---
title: "GoogleAIGeminiChatGenerator"
id: googleaigeminichatgenerator
slug: "/googleaigeminichatgenerator"
description: "This component enables chat completion using Google Gemini models."
---

# GoogleAIGeminiChatGenerator

This component enables chat completion using Google Gemini models.

:::warning Deprecation Notice

This integration uses the deprecated google-generativeai SDK, which will lose support after August 2025.

We recommend switching to the new [GoogleGenAIChatGenerator](googlegenaichatgenerator.mdx) integration instead.
:::

<div className="key-value-table">

|                                        |                                                                                                      |
| :------------------------------------- | :--------------------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)                                                 |
| **Mandatory init variables**           | `api_key`: A Google AI Studio API key. Can be set with `GOOGLE_API_KEY` env var.                     |
| **Mandatory run variables**            | `messages`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects representing the chat |
| **Output variables**                   | `replies`: A list of alternative replies of the model to the input chat                              |
| **API reference**                      | [Google AI](/reference/integrations-google-ai)                                                              |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_ai          |

</div>

`GoogleAIGeminiChatGenerator` supports `gemini-2.5-pro-exp-03-25`, `gemini-2.0-flash`, `gemini-1.5-pro`, and `gemini-1.5-flash` models.

For available models, see https://ai.google.dev/gemini-api/docs/models/gemini.

### Parameters Overview

`GoogleAIGeminiChatGenerator` uses a Google Studio API key for authentication. You can write this key in an `api_key` parameter or as a `GOOGLE_API_KEY` environment variable (recommended).

To get an API key, visit the [Google AI Studio](https://aistudio.google.com/) website.

### Streaming

This Generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter.

## Usage

To begin working with `GoogleAIGeminiChatGenerator`, install the `google-ai-haystack` package:

```shell
pip install google-ai-haystack
```

### On its own

Basic usage:

```python
import os
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiChatGenerator

os.environ["GOOGLE_API_KEY"] = "<MY_API_KEY>"
gemini_chat = GoogleAIGeminiChatGenerator()

messages = [ChatMessage.from_user("Tell me the name of a movie")]
res = gemini_chat.run(messages)

print(res["replies"][0].text)

messages += [res["replies"], ChatMessage.from_user("Who's the main actor?")]
res = gemini_chat.run(messages)

print(res["replies"][0].text)
```

When chatting with Gemini, you can also easily use function calls. First, define the function locally and convert into a [Tool](../../tools/tool.mdx):

```python
from typing import Annotated
from haystack.tools import create_tool_from_function

## example function to get the current weather
def get_current_weather(
    location: Annotated[str, "The city for which to get the weather, e.g. 'San Francisco'"] = "Munich",
    unit: Annotated[str, "The unit for the temperature, e.g. 'celsius'"] = "celsius",
) -> str:
    return f"The weather in {location} is sunny. The temperature is 20 {unit}."

tool = create_tool_from_function(get_current_weather)
```

Create a new instance of `GoogleAIGeminiChatGenerator` to set the tools and a [ToolInvoker](../tools/toolinvoker.mdx) to invoke the tools.

```python
import os
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiChatGenerator
from haystack.components.tools import ToolInvoker

os.environ["GOOGLE_API_KEY"] = "<MY_API_KEY>"

gemini_chat = GoogleAIGeminiChatGenerator(model="gemini-2.0-flash", tools=[tool])

tool_invoker = ToolInvoker(tools=[tool])

```

And then ask a question:

```python
from haystack.dataclasses import ChatMessage

messages = [ChatMessage.from_user("What is the temperature in celsius in Berlin?")]
res = gemini_chat.run(messages=messages)

print(res["replies"][0].tool_calls)

tool_messages = tool_invoker.run(messages=replies)["tool_messages"]
messages = user_message + replies + tool_messages

messages += res["replies"][0] + [ChatMessage.from_function(content=weather, name="get_current_weather")]

final_replies = gemini_chat.run(messages=messages)["replies"]
print(final_replies[0].text)
```

### In a pipeline

```python
import os
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack import Pipeline
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiChatGenerator

## no parameter init, we don't use any runtime template variables
prompt_builder = ChatPromptBuilder()

os.environ["GOOGLE_API_KEY"] = "<MY_API_KEY>"
gemini_chat = GoogleAIGeminiChatGenerator()

pipe = Pipeline()
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("gemini", gemini_chat)
pipe.connect("prompt_builder.prompt", "gemini.messages")

location = "Rome"
messages = [ChatMessage.from_user("Tell me briefly about {{location}} history")]
res = pipe.run(data={"prompt_builder": {"template_variables":{"location": location}, "template": messages}})

print(res)

```
