README: Customer Feedback LLM

📝 Project Description

This project pulls customer feedback from JIRA (using a JQL query) and uploads a structured .json file into a specific folder on Google Drive. The output file can then be manually uploaded to tools like NotebookLM for query-based analysis.

Problem it Solves

Product Managers need a streamlined way to query customer feedback without manually searching through Jira tickets.

Target Users

Product Managers, Product Marketers, Product Owners, etc.

Dependencies

I am a non-technical user who is not familiar with writing or hosting code. I will likely need to vibe code most of the functionality I need.

I also want to make this project as accessible to my team as possible, in the event that others need to contribute in the future. This means I do not want to host the code locally.

I have access to an Enterprise Github account, Enterprise Gemini account, Enterprise GPT account, JIRA, Slack, NotebookLM…etc. I may need to independently seek out vibe coding tools or other code-writing platforms.

✨ What It Does

• Authenticates with JIRA Cloud using API token
• Fetches all Feature Request issues from the PFR project using JQL
• Outputs a jira_issues.json file with relevant metadata
• Adds a last_updated timestamp to the top of the JSON structure
• Uploads the file to a Google Drive folder using a service account
• Compatible with Shared Drives and folder shortcuts
• Enables the Notebook LLM to query and analyze customer feedback.

🛠️ Prerequisites

• Jira API Access: A Jira account with access to the target service board and a Jira API Token.
• Development Environment: This project is built using Python 3.9 and hosted in a GitHub repository.
• Google Drive folder The folder must be shared with your service account email. You can find your folder ID by visiting the folder and copying the string after /folders/ in the URL.

🚀 Setup & Installation

This project is configured to run automatically using GitHub Actions. Manual installation is not required for the automated workflow. To work with the code locally, follow these steps:

1. Clone the Repository:

   git clone [https://github.com/isharman/customer-feedback.git](https://github.com/isharman/customer-feedback.git)
   cd customer-feedback

2. Install Dependencies:
   • This project's dependencies are listed in requirements.txt.
   • To install them, run pip install -r requirements.txt.

⚙️ Configuration

• Environment Variables & Security: For security, you must NEVER commit your Jira API token to GitHub. Instead, your JIRA_API_TOKEN and JIRA_EMAIL, along with the JIRA_SERVER_URL and GOOGLE_SERVICE_ACCOUNT_JSON, are saved as GitHub Secrets.
• Jira Server URL: https://yexttest.atlassian.net
• JQL Query: The script uses the following JQL to fetch issues: project = "PFR" AND issuetype = "Feature Request" ORDER BY created DESC
• Google Drive Folder ID: The script requires the ID of the Google Drive folder where the jira_issues.json file will be uploaded. This is configured directly in the jira_importer.py script.

How It Works

📤 Output

• The script writes a jira_issues.json file locally.
• That file is uploaded (or updated) into the target folder on Google Drive.
• Output format is clean, structured, and ready for import into tools like NotebookLM.

🏃 Usage

• Automated Execution: The script runs automatically every Monday at midnight UTC GitHub Actions. Example cron job is included in the workflow YAML (.github/workflows/...).
• Manual Execution (for testing): You can manually trigger the workflow from the Actions tab in your GitHub repository to run the script at any time.

📚 Fields Exported

Each JIRA issue includes:

• key
• summary
• description
• reporter
• assignee
• created
• status
• product_area (custom field)
• idea_priority (custom field)
• workaround (custom field)

🧠 Using with NotebookLM

Once the .json file is uploaded to Drive:

1. Open notebooklm.google
2. Create a new notebook
3. Add the Google Drive file as a source
4. Start asking questions!