Integration: Google AI
Use Google AI Models with Haystack
Table of Contents
Overview
Google AI is a machine learning (ML) platform that lets you train and deploy ML models and AI applications, and customize large language models (LLMs) for use in your AI-powered applications. This integration enables the usage of Google generative models via their Makersuite REST API.
Haystack supports all the available multimodal Gemini models for tasks such as text generation, function calling, visual question answering, code generation, and image captioning.
Installation
Install the Google AI integration:
pip install google-ai-haystack
Usage
Once installed, you will have access to various Haystack Generators:
-
GoogleAIGeminiGenerator: Use this component with Gemini models ‘gemini-pro’, ‘gemini-pro-vision’, ‘gemini-ultra’ for text generation and multimodal prompts. -
GoogleAIGeminiChatGenerator: Use this component with Gemini models ‘gemini-pro’, ‘gemini-pro-vision’ and ‘gemini-ultra’ for text generation, multimodal prompts and function calling in chat completion setting.
To use Google Gemini models you need an API key. You can either pass it as init argument or set a GOOGLE_API_KEY environment variable. If neither is set you won’t be able to use the generators.
To get an API key visit Google Makersuite.
Text Generation with gemini-pro
To use Gemini model for text generation, set the GOOGLE_API_KEY environment variable and then initialize a GoogleAIGeminiGenerator with "gemini-pro":
import os
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiGenerator
os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
gemini_generator = GoogleAIGeminiGenerator(model="gemini-pro")
result = gemini_generator.run(parts = ["What is assemblage in art?"])
print(result["answers"][0])
Output:
Assemblage in art refers to the creation of a three-dimensional artwork by combining various found objects...
Multimodality with gemini-pro-vision
To use gemini-pro-vision model for visual question answering, initialize a GoogleAIGeminiGenerator with "gemini-pro-vision" and project_id. Then, run it with the images as well as the prompt:
import requests
import os
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
]
os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
gemini_generator = GoogleAIGeminiGenerator(model="gemini-pro-vision")
result = gemini_generator.run(parts = ["What can you tell me about these robots?", *images])
for answer in result["answers"]:
print(answer)
Output:
The first image is of C-3PO and R2-D2 from the Star Wars franchise...
The second image is of Maria from the 1927 film Metropolis...
The third image is of Gort from the 1951 film The Day the Earth Stood Still...
The fourth image is of Marvin from the 1977 film The Hitchhiker's Guide to the Galaxy...
Function calling
When chatting with Gemini we can also use function calls.
import os
from google.ai.generativelanguage import FunctionDeclaration, Tool
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiChatGenerator
# Define a function that return always some nice weather
def get_current_weather(location: str, unit: str = "celsius"):
return {"weather": "sunny", "temperature": 21.8, "unit": unit}
# Class that defines the arguments of a function so Gemini
# knows how it should be called
get_current_weather_func = FunctionDeclaration(
name="get_current_weather",
description="Get the current weather in a given location",
parameters={
"type_": "OBJECT",
"properties": {
"location": {"type_": "STRING", "description": "The city and state, e.g. San Francisco, CA"},
"unit": {
"type_": "STRING",
"enum": [
"celsius",
"fahrenheit",
],
},
},
"required": ["location"],
},
)
tool = Tool(function_declarations=[get_current_weather_func])
os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
gemini_chat = GoogleAIGeminiChatGenerator(model="gemini-pro", tools=[tool])
messages = [
ChatMessage.from_user(content="What is the temperature in celsius in Berlin?")
]
res = gemini_chat.run(messages=messages)
weather = get_current_weather(**res["replies"][0].content)
messages += res["replies"] + [
ChatMessage.from_function(content=weather, name="get_current_weather")
]
res = gemini_chat.run(messages=messages)
print(res["replies"][0].content)
Will output:
In Berlin, the weather is sunny with a temperature of 21.8 degrees Celsius.
Code generation
Gemini can also easily generate code, here’s an example:
import os
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiGenerator
os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
gemini_generator = GoogleAIGeminiGenerator(model="gemini-pro")
result = gemini_generator.run("Write a code for calculating fibonacci numbers in JavaScript")
print(result["answers"][0])
Output:
// Recursive approach
function fibonacciRecursive(n) {
if (n <= 1) {
return n;
} else {
return fibonacciRecursive(n - 1) + fibonacciRecursive(n - 2);
}
}
// Iterative approach
function fibonacciIterative(n) {
if (n <= 1) {
return n;
}
let fibSequence = [0, 1];
while (fibSequence.length < n + 1) {
let nextNumber =
fibSequence[fibSequence.length - 1] + fibSequence[fibSequence.length - 2];
fibSequence.push(nextNumber);
}
return fibSequence[n];
}
// Usage
console.log(fibonacciRecursive(7)); // Output: 13
console.log(fibonacciIterative(7)); // Output: 13