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Update ADK doc according to issue #989 - 1 #990

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4 changes: 2 additions & 2 deletions docs/get-started/python.md
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Expand Up @@ -3,7 +3,7 @@
This guide shows you how to get up and running with Agent Development Kit
(ADK) for Python. Before you start, make sure you have the following installed:

* Python 3.9 or later
* Python 3.10 or later
* `pip` for installing packages

## Installation
Expand Down Expand Up @@ -146,4 +146,4 @@ upper left corner and type a request.
Now that you have ADK installed and your first agent running, try building
your own agent with our build guides:

* [Build your agent](/adk-docs/tutorials/)
* [Build your agent](/adk-docs/tutorials/)
4 changes: 2 additions & 2 deletions docs/get-started/quickstart.md
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Expand Up @@ -6,7 +6,7 @@ setting up a basic agent with multiple tools, and running it locally either in t
<!-- <img src="../../assets/quickstart.png" alt="Quickstart setup"> -->

This quickstart assumes a local IDE (VS Code, PyCharm, IntelliJ IDEA, etc.)
with Python 3.9+ or Java 17+ and terminal access. This method runs the
with Python 3.10+ or Java 17+ and terminal access. This method runs the
application entirely on your machine and is recommended for internal development.

## 1. Set up Environment & Install ADK { #set-up-environment-install-adk }
Expand Down Expand Up @@ -443,4 +443,4 @@ You've successfully created and interacted with your first agent using ADK!
section for deeper dives into project structure, configuration, and other
interfaces.
* **Understand Core Concepts:** Learn about
[agents concepts](../agents/index.md).
[agents concepts](../agents/index.md).
4 changes: 2 additions & 2 deletions docs/grounding/google_search_grounding.md
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Expand Up @@ -19,7 +19,7 @@ As an additional resource, [Gemini Fullstack Agent Development Kit (ADK) Quickst

## Google Search Grounding Quickstart

This quickstart guides you through creating an ADK agent with Google Search grounding feature. This quickstart assumes a local IDE (VS Code or PyCharm, etc.) with Python 3.9+ and terminal access.
This quickstart guides you through creating an ADK agent with Google Search grounding feature. This quickstart assumes a local IDE (VS Code or PyCharm, etc.) with Python 3.10+ and terminal access.

### 1. Set up Environment & Install ADK { #set-up-environment-install-adk }

Expand Down Expand Up @@ -297,4 +297,4 @@ Google Search Grounding transforms AI agents from static knowledge repositories
- Deliver comprehensive answers with verifiable facts
- Enhance user experience with relevant search suggestions

The grounding process seamlessly connects user queries to Google's vast search index, enriching responses with up-to-date context while maintaining the conversational flow. With proper implementation and display of grounded responses, your agents become powerful tools for information discovery and decision-making.
The grounding process seamlessly connects user queries to Google's vast search index, enriching responses with up-to-date context while maintaining the conversational flow. With proper implementation and display of grounded responses, your agents become powerful tools for information discovery and decision-making.
4 changes: 2 additions & 2 deletions docs/grounding/vertex_ai_search_grounding.md
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Expand Up @@ -15,7 +15,7 @@ In this guide, you'll discover:

## Vertex AI Search Grounding Quickstart

This quickstart guides you through creating an ADK agent with Vertex AI Search grounding feature. This quickstart assumes a local IDE (VS Code or PyCharm, etc.) with Python 3.9+ and terminal access.
This quickstart guides you through creating an ADK agent with Vertex AI Search grounding feature. This quickstart assumes a local IDE (VS Code or PyCharm, etc.) with Python 3.10+ and terminal access.

### 1. Prepare Vertex AI Search { #prepare-vertex-ai-search }

Expand Down Expand Up @@ -316,4 +316,4 @@ Vertex AI Search Grounding transforms AI agents from general-purpose assistants
- Deliver comprehensive answers with verifiable enterprise facts
- Maintain data privacy within your Google Cloud environment

The grounding process seamlessly connects user queries to your organization's knowledge base, enriching responses with relevant context from your private documents while maintaining the conversational flow. With proper implementation, your agents become powerful tools for enterprise information discovery and decision-making.
The grounding process seamlessly connects user queries to your organization's knowledge base, enriching responses with relevant context from your private documents while maintaining the conversational flow. With proper implementation, your agents become powerful tools for enterprise information discovery and decision-making.