Add author info for Gary Badwal in Google GenAI integration (TextEmbedder & DocumentEmbedder)

integrations/google-genai.md

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+---
+layout: integration
+name: Google Gen AI
+description: Use Google's Gemini models with Haystack via the new Google Gen AI SDK
+authors:
+  - name: deepset
+    socials:
+      github: deepset-ai
+      twitter: deepset_ai
+      linkedin: https://www.linkedin.com/company/deepset-ai/
+  - name: Gary Badwal
+    socials:
+      website: garybadwal.com
+      github: garybadwal
+      twitter: garybadwal_
+      linkedin: https://www.linkedin.com/in/garybadwal/
+pypi: https://pypi.org/project/google-genai-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_genai
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/googleai.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Chat Generation with `gemini-2.0-flash`](#chat-generation-with-gemini-20-flash)
+  - [Streaming Chat Generation](#streaming-chat-generation)
+  - [Function calling](#function-calling)
+  - [Document Embedding](#document-embedding)
+  - [Text Embedding](#text-embedding)
+
+## Overview
+
+[Google Gen AI](https://ai.google.dev/) provides access to Google's Gemini models through the new Google Gen AI SDK. This integration enables the usage of Google's latest generative models via the updated API interface.
+
+Haystack supports the latest [Gemini models](https://ai.google.dev/models/gemini) like `gemini-2.0-flash` and `text-embedding-004` for tasks such as **chat completion**, **function calling**, **streaming responses** and **embedding generation**.
+
+## Installation
+
+Install the Google Gen AI integration:
+
+```bash
+pip install google-genai-haystack
+```
+
+## Usage
+
+Once installed, you will have access to the Haystack Chat components:
+
+- [`GoogleGenAIChatGenerator`](https://docs.haystack.deepset.ai/docs/googlegenaichatgenerator): Use this component with [Gemini models](https://ai.google.dev/gemini-api/docs/models/gemini#model-variations), such as '**gemini-2.0-flash**' for chat completion and function calling.
+- `GoogleGenAIDocumentEmbedder`: Use this component with [Google GenAI models](https://ai.google.dev/gemini-api/docs/embeddings#embeddings-models), such as '**text-embedding-004**' for generating embeddings.
+- `GoogleGenAIChatGenerator`: Use this component with [Google GenAI models](https://ai.google.dev/gemini-api/docs/embeddings#embeddings-models), such as '**text-embedding-004**' for generating embeddings.
+
+To use Google Gemini models you need an API key. You can either pass it as init argument or set a `GOOGLE_API_KEY` or `GEMINI_API_KEY` environment variable. If neither is set you won't be able to use the generator.
+
+To get an API key visit [Google AI Studio](https://aistudio.google.com/).
+
+### Chat Generation with `gemini-2.0-flash`
+
+To use Gemini model for chat generation, set the `GOOGLE_API_KEY` or `GEMINI_API_KEY` environment variable and then initialize a `GoogleGenAIChatGenerator` with `"gemini-2.0-flash"`:
+
+```python
+import os
+from haystack.dataclasses.chat_message import ChatMessage
+from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator
+
+os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
+
+# Initialize the chat generator
+chat_generator = GoogleGenAIChatGenerator(model="gemini-2.0-flash")
+
+# Generate a response
+messages = [ChatMessage.from_user("Tell me about the future of AI")]
+response = chat_generator.run(messages=messages)
+print(response["replies"][0].text)
+```
+
+Output:
+
+```shell
+The future of AI is incredibly exciting and multifaceted, with developments spanning multiple domains...
+```
+
+### Streaming Chat Generation
+
+For real-time streaming responses, you can use the streaming callback functionality:
+
+```python
+import os
+from haystack.dataclasses.chat_message import ChatMessage
+from haystack.dataclasses import StreamingChunk
+from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator
+
+os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
+
+def streaming_callback(chunk: StreamingChunk):
+    print(chunk.content, end='', flush=True)
+
+# Initialize with streaming callback
+chat_generator = GoogleGenAIChatGenerator(
+    model="gemini-2.0-flash",
+    streaming_callback=streaming_callback
+)
+
+# Generate a streaming response
+messages = [ChatMessage.from_user("Write a short story about robots")]
+response = chat_generator.run(messages=messages)
+# Text will stream in real-time via the callback
+```
+
+### Function calling
+
+When chatting with Gemini models, you can also use function calls for tool integration:
+
+```python
+import os
+from haystack.dataclasses.chat_message import ChatMessage
+from haystack.tools import Tool
+
+os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
+
+# Define a simple weather function
+def get_weather(city: str) -> str:
+    return f"The weather in {city} is sunny and 25°C"
+
+# Create a tool from the function
+weather_tool = Tool(
+    name="get_weather",
+    description="Get weather information for a city",
+    parameters={
+        "type": "object",
+        "properties": {
+            "city": {"type": "string", "description": "The city name"}
+        },
+        "required": ["city"]
+    },
+    function=get_weather
+)
+
+# Initialize chat generator with tools
+chat_generator = GoogleGenAIChatGenerator(
+    model="gemini-2.0-flash",
+    tools=[weather_tool]
+)
+
+# Generate response with tool usage
+messages = [ChatMessage.from_user("What's the weather like in Paris?")]
+response = chat_generator.run(messages=messages)
+
+# The model will call the weather function and provide a natural response
+print(response["replies"][0].text)
+```
+
+Output:
+
+```shell
+The weather in Paris is sunny and 25°C.
+``` 
+
+### Document Embedding
+
+To use Google model for embedding generation, set the `GOOGLE_API_KEY` or `GEMINI_API_KEY` environment variable and then initialize a `GoogleGenAIDocumentEmbedder`:
+
+```python
+import os
+from haystack import Document
+from haystack_integrations.components.embedders.google_genai import GoogleGenAIDocumentEmbedder
+
+os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
+
+# Initialize the embedder
+embedder = GoogleGenAIDocumentEmbedder()
+
+# Generate a response
+doc = Document(content="some text")
+docs_w_embeddings = embedder.run(documents=[doc])["documents"]
+```
+
+### Text Embedding
+
+To use Google model for embedding generation, set the `GOOGLE_API_KEY` or `GEMINI_API_KEY` environment variable and then initialize a `GoogleGenAITextEmbedder`:
+
+```python
+import os
+from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder
+
+os.environ["GOOGLE_API_KEY"] = "YOUR-GOOGLE-API-KEY"
+
+text_to_embed = "I love pizza!"
+
+# Initialize the text embedder
+text_embedder = GoogleGenAITextEmbedder()
+
+# Generate a response
+print(text_embedder.run(text_to_embed))
+```
+
+Output:
+
+```shell
+{'embedding': [-0.052871075, -0.035282962, ...., -0.04802792], 
+'meta': {'model': 'text-embedding-004'}}
+```