https://github.com/aixerum/langgraph-email-automation
A Langgraph system for streamlining client interactions, ensuring every customer receives a quick and accurate response. 🌟
https://github.com/aixerum/langgraph-email-automation
aiagent email groq-api langgraph rag
Last synced: 5 months ago
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A Langgraph system for streamlining client interactions, ensuring every customer receives a quick and accurate response. 🌟
- Host: GitHub
- URL: https://github.com/aixerum/langgraph-email-automation
- Owner: AIXerum
- Created: 2024-10-11T06:44:50.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-10-17T07:36:02.000Z (12 months ago)
- Last Synced: 2025-04-30T10:12:52.684Z (5 months ago)
- Topics: aiagent, email, groq-api, langgraph, rag
- Language: Python
- Homepage:
- Size: 264 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Customer Support Email Automation with AI Agents and RAG
**A Langgraph system for streamlining client interactions, ensuring every customer receives a quick and accurate response. 🌟**
## Introduction
In today's fast-paced environment, customers demand quick, accurate, and personalized responses—expectations that can overwhelm traditional support teams. Managing large volumes of emails, categorizing them, crafting appropriate replies, and ensuring quality consumes significant time and resources, often leading to delays or errors, which can harm customer satisfaction.
**Customer Support Email Automation** is an AI solution designed to enhance customer communication for businesses. Leveraging a Langgraph-driven workflow, multiple AI agents collaborate to efficiently manage, categorize, and respond to customer emails. The system also implements RAG (Retrieval-Augmented Generation) technology to deliver accurate responses to any business or product-related questions.
## Features
### Smart Email Management with AI Agents
- Continuously monitors the agency's Gmail inbox
- Categorizes emails into 'customer complaint,' 'product inquiry,' 'customer feedback,' or 'unrelated'
- Automatically handles irrelevant emails to maintain efficiency
### AI Response Generation
- Quickly drafts emails for customer complaints and feedback using Langgraph
- Utilizes RAG techniques to answer product-related questions accurately
- Creates personalized email content tailored to each customer's needs
### Quality Assurance with AI
- Automatically checks email quality, formatting, and relevance
- Ensures every response meets high standards before reaching the client
### Advanced AI Technology Stack
- Employs Langchain/Langgraph to manage the workflow of the AI agents and LLMs
- Utilizes multiple AI agents working in tandem to process various types of emails
## How It Works
1. **Email Monitoring**: The system constantly checks for new emails in the agency's Gmail inbox using the Gmail API.
2. **Email Categorization**: AI agents sort each email into predefined categories.
3. **Response Generation**:
- For complaints or feedback: The system quickly drafts a tailored email response.
- For product questions: The system uses RAG to retrieve accurate information from agency documents and generates a response.
4. **Quality Assurance**: Each draft email undergoes AI quality and formatting checks.
5. **Sending**: Approved emails are sent to the client promptly, ensuring timely communication.
## Benefits
- **Time Efficiency**: Significantly reduces time spent on email management.
- **Enhanced Accuracy**: Improves the precision and relevance of email responses through AI and RAG.
- **Consistency**: Provides a uniform experience across all customer communications.
- **Improved Focus**: Allows support teams to concentrate on complex issues rather than routine email tasks.
## System Flowchart
This is the detailed flow of the system:
[](https://mermaid.live/edit#pako:eNqdk8GO2jAQhl9lZKSeQELlAjlUgiSgSi1qd9sKkfRg4jFYJDa1nWXpZt-9A5jdVNrDKjk5nv-bGf_2PLHCCGQRk6U5FjtuPfxIcg30TbN4h8UelniEtOKqdKA0LL5OP3_5HRQwGHxqlqYlaWCdpVrAN2sKdO5_4Wuiuam1aGCWxdzj1lj1F6-hAMwuwE9tsSQBKdft_bh23lRoITbVoeRKezAW5ohiw4t9A0mWWC79GxmpLVEXFNF_amVPDcTZ_Un7HbpzB99rtAodSMp2N10EMj6TsMoW6GGq3ZHqSmuqlmJ1UbxVNblE0uwXJZana4jK8FL5E3wgG2zFvVd6G_Tppcvp4WDNAzYwz-6RzGxnnF8dOB8cPb4YkwaHUTi4wwfllNFkRJtZ4mNojozP9TXk_KlEuh-pyjLqyYnsO2_NHqPeeDwO68FRCb-LPh4e-4UpjY16w-Gwjc8Cvtm84qPR6L14fKu-2XTBk1t12QlPX84-6YLPAz6RnfDVrXo3fB1wIcT7cdZnNDz0EATN_dM5Xc5oBCrMWURLwe0-Z7l-Jh2vvaEBKVjkbY19Zk293bFI8tLRX30QNJ2J4lvLq7D7_A-v-mU8)
## How to Run
### Prerequisites
- Python 3.7+
- Groq api key
- Google Gemini api key (for embeddings)
- Gmail API credentials
- Necessary Python libraries (listed in `requirements.txt`)
### Setup
1. **Clone the repository:**
```sh
git clone https://github.com/AIXerum/LangGraph-Email-Automation.git
cd LangGraph-Email-Automation
```
2. **Create and activate a virtual environment:**
```sh
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install the required packages:**
```sh
pip install -r requirements.txt
```
4. **Set up environment variables:**
Create a `.env` file in the root directory of the project and add your GMAIL address, we are using the Groq llama-3.1-70b model and the Google gemini embedding model so you must also get API keys to access them:
```env
MY_EMAIL=your_email@gmail.com
GROQ_API_KEY=your_groq_api_key
GOOGLE_API_KEY=your_gemini_api_key
```
5. **Ensure Gmail API is enabled:**
Follow [this guide](https://developers.google.com/gmail/api/quickstart/python) to enable Gmail API and obtain your credentials.
### Running the Application
1. **Start the workflow:**
```sh
python main.py
```
The application will start checking for new emails, categorizing them, synthesizing queries, drafting responses, and verifying email quality.
2. **Deploy as API:** you can deploy the workflow as an API using Langserve and FastAPI by running the command below:
```sh
python deploy_api.py
```
The workflow api will be running on `localhost:8000`, you can consult the API docs on `/docs` and you can use the langsergve playground (on the route `/playground`) to test it out.
### Customization
You can customize the behavior of each agent by modifying the corresponding methods in the `Nodes` class or the agents prompt `prompts` located in the `src` directory.
You can also add your own agency data into the `data` folder, then you must create your own vector store by running (update first the data path):
```sh
python create_index.py
```