# Triangle

**Repository Path**: mirrors_microsoft/Triangle

## Basic Information

- **Project Name**: Triangle
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-16
- **Last Updated**: 2025-12-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Triangle: Empowering Incident Triage with Multi-Agents

Triangle is an end-to-end incident triage system using multiple LLM agents to route incidents to appropriate teams. It addresses challenges in cloud service incident management through semantic distillation and multi-role agent negotiation. Experiments show Triangle improves triage accuracy by over 20% while reducing response time. The system has been successfully deployed at a leading technology company serving millions of users.

## 🌟 Overview

This project implements an intelligent incident triage system using multiple Large Language Model (LLM) agents to analyze and route incidents to the most suitable teams. The system combines TF-IDF similarity matching with semantic analysis to achieve accurate incident assignment. By leveraging both rule-based and machine-learning components, Triangle ensures adaptability, scalability, and continuous improvement over time.

### Key Goals

1. **Efficiency**: Reduce incident response time and streamline communication across different teams.  
2. **Accuracy**: Increase correct team assignment by understanding the semantic context of incoming incidents.  
3. **Scalability**: Seamlessly integrate new capabilities and scale to handle an increasing number of incidents.  
4. **Extensibility**: Allow new routing policies, data sources, and integration points to be added with minimal overhead.

## 🚀 Features

-  **Multi-Agent Architecture**  
  - **Triage Decider**: Makes final routing decisions.  
  - **Team Manager**: Handles team information and negotiations.  
  - **Analyzer**: Performs semantic analysis and TF-IDF matching.

-  **Intelligent Matching**  
  - TF-IDF based similarity scoring.  
  - Semantic analysis of incident descriptions.  
  - Multi-hop routing capability for complex incident redirections.  
  - Team function phrase matching for increased accuracy.

-  **Performance Tracking**  
  - Real-time accuracy monitoring through dashboards.  
  - Detailed logging of decisions for post-mortem analysis.  
  - Result analysis and visualization for iterative improvements.

-  **Confidence Estimation**  
  - Confidence scores for each triage decision.  
  - Threshold-based auto-assignment or manual review process.

## ⚙️ Architecture

The system consists of three main components, each performing specialized tasks to ensure consistency and efficiency:

1. **Triage Decider (TriageDecider)**  
   - Gathers incident data from the Analyzer and Team Manager.  
   - Matches incidents with relevant teams based on confidence scores.  
   - Provides traceable reasoning for each routing decision.

2. **Team Manager (TeamManager)**  
   - Maintains team capability profiles and summary key phrases.  
   - Negotiates and escalates incidents when multiple teams are possible matches.  
   - Ensures that team availability and load constraints are respected.

3. **Analyzer (Analyser)**  
   - Performs TF-IDF analysis to derive initial similarity ranks.  
   - Conducts semantic distillation of incident data.  
   - Merges findings to produce final similarity and confidence metrics.

## ⏱ Performance Tracking and Metrics

Triangle continuously monitors performance indicators to evaluate its effectiveness:

| Metric                  | Description                                              |  
|-------------------------|----------------------------------------------------------|  
| Accuracy                | Percentage of correct team assignments                   |  
| Response Time           | Average triage completion time                           |  
| Escalation Rate         | Frequency of manual interventions or reassignments       |  
| Confidence Score Mean   | Average confidence for automated triage decisions        |  

By measuring these metrics over time, Triangle helps identify improvements and ensure consistent, data-driven enhancements to incident triage workflows.

## 📋 Requirements

-  Python 3.8+  
-  Azure OpenAI API access  
-  Required Python packages (see requirements.txt)

### Recommended Environment

-  A stable internet connection for reliable LLM access.  
-  Sufficient resource allocation (CPU/Memory) for larger incident volumes.

## 🛠️ Installation & Setup

1. **Clone the repository**  
   ```bash
   git clone <url>
   cd triangle
   ```

2. **Setting Up the Virtual Environment**

   Follow these steps to create and activate a virtual environment, then install the required packages from `requirements.txt`.

### Prerequisites

-  **Python Installation**: Ensure Python is installed on your system. You can download it from the [official Python website](https://www.python.org/downloads/).
-  **Pip Verification**: Verify `pip` is installed by running `pip --version` in your terminal or command prompt.

### Steps

1. **Create a Virtual Environment**

   Open your terminal (Linux/Mac) or command prompt (Windows) and navigate to your project directory:

   ```bash
   cd /path/to/your/project
   ```

   Create a virtual environment named `venv`:

   ```bash
   python -m venv venv
   ```

2. **Activate the Virtual Environment**

   - **Windows**:
     ```bash
     .\venv\Scripts\activate
     ```
   - **Linux/Mac**:
     ```bash
     source venv/bin/activate
     ```

   After activation, your terminal prompt will change to indicate that the virtual environment is active.

3. **Install Required Packages**

   With the virtual environment activated, install the dependencies listed in `requirements.txt`:

   ```bash
   pip install -r requirements.txt
   ```

   This command will read the `requirements.txt` file and install all necessary packages.

4. **Configure Azure OpenAI Credentials**

   Create a `config.json` file with your Azure OpenAI credentials:

   ```json
   {
       "ENDPOINT_URL": "your_azure_endpoint",
       "DEPLOYMENT_NAME": "your_deployment_name",
       "API_VERSION": "your_api_version",
       "API_KEY": "your_api_key"
   }
   ```

## 📊 Data Format

### Team Data (person.json)
```json
[
    {
        "name": "team_name",
        "summary_key_phrases": ["key_phrase1", "key_phrase2", ...]
    }
]
```

### Incident Data (dataset.json)
```json
[
    {
        "case": "incident_id",
        "message": "incident_description",
        "last_person": "assigned_team"
    }
]
```

### Advanced Topics for Data Management

-  **Data Versioning**: Use Git LFS or specialized tools to manage large datasets and historical changes.  
-  **Privacy & Security**: Ensure that only sanitized or anonymized data is shared where needed, and follow your organization's data handling policies.

## 🎯 Usage

Run the main triage system:

```bash
python triangle.py
```

You can customize parameters in `triangle.py` to adjust agent behaviors, logging levels, or threshold settings for confidence scores.

## 📈 Results

When the triage process completes, the system generates detailed results in the `results` directory, including:

-  Assignment decisions for each incident  
-  Confidence scores  
-  Performance metrics  
-  Routing paths

Review these logs continuously to identify recurring issues and potential improvements in your triage logic.


## 📖 Contributing

We welcome contributions from the community to make Triangle even better:

1. **Fork the Repo** and create your branch from `main`.  
2. **Implement Features** or bug fixes in alignment with the project’s guidelines.  
3. **Open a Pull Request**, detailing your changes, improvements, and testing for easy review.


## ❓ FAQ

| Question                                | Answer                                                                                            |
|----------------------------------------|---------------------------------------------------------------------------------------------------|
| How do I add a new team?               | Add a new JSON object in person.json and include relevant key phrases that describe the team’s domain. |
| How do I retrain or refine the model?  | Update your training scripts using new incident data, then adjust the Analyzer module accordingly. |
| Is on-prem deployment supported?       | Yes, you can run Triangle self-hosted, but you need a stable internal environment for the LLM API. |

## 🔎 Limitations & Future Work

1. **Language Coverage**: While the system supports English data, non-English data may require additional adjustments in the Analyzer.  
2. **Contextual Knowledge**: Domain-specific knowledge bases can help enrich the semantic matching but are currently not fully integrated.  
3. **LLM Dependence**: Triage decisions depend on the accuracy, availability, and cost of LLM services.  
4. **Future Enhancements**: Plans include adding advanced multi-modal interfaces (voice, images) and incorporating user feedback loops for continuous learning.