Google AI integration for Haystack
Module haystack_integrations.components.generators.google_ai.gemini
GoogleAIGeminiGenerator
Generates text using multimodal Gemini models through Google AI Studio.
Usage example
from haystack.utils import Secret
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiGenerator
gemini = GoogleAIGeminiGenerator(model="gemini-pro", api_key=Secret.from_token("<MY_API_KEY>"))
res = gemini.run(parts = ["What is the most interesting thing you know?"])
for answer in res["replies"]:
print(answer)
Multimodal example
import requests
from haystack.utils import Secret
from haystack.dataclasses.byte_stream import ByteStream
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiGenerator
BASE_URL = (
"https://raw.githubusercontent.com/deepset-ai/haystack-core-integrations"
"/main/integrations/google_ai/example_assets"
)
URLS = [
f"{BASE_URL}/robot1.jpg",
f"{BASE_URL}/robot2.jpg",
f"{BASE_URL}/robot3.jpg",
f"{BASE_URL}/robot4.jpg"
]
images = [
ByteStream(data=requests.get(url).content, mime_type="image/jpeg")
for url in URLS
]
gemini = GoogleAIGeminiGenerator(model="gemini-1.5-flash", api_key=Secret.from_token("<MY_API_KEY>"))
result = gemini.run(parts = ["What can you tell me about this robots?", *images])
for answer in result["replies"]:
print(answer)
GoogleAIGeminiGenerator.__init__
def __init__(*,
api_key: Secret = Secret.from_env_var("GOOGLE_API_KEY"),
model: str = "gemini-1.5-flash",
generation_config: Optional[Union[GenerationConfig,
Dict[str, Any]]] = None,
safety_settings: Optional[Dict[HarmCategory,
HarmBlockThreshold]] = None,
streaming_callback: Optional[Callable[[StreamingChunk],
None]] = None)
Initializes a GoogleAIGeminiGenerator instance.
To get an API key, visit: https://makersuite.google.com
Arguments:
api_key: Google AI Studio API key.model: Name of the model to use. For available models, see https://ai.google.dev/gemini-api/docs/models/geminigeneration_config: The generation configuration to use. This can either be aGenerationConfigobject or a dictionary of parameters. For available parameters, see theGenerationConfigAPI reference.safety_settings: The safety settings to use. A dictionary withHarmCategoryas keys andHarmBlockThresholdas values. For more information, see the API referencestreaming_callback: A callback function that is called when a new token is received from the stream. The callback function accepts StreamingChunk as an argument.
GoogleAIGeminiGenerator.to_dict
def to_dict() -> Dict[str, Any]
Serializes the component to a dictionary.
Returns:
Dictionary with serialized data.
GoogleAIGeminiGenerator.from_dict
def from_dict(cls, data: Dict[str, Any]) -> "GoogleAIGeminiGenerator"
Deserializes the component from a dictionary.
Arguments:
data: Dictionary to deserialize from.
Returns:
Deserialized component.
GoogleAIGeminiGenerator.run
.output_types(replies=List[str])
def run(parts: Variadic[Union[str, ByteStream, Part]],
streaming_callback: Optional[Callable[[StreamingChunk], None]] = None)
Generates text based on the given input parts.
Arguments:
parts: A heterogeneous list of strings,ByteStreamorPartobjects.streaming_callback: A callback function that is called when a new token is received from the stream.
Returns:
A dictionary containing the following key:
replies: A list of strings containing the generated responses.
Module haystack_integrations.components.generators.google_ai.chat.gemini
GoogleAIGeminiChatGenerator
Completes chats using Gemini models through Google AI Studio.
It uses the ChatMessage
dataclass to interact with the model.
Usage example
from haystack.utils import Secret
from haystack.dataclasses.chat_message import ChatMessage
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiChatGenerator
gemini_chat = GoogleAIGeminiChatGenerator(model="gemini-1.5-flash", api_key=Secret.from_token("<MY_API_KEY>"))
messages = [ChatMessage.from_user("What is the most interesting thing you know?")]
res = gemini_chat.run(messages=messages)
for reply in res["replies"]:
print(reply.text)
messages += res["replies"] + [ChatMessage.from_user("Tell me more about it")]
res = gemini_chat.run(messages=messages)
for reply in res["replies"]:
print(reply.text)
With function calling:
from typing import Annotated
from haystack.utils import Secret
from haystack.dataclasses.chat_message import ChatMessage
from haystack.components.tools import ToolInvoker
from haystack.tools import create_tool_from_function
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiChatGenerator
# 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)
tool_invoker = ToolInvoker(tools=[tool])
gemini_chat = GoogleAIGeminiChatGenerator(
model="gemini-2.0-flash-exp",
api_key=Secret.from_token("<MY_API_KEY>"),
tools=[tool],
)
user_message = [ChatMessage.from_user("What is the temperature in celsius in Berlin?")]
replies = gemini_chat.run(messages=user_message)["replies"]
print(replies[0].tool_calls)
# actually invoke the tool
tool_messages = tool_invoker.run(messages=replies)["tool_messages"]
messages = user_message + replies + tool_messages
# transform the tool call result into a human readable message
final_replies = gemini_chat.run(messages=messages)["replies"]
print(final_replies[0].text)
GoogleAIGeminiChatGenerator.__init__
def __init__(*,
api_key: Secret = Secret.from_env_var("GOOGLE_API_KEY"),
model: str = "gemini-1.5-flash",
generation_config: Optional[Union[GenerationConfig,
Dict[str, Any]]] = None,
safety_settings: Optional[Dict[HarmCategory,
HarmBlockThreshold]] = None,
tools: Optional[List[Tool]] = None,
tool_config: Optional[content_types.ToolConfigDict] = None,
streaming_callback: Optional[Callable[[StreamingChunk],
None]] = None)
Initializes a GoogleAIGeminiChatGenerator instance.
To get an API key, visit: https://aistudio.google.com/
Arguments:
api_key: Google AI Studio API key. To get a key, see Google AI Studio.model: Name of the model to use. For available models, see https://ai.google.dev/gemini-api/docs/models/gemini.generation_config: The generation configuration to use. This can either be aGenerationConfigobject or a dictionary of parameters. For available parameters, see the API reference.safety_settings: The safety settings to use. A dictionary withHarmCategoryas keys andHarmBlockThresholdas values. For more information, see the API referencetools: A list of tools for which the model can prepare calls.tool_config: The tool config to use. See the documentation for ToolConfig.streaming_callback: A callback function that is called when a new token is received from the stream. The callback function accepts StreamingChunk as an argument.
GoogleAIGeminiChatGenerator.to_dict
def to_dict() -> Dict[str, Any]
Serializes the component to a dictionary.
Returns:
Dictionary with serialized data.
GoogleAIGeminiChatGenerator.from_dict
def from_dict(cls, data: Dict[str, Any]) -> "GoogleAIGeminiChatGenerator"
Deserializes the component from a dictionary.
Arguments:
data: Dictionary to deserialize from.
Returns:
Deserialized component.
GoogleAIGeminiChatGenerator.run
.output_types(replies=List[ChatMessage])
def run(messages: List[ChatMessage],
streaming_callback: Optional[Callable[[StreamingChunk], None]] = None,
*,
tools: Optional[List[Tool]] = None)
Generates text based on the provided messages.
Arguments:
messages: A list ofChatMessageinstances, representing the input messages.streaming_callback: A callback function that is called when a new token is received from the stream.tools: A list of tools for which the model can prepare calls. If set, it will override thetoolsparameter set during component initialization.
Returns:
A dictionary containing the following key:
replies: A list containing the generated responses asChatMessageinstances.