# Gemini API Examples in Google Cloud: Navigating the generative-ai Repository > Find Gemini API examples in the GoogleCloudPlatform/generative-ai repository. Explore workshops, gemini, and open-models folders for practical applications. - Repository: [Google Cloud Platform/generative-ai](https://github.com/GoogleCloudPlatform/generative-ai) - Tags: tutorial - Published: 2026-03-09 --- **The GoogleCloudPlatform/generative-ai repository does not contain a `generative-ai-gcp` directory, but it houses extensive Gemini API examples across the `workshops`, `gemini`, and `open-models` folders.** The GoogleCloudPlatform/generative-ai repository serves as the definitive reference for implementing Google's Gemini API on Vertex AI. While the specific `generative-ai-gcp` directory does not exist, the repository provides production-ready code samples spanning from basic SDK initialization to advanced multimodal RAG systems and fine-tuning workflows. ## Where to Find Gemini API Examples in the Repository Despite the absence of a `generative-ai-gcp` folder, Gemini API implementations are distributed strategically throughout the repository's architecture. The primary locations include the `workshops` directory for instructional notebooks, the `gemini` folder for use-case-specific implementations, and `open-models` for deployment scenarios. These locations collectively demonstrate the full lifecycle of Gemini API requests, from authentication and model selection to streaming responses and token-count optimization. ## Core Gemini API Implementation Patterns The repository showcases consistent architectural patterns for Gemini API integration using the Vertex AI Python SDK. Each pattern targets specific operational requirements while maintaining standardized initialization workflows. ### SDK Initialization and Authentication Every notebook begins with the `vertexai` module import and explicit project configuration. In `workshops/rag-ops/1_prototyping_gemini.ipynb`, the initialization follows this pattern: ```python import vertexai vertexai.init(project="my-gcp-project", location="us-central1") ``` The SDK automatically respects default environment variables when explicit parameters are omitted, enabling seamless transitions between development and production environments. ### Model Instantiation and Configuration Gemini model access occurs through the `GenerativeModel` class imported from `vertexai.generative_models`. The repository demonstrates instantiation with specific model IDs such as `gemini-2.0-flash` or custom fine-tuned endpoints. For example, `gemini/use-cases/retrieval-augmented-generation/intro_multimodal_rag.ipynb` illustrates loading both standard and specialized model versions to handle multimodal RAG pipelines. ### Multimodal Content and Prompt Construction The examples extensively utilize the `Part` class for constructing complex prompts that combine text, images, video, and documents. In `workshops/ai-agents/ai_agents_for_engineers.ipynb`, prompts are assembled as mixed-type lists: ```python from vertexai.generative_models import Part prompt = [ Part.from_image_uri("gs://my-bucket/sample.jpg"), "Describe the scene in plain English, then list three key insights." ] ``` This pattern supports ingestion of PDFs, audio files, and video content through methods like `Part.from_file()` and `Part.from_image()`. ### Safety Settings and Generation Configuration Production implementations require explicit safety filters and generation parameters. The repository consistently implements `GenerationConfig` for controlling temperature, maximum output tokens, and top-p sampling thresholds. Safety constraints are enforced using `HarmCategory` enumerations paired with `HarmBlockThreshold` levels, as shown in the prototyping workshops: ```python from vertexai.generative_models import GenerationConfig, HarmCategory, HarmBlockThreshold gen_config = GenerationConfig( temperature=0.7, max_output_tokens=1024, top_p=0.95, ) safety_settings = { HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, } ``` ## Complete Gemini API Implementation Example The following implementation synthesizes patterns from `workshops/rag-ops/1_prototyping_gemini.ipynb` and related notebooks, demonstrating SDK initialization, multimodal prompting, streaming responses, and token counting: ```python # 1️⃣ Initialise the Vertex AI SDK (uses default env vars if set) import vertexai vertexai.init(project="my-gcp-project", location="us-central1") # 2️⃣ Import Gemini classes from vertexai.generative_models import ( GenerativeModel, GenerationConfig, HarmCategory, HarmBlockThreshold, Part, ) # 3️⃣ Choose a Gemini model (flash for quick prototyping) MODEL_ID = "gemini-2.0-flash" model = GenerativeModel(MODEL_ID) # 4️⃣ Define generation parameters and safety filters gen_config = GenerationConfig( temperature=0.7, max_output_tokens=1024, top_p=0.95, ) safety_settings = { HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, } # 5️⃣ Build a multimodal prompt (image + text) prompt = [ Part.from_image_uri("gs://my-bucket/sample.jpg"), "Describe the scene in plain English, then list three key insights." ] # 6️⃣ Generate a response (streaming version) response = model.generate_content( prompt, generation_config=gen_config, safety_settings=safety_settings, stream=True, ) # 7️⃣ Consume streamed chunks for chunk in response: print(chunk.text) # partial output print("-" * 80) # 8️⃣ Token-count helper (cost estimation) token_info = model.count_tokens(prompt) print(f"Tokens: {token_info.total_tokens}, Billable chars: {token_info.total_billable_characters}") ``` ## Key Notebooks for Specific Use Cases The repository organizes Gemini API examples by functional domain, allowing developers to locate relevant implementations efficiently: - **`workshops/rag-ops/1_prototyping_gemini.ipynb`**: Demonstrates fundamental API patterns including model loading, streaming responses, and token utilization tracking. - **`workshops/rag-ops/2.3_mvp_rag.ipynb`**: Implements end-to-end Retrieval-Augmented Generation using Gemini with chunked embeddings and context stitching. - **`gemini/use-cases/retrieval-augmented-generation/intro_multimodal_rag.ipynb`**: Shows multimodal RAG implementations combining text, PDF documents, and audio inputs. - **`gemini/use-cases/video-analysis/video_analysis_with_youtube_data_api_and_batch_prediction.ipynb`**: Illustrates video-to-text extraction pipelines and subsequent Gemini summarization. - **`gemini/tuning/sft_gemini_visual_defect_detection.ipynb`**: Loads fine-tuned Gemini endpoints via `GenerativeModel(custom_endpoint_name)` for specialized visual inspection tasks. - **`workshops/ai-agents/ai_agents_for_engineers.ipynb`**: Covers zero-shot, few-shot, and chain-of-thought prompting patterns. - **[`setup-env/README.md`](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/setup-env/README.md)**: Provides environment configuration instructions for GCP project setup and service account permissions. ## Summary - The `generative-ai-gcp` directory does not exist within the GoogleCloudPlatform/generative-ai repository. - Gemini API examples are distributed across `workshops`, `gemini`, and `open-models` directories. - All implementations utilize the `vertexai` Python SDK with consistent initialization via `vertexai.init()`. - The `GenerativeModel` class supports both standard model IDs (`gemini-2.0-flash`) and custom fine-tuned endpoints. - Multimodal inputs are handled through the `Part` class, supporting images, video, audio, and documents. - Production deployments require explicit `GenerationConfig` and `HarmCategory` safety configurations. - The `count_tokens()` method enables precise cost estimation before API invocation. ## Frequently Asked Questions ### Is there a generative-ai-gcp directory in the GoogleCloudPlatform/generative-ai repository? No, the repository does not contain a directory named `generative-ai-gcp`. Instead, Gemini API examples are organized within the `workshops`, `gemini`, and `open-models` directories. These folders contain comprehensive notebooks demonstrating various implementation patterns. ### How do I initialize the Gemini API using the examples provided? Initialize the SDK by importing `vertexai` and calling `vertexai.init(project="your-project", location="your-region")`. The examples in `workshops/rag-ops/1_prototyping_gemini.ipynb` demonstrate this pattern, including support for default environment variables when explicit parameters are not provided. ### Can I process multimodal inputs using these Gemini API examples? Yes, the repository extensively demonstrates multimodal processing using the `Part` class from `vertexai.generative_models`. Notebooks like `workshops/ai-agents/ai_agents_for_engineers.ipynb` show how to combine text, images, PDFs, and video content within a single prompt using methods such as `Part.from_image()` and `Part.from_file()`. ### Where can I find examples of fine-tuned Gemini models in the repository? The `gemini/tuning/sft_gemini_visual_defect_detection.ipynb` notebook demonstrates loading and utilizing fine-tuned Gemini endpoints. It shows how to instantiate `GenerativeModel` with a custom endpoint name rather than a standard model ID, enabling specialized inference for specific domains like visual defect detection.