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swift-chat
A Cross-platform AI chat application built with React Native and powered by Amazon Bedrock
Stars: 157
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SwiftChat is a fast and responsive AI chat application developed with React Native and powered by Amazon Bedrock. It offers real-time streaming conversations, AI image generation, multimodal support, conversation history management, and cross-platform compatibility across Android, iOS, and macOS. The app supports multiple AI models like Amazon Bedrock, Ollama, DeepSeek, and OpenAI, and features a customizable system prompt assistant. With a minimalist design philosophy and robust privacy protection, SwiftChat delivers a seamless chat experience with various features like rich Markdown support, comprehensive multimodal analysis, creative image suite, and quick access tools. The app prioritizes speed in launch, request, render, and storage, ensuring a fast and efficient user experience. SwiftChat also emphasizes app privacy and security by encrypting API key storage, minimal permission requirements, local-only data storage, and a privacy-first approach.
README:
π Your Personal AI Assistant - Fast, Private, and Cross-platform
- Download for Android
- Download for macOS
- For iOS: Currently available through local build with Xcode
SwiftChat is a fast and responsive AI chat application developed with React Native and powered by Amazon Bedrock, with compatibility extending to other model providers such as Ollama, DeepSeek, and OpenAI. With its minimalist design philosophy and robust privacy protection, it delivers real-time streaming conversations and AI image generation capabilities across Android, iOS, and macOS platforms.
Key Features:
- Real-time streaming chat with AI
- Rich Markdown Support: Tables, Code Blocks, LaTeX and More
- AI image generation with progress
- Multimodal support (images, videos & documents)
- Conversation history list view and management
- Cross-platform support (Android, iOS, macOS)
- Tablet-optimized for iPad and Android tablets
- Fast launch and responsive performance
- Multiple AI model supported (Amazon Bedrock, Ollama, DeepSeek and OpenAI, From v1.10.0 π)
- Fully Customizable System Prompt Assistant (New feature from v1.9.0 π)
Supported Features For Amazon Nova
- Stream conversations with Amazon Nova Micro, Lite and Pro
- Understand images, documents and videos with Nova Lite and Pro
- Record 30-second videos directly on Android and iOS for Nova analysis
- Upload large videos (1080p/4K) beyond 8MB with auto compression
- Support using natural language to make Nova Canvas generate images, remove backgrounds, replace backgrounds, and create images in similar styles.
- Support LaTeX formula rendering (inline and display modes) for Amazon Nova.
Comprehensive Multimodal Analysis: Text, Image, Document and Video
System Prompt Assistant: Useful Preset System Prompts with Full Management Capabilities (Add/Edit/Sort/Delete)
Creative Image Suite: Generation, Style Replication, Background Removal & Replacement
Rich Markdown Support: Paragraph, Code Blocks, Tables, LaTeX and More
We redesigned the UI with optimized font sizes and line spacing for a more elegant and clean presentation. All of these features are also seamlessly displayed on Android and macOS with native UI
Note: Some animated images have been sped up for demonstration. If you experience lag, please view on Chrome, Firefox, or Edge browser on your computer.
By default, we use AWS App Runner, which is commonly used to host Python FastAPI servers, offering high performance, scalability and low latency.
Alternatively, we provide the option to replace App Runner with AWS Lambda using Function URL for a more cost-effective solution, as shown in this example.
Ensure you have access to Amazon Bedrock foundation models. SwiftChat default settings are:
- Region:
us-west-2
- Text Model:
Amazon Nova Pro
- Image Model:
Stable Diffusion 3.5 Large
If you are using the image generation feature, please make sure you have enabled access to the Amazon Nova Lite
model.
Please follow
the Amazon Bedrock User Guide to
enable your models.
π§ Configuration Steps (Click to expand)
-
Sign in to your AWS console and right-click Parameter Store to open it in a new tab.
-
Check whether you are in the supported region, then click on the Create parameter button.
-
Fill in the parameters below, leaving other options as default:
-
Name: Enter a parameter name (e.g., "SwiftChatAPIKey", will be used as
ApiKeyParam
in Step 2). -
Type: Select
SecureString
-
Value: Enter any string without spaces.(this will be your
API Key
in Step 3)
-
-
Click Create parameter.
-
Click one of the following buttons to launch the CloudFormation Stack in the same region where your API Key was created.
-
Click Next, On the "Specify stack details" page, provide the following information:
- Fill the
ApiKeyParam
with the parameter name you used for storing the API key (e.g., "SwiftChatAPIKey"). - For App Runner, choose an
InstanceTypeParam
based on your needs.
- Fill the
-
Click Next, Keep the "Configure stack options" page as default, Read the Capabilities and Check the "I acknowledge that AWS CloudFormation might create IAM resources" checkbox at the bottom.
-
Click Next, In the "Review and create" Review your configuration and click Submit.
Wait about 3-5 minutes for the deployment to finish, then click the CloudFormation stack and go to Outputs tab, you
can find the API URL which looks like: https://xxx.xxx.awsapprunner.com
or https://xxx.lambda-url.xxx.on.aws
- Launch the App, open the drawer menu, and tap Settings.
- Paste the
API URL
andAPI Key
(The Value you typed in Parameter Store) then select the Region. - Click the top right β icon to save your configuration and start your chat.
Congratulations π Your SwiftChat App is ready to use!
- US East (N. Virginia): us-east-1
- US West (Oregon): us-west-2
- Asia Pacific (Mumbai): ap-south-1
- Asia Pacific (Singapore): ap-southeast-1
- Asia Pacific (Sydney): ap-southeast-2
- Asia Pacific (Tokyo): ap-northeast-1
- Canada (Central): ca-central-1
- Europe (Frankfurt): eu-central-1
- Europe (London): eu-west-2
- Europe (Paris): eu-west-3
- South America (SΓ£o Paulo): sa-east-1
π§ Configure Ollama (Click to expand)
- Navigate to the Settings Page and select the Ollama tab.
- Enter your Ollama Server URL. For example:
http://localhost:11434
- Once the correct Server URL is entered, you can select your desired Ollama models from the Text Model dropdown list.
π§ Configure DeepSeek (Click to expand)
- Go to the Settings Page and select the DeepSeek tab.
- Input your DeepSeek API Key.
- Choose DeepSeek models from the Text Model dropdown list. Currently, the following DeepSeek models are supported:
DeepSeek-V3
DeepSeek-R1
π§ Configure OpenAI (Click to expand)
- Navigate to the Settings Page and select the OpenAI tab.
- Enter your OpenAI API Key.
- Select OpenAI models from the Text Model dropdown list. The following OpenAI models are currently supported:
GPT-4o
GPT-4o mini
Additionally, if you have deployed the ClickStream Server, you can enable the Use Proxy option to forward your requests.
Quick Access Tools: Code Copy, Selection Mode, Scroll Controls and Token Counter
We feature streamlined chat History, Settings pages, and intuitive Usage statistics:
Similarly, for the Mac version, we not only support the display of history, but also added a permanent sidebar display mode after v1.9.0, Below is a demo animation for how to add custom system prompt.
- [x] Text copy support:
- Copy button in message header
- Copy button in code blocks
- Direct Select and copy code on macOS (double click or long click on iOS)
- Long press text to copy entire sentence (Right-click on macOS)
- [x] Text selection mode by tapping message title or double-clicking text
- [x] Message timeline view in history
- [x] Delete messages through long press in history
- [x] Click to preview for uploaded documents and images
- [x] Support Markdown format for both questions and answers
- [x] Maximum 20 images and 5 documents per conversation
- [x] Support image generation with Chinese prompts(Make sure
Amazon Nova Lite
is enabled in your selected region) - [x] View and zoom generated images
- [x] Long press images to save or share
- [x] Automatic image compression to improve response speed
- [x] Haptic feedback for Android and iOS (can be disabled in Settings)
- [x] Support landscape mode on Android/iOS devices
- [x] Double tap title bar to scroll to top
- [x] Click bottom arrow to view latest messages
- [x] View current session token usage by tapping Chat title
- [x] Check detailed token usage and image generation count in Settings
- [x] In-app upgrade notifications (Android & macOS)
We have optimized the layout for landscape mode. As shown below, you can comfortably view table/code contents in landscape orientation.
π Fast Launch Speed
- Thanks to the AOT (Ahead of Time) compilation of RN Hermes engine
- Added lazy loading of complex components
- App launches instantly and is immediately ready for input
π Fast Request Speed
- Speed up end-to-end API requests through image compression
- Deploying APIs in the same region as Bedrock provides lower latency
π± Fast Render Speed
- Using
useMemo
and custom caching to creates secondary cache for session content - Reduce unnecessary re-renders and speed up streaming messages display
- All UI components are rendered as native components
π¦ Fast Storage Speed
- By using react-native-mmkv Messages can be read, stored, and updated 10x faster than AsyncStorage
- Optimized session content and session list storage structure to accelerates history list display
- Encrypted API key storage
- Minimal permission requirements
- Local-only data storage
- No user behavior tracking
- No data collection
- Privacy-first approach
First, clone this repository. All app code is located in the react-native
folder. Before proceeding, execute the
following command to download dependencies.
cd react-native && npm i && npm start
open a new terminal and execute:
npm run android
also open a new terminal, for the first time you need to install the native dependencies
by execute cd ios && pod install && cd ..
, then execute the follow command:
npm run ios
- Execute
npm start
. - Double click
ios/SwiftChat.xcworkspace
to open the project in your Xcode. - Change the build destination to
My Mac (Mac Catalyst)
then click the βΆ Run button.
Please refer API Reference
- Android and macOS: Navigate to Settings Page, if there is a new version, you will find it at the bottom of this page, then click the app version to download and install it.
- iOS: If a new version is released in the Release page, update your local code, rebuild and install your app by Xcode.
Note: After downloading a new version, please check the release notes to see if an API version update is required.
-
For AppRunner: Click and open App Runner Services page,
find and open
swiftchat-api
, click top right Deploy button. -
For Lambda: Click and open Lambda Services, find and open
your Lambda which start with
SwiftChatLambda-xxx
, click the Deploy new image button and click Save.
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.
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LLamaSharp
LLamaSharp is a cross-platform library to run π¦LLaMA/LLaVA model (and others) on your local device. Based on llama.cpp, inference with LLamaSharp is efficient on both CPU and GPU. With the higher-level APIs and RAG support, it's convenient to deploy LLM (Large Language Model) in your application with LLamaSharp.
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gpt4all
GPT4All is an ecosystem to run powerful and customized large language models that work locally on consumer grade CPUs and any GPU. Note that your CPU needs to support AVX or AVX2 instructions. Learn more in the documentation. A GPT4All model is a 3GB - 8GB file that you can download and plug into the GPT4All open-source ecosystem software. Nomic AI supports and maintains this software ecosystem to enforce quality and security alongside spearheading the effort to allow any person or enterprise to easily train and deploy their own on-edge large language models.
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ChatGPT-Telegram-Bot
ChatGPT Telegram Bot is a Telegram bot that provides a smooth AI experience. It supports both Azure OpenAI and native OpenAI, and offers real-time (streaming) response to AI, with a faster and smoother experience. The bot also has 15 preset bot identities that can be quickly switched, and supports custom bot identities to meet personalized needs. Additionally, it supports clearing the contents of the chat with a single click, and restarting the conversation at any time. The bot also supports native Telegram bot button support, making it easy and intuitive to implement required functions. User level division is also supported, with different levels enjoying different single session token numbers, context numbers, and session frequencies. The bot supports English and Chinese on UI, and is containerized for easy deployment.
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twinny
Twinny is a free and open-source AI code completion plugin for Visual Studio Code and compatible editors. It integrates with various tools and frameworks, including Ollama, llama.cpp, oobabooga/text-generation-webui, LM Studio, LiteLLM, and Open WebUI. Twinny offers features such as fill-in-the-middle code completion, chat with AI about your code, customizable API endpoints, and support for single or multiline fill-in-middle completions. It is easy to install via the Visual Studio Code extensions marketplace and provides a range of customization options. Twinny supports both online and offline operation and conforms to the OpenAI API standard.
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sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.
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teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.
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ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.
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classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.
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chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.
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BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
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uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.
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griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.