GoogleAIGeminiChatGenerator

This component enables chat completion using Google Gemini models.

Deprecation Notice

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

We recommend switching to the new GoogleGenAIChatGenerator integration instead.

Most common position in a pipeline	After a ChatPromptBuilder
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 objects representing the chat
Output variables	“replies”: A list of alternative replies of the model to the input chat
API reference	Google AI
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_ai

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 website.

Streaming

This Generator supports streaming 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:

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 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)