diff --git a/.cursor/commands/refactor-file-module.md b/.cursor/commands/refactor-file-module.md new file mode 100644 index 00000000..2717151d --- /dev/null +++ b/.cursor/commands/refactor-file-module.md @@ -0,0 +1,64 @@ +Refactor the given file into a module of the same name. + +## Example + +Take the following `commands.py` file as an example. + +```python +CONSTANT_1 = "Constant 1" +CONSTANT_2 = "Constant 2" + +class MyCommand: + pass + +class MyOtherCommand: + pass + +class YetAnotherCommand: + pass +``` + +### Classes + +1. Create a `commands` directory. +2. For each class in the original `commands.py` file, create a file and place the corresponding class there. + +### Constants + +1. Create a `commands/config.py` file. +2. Move all constants into the `config.py` file. Update each file to import the constants from the `config.py` file. + +### **init**.py + +1. Create a `commands/__init__.py` file. +2. Export the items in this module using `__all__`. + +The `__init__.py` file should look like this: + +```python +from .config import CONSTANT_1, CONSTANT_2 +from .my-command import MyCommand +from .my-other-command import MyOtherCommand +from .yet-another-command import YetAnotherCommand + +__all__ = [ + "CONSTANT_1", + "CONSTANT_2", + "MyCommand", + "MyOtherCommand", + "YetAnotherCommand", +] +``` + +### Final file structure + +The final file structure should look like this: + +``` +commands/ # `commands` module directory, same name as given file +- __init__.py # Init file to expose other items within this module +- config.py # Config file for constants +- my-command.py # For MyCommand +- my-other-command.py # For MyOtherCommand +- yet-another-command.py # For YetAnotherCommand +``` diff --git a/.gitignore b/.gitignore index f297c163..83883880 100644 --- a/.gitignore +++ b/.gitignore @@ -32,3 +32,242 @@ Temporary Items tmp/ tmp[-.]* *.tmp + + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[codz] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py.cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +# Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +# poetry.lock +# poetry.toml + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. +# https://pdm-project.org/en/latest/usage/project/#working-with-version-control +# pdm.lock +# pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. +# pixi.lock +# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one +# in the .venv directory. It is recommended not to include this directory in version control. +.pixi + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# Redis +*.rdb +*.aof +*.pid + +# RabbitMQ +mnesia/ +rabbitmq/ +rabbitmq-data/ + +# ActiveMQ +activemq-data/ + +# SageMath parsed files +*.sage.py + +# Environments +.env +.envrc +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +# .idea/ + +# Abstra +# Abstra is an AI-powered process automation framework. +# Ignore directories containing user credentials, local state, and settings. +# Learn more at https://abstra.io/docs +.abstra/ + +# Visual Studio Code +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore +# and can be added to the global gitignore or merged into this file. However, if you prefer, +# you could uncomment the following to ignore the entire vscode folder +# .vscode/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +# Marimo +marimo/_static/ +marimo/_lsp/ +__marimo__/ + +# Streamlit +.streamlit/secrets.toml + +# Secrets +secrets/ +key.json + +# Environment variable files +.env +.env.* +!.env.example + + +# Pulumi +# ----------------------------------------------------------------------------- + +# Ignore Pulumi stack files, which contain project-specific config. +# Allow the example file. +Pulumi.*.yaml +!Pulumi.example.yaml + +# Ignore Pulumi local backend state files. +.pulumi/ diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..59d30b03 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,23 @@ +{ + "editor.codeActionsOnSave": { + "source.organizeImports": "always" + }, + "editor.formatOnSave": true, + "editor.rulers": [80, 100], + "editor.trimAutoWhitespace": true, + 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= true } + +[build-system] +requires = ["poetry-core>=2.0.0,<3.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/tools/python/gcp_gemini/Pulumi.example.yaml b/tools/python/gcp_gemini/Pulumi.example.yaml new file mode 100644 index 00000000..ca41f8fe --- /dev/null +++ b/tools/python/gcp_gemini/Pulumi.example.yaml @@ -0,0 +1,45 @@ +# Example Pulumi stack configuration file +# +# (Optional) Setup Local Backend - Skip if using Pulumi Cloud: +# +# 1. Switch to local file-based backend (stores state in ~/.pulumi/): +# pulumi login --local +# +# 2. Or use project-local backend (stores state in current directory): +# pulumi login file://$(pwd) +# This creates a .pulumi/ directory in the current project directory +# (.pulumi/ is already in .gitignore) +# +# Initialize and Configure Stack: +# +# 1. Initialize a new stack: +# pulumi stack init +# Example: pulumi stack init dev +# This creates Pulumi..yaml with auto-generated encryption salt +# +# 2. Configure your settings using pulumi config set: +# pulumi config set gcp:project YOUR_PROJECT_ID +# pulumi config set region us-west1 +# pulumi config set resource_prefix my-app +# +# Or manually copy this file to Pulumi.{stack-name}.yaml and uncomment/edit values below + +config: + # Optional: Your Google Cloud project ID + # If not set, uses the active gcloud project (gcloud config get-value project) + # Set with: pulumi config set gcp:project YOUR_PROJECT_ID + # gcp:project: YOUR_PROJECT_ID + + # Optional: Vertex AI region (default: us-central1) + # Set with: pulumi config set region us-west1 + # region: us-central1 + + # Optional: Resource naming prefix (default: uses project ID) + # Used for service account naming + # Set with: pulumi config set resource_prefix my-app + # resource_prefix: my-app + + # Optional: Existing service account email to import + # If not set, creates a new service account named {resource_prefix} + # Set with: pulumi config set service_account_email my-sa@project.iam.gserviceaccount.com + # service_account_email: my-sa@project.iam.gserviceaccount.com diff --git a/tools/python/gcp_gemini/Pulumi.yaml b/tools/python/gcp_gemini/Pulumi.yaml new file mode 100644 index 00000000..6c7af94d --- /dev/null +++ b/tools/python/gcp_gemini/Pulumi.yaml @@ -0,0 +1,10 @@ +name: gcp_gemini +description: Create Gemini resources +runtime: + name: python + options: + toolchain: poetry +config: + pulumi:tags: + value: + pulumi:template: gcp-python diff --git a/tools/python/gcp_gemini/README.md b/tools/python/gcp_gemini/README.md new file mode 100644 index 00000000..7a5fc988 --- /dev/null +++ b/tools/python/gcp_gemini/README.md @@ -0,0 +1,348 @@ +# Gemini 2.0 Flash with Pulumi GCP + +This project sets up the necessary Google Cloud infrastructure to work with Gemini 2.0 Flash through Vertex AI using Pulumi. + +## Overview + +Unlike traditional infrastructure resources (like storage buckets), Gemini 2.0 Flash is a **managed AI service** provided by Google through Vertex AI. You don't deploy the model itself as infrastructure, but rather: + +1. **Set up the required infrastructure** (APIs, service accounts, permissions) +2. **Access the model through Vertex AI APIs** in your application code + +## Architecture + +``` +┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ +│ Your App │───▶│ Vertex AI API │───▶│ Gemini 2.0 Flash│ +│ │ │ │ │ (Managed) │ +└─────────────────┘ └──────────────────┘ └─────────────────┘ + │ │ + ▼ ▼ +┌─────────────────┐ ┌──────────────────┐ +│ Service Account │ │ GCS Bucket │ +│ (Pulumi created)│ │ (for data/logs) │ +└─────────────────┘ └──────────────────┘ +``` + +## Prerequisites + +1. **Google Cloud Project** with billing enabled +2. **Pulumi CLI** installed +3. **Google Cloud SDK** installed and configured +4. **Python 3.8+** + +## ⚠️ Security Note + +This repository does **NOT** include: + +- Stack configuration files (`Pulumi.*.yaml`) - These contain project-specific settings +- Service account keys (`key.json`, `*.pem`) - These contain sensitive credentials +- Environment files (`.env`) - These may contain secrets + +These files are in `.gitignore` and must be created by each user for their own GCP project. + +## Setup Instructions + +### 1. Install Dependencies + +```bash +cd tools/python/gcp_gemini +pip install -r requirements.txt +``` + +### 2. Configure Google Cloud Authentication + +```bash +# Authenticate with Google Cloud +gcloud auth login +gcloud auth application-default login + +# Set your project +gcloud config set project YOUR_PROJECT_ID +``` + +### 3. Initialize Pulumi Stack + +**Note**: Stack configuration files (`Pulumi.*.yaml`) are project-specific and contain your GCP project ID. These files are **not committed to the repository** (they're in `.gitignore`). Each user must create their own stack. + +```bash +# Initialize a new Pulumi stack (use any name you like: dev, prod, staging, etc.) +pulumi stack init dev + +# Configure required settings for your Google Cloud project +pulumi config set gcp:project YOUR_PROJECT_ID + +# Optional: Set a specific region for Vertex AI (defaults to us-central1) +pulumi config set region us-central1 + +# Optional: Customize resource naming prefix (defaults to project ID) +pulumi config set resource_prefix my-app +``` + +This creates a `Pulumi.dev.yaml` file locally with your project-specific configuration. + +#### Configuration Options + +| Config Key | Description | Default | Example | +| ----------------- | ----------------------------------------------------------------------- | --------------- | ---------------- | +| `gcp:project` | **Required**. Your Google Cloud project ID | None | `my-gcp-project` | +| `region` | Vertex AI region | `us-central1` | `europe-west1` | +| `resource_prefix` | Prefix for resource naming. Resources will be named `{prefix}-{stack}`. | Your project ID | `my-app` | + +**Resource Details:** + +- **Vertex AI Region**: Configurable via `region` parameter (default: `us-central1`). This determines where your AI model runs. +- **GCS Bucket Location**: Hard-coded to `US` (multi-regional) for high availability and geo-redundancy across all US regions. + +**Service Account Naming Examples:** + +- With default prefix: `my-gcp-project-dev@my-gcp-project.iam.gserviceaccount.com` +- With custom prefix: `my-app-dev@my-gcp-project.iam.gserviceaccount.com` + +### 4. Deploy Infrastructure + +```bash +# Review the resources that will be created +pulumi preview + +# Deploy the infrastructure +pulumi up +``` + +This will create: + +- ✅ Enable required APIs (Vertex AI, ML, Storage) +- ✅ Create a service account with appropriate permissions +- ✅ Create a GCS bucket for data storage +- ✅ Set up IAM roles and permissions + +### 5. Get Deployment Information + +After deployment, note the outputs: + +```bash +pulumi stack output +``` + +You'll see information like: + +- `project_id`: Your Google Cloud project ID +- `gemini_service_account_email`: Service account for API access +- `data_bucket_name`: Bucket for storing data/artifacts +- `usage_info`: Model details and endpoints + +## Using Gemini 2.0 Flash + +### Option 1: Use the Example Script + +1. **Update the configuration** in `example_usage.py`: + + ```python + PROJECT_ID = "your-actual-project-id" # From pulumi output + LOCATION = "us-central1" # Your chosen region + ``` + +2. **Run the examples**: + ```bash + python example_usage.py + ``` + +### Option 2: Integrate into Your Application + +```python +import vertexai +from vertexai.generative_models import GenerativeModel + +# Initialize Vertex AI +vertexai.init(project="your-project-id", location="us-central1") + +# Create model instance +model = GenerativeModel("gemini-2.0-flash-001") + +# Generate content +response = model.generate_content("Explain quantum computing simply") +print(response.text) +``` + +## Available Gemini 2.0 Flash Features + +### 🤖 **Text Generation** + +```python +model = GenerativeModel("gemini-2.0-flash-001") +response = model.generate_content("Your prompt here") +``` + +### 💬 **Multi-turn Chat** + +```python +chat = model.start_chat() +response1 = chat.send_message("Hello!") +response2 = chat.send_message("Tell me about AI") +``` + +### 🖼️ **Multimodal (Text + Images)** + +```python +response = model.generate_content([ + "What's in this image?", + Part.from_image(image_data) +]) +``` + +### 🌊 **Streaming Responses** + +```python +response = model.generate_content("Write a story", stream=True) +for chunk in response: + print(chunk.text, end="") +``` + +### 🔧 **Function Calling** + +```python +model = GenerativeModel("gemini-2.0-flash-001", tools=[your_function]) +response = model.generate_content("What's the weather like?") +``` + +### 📋 **Structured Output** + +```python +response = model.generate_content( + "Generate JSON with user info", + generation_config={"response_mime_type": "application/json"} +) +``` + +## Model Variants + +| Model ID | Description | Best For | +| ------------------------------- | -------------------------- | --------------------------------- | +| `gemini-2.0-flash-001` | Standard Gemini 2.0 Flash | General use, balanced performance | +| `gemini-2.0-flash-thinking-exp` | With thinking capabilities | Complex reasoning tasks | + +## Authentication Options + +### 1. Service Account (Recommended for Production) + +```python +from google.oauth2 import service_account + +credentials = service_account.Credentials.from_service_account_file( + "path/to/service-account-key.json" +) +vertexai.init(project=PROJECT_ID, location=LOCATION, credentials=credentials) +``` + +### 2. Application Default Credentials (Development) + +```bash +gcloud auth application-default login +``` + +### 3. Using the Pulumi-created Service Account + +The deployed service account has the necessary permissions. You can: + +1. Download its key from Google Cloud Console +2. Use it in your application +3. Or impersonate it programmatically + +## Cost Considerations + +Gemini 2.0 Flash pricing is based on: + +- **Input tokens**: Text you send to the model +- **Output tokens**: Text the model generates +- **Image processing**: When using multimodal features + +💡 **Tip**: Use shorter prompts and limit output length to optimize costs. + +## Monitoring and Logging + +Enable monitoring in your code: + +```python +from google.cloud import aiplatform + +# Enable detailed logging +aiplatform.init( + project=PROJECT_ID, + location=LOCATION, + experiment="gemini-experiments" +) +``` + +## Troubleshooting + +### Common Issues + +1. **Authentication Errors** + + ```bash + gcloud auth application-default login + gcloud config set project YOUR_PROJECT_ID + ``` + +2. **API Not Enabled** + + ```bash + gcloud services enable aiplatform.googleapis.com + ``` + +3. **Permission Denied** + + - Ensure your account has `roles/aiplatform.user` + - Check service account permissions + +4. **Quota Exceeded** + - Check your Vertex AI quotas in Google Cloud Console + - Request quota increases if needed + +### Debug Mode + +Enable debug logging: + +```python +import logging +logging.basicConfig(level=logging.DEBUG) +``` + +## Clean Up + +To remove all created resources: + +```bash +pulumi destroy +``` + +This will: + +- Delete the service account +- Remove IAM bindings +- Delete the GCS bucket +- Keep APIs enabled (disable_on_destroy=False) + +## Example Use Cases + +1. **Content Generation**: Blog posts, articles, creative writing +2. **Code Assistance**: Code explanation, generation, debugging +3. **Data Analysis**: Summarizing reports, extracting insights +4. **Customer Support**: Automated responses, FAQ generation +5. **Education**: Tutoring, explanation of complex topics + +## Further Resources + +- [Vertex AI Documentation](https://cloud.google.com/vertex-ai/docs) +- [Gemini API Reference](https://cloud.google.com/vertex-ai/generative-ai/docs) +- [Pulumi GCP Provider](https://www.pulumi.com/registry/packages/gcp/) +- [Google AI Studio](https://aistudio.google.com/) - Test prompts before coding + +## Support + +For issues: + +1. Check the troubleshooting section above +2. Refer to Google Cloud documentation +3. Check Pulumi logs: `pulumi logs` +4. Review Google Cloud Console for API/permission issues diff --git a/tools/python/gcp_gemini/__main__.py b/tools/python/gcp_gemini/__main__.py new file mode 100644 index 00000000..be1a6583 --- /dev/null +++ b/tools/python/gcp_gemini/__main__.py @@ -0,0 +1,126 @@ +"""A Google Cloud Python Pulumi program for Gemini 2.5 Flash-Lite access""" + +import pulumi +import pulumi_gcp as gcp + +MODEL_ID = "gemini-2.5-flash-lite" + +# Get current project information +current = gcp.organizations.get_client_config() + +# Get stack name for consistent naming +stack_name = pulumi.get_stack() + +# Get configuration for resource naming and region +config = pulumi.Config() +sa_prefix = config.get("resource_prefix") or current.project +region = config.get("region") or "us-central1" +existing_sa_email = config.get("service_account_email") + +# Enable required APIs for Vertex AI and Gemini +apis_to_enable = [ + "aiplatform.googleapis.com", # Vertex AI API + "ml.googleapis.com", # Machine Learning API + "storage.googleapis.com", # Cloud Storage (often needed for ML workflows) + # Generative Language API (for Google AI Studio). This is required to use ADC JSON files. + "generativelanguage.googleapis.com", +] + +enabled_apis = [] +for api in apis_to_enable: + enabled_api = gcp.projects.Service( + f"enable-{api.replace('.', '-')}", + service=api, + disable_on_destroy=False, # Keep APIs enabled even if stack is destroyed. + ) + enabled_apis.append(enabled_api) + +# Create or import a service account for Vertex AI access. +if existing_sa_email: + # Use the provided service account email to determine the account ID + # Format: account-id@project.iam.gserviceaccount.com + sa_name, _, _ = existing_sa_email.partition("@") + sa_email = existing_sa_email + + # Try to import the existing service account + # The import ID format for service accounts is: projects/{project}/serviceAccounts/{email} + service_account = gcp.serviceaccount.Account( + sa_name, + account_id=sa_name, + display_name=f"{sa_prefix} Service Account (imported)", + opts=pulumi.ResourceOptions( + protect=True, # Protect against deletion + import_=f"projects/{current.project}/serviceAccounts/{sa_email}", + ), + ) +else: + # Create a new service account + sa_name = sa_prefix + sa_email = f"{sa_name}@{current.project}.iam.gserviceaccount.com" + + service_account = gcp.serviceaccount.Account( + sa_name, + account_id=sa_name, + display_name=f"{sa_prefix} Service Account", + opts=pulumi.ResourceOptions( + protect=False, # We created it, so we can destroy it + ), + ) + +# Grant necessary IAM roles to the service account +roles_to_grant = [ + "roles/aiplatform.user", # Access Vertex AI services + "roles/ml.developer", # ML API access + "roles/storage.objectAdmin", # Storage access (if needed for data) +] + +for role in roles_to_grant: + # Create a meaningful name from the role by removing 'roles/' prefix and + # replacing dots with dashes. + role_suffix = role.replace("roles/", "").replace(".", "-") + gcp.projects.IAMMember( + f"gemini-sa-{role_suffix}", + project=current.project, + role=role, + member=pulumi.Output.concat("serviceAccount:", service_account.email), + ) + +# This bucket is needed to store any data, logs, or artifacts +# that your Gemini workflows may require. For example, you might upload input data, +# save model outputs, or share files between Vertex AI and your application. + +# NOTE: Bucket location can be regional (lowercase like us-central1) or multi-regional (US, EU, ASIA). +# Default to US for multi-regional buckets. +BUCKET_LOCATION = "US" +gemini_bucket = gcp.storage.Bucket( + "gemini-data-bucket", + name=pulumi.Output.concat(current.project, "-gemini-data"), + location=BUCKET_LOCATION, + uniform_bucket_level_access=True, + public_access_prevention="enforced", + # Enable versioning for model artifacts + versioning=gcp.storage.BucketVersioningArgs(enabled=True), +) + +# Grant the service account access to the bucket +gcp.storage.BucketIAMMember( + "gemini-bucket-access", + bucket=gemini_bucket.name, + role="roles/storage.objectAdmin", + member=pulumi.Output.concat("serviceAccount:", service_account.email), +) + +# Export important information +pulumi.export("project_id", current.project) +pulumi.export("gemini_service_account_email", service_account.email) +pulumi.export("data_bucket_name", gemini_bucket.name) + +# Export example usage information +pulumi.export( + "usage_info", + { + "model_id": MODEL_ID, + "endpoint_base": f"https://{region}-aiplatform.googleapis.com", + "location": region, + }, +) diff --git a/tools/python/gcp_gemini/example_usage.py b/tools/python/gcp_gemini/example_usage.py new file mode 100644 index 00000000..23337495 --- /dev/null +++ b/tools/python/gcp_gemini/example_usage.py @@ -0,0 +1,216 @@ +""" +Example script showing how to use Gemini 2.5 Flash-Lite after Pulumi deployment. + +This script demonstrates various ways to interact with Gemini 2.5 Flash-Lite through Vertex AI. + +Prerequisites: +1. Deploy the infrastructure: pulumi up +2. Authenticate with GCP: gcloud auth application-default login + +Configuration is automatically read from Pulumi stack outputs. +No environment variables or .env files needed! + +To set Pulumi configuration (all optional): + pulumi config set gcp:project YOUR_PROJECT_ID # Optional (default: active gcloud project) + pulumi config set region us-central1 # Optional (default: us-central1) + pulumi config set resource_prefix my-app # Optional (default: project ID) +""" + +import json +import subprocess +import sys + +from google import genai + + +def get_pulumi_outputs(): + """Get configuration from Pulumi stack outputs.""" + try: + result = subprocess.run( + ["pulumi", "stack", "output", "--json"], + capture_output=True, + text=True, + check=True, + ) + return json.loads(result.stdout) + except subprocess.CalledProcessError as e: + print(f"❌ Error reading Pulumi stack outputs: {e}") + print("\nMake sure you have:") + print("1. Run 'pulumi up' to deploy the infrastructure") + print("2. Run this script from the gcp_gemini directory") + sys.exit(1) + except FileNotFoundError: + print("❌ Error: Pulumi CLI not found") + print("Install Pulumi: https://www.pulumi.com/docs/get-started/install/") + sys.exit(1) + + +# Get configuration from Pulumi stack outputs +outputs = get_pulumi_outputs() +PROJECT_ID = outputs["project_id"] +LOCATION = outputs["usage_info"]["location"] +MODEL_NAME = outputs["usage_info"]["model_id"] + +# Initialize the client for Vertex AI +client = genai.Client(vertexai=True, project=PROJECT_ID, location=LOCATION) + + +def initialize_vertex_ai(): + """Initialize Vertex AI with project and location.""" + # Client initialized globally above + print(f"✅ Using project: {PROJECT_ID} in location: {LOCATION}") + print(f"✅ Model: {MODEL_NAME}") + + +def simple_text_generation(): + """Demonstrate basic text generation with Gemini 2.5 Flash-Lite.""" + print("\n🤖 Simple Text Generation Example:") + + # Generate response + prompt = "Explain quantum computing in simple terms." + response = client.models.generate_content(model=MODEL_NAME, contents=prompt) + + print(f"Prompt: {prompt}") + print(f"Response: {response.text}") + + return response + + +def chat_conversation(): + """Demonstrate a multi-turn chat conversation.""" + print("\n💬 Chat Conversation Example:") + + # First message + message1 = "Hi! I'm learning about AI. Can you help?" + response1 = client.models.generate_content(model=MODEL_NAME, contents=message1) + print(f"User: {message1}") + print(f"Gemini: {response1.text}") + + # Follow-up message (simple approach without conversation history) + message2 = "What's the difference between machine learning and deep learning?" + response2 = client.models.generate_content(model=MODEL_NAME, contents=message2) + print(f"User: {message2}") + print(f"Gemini: {response2.text}") + + return response2 + + +def structured_output_example(): + """Demonstrate structured output generation.""" + print("\n📋 Structured Output Example:") + + prompt = """ + Create a JSON response with information about Python programming language. + Include: name, creator, year_created, main_uses (array), and popularity_rank. + Format the response as valid JSON. + """ + + response = client.models.generate_content(model=MODEL_NAME, contents=prompt) + print(f"Structured Response: {response.text}") + + return response + + +def multimodal_example(): + """Demonstrate multimodal capabilities (text + image).""" + print("\n🖼️ Multimodal Example (requires image):") + + # Note: This is an example - you'd need to provide an actual image + # For now, we'll just show the text-only approach + prompt = """ + Describe what you would look for when analyzing an image of a sunset. + What visual elements would you identify? + """ + + response = client.models.generate_content(model=MODEL_NAME, contents=prompt) + print(f"Response: {response.text}") + + return response + + +def function_calling_example(): + """Demonstrate function calling capabilities.""" + print("\n🔧 Function Calling Example:") + + # Note: Function calling format may differ in google-genai SDK + # This example shows basic usage without tools for now + response = client.models.generate_content( + model=MODEL_NAME, contents="What's the weather like in San Francisco?" + ) + print(f"Response: {response.text}") + + return response + + +def streaming_example(): + """Demonstrate streaming responses.""" + print("\n🌊 Streaming Response Example:") + + prompt = "Write a short story about a robot learning to paint." + + print("Streaming response:") + response = client.models.generate_content_stream(model=MODEL_NAME, contents=prompt) + + full_response = "" + for chunk in response: + if chunk.text: + print(chunk.text, end="", flush=True) + full_response += chunk.text + + print("\n") + return full_response + + +def batch_processing_example(): + """Demonstrate processing multiple prompts.""" + print("\n📦 Batch Processing Example:") + + prompts = [ + "What is artificial intelligence?", + "Explain machine learning in one sentence.", + "What are the benefits of cloud computing?", + "How does blockchain work?", + ] + + responses = [] + for i, prompt in enumerate(prompts, 1): + response = client.models.generate_content(model=MODEL_NAME, contents=prompt) + responses.append(response.text) + print(f"{i}. Q: {prompt}") + print(f" A: {response.text[:100]}...") + print() + + return responses + + +def main(): + """Run all examples.""" + print("🚀 Gemini 2.5 Flash-Lite Examples") + print("=" * 50) + + try: + # Initialize + initialize_vertex_ai() + + # Run examples + simple_text_generation() + chat_conversation() + structured_output_example() + multimodal_example() + function_calling_example() + streaming_example() + batch_processing_example() + + print("\n✅ All examples completed successfully!") + + except Exception as e: + print(f"❌ Error: {e}") + print("\nMake sure you have:") + print("1. Run 'pulumi up' to deploy the infrastructure") + print("2. Set up authentication (gcloud auth application-default login)") + print("3. Set Pulumi config (pulumi config set gcp:project YOUR_PROJECT_ID)") + print("4. 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