WhatsApp AI Assistant

Welcome to the WhatsApp AI Assistant repository, where you'll find a remarkable WhatsApp chatbot designed to function as your very own AI-powered personal assistant. This chatbot leverages the power of Language Model (LLM) technology.

Sydney	LangChain
sydney_demo.mp4	final.mp4

Feature Comparison

Feature	Sydney (BingAI Jailbreak)	LangChain
Google/Bing Searching	✅	✅
Google Calendar	❌	✅
Google Routes	❌	✅
Gmail	❌	❌
Communication Capability	✅	✅
Group Chat Compatibility	✅	✅
Voice Message Capability	✅	✅
Create Basic Text Reminders	✅	❌
Image Recognition	✅	❌
Image Generation	❌	✅
PDF Reading	❌	❌

Getting Started

Prerequisites

• Node.js >= 18.15.0
• Node.js version >= 20.x.x users you should use node --loader ts-node/esm src/index.ts instead of pnpm start
• A spare WhatsApp number

Installation

Sydney/BingChat

1. Clone this repository

git clone https://github.com/WAppAI/assistant.git

2. Install the dependencies

pnpm install

3. Rename .env.example to .env

cp .env.example .env

4. Login with your Bing account and edit .env's BING_COOKIES environment variable to the cookies string from bing.com. For detailed instructions here.

   NOTE: Occasionally, you might encounter an error stating, User needs to solve CAPTCHA to continue. To resolve this issue, please solve the captcha [here]https://www.bing.com/turing/captcha/challenge, while logged in with the same account associated with your BING_COOKIES.

5. Read and fill in the remaining information in the .env file.

6. Run

pnpm build

7. Start the bot

pnpm start

8. Connect your WhatsApp account to the bot by scanning the generated QR Code in the CLI.

9. Send a message to your WhatsApp account to start a conversation with Sydney!

LangChain

1. Clone this repository

git clone https://github.com/WAppAI/assistant.git

2. Install the dependencies

pnpm install

3. Rename .env.example to .env

cp .env.example .env

4. Read and fill in the remaining information in the .env file.

5. Instructions on how to use langchain tools like Google Calendar and search will be in the .env

6. Run

pnpm build

6. Start the bot

pnpm start

7. Connect your WhatsApp account to the bot by scanning the generated QR Code in the CLI.

8. Send a message to your WhatsApp account to start a conversation with the bot!

Deploying with Docker

1. Clone this repository

git clone https://github.com/WAppAI/assistant.git

2. Rename .env.example to .env

cp .env.example .env

3. Read and fill in the remaining information in the .env file.

4. Instructions on how to use langchain tools like Google Calendar and search will be in the .env

5. Build and start the Docker container

pnpm docker:build:start

6. Access the container logs to read the QR code.

docker logs whatsapp-assistant

7. Scan the QR code with your WhatsApp account to connect the bot.

8. Send a message to your WhatsApp account to start a conversation with the bot!

Usage

The AI's are designed to respond to natural language queries from users. You can ask them questions, or just have a casual conversation.

Voice Messages

When dealing with voice messages, you have 3 options for transcription: using groq's Whisper API for free (recommended), utilizing the Whisper API or the local method. Each option has its own considerations, including cost and performance.

Groq API:

• Setup:
  1. Obtain a Groq API key from Groq Console.
  2. In the .env file, set TRANSCRIPTION_ENABLED to "true" and TRANSCRIPTION_METHOD to "whisper-groq".

Whisper API:

• Cost: Utilizing the Whisper API incurs a cost of US$0.06 per 10 minutes of audio.
• Setup:
  1. Obtain an OpenAI API key and place it in the .env file under the OPENAI_API_KEY variable.
  2. In the .env file, set TRANSCRIPTION_ENABLED to "true" and TRANSCRIPTION_METHOD to "whisper-api".

Local Mode:

• Cost: The local method is free but may be slower and less precise.
• Setup:
  1. Download a model of your choice from here. Download any .bin file and place it in the ./whisper/models folder.
  2. Modify the .env file by changing TRANSCRIPTION_ENABLED to "true", TRANSCRIPTION_METHOD to "local", and "TRANSCRIPTION_MODEL" with the name of the model you downloaded. While setting a language in TRANSCRIPTION_LANGUAGE is not mandatory, it is recommended for better performance.

Group Chat

To utilize it in a group chat, you will need to either mention it by using her username with the "@" symbol (e.g., @Sydney) or reply directly to her last message.

Available commands

• !help: Displays a message listing all available commands.
• !help followed by a specific command, e.g., !help reset: Provides detailed information about the selected command.
• If you wish to customize the command prefix, you can do so in the .env file to better suit your preferences.

Contribute

Your contributions to Sydney are welcome in any form. Whether you'd like to:

• Report Issues: If you come across bugs or have ideas for new features, please open an issue to discuss and track these items.

• Submit Pull Requests (PRs): Feel free to contribute directly by opening pull requests. Your contributions are greatly appreciated and help improve Sydney.

• If you want us to add a feature open an issue asking for it.

• In the Projects tab, you'll find a Kanban board that outlines our current objectives and progress.

Your involvement is valued, and you're encouraged to contribute in the way that suits you best.

Hire Us

Both creators of this project, Veigamann and Luisotee, are currently seeking new job opportunities.

If you have any job opportunities, please feel free to contact us through the emails provided in our GitHub profiles.