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VertexAIGeminiChatGenerator

VertexAIGeminiChatGenerator enables chat completion using Google Gemini models.

Most common position in a pipelineAfter a ChatPromptBuilder
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 referenceGoogle Vertex
GitHub linkhttps://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_vertex

VertexAIGeminiChatGenerator supports both gemini-pro and gemini-1.5-flash models.

For available models, see https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models.

📘

To explore the full capabilities of Gemini check out this article and the related 🧑‍🍳 Cookbook.

Parameters Overview

VertexAIGeminiChatGenerator uses Google Cloud Application Default Credentials (ADCs) for authentication. For more information on how to set up ADCs, see the official documentation.

Keep in mind that it’s essential to use an account that has access to a project authorized to use Google Vertex AI endpoints.

You can find your project ID in the GCP resource manager or locally by running gcloud projects list in your terminal. For more info on the gcloud CLI, see its official documentation.

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

You need to install the google-vertex-haystack package to use the VertexAIGeminiChatGenerator:

pip install google-vertex-haystack

On its own

Basic usage:

from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.google_vertex import VertexAIGeminiChatGenerator


gemini_chat = VertexAIGeminiChatGenerator()

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

print(res["replies"][0].content)
>>> The Shawshank Redemption

messages += [res["replies"][0], ChatMessage.from_user("Who's the main actor?")]
res = gemini_chat.run(messages)

print(res["replies"][0].content)
>>> Tim Robbins

When chatting with Gemini Pro, you can also easily use function calls. First, define the function locally and convert into a Tool:

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 VertexAIGeminiChatGenerator to set the tools and a ToolInvoker to invoke the tools.:

from haystack_integrations.components.generators.google_vertex import VertexAIGeminiChatGenerator
from haystack.components.tools import ToolInvoker

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

tool_invoker = ToolInvoker(tools=[tool])

And then ask our question:

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)
>>> [ToolCall(tool_name='get_current_weather', 
>>>           arguments={'unit': 'celsius', 'location': 'Berlin'}, id=None)]

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)
>>> The temperature in Berlin is 20 degrees Celsius.

In a pipeline

from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack import Pipeline
from haystack_integrations.components.generators.google_vertex import VertexAIGeminiChatGenerator


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

pipe = Pipeline()
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("gemini", gemini)
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)

>>> - **753 B.C.:** Traditional date of the founding of Rome by Romulus and Remus.
>>> - **509 B.C.:** Establishment of the Roman Republic, replacing the Etruscan monarchy.
>>> - **492-264 B.C.:** Series of wars against neighboring tribes, resulting in the expansion of the Roman Republic's territory.
>>> - **264-146 B.C.:** Three Punic Wars against Carthage, resulting in the destruction of Carthage and the Roman Republic becoming the dominant power in the Mediterranean.
>>> - **133-73 B.C.:** Series of civil wars and slave revolts, leading to the rise of Julius Caesar.
>>> - **49 B.C.:** Julius Caesar crosses the Rubicon River, starting the Roman Civil War.
>>> - **44 B.C.:** Julius Caesar is assassinated, leading to the Second Triumvirate of Octavian, Mark Antony, and Lepidus.
>>> - **31 B.C.:** Battle of Actium, where Octavian defeats Mark Antony and Cleopatra, becoming the sole ruler of Rome.
>>> - **27 B.C.:** The Roman Republic is transformed into the Roman Empire, with Octavian becoming the first Roman emperor, known as Augustus.
>>> - **1st century A.D.:** The Roman Empire reaches its greatest extent, stretching from Britain to Egypt.
>>> - **3rd century A.D.:** The Roman Empire begins to decline, facing internal instability, invasions by Germanic tribes, and the rise of Christianity.
>>> - **476 A.D.:** The last Western Roman emperor, Romulus Augustulus, is overthrown by the Germanic leader Odoacer, marking the end of the Roman Empire in the West.

Additional References

🧑‍🍳 Cookbook: Function Calling and Multimodal QA with Gemini