# Where to Find generative-ai Repository Documentation: A Complete Guide to GoogleCloudPlatform/generative-ai

> Find all generative ai repository documentation easily. This guide shows you where to locate README files within the GoogleCloudPlatform/generative-ai repository for Gemini, Genkit, and Agents.

- Repository: [Google Cloud Platform/generative-ai](https://github.com/GoogleCloudPlatform/generative-ai)
- Tags: getting-started
- Published: 2026-03-09

---

**All generative-ai repository documentation lives in Markdown README files distributed throughout the repository, starting with the root [`README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/README.md) and extending into topical subdirectories like `gemini/`, `genkit/`, and `agents/`.**

The GoogleCloudPlatform/generative-ai repository is a comprehensive collection of self-contained examples, Jupyter notebooks, and sample applications for Google Cloud's generative AI services. Unlike traditional documentation sites, this project organizes all generative-ai repository documentation within the source tree itself, using hierarchical README files to guide developers through setup, implementation patterns, and advanced use cases.

## Repository Documentation Structure

The documentation follows a hierarchical structure that mirrors the codebase organization. Each major component maintains its own README file explaining specific implementations, prerequisites, and usage instructions.

### Root-Level Documentation

The primary entry point for all generative-ai repository documentation is the root [`README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/README.md) file. This document provides:

- High-level repository overview and architecture
- Table of contents mapping to each component
- Contribution guidelines and help links
- Related repository references

Source: [`README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/README.md) at repository root.

### Component-Specific Documentation

Major functional areas each maintain dedicated documentation directories:

| Component | Documentation Location | Contents |
|-----------|------------------------|----------|
| **Gemini** | [`gemini/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/README.md) | Multimodal model guides, agent-engine samples, function-calling tutorials, multimodal-live-api examples |
| **Vertex AI Search** | [`search/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/search/README.md) | RAG implementation guides, Cloud Function examples, enterprise search patterns |
| **RAG & Grounding** | [`rag-grounding/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/rag-grounding/README.md) | Dual-LLM pipelines, grounding notebooks, retrieval patterns |
| **Vision (Imagen)** | [`vision/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/vision/README.md) | Image generation, editing, captioning, and VQA samples |
| **Audio/Speech** | [`audio/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/audio/README.md) | Chirp speech-to-text, translation, and real-time transcription |
| **GenKit** | [`genkit/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/genkit/README.md) | LLM-as-a-service framework guides, postcard-generator samples |
| **Agents (ADK)** | [`agents/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/agents/README.md) | Agent Development Kit samples, multi-agent orchestration, memory-bank demos |
| **Tools** | [`tools/llmevalkit/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/tools/llmevalkit/README.md) | LLM evaluation toolkit documentation |
| **Resources** | [`RESOURCES.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/RESOURCES.md) | Curated blogs, YouTube playlists, external tutorials |

## Key Documentation Files and Paths

For developers navigating the generative-ai repository documentation, these specific files serve as the primary reference points:

- **Root Overview**: [`README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/README.md) — Entry point with repository-wide navigation
- **Gemini Samples**: [`gemini/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/README.md) — Index of all Gemini-specific implementations including [`gemini/getting-started/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/getting-started/README.md) for initial setup
- **Environment Setup**: [`setup-env/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/setup-env/README.md) — Step-by-step instructions for installing the Vertex AI Python SDK, configuring GCP credentials, and launching notebooks in Colab or Workbench
- **RAG Implementation**: [`rag-grounding/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/rag-grounding/README.md) and [`search/retrieval-augmented-generation/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/search/retrieval-augmented-generation/README.md) — Dual-LLM pipeline documentation
- **Framework Guides**: [`genkit/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/genkit/README.md) and [`agents/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/agents/README.md) — Documentation for the GenKit framework and Agent Development Kit (ADK)

All documentation follows the standard GitHub URL pattern:

```

https://github.com/GoogleCloudPlatform/generative-ai/blob/main/<path-to-file>

```

## Practical Code Examples from Documentation

The generative-ai repository documentation includes runnable code samples demonstrating key implementation patterns.

### Calling Gemini with Vertex AI Python SDK

As documented in [`gemini/getting-started/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/getting-started/README.md) and related notebooks:

```python

# Install the SDK (see setup-env/README.md)

# pip install "google-cloud-aiplatform[preview]"

from google.cloud import aiplatform

# Initialise the Vertex AI client – replace <PROJECT_ID> and <REGION>

aiplatform.init(project="<PROJECT_ID>", location="<REGION>")

# Choose a Gemini model (e.g., gemini-1.5-flash)

model = aiplatform.LanguageModel(
    "gemini-1.5-flash-001",
    temperature=0.7,
    max_output_tokens=1024,
)

# Simple text generation

response = model.generate_content("Explain retrieval‑augmented generation in one sentence.")
print(response.text)

```

### Deploying a GenKit Sample Application

Following the guides in [`genkit/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/genkit/README.md) and [`genkit/postcard-generator/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/genkit/postcard-generator/README.md):

```bash

# From the repository root

cd genkit/postcard-generator

# Install dependencies

npm ci   # or `pip install -r requirements.txt` for the Python version

# Start the local development server

npm run dev   # Python version: `python -m genkit start`

```

Access the application at `http://localhost:3000` as documented in [`genkit/postcard-generator/docs/local.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/genkit/postcard-generator/docs/local.md).

### Running RAG Notebooks

As instructed in [`rag-grounding/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/rag-grounding/README.md):

```bash

# From the repository root

cd rag-grounding
jupyter lab  # opens the notebooks listed in the README

```

The notebook `rag_with_dual_llms.ipynb` implements the dual-LLM pipeline using Vertex AI Search and Gemini, documented in [`search/retrieval-augmented-generation/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/search/retrieval-augmented-generation/README.md).

## Summary

- **All generative-ai repository documentation resides within the repository itself**, primarily in hierarchical [`README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/README.md) files rather than external documentation sites.
- **Start with the root [`README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/README.md)** for the repository overview and navigation table pointing to all components.
- **Consult component-specific READMEs** in directories like `gemini/`, `genkit/`, `agents/`, `search/`, and `rag-grounding/` for implementation details.
- **Use [`setup-env/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/setup-env/README.md)** for environment configuration and authentication setup.
- **Reference [`RESOURCES.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/RESOURCES.md)** for external learning materials including blogs and video tutorials.

## Frequently Asked Questions

### Where is the main entry point for generative-ai repository documentation?

The main entry point is the root [`README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/README.md) file at the repository base. This file contains the "Using this repository" table that maps to every component including Gemini, Vertex AI Search, GenKit, and Agents Development Kit (ADK).

### How do I find documentation for specific Google AI models like Gemini?

Navigate to the `gemini/` directory and open [`gemini/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/README.md). This file indexes all Gemini-specific samples including function-calling examples, multimodal-live-api implementations, and agent-engine tutorials. For setup instructions, also consult [`setup-env/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/setup-env/README.md).

### Is there documentation for deploying agents and multi-agent systems?

Yes. The [`agents/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/agents/README.md) file provides documentation for the Agents Development Kit (ADK), including sample agents, multi-agent orchestration patterns, memory-bank demonstrations, and Terraform deployment guides. Each sample agent typically includes its own README with specific implementation details.

### Where can I find setup and environment configuration instructions?

Environment setup documentation is located in [`setup-env/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/setup-env/README.md). This file provides step-by-step instructions for installing the Vertex AI Python SDK, configuring Google Cloud Platform credentials, and launching notebooks in Colab or Vertex AI Workbench. This is the recommended starting point before running any samples.