Virtual Organization Deployment

Description

Utilizes large language models to perform analytical tasks requested by the user.

Installation

1. Install required dependencies

pip install -r requirements.txt

2. Use LM Studio to host the large language model and load the model

• Download and install LM Studio

• Open a terminal and run lms server start

Test Agentic System

python okr_alignment_system/examples/alignment_example.py

Features

• Alignment Analysis: Measures how well current Jira issues align with your OKRs
• Gap Identification: Identifies missing work and gaps in OKR coverage
• Issue Recommendations: Suggests specific new issues to improve alignment
• Local LLM Support: Uses LM Studio for privacy and cost-effective analysis
• Flexible Input: Accepts text queries about alignment in natural language

Environment Variables

You can set these environment variables instead of passing them as arguments:

• JIRA_URL: Your Jira base URL
• JIRA_USERNAME: Your Jira username/email
• JIRA_API_TOKEN: Your Jira API token
• LM_STUDIO_URL: LM Studio API URL (default: http://localhost:1234/v1)
• LM_STUDIO_MODEL: Specific model name to use

Output

The agent provides:

1. Overall Alignment Score (0-100): Overall measure of alignment
2. Per-OKR Analysis:
   • Alignment score for each OKR
   • Contributing issues
   • Gap analysis
   • Recommendations
3. Suggested Issues: Specific new issues to create with:
   • Title and description
   • Related OKR
   • Priority
   • Rationale