Linear AI Duplicate Detection System

An AI-powered system to automatically detect and flag duplicate tickets in Linear using semantic similarity search and GPT-4 analysis. This system is designed for bug bounty programs where maintaining chronological ticket integrity is crucial.

🎯 What This System Does

Fetches Linear Issues: Downloads all tickets from your Linear workspace
Semantic Search: Uses OpenAI embeddings to find similar tickets based on content
AI Analysis: GPT-4 analyzes similarities and determines duplicates with confidence scores
Automated Actions: Labels duplicates, links related issues, and adds detailed comments
Chronological Protection: Ensures newer tickets are never marked as duplicates of older ones (critical for bug bounty programs)

📁 Project Structure

bug_bounty/
├── ai_flag_duplicates_improved.py    # Main duplicate detection script (CURRENT VERSION)
├── fetch_linear_issues.py           # Script to fetch Linear issues to JSON
├── requirements.txt                 # Python dependencies
├── .env.example                     # Environment variables template
├── .gitignore                       # Git ignore rules
├── README_duplicate_detection.md    # This file
├── improvements_summary.md          # Technical improvements documentation
├── ai_flag_duplicates_updated.py    # Previous version (deprecated)
└── ai_flag_duplicates.py           # Original version (deprecated)

🚀 Quick Start Guide

1. Environment Setup

# Clone/download the project
cd /path/to/bug_bounty

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Configure API Keys

Copy the template and add your API keys:

# Copy the template
cp .env.example .env

# Edit .env with your actual API keys

Your .env file should contain:

# Required API Keys
CHROMA_OPENAI_API_KEY=your-openai-api-key-here
LINEAR_OAUTH_ACCESS_TOKEN=your-linear-api-token-here

# Optional Configuration
LINEAR_TEAM_ID=your-team-id  # defaults to the ID in script
CHROMA_DB_PATH=/path/to/chromadb  # defaults to ~/.chroma/bug-bounty
CONFIDENCE_THRESHOLD=65  # 0-100, default 65
MAX_CANDIDATES=10  # number of similar tickets to analyze, default 10

To get your API keys:

OpenAI: Go to https://platform.openai.com/api-keys
Linear: Go to Linear Settings > API > Create personal API key

3. Fetch Your Linear Issues

# Download all Linear issues to JSON file
python fetch_linear_issues.py

# This creates ~/linear_issues.json with all your tickets

4. Initialize the System

# Seed ChromaDB with your issues (one-time setup)
python ai_flag_duplicates_improved.py --seed

# This creates embeddings for all tickets and stores them locally

5. Test the System

# Test on a single ticket (dry run)
python ai_flag_duplicates_improved.py --tickets BUG-123 --dry-run

# Process a single ticket for real
python ai_flag_duplicates_improved.py --tickets BUG-123

📖 Detailed Usage

Fetching Linear Issues

The fetch_linear_issues.py script downloads all issues from your Linear workspace:

# Basic usage - saves to ~/linear_issues.json
python fetch_linear_issues.py

# Custom output location
python fetch_linear_issues.py /path/to/custom/issues.json

# Filter by specific team (optional)
export LINEAR_TEAM_ID=your-team-id
python fetch_linear_issues.py

When to re-fetch:

New tickets have been created in Linear
You want to refresh ticket data (descriptions, states, etc.)
You're switching to a different team or workspace

ChromaDB Seeding

The seeding process creates embeddings for semantic similarity search:

# Initial seeding (one-time setup)
python ai_flag_duplicates_improved.py --seed

# Update existing entries (after re-fetching Linear data)
python ai_flag_duplicates_improved.py --seed --update-existing

# Custom issues file location
python ai_flag_duplicates_improved.py --seed --issues-file /path/to/issues.json

Processing Tickets

(THIS IS WHAT YOU WILL USE 99% OF THE TIME)

Single and Multi Ticket Processing

# Process one ticket
python ai_flag_duplicates_improved.py --tickets BUG-123

# Process multiple specific tickets
python ai_flag_duplicates_improved.py --tickets BUG-123 BUG-456 BUG-789

Other Processing Options

(Mostly for debugging purposes)

Bulk Processing

(don't do this because it will reprocess all the old tickets)

# Process all tickets (don't do this because it will reprocess all the old tickets)
python ai_flag_duplicates_improved.py --all

# Process with custom confidence threshold
python ai_flag_duplicates_improved.py --all --confidence-threshold 80

Dry Run Mode

# See what would happen without making changes in Linear
python ai_flag_duplicates_improved.py --tickets BUG-123 --dry-run
python ai_flag_duplicates_improved.py --all --dry-run

Advanced Options

# Debug mode with detailed logging
python ai_flag_duplicates_improved.py --tickets BUG-123 --log-level DEBUG

# Log to file
python ai_flag_duplicates_improved.py --all --log-file duplicate_detection.log

# Combine multiple options
python ai_flag_duplicates_improved.py \
    --all \
    --dry-run \
    --log-level INFO \
    --log-file run_$(date +%Y%m%d_%H%M%S).log \
    --confidence-threshold 70

🔧 Configuration Options

Environment Variables

Variable	Description	Default
`CHROMA_OPENAI_API_KEY`	OpenAI API key (required)	None
`LINEAR_OAUTH_ACCESS_TOKEN`	Linear API token (required)	None
`LINEAR_TEAM_ID`	Filter issues by team ID	Auto-detected
`CHROMA_DB_PATH`	ChromaDB storage location	`~/.chroma/bug-bounty`
`CONFIDENCE_THRESHOLD`	Min confidence for duplicates (0-100)	65
`MAX_CANDIDATES`	Max similar tickets to analyze	10

Command Line Arguments

Argument	Description
`--seed`	Seed ChromaDB with issues
`--update-existing`	Update existing ChromaDB entries
`--tickets BUG-123 BUG-456`	Process specific tickets
`--all`	Process all tickets
`--issues-file PATH`	Custom issues JSON file path
`--dry-run`	Test without making changes
`--log-level DEBUG`	Set logging level
`--log-file FILE`	Log to file
`--confidence-threshold 80`	Override confidence threshold

🎯 Common Workflows

1. Initial Setup (New Teammate)

# 1. Setup environment
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt

# 2. Configure API keys
cp .env.example .env
# Edit .env with your actual API keys

# 3. Fetch Linear data
python fetch_linear_issues.py

# 4. Seed ChromaDB
python ai_flag_duplicates_improved.py --seed

# 5. Test on a few tickets
python ai_flag_duplicates_improved.py --tickets BUG-1 BUG-2 BUG-3 --dry-run

2. Regular Maintenance (Processing New Tickets)

# 1. Update Linear data
python fetch_linear_issues.py

# 2. Update ChromaDB with new tickets
python ai_flag_duplicates_improved.py --seed --update-existing

# 3. Process new tickets (example: BUG-559 through BUG-780)
python ai_flag_duplicates_improved.py --tickets BUG-559 BUG-560 BUG-561
# OR process all new tickets at once

3. Bulk Backfill (One-time Process)

# Process all existing tickets
python ai_flag_duplicates_improved.py --all --log-file backfill_$(date +%Y%m%d).log

📊 Understanding the Output

Console Output

2024-01-10 10:15:23 - INFO - Processing ticket: BUG-123
2024-01-10 10:15:24 - INFO - Found 8 potential duplicate candidates
2024-01-10 10:15:24 - INFO - Date filtering reduced candidates from 12 to 8
2024-01-10 10:15:26 - INFO - LLM identified 1 duplicates above confidence threshold
2024-01-10 10:15:27 - INFO - Successfully added label to issue BUG-123
2024-01-10 10:15:28 - INFO - Successfully linked BUG-123 as duplicate of BUG-456
==================================================
SUMMARY
==================================================
Total tickets processed: 150
Total duplicates found: 23
✅ Processing completed

Linear Comments

The system adds professional comments to tickets:

🤖 AI Duplicate Detection

This ticket appears to be a duplicate of BUG-456.

Confidence: 85%

Analysis: Both tickets describe the same API timeout issue with identical error messages and stack traces

Please review this assessment. If incorrect, you can unlink the issues.

Linear Actions Taken

Label: Adds "ai-flagged-duplicate" label to tickets
Link: Creates "related" relationships between duplicate tickets
Comment: Adds detailed explanation of the duplicate detection

🔍 How It Works

1. Semantic Similarity Search

Uses OpenAI's text-embedding-3-small model to create embeddings
Stores embeddings in ChromaDB for fast vector search
Finds tickets with similar titles and descriptions

2. Chronological Protection

Only considers tickets created before the current ticket
Prevents newer tickets from being marked as duplicates of older ones
Critical for bug bounty programs where first reporter gets credit

3. AI Analysis

GPT-4-mini analyzes semantic similarities
Considers exact duplicates, root cause duplicates, and pattern duplicates
Provides confidence scores and detailed explanations

4. Conservative Approach

Default 65% confidence threshold prevents false positives
Dry run mode for testing
All actions are reversible in Linear

🛠️ Troubleshooting

Common Issues

1. "API keys must be set as environment variables"

Make sure you copied .env.example to .env and added your actual API keys
Verify the keys are not expired or invalid
Check that the .env file is in the project root directory

2. "Issues file not found"

Run python fetch_linear_issues.py first
Check if ~/linear_issues.json exists
Use --issues-file to specify custom path

3. "Could not find internal ID for identifier"

The ticket might not exist in your fetched issues
Re-run python fetch_linear_issues.py to update your data
Check if the ticket ID is correct

4. "GraphQL errors" or "400 Bad Request"

Linear API token might be expired or invalid
Check your LINEAR_OAUTH_ACCESS_TOKEN in .env
Verify you have the correct permissions in Linear

Debug Mode

For detailed troubleshooting, use debug mode:

python ai_flag_duplicates_improved.py --tickets BUG-123 --log-level DEBUG --dry-run

This will show:

Detailed API requests and responses
ChromaDB query results
LLM prompt and response
Step-by-step processing logic

📈 Performance & Costs

Processing Speed

ChromaDB seeding: ~1-2 minutes for 500 tickets
Individual ticket: ~7-8 seconds (including AI analysis)
Bulk processing: ~15-30 minutes for 500 tickets

API Costs (Approximate)

OpenAI Embeddings: ~$0.0001 per ticket
GPT-4-mini Analysis: ~$0.001 per ticket
Total: ~$0.0011 per ticket processed

Rate Limiting

Built-in delays between API calls
Configurable rate limiting
Automatic retry logic for failed requests

🔒 Security & Privacy

API keys stored in local .env file (not in code)
ChromaDB stored locally (not in cloud)
No ticket data sent to external services except OpenAI for analysis
All actions logged for audit trail

🔄 Maintenance

Regular Tasks

Update Linear data: Run fetch_linear_issues.py periodically
Update ChromaDB: Use --seed --update-existing after fetching new data
Process new tickets: Run duplicate detection on new tickets
Monitor logs: Check for errors or API issues

Monitoring

Check log files for errors
Monitor OpenAI API usage
Review false positives/negatives in Linear
Adjust confidence threshold if needed

📚 Additional Resources

improvements_summary.md: Technical details about improvements made
Linear API Documentation: https://developers.linear.app/
OpenAI API Documentation: https://platform.openai.com/docs
ChromaDB Documentation: https://docs.trychroma.com/

🆘 Support

If you encounter issues:

Check the troubleshooting section above
Run with --log-level DEBUG for detailed output
Check log files for error messages
Verify API keys and permissions
Test with --dry-run mode first

The system is designed to be safe and reversible - all actions can be undone manually in Linear if needed.

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
.env.example		.env.example
.gitignore		.gitignore
README.md		README.md
ai_flag_duplicates_improved.py		ai_flag_duplicates_improved.py
fetch_linear_issues.py		fetch_linear_issues.py
improvements_summary.md		improvements_summary.md
linear_issues.json		linear_issues.json
requirements.txt		requirements.txt
test_chroma_cloud.py		test_chroma_cloud.py

agencyenterprise/linear-duplicate-detection

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