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+---
+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,
+    timeout: float | None = None,
+    max_retries: int | 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.
+- **timeout** (<code>float | None</code>) – The timeout in seconds for the underlying Google GenAI client network requests.
+- **max_retries** (<code>int | None</code>) – The maximum number of retries for the underlying Google GenAI client network requests.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Create the synchronous Google Gen AI client.
+
+#### warm_up_async
+
+```python
+warm_up_async() -> None
+```
+
+Create the asynchronous Google Gen AI client.
+
+#### close
+
+```python
+close() -> None
+```
+
+Close the synchronous Google Gen AI client.
+
+#### close_async
+
+```python
+close_async() -> None
+```
+
+Close the asynchronous Google Gen AI 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]) -> 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,
+    timeout: float | None = None,
+    max_retries: int | 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 and are guaranteed to stay within it.
+  If None, file paths are treated as absolute paths with no containment check.
+  If document metadata, in particular `file_path_meta_field`, may be influenced by untrusted input,
+  set `root_path` to a dedicated data directory so that path-traversal beyond it is rejected.
+- **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.
+- **timeout** (<code>float | None</code>) – The timeout in seconds for the underlying Google GenAI client network requests.
+- **max_retries** (<code>int | None</code>) – The maximum number of retries for the underlying Google GenAI client network requests.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Create the synchronous Google Gen AI client.
+
+#### warm_up_async
+
+```python
+warm_up_async() -> None
+```
+
+Create the asynchronous Google Gen AI client.
+
+#### close
+
+```python
+close() -> None
+```
+
+Close the synchronous Google Gen AI client.
+
+#### close_async
+
+```python
+close_async() -> None
+```
+
+Close the asynchronous Google Gen AI 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]) -> GoogleGenAIMultimodalDocumentEmbedder
+```
+
+Deserializes the component from a dictionary.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
+
+**Returns:**
+
+- <code>GoogleGenAIMultimodalDocumentEmbedder</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.
+
+**Raises:**
+
+- <code>TypeError</code> – If the input is not a list of `Documents`.
+- <code>ValueError</code> – If a document is missing the file path metadata field, its file path escapes `root_path`, or its
+  MIME type is not supported.
+- <code>RuntimeError</code> – If the conversion of some documents fails.
+
+#### 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.
+
+**Raises:**
+
+- <code>TypeError</code> – If the input is not a list of `Documents`.
+- <code>ValueError</code> – If a document is missing the file path metadata field, its file path escapes `root_path`, or its
+  MIME type is not supported.
+- <code>RuntimeError</code> – If the conversion of some documents fails.
+
+## 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,
+    timeout: float | None = None,
+    max_retries: int | 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.
+- **timeout** (<code>float | None</code>) – The timeout in seconds for the underlying Google GenAI client network requests.
+- **max_retries** (<code>int | None</code>) – The maximum number of retries for the underlying Google GenAI client network requests.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Create the synchronous Google Gen AI client.
+
+#### warm_up_async
+
+```python
+warm_up_async() -> None
+```
+
+Create the asynchronous Google Gen AI client.
+
+#### close
+
+```python
+close() -> None
+```
+
+Close the synchronous Google Gen AI client.
+
+#### close_async
+
+```python
+close_async() -> None
+```
+
+Close the asynchronous Google Gen AI 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]) -> 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-3.8-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 and Gemini 3 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-3.8-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-3.8-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-3.8-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-3.8-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-3.8-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-3.8-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-3.8-flash",
+    "gemini-3.7-flash",
+    "gemini-3.6-flash",
+    "gemini-3.5-flash",
+    "gemini-3.5-flash-lite",
+    "gemini-3.1-pro-preview",
+    "gemini-3.1-flash-lite",
+    "gemini-3-flash-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-3.8-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-3.8-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.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Create the synchronous Google Gen AI client.
+
+#### warm_up_async
+
+```python
+warm_up_async() -> None
+```
+
+Create the asynchronous Google Gen AI client.
+
+#### close
+
+```python
+close() -> None
+```
+
+Close the synchronous Google Gen AI client.
+
+#### close_async
+
+```python
+close_async() -> None
+```
+
+Close the asynchronous Google Gen AI 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] | str,
+    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\[ChatMessage\] | str</code>) – A list of ChatMessage instances representing the input messages.
+  If a string is provided, it is converted to a list containing a ChatMessage with user role.
+- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – Configuration for generation. These are merged per key with the
+  `generation_kwargs` passed during component initialization: keys provided here take precedence,
+  keys set only at initialization are kept. 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] | str,
+    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\[ChatMessage\] | str</code>) – A list of ChatMessage instances representing the input messages.
+  If a string is provided, it is converted to a list containing a ChatMessage with user role.
+- **generation_kwargs** (<code>dict\[str, Any\] | None</code>) – Configuration for generation. These are merged per key with the
+  `generation_kwargs` passed during component initialization: keys provided here take precedence,
+  keys set only at initialization are kept. 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.
+
+## haystack_integrations.token_counters.google_genai.token_counter
+
+### GoogleGenAITokenCounter
+
+Counts input tokens for Gemini models with Google's token counting API.
+
+Unlike local token counters, this counter sends the input to the `countTokens` endpoint of the Google Gen AI
+SDK, so the returned count includes the model-specific formatting Gemini applies to messages.
+
+Inputs are assembled exactly as `GoogleGenAIChatGenerator` sends them: a leading system message becomes the
+system instruction and the remaining messages become the request contents.
+
+### Backend support for system instructions and tools
+
+The Google Gen AI SDK only accepts a system instruction and tool schemas on `countTokens` when the client
+targets Vertex AI. On the Gemini Developer API, a leading system message is therefore measured as a user turn,
+which gives a close approximation rather than the exact count, and tools raise a `ValueError` instead of
+silently returning a count that omits their schemas. Counting plain messages works on either backend.
+
+## Usage Example:
+
+```python
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.token_counters.google_genai import GoogleGenAITokenCounter
+
+counter = GoogleGenAITokenCounter("gemini-3.8-flash")
+messages = [ChatMessage.from_user("Hello, how are you?")]
+token_count = counter.count(messages)
+print(f"Token count: {token_count}")
+```
+
+#### __init__
+
+```python
+__init__(
+    model: str,
+    *,
+    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,
+    timeout: float | None = None,
+    max_retries: int | None = None
+) -> None
+```
+
+Initialize the counter.
+
+**Parameters:**
+
+- **model** (<code>str</code>) – The model whose tokenization should be used. Token counts are model-specific, so count
+  against the same model you intend to generate with.
+- **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.
+- **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.
+- **timeout** (<code>float | None</code>) – Timeout for Google Gen AI client calls. If not set, it defaults to the default set by the
+  Google Gen AI 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 Gen AI client.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initialize the Google Gen AI client.
+
+#### count
+
+```python
+count(messages: list[ChatMessage], tools: ToolsType | None = None) -> int
+```
+
+Return the number of input tokens Gemini will use for the given messages and tools.
+
+**Parameters:**
+
+- **messages** (<code>list\[ChatMessage\]</code>) – The messages to measure. A leading system message is measured as the system instruction on
+  Vertex AI and as a user turn on the Gemini Developer API, which cannot measure system instructions.
+- **tools** (<code>ToolsType | None</code>) – Tools whose schemas are sent alongside the messages, and so consume tokens too.
+
+**Returns:**
+
+- <code>int</code> – The token count, or `0` when there is nothing to measure.
+
+**Raises:**
+
+- <code>ValueError</code> – If tools are passed while targeting the Gemini Developer API, which cannot measure them.
+
+#### close
+
+```python
+close() -> None
+```
+
+Close the Google Gen AI client and its underlying HTTP resources.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serialize the counter.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – A dictionary representation of the counter.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> GoogleGenAITokenCounter
+```
+
+Deserialize the counter.
+
+**Parameters:**
+
+- **data** (<code>dict\[str, Any\]</code>) – The dictionary to deserialize from.
+
+**Returns:**
+
+- <code>GoogleGenAITokenCounter</code> – The deserialized counter.