🛠️ Support Ticket Resolution Agent with Multi-Step Review Loop

This project is an AI-powered support ticket agent built with LangGraph, Azure OpenAI, and FAISS + Hugging Face Transformers for context-aware ticket resolution. It classifies, drafts, reviews, and escalates support tickets through a multi-step reasoning loop using LangGraph’s dynamic state machine.

---

🚀 Features

• Ticket Classification: Categorizes incoming support tickets using an LLM.
• Context Retrieval: Uses FAISS + Hugging Face embeddings to retrieve relevant support knowledge.
• LLM Drafting: Automatically drafts a response using Azure OpenAI based on category and context.
• Review & Feedback Loop: Simulates a quality reviewer that can approve or reject drafts with feedback.
• Escalation Path: If a draft is rejected more than twice, the ticket is escalated and logged.

---

📁 Project Structure

.
├── main.py                   # Graph definition and execution
├── nodes/
│   ├── classify_node.py      # Classifies ticket category
│   ├── retrieve_node.py      # Retrieves relevant context using FAISS
│   ├── draft_node.py         # Drafts response using LLM
│   ├── review_node.py        # Simulates LLM review of draft
│   └── escalate_node.py      # Escalates and logs tickets after 2 failed reviews
├── utils/
│   └── rag_utils.py          # FAISS setup with HuggingFace embeddings
├── .env                      # Azure OpenAI credentials
├── escalation_log.csv        # Log of escalated tickets
└── README.md

---

🧠 Technologies Used

• 🧩 LangGraph: To model the reasoning flow between nodes.
• 🤖 Azure OpenAI: For classification, drafting, and review.
• 🔍 FAISS + Hugging Face: For semantic search using all-MiniLM-L6-v2.
• 🧪 LangChain: For LLM chaining and prompt management.

---

⚙️ Setup

1. Clone this repo:

   git clone https://github.com/your-org/support-ticket-agent.git
   cd support-ticket-agent

2. Create and activate Python 3.10 virtual environment:

   py -3.10 -m venv venv
   venv\Scripts\activate   # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Add your .env file:

   OPENAI_API_KEY=
   OPENAI_API_BASE=
   OPENAI_API_VERSION=
   OPENAI_API_TYPE=
   OPENAI_DEPLOYMENT_NAME=
   

5. Run the app:

   python main.py

---

🧪 Example

ticket = {
    "subject": "Charged twice for subscription",
    "description": "I was billed twice this month for the same plan."
}

Depending on review outcomes, the agent will loop through retrieve → draft → review. If rejected twice, it logs the issue via escalate_node.

---

📌 Notes

• FAISS is initialized once in rag_utils.py using Hugging Face sentence transformers.
• You can expand the knowledge base by editing the docs_by_category dictionary.
• Escalated tickets are saved to escalation_log.csv.

---