AICore Chat

AICore Chat is a modern, Jetpack Compose Android application that showcases Google's on-device AICore SDK with a fully offline-capable chat experience. The app focuses on demonstrating how Gemini Nano can be embedded inside a polished messaging workflow that feels comparable to cloud-backed assistants while keeping all inference on device.

✨ Features

• On-Device Gemini Nano – All text generation happens locally through the experimental com.google.ai.edge.aicore:aicore SDK for private and low-latency responses.
• Streaming Conversation Flow – Messages stream token-by-token with controls to stop generation, retry, copy, clear, and jump back to the latest turn in long threads.
• Multi-Session Workspace – A navigation drawer lets you create, rename, and delete conversations, with automatic cleanup of empty chats and in-memory persistence of history.
• Personalized Onboarding & Settings – Collect a preferred name, toggle personal context, bio context, web search, multimodal support, memory usage, and custom instructions before entering the chat.
• Local Memory Management – Curate what the assistant should remember about you by adding, editing, toggling, or deleting memory entries and optional biographical details stored on device.
• Multimodal Attachments – Attach camera shots or gallery images. ML Kit's on-device Image Description API generates captions that are injected into the prompt when multimodal mode is enabled.
• Contextual Awareness Tools – Opt-in personal context includes device model, battery, locale, storage, time, and coarse location. A built-in web search tool (DuckDuckGo HTML results) fetches fresh snippets when online.
• Device Support Guardrails – The app verifies AICore availability and gracefully surfaces onboarding, unsupported, and loading states before the main chat renders.

🛠️ Tech Stack & Key Libraries

• Language: Kotlin
• UI Toolkit: Jetpack Compose with Material 3 components
• AI Runtime: Google AICore SDK
• Async & State: Kotlin Coroutines, Flow, and AndroidViewModel
• Location & Services: Google Play Services Location, Android connectivity & battery APIs
• Multimodal Support: ML Kit Generative AI Image Description and Coil for image loading
• Architecture: MVVM-inspired with dedicated repositories for chat sessions, memory, and tool integrations

⚙️ How It Works

1. ViewModel Orchestration – ChatViewModel bootstraps settings, restores sessions via ChatRepository, loads memory and bio data from MemoryRepository, and prepares the AICore GenerativeModel.
2. Prompt Assembly – Each turn composes a system preamble, few-shot examples, optional personal context, custom instructions, relevant memories, pending image descriptions, and the full [USER] / [ASSISTANT] formatted transcript.
3. Tooling Pipeline – Web searches are requested with [SEARCH] tags when enabled and online, image attachments run through ImageDescriptionService, and location/device metadata is injected through PersonalContextBuilder.
4. Streaming & Persistence – Responses stream into a StateFlow, updating Compose UI in real time while persisting chat content back to disk so session switching is instantaneous.

🚀 Getting Started

Prerequisites

• Android Studio
• An Android device or emulator running API level 31 or higher with the AICore Gemini Nano preview installed

Build and Run

1. Clone the repository:

   git clone <repository-url>

2. Open in Android Studio:
   • Open Android Studio.
   • Click on File -> Open and select the cloned project directory.
3. Sync Gradle:
   • Let Android Studio sync the project and download all the required dependencies.
4. Run the app:
   • Select a target device (emulator or physical device).
   • Click the "Run" button (▶️).

Running Quality Checks

The project ships with a consolidated quality script that runs formatting, static analysis, Android Lint, and the JVM test suite:

./scripts/quality.sh

Under the hood this executes spotlessCheck, detekt, lint, and test using JDK 17. Run it locally (or in CI) before opening pull requests to keep the codebase consistent.

For a one-stop build helper you can use run_all.sh from the repository root:

# Run full clean build + QA gates + assembleDebug
./run_all.sh

# Only run unit tests
./run_all.sh tests

# Build the app
./run_all.sh assemble

# Execute connected Android tests (requires device/emulator)
./run_all.sh connected

If you intentionally fix or introduce code that changes the current lint/detekt findings, regenerate the baselines first and re-run the script:

./gradlew lintDebug       # updates app/lint-baseline.xml
./gradlew detektBaseline  # updates config/detekt/baseline.xml
./scripts/quality.sh

Permissions

The sample declares the following runtime capabilities:

• INTERNET and ACCESS_NETWORK_STATE for web search and connectivity checks
• ACCESS_FINE_LOCATION and ACCESS_COARSE_LOCATION to include coarse device context when enabled
• Scoped storage access via a FileProvider for photo capture attachments

🗂️ Project Structure

• app/src/main/java/org/dylanneve1/aicorechat/MainActivity.kt – Hosts the Compose hierarchy, onboarding flow, and device support gating.
• app/src/main/java/org/dylanneve1/aicorechat/data/ – ChatViewModel, session & memory repositories, prompt utilities, and integrations for search, personal context, and image description.
• app/src/main/java/org/dylanneve1/aicorechat/ui/ – Compose screens for chat, onboarding, settings, memory management, and shared UI components/themes.
• app/src/main/java/org/dylanneve1/aicorechat/util/ – Utility helpers for device checks, formatting, and token cleanup.
• scripts/ – Automation helpers such as quality.sh for enforcing formatting and analysis gates.

📄 License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details.