of documents with embeddings.
- `meta`: Information about the usage of the model.

## haystack_integrations.components.embedders.google_genai.multimodal_document_embedder

### GoogleGenAIMultimodalDocumentEmbedder

Computes non-textual document embeddings using Google AI models.

It supports images, PDFs, video and audio files. They are mapped to vectors in a single vector space.

To embed textual documents, use the GoogleGenAIDocumentEmbedder.
To embed a string, like a user query, use the GoogleGenAITextEmbedder.

### Authentication examples

**1. Gemini Developer API (API Key Authentication)**

````python
from haystack_integrations.components.embedders.google_genai import GoogleGenAIMultimodalDocumentEmbedder

# export the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
document_embedder = GoogleGenAIMultimodalDocumentEmbedder(model="gemini-embedding-2-preview")

**2. Vertex AI (Application Default Credentials)**
```python
from haystack_integrations.components.embedders.google_genai import GoogleGenAIMultimodalDocumentEmbedder

# Using Application Default Credentials (requires gcloud auth setup)
document_embedder = GoogleGenAIMultimodalDocumentEmbedder(
    api="vertex",
    vertex_ai_project="my-project",
    vertex_ai_location="us-central1",
    model="gemini-embedding-2-preview"
)
````

**3. Vertex AI (API Key Authentication)**

```python
from haystack_integrations.components.embedders.google_genai import GoogleGenAIMultimodalDocumentEmbedder

# export the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
document_embedder = GoogleGenAIMultimodalDocumentEmbedder(
    api="vertex",
    model="gemini-embedding-2-preview"
)
```

### Usage example

```python
from haystack import Document
from haystack_integrations.components.embedders.google_genai import GoogleGenAIMultimodalDocumentEmbedder

doc = Document(content=None, meta={"file_path": "path/to/image.jpg"})

document_embedder = GoogleGenAIMultimodalDocumentEmbedder()

result = document_embedder.run([doc])
print(result['documents'][0].embedding)

# [0.017020374536514282, -0.023255806416273117, ...]
```

#### __init__

```python
__init__(
    *,
    api_key: Secret = Secret.from_env_var(
        ["GOOGLE_API_KEY", "GEMINI_API_KEY"], strict=False
    ),
    api: Literal["gemini", "vertex"] = "gemini",
    vertex_ai_project: str | None = None,
    vertex_ai_location: str | None = None,
    file_path_meta_field: str = "file_path",
    root_path: str | None = None,
    image_size: tuple[int, int] | None = None,
    model: str = "gemini-embedding-2",
    batch_size: int = 6,
    progress_bar: bool = True,
    config: dict[str, Any] | None = None
) -> None
```

Creates an GoogleGenAIMultimodalDocumentEmbedder component.

**Parameters:**

- **api_key** (<code>Secret</code>) – Google API key, defaults to the `GOOGLE_API_KEY` and `GEMINI_API_KEY` environment variables.
  Not needed if using Vertex AI with Application Default Credentials.
  Go to https://aistudio.google.com/app/apikey for a Gemini API key.
  Go to https://cloud.google.com/vertex-ai/generative-ai/docs/start/api-keys for a Vertex AI API key.
- **api** (<code>Literal['gemini', 'vertex']</code>) – Which API to use. Either "gemini" for the Gemini Developer API or "vertex" for Vertex AI.
- **vertex_ai_project** (<code>str | None</code>) – Google Cloud project ID for Vertex AI. Required when using Vertex AI with
  Application Default Credentials.
- **vertex_ai_location** (<code>str | None</code>) – Google Cloud location for Vertex AI (e.g., "us-central1", "europe-west1").
  Required when using Vertex AI with Application Default Credentials.
- **file_path_meta_field** (<code>str</code>) – The metadata field in the Document that contains the file path to the file to embed.
- **root_path** (<code>str | None</code>) – The root directory path where document files are located. If provided, file paths in
  document metadata will be resolved relative to this path. If None, file paths are treated as absolute paths.
- **image_size** (<code>tuple\[int, int\] | None</code>) – Only used for images and PDF pages. If provided, resizes the image to fit within the specified dimensions
  (width, height) while maintaining aspect ratio. This reduces file size, memory usage, and processing time,
  which is beneficial when working with models that have resolution constraints or when transmitting images
  to remote services.
- **model** (<code>str</code>) – The name of the model to use for calculating embeddings.
- **batch_size** (<code>int</code>) – Number of documents to embed at once. Maximum batch size varies depending on the input type.
  See [Google AI documentation](https://ai.google.dev/gemini-api/docs/embeddings#supported-modalities) for
  more information.
- **progress_bar** (<code>bool</code>) – If `True`, shows a progress bar when running.
- **config** (<code>dict\[str, Any\] | None</code>) – A dictionary of keyword arguments to configure embedding content configuration.
  You can for example set the output dimensionality of the embedding: `{"output_dimensionality": 768}`.
  See [Google API documentation](https://googleapis.github.io/python-genai/genai.html#genai.types.EmbedContentConfig)
  for the available options.

#### run

```python
run(documents: list[Document]) -> dict[str, list[Document]] | dict[str, Any]
```

Embeds a list of documents.

**Parameters:**

- **documents** (<code>list\[Document\]</code>) – A list of documents to embed.

**Returns:**

- <code>dict\[str, list\[Document\]\] | dict\[str, Any\]</code> – A dictionary with the following keys:
- `documents`: A list of documents with embeddings.
- `meta`: Information about the usage of the model.

#### run_async

```python
run_async(
    documents: list[Document],
) -> dict[str, list[Document]] | dict[str, Any]
```

Embeds a list of documents asynchronously.

**Parameters:**

- **documents** (<code>list\[Document\]</code>) – A list of documents to embed.

**Returns:**

- <code>dict\[str, list\[Document\]\] | dict\[str, Any\]</code> – A dictionary with the following keys:
- `documents`: A list of documents with embeddings.
- `meta`: Information about the usage of the model.

## haystack_integrations.components.embedders.google_genai.text_embedder

### GoogleGenAITextEmbedder

Embeds strings using Google AI models.

You can use it to embed user query and send it to an embedding Retriever.

### Authentication examples

**1. Gemini Developer API (API Key Authentication)**

````python
from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder

# export the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
text_embedder = GoogleGenAITextEmbedder(model="gemini-embedding-001")

**2. Vertex AI (Application Default Credentials)**
```python
from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder

# Using Application Default Credentials (requires gcloud auth setup)
text_embedder = GoogleGenAITextEmbedder(
    api="vertex",
    vertex_ai_project="my-project",
    vertex_ai_location="us-central1",
    model="gemini-embedding-001"
)
````

**3. Vertex AI (API Key Authentication)**

```python
from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder

# export the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
text_embedder = GoogleGenAITextEmbedder(
    api="vertex",
    model="gemini-embedding-001"
)
```

### Usage example

```python
from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder

text_to_embed = "I love pizza!"

text_embedder = GoogleGenAITextEmbedder()

print(text_embedder.run(text_to_embed))

# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
# 'meta': {'model': 'gemini-embedding-001-v2',
#          'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}
```

#### __init__

```python
__init__(
    *,
    api_key: Secret = Secret.from_env_var(
        ["GOOGLE_API_KEY", "GEMINI_API_KEY"], strict=False
    ),
    api: Literal["gemini", "vertex"] = "gemini",
    vertex_ai_project: str | None = None,
    vertex_ai_location: str | None = None,
    model: str = "gemini-embedding-001",
    prefix: str = "",
    suffix: str = "",
    config: dict[str, Any] | None = None
) -> None
```

Creates an GoogleGenAITextEmbedder component.

**Parameters:**

- **api_key** (<code>Secret</code>) – Google API key, defaults to the `GOOGLE_API_KEY` and `GEMINI_API_KEY` environment variables.
  Not needed if using Vertex AI with Application Default Credentials.
  Go to https://aistudio.google.com/app/apikey for a Gemini API key.
  Go to https://cloud.google.com/vertex-ai/generative-ai/docs/start/api-keys for a Vertex AI API key.
- **api** (<code>Literal['gemini', 'vertex']</code>) – Which API to use. Either "gemini" for the Gemini Developer API or "vertex" for Vertex AI.
- **vertex_ai_project** (<code>str | None</code>) – Google Cloud project ID for Vertex AI. Required when using Vertex AI with
  Application Default Credentials.
- **vertex_ai_location** (<code>str | None</code>) – Google Cloud location for Vertex AI (e.g., "us-central1", "europe-west1").
  Required when using Vertex AI with Application Default Credentials.
- **model** (<code>str</code>) – The name of the model to use for calculating embeddings.
  The default model is `gemini-embedding-001`.
- **prefix** (<code>str</code>) – A string to add at the beginning of each text. It can be used to specify a task type for
  `gemini-embedding-2`. For available task types, see
  [Gemini documentation](https://ai.google.dev/gemini-api/docs/embeddings#task-types).
- **suffix** (<code>str</code>) – A string to add at the end of each text to embed.
- **config** (<code>dict\[str, Any\] | None</code>) – A dictionary of keyword arguments to configure embedding content configuration.
  See [Google API documentation](https://googleapis.github.io/python-genai/genai.html#genai.types.EmbedContentConfig)
  for the available options.
  Specifying task types in `config` does not take effect for `gemini-embedding-2`.
  See [Gemini documentation](https://ai.google.dev/gemini-api/docs/embeddings#task-types) for more
  information.

#### to_dict

```python
to_dict() -> dict[str, Any]
```

Serializes the component to a dictionary.

**Returns:**

- <code>dict\[str, Any\]</code> – Dictionary with serialized data.

#### from_dict

```python
from_dict(data: dict[str, Any]) -> GoogleGenAITextEmbedder
```

Deserializes the component from a dictionary.

**Parameters:**

- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.

**Returns:**

- <code>GoogleGenAITextEmbedder</code> – Deserialized component.

#### run

```python
run(text: str) -> dict[str, list[float]] | dict[str, Any]
```

Embeds a single string.

**Parameters:**

- **text** (<code>str</code>) – Text to embed.

**Returns:**

- <code>dict\[str, list\[float\]\] | dict\[str, Any\]</code> – A dictionary with the following keys:
- `embedding`: The embedding of the input text.
- `meta`: Information about the usage of the model.

#### run_async

```python
run_async(text: str) -> dict[str, list[float]] | dict[str, Any]
```

Asynchronously embed a single string.

This is the asynchronous version of the `run` method. It has the same parameters and return values
but can be used with `await` in async code.

**Parameters:**

- **text** (<code>str</code>) – Text to embed.

**Returns:**

- <code>dict\[str, list\[float\]\] | dict\[str, Any\]</code> – A dictionary with the following keys:
- `embedding`: The embedding of the input text.
- `meta`: Information about the usage of the model.

## haystack_integrations.components.generators.google_genai.chat.chat_generator

### GoogleGenAIChatGenerator

A component for generating chat completions using Google's Gemini models via the Google Gen AI SDK.

Supports models like gemini-2.5-flash and other Gemini variants. For Gemini 2.5 series models,
enables thinking features via `generation_kwargs={"thinking_budget": value}`.

### Thinking Support (Gemini 2.5 Series)

- **Reasoning transparency**: Models can show their reasoning process
- **Thought signatures**: Maintains thought context across multi-turn conversations with tools
- **Configurable thinking budgets**: Control token allocation for reasoning

Configure thinking behavior:

- `thinking_budget: -1`: Dynamic allocation (default)
- `thinking_budget: 0`: Disable thinking (Flash/Flash-Lite only)
- `thinking_budget: N`: Set explicit token budget

### Multi-Turn Thinking with Thought Signatures

Gemini uses **thought signatures** when tools are present - encrypted "save states" that maintain
context across turns. Include previous assistant responses in chat history for context preservation.

### Authentication

**Gemini Developer API**: Set `GOOGLE_API_KEY` or `GEMINI_API_KEY` environment variable
**Vertex AI**: Use `api="vertex"` with Application Default Credentials or API key

### Authentication Examples

**1. Gemini Developer API (API Key Authentication)**

```python
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

# export the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
chat_generator = GoogleGenAIChatGenerator(model="gemini-2.5-flash")
```

**2. Vertex AI (Application Default Credentials)**

```python
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

# Using Application Default Credentials (requires gcloud auth setup)
chat_generator = GoogleGenAIChatGenerator(
    api="vertex",
    vertex_ai_project="my-project",
    vertex_ai_location="us-central1",
    model="gemini-2.5-flash",
)
```

**3. Vertex AI (API Key Authentication)**

```python
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

# export the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
chat_generator = GoogleGenAIChatGenerator(
    api="vertex",
    model="gemini-2.5-flash",
)
```

### Usage example

```python
from haystack.dataclasses.chat_message import ChatMessage
from haystack.tools import Tool, Toolset
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

# Initialize the chat generator with thinking support
chat_generator = GoogleGenAIChatGenerator(
    model="gemini-2.5-flash",
    generation_kwargs={"thinking_budget": 1024}  # Enable thinking with 1024 token budget
)

# Generate a response
messages = [ChatMessage.from_user("Tell me about the future of AI")]
response = chat_generator.run(messages=messages)
print(response["replies"][0].text)

# Access reasoning content if available
message = response["replies"][0]
if message.reasonings:
    for reasoning in message.reasonings:
        print("Reasoning:", reasoning.reasoning_text)

# Tool usage example with thinking
def weather_function(city: str):
    return f"The weather in {city} is sunny and 25°C"

weather_tool = Tool(
    name="weather",
    description="Get weather information for a city",
    parameters={"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]},
    function=weather_function
)

# Can use either List[Tool] or Toolset
chat_generator_with_tools = GoogleGenAIChatGenerator(
    model="gemini-2.5-flash",
    tools=[weather_tool],  # or tools=Toolset([weather_tool])
    generation_kwargs={"thinking_budget": -1}  # Dynamic thinking allocation
)

messages = [ChatMessage.from_user("What's the weather in Paris?")]
response = chat_generator_with_tools.run(messages=messages)
```

### Usage example with structured output

```python
from pydantic import BaseModel
from haystack.dataclasses.chat_message import ChatMessage
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

class City(BaseModel):
    name: str
    country: str
    population: int

chat_generator = GoogleGenAIChatGenerator(
    model="gemini-2.5-flash",
    generation_kwargs={"response_format": City}
)

messages = [ChatMessage.from_user("Tell me about Paris")]
response = chat_generator.run(messages=messages)
print(response["replies"][0].text)  # JSON output matching the City schema
```

### Usage example with FileContent embedded in a ChatMessage

```python
from haystack.dataclasses import ChatMessage, FileContent
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

file_content = FileContent.from_url("https://arxiv.org/pdf/2309.08632")
chat_message = ChatMessage.from_user(content_parts=[file_content, "Summarize this paper in 100 words."])
chat_generator = GoogleGenAIChatGenerator()
response = chat_generator.run(messages=[chat_message])
```

#### SUPPORTED_MODELS

```python
SUPPORTED_MODELS: list[str] = [
    "gemini-3.1-pro-preview",
    "gemini-3-flash-preview",
    "gemini-3.1-flash-lite-preview",
    "gemini-2.5-pro",
    "gemini-2.5-flash",
    "gemini-2.5-flash-lite",
]

```

A non-exhaustive list of chat models supported by this component.

See https://ai.google.dev/gemini-api/docs/models for the full list of models and up-to-date model IDs.

#### __init__

```python
__init__(
    *,
    api_key: Secret = Secret.from_env_var(
        ["GOOGLE_API_KEY", "GEMINI_API_KEY"], strict=False
    ),
    api: Literal["gemini", "vertex"] = "gemini",
    vertex_ai_project: str | None = None,
    vertex_ai_location: str | None = None,
    model: str = "gemini-2.5-flash",
    generation_kwargs: dict[str, Any] | None = None,
    safety_settings: list[dict[str, Any]] | None = None,
    streaming_callback: StreamingCallbackT | None = None,
    tools: ToolsType | None = None,
    timeout: float | None = None,
    max_retries: int | None = None
) -> None
```

Initialize a GoogleGenAIChatGenerator instance.

**Parameters:**

- **api_key** (<code>Secret</code>) – Google API key, defaults to the `GOOGLE_API_KEY` and `GEMINI_API_KEY` environment variables.
  Not needed if using Vertex AI with Application Default Credentials.
  Go to https://aistudio.google.com/app/apikey for a Gemini API key.
  Go to https://cloud.google.com/vertex-ai/generative-ai/docs/start/api-keys for a Vertex AI API key.
- **api** (<code>Literal['gemini', 'vertex']</code>) – Which API to use. Either "gemini" for the Gemini Developer API or "vertex" for Vertex AI.
- **vertex_ai_project** (<code>str | None</code>) – Google Cloud project ID for Vertex AI. Required when using Vertex AI with
  Application Default Credentials.
- **vertex_ai_location** (<code>str | None</code>) – Google Cloud location for Vertex AI (e.g., "us-central1", "europe-west1").
  Required when using Vertex AI with Application Default Credentials.
- **model** (<code>str</code>) – Name of the model to use (e.g., "gemini-2.5-flash")
- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – Configuration for generation (temperature, max_tokens, etc.).
  For Gemini 2.5 series, supports `thinking_budget` to configure thinking behavior:
- `thinking_budget`: int, controls thinking token allocation
  - `-1`: Dynamic (default for most models)
  - `0`: Disable thinking (Flash/Flash-Lite only)
  - Positive integer: Set explicit budget
    For Gemini 3 series and newer, supports `thinking_level` to configure thinking depth:
- `thinking_level`: str, controls thinking (https://ai.google.dev/gemini-api/docs/thinking#levels-budgets)
  - `minimal`: Matches the "no thinking" setting for most queries. The model may think very minimally for
    complex coding tasks. Minimizes latency for chat or high throughput applications.
  - `low`: Minimizes latency and cost. Best for simple instruction following, chat, or high-throughput
    applications.
  - `medium`: Balanced thinking for most tasks.
  - `high`: (Default, dynamic): Maximizes reasoning depth. The model may take significantly longer to reach
    a first token, but the output will be more carefully reasoned.
- **safety_settings** (<code>list\[dict\[str, Any\]\] | None</code>) – Safety settings for content filtering
- **streaming_callback** (<code>StreamingCallbackT | None</code>) – A callback function that is called when a new token is received from the stream.
- **tools** (<code>ToolsType | None</code>) – A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
  Each tool should have a unique name.
- **timeout** (<code>float | None</code>) – Timeout for Google GenAI client calls. If not set, it defaults to the default set by the Google GenAI
  client.
- **max_retries** (<code>int | None</code>) – Maximum number of retries to attempt for failed requests. If not set, it defaults to the default set by
  the Google GenAI client.

#### to_dict

```python
to_dict() -> dict[str, Any]
```

Serializes the component to a dictionary.

**Returns:**

- <code>dict\[str, Any\]</code> – Dictionary with serialized data.

#### from_dict

```python
from_dict(data: dict[str, Any]) -> GoogleGenAIChatGenerator
```

Deserializes the component from a dictionary.

**Parameters:**

- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.

**Returns:**

- <code>GoogleGenAIChatGenerator</code> – Deserialized component.

#### run

```python
run(
    messages: list[ChatMessage],
    generation_kwargs: dict[str, Any] | None = None,
    safety_settings: list[dict[str, Any]] | None = None,
    streaming_callback: StreamingCallbackT | None = None,
    tools: ToolsType | None = None,
) -> dict[str, Any]
```

Run the Google Gen AI chat generator on the given input data.

**Parameters:**

- **messages** (<code>list\[ChatMessage\]</code>) – A list of ChatMessage instances representing the input messages.
- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – Configuration for generation. If provided, it will override
  the default config. Supports `thinking_budget` for Gemini 2.5 series thinking configuration.
- **safety_settings** (<code>list\[dict\[str, Any\]\] | None</code>) – Safety settings for content filtering. If provided, it will override the
  default settings.
- **streaming_callback** (<code>StreamingCallbackT | None</code>) – A callback function that is called when a new token is
  received from the stream.
- **tools** (<code>ToolsType | None</code>) – A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
  If provided, it will override the tools set during initialization.

**Returns:**

- <code>dict\[str, Any\]</code> – A dictionary with the following keys:
- `replies`: A list containing the generated ChatMessage responses.

**Raises:**

- <code>RuntimeError</code> – If there is an error in the Google Gen AI chat generation.
- <code>ValueError</code> – If a ChatMessage does not contain at least one of TextContent, ToolCall, or
  ToolCallResult or if the role in ChatMessage is different from User, System, Assistant.

#### run_async

```python
run_async(
    messages: list[ChatMessage],
    generation_kwargs: dict[str, Any] | None = None,
    safety_settings: list[dict[str, Any]] | None = None,
    streaming_callback: StreamingCallbackT | None = None,
    tools: ToolsType | None = None,
) -> dict[str, Any]
```

Async version of the run method. Run the Google Gen AI chat generator on the given input data.

**Parameters:**

- **messages** (<code>list\[ChatMessage\]</code>) – A list of ChatMessage instances representing the input messages.
- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – Configuration for generation. If provided, it will override
  the default config. Supports `thinking_budget` for Gemini 2.5 series thinking configuration.
  See https://ai.google.dev/gemini-api/docs/thinking for possible values.
- **safety_settings** (<code>list\[dict\[str, Any\]\] | None</code>) – Safety settings for content filtering. If provided, it will override the
  default settings.
- **streaming_callback** (<code>StreamingCallbackT | None</code>) – A callback function that is called when a new token is
  received from the stream.
- **tools** (<code>ToolsType | None</code>) – A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
  If provided, it will override the tools set during initialization.

**Returns:**

- <code>dict\[str, Any\]</code> – A dictionary with the following keys:
- `replies`: A list containing the generated ChatMessage responses.

**Raises:**

- <code>RuntimeError</code> – If there is an error in the async Google Gen AI chat generation.
- <code>ValueError</code> – If a ChatMessage does not contain at least one of TextContent, ToolCall, or
  ToolCallResult or if the role in ChatMessage is different from User, System, Assistant.
