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JIRA JQL Generator Slack Bot
This video demonstrates how to use the JIRA JQL Generator Slack Bot template.
import os
import re
from slack_bolt import App
from slack_bolt.adapter.socket_mode import SocketModeHandler
from abilities import llm_prompt
from jira import JIRA
# Install the Slack app and get xoxb- token in advance
app = App(token=os.environ["SLACK_BOT_TOKEN"])
# Dictionary to store the generated JQL and the user who requested it
jql_dict = {}
# List of reserved JQL words
reserved_words = ["and", "or", "not", "empty", "null", "contains", "issue", "project", "text", "word", "in", "is", "was", "during"]
@app.command("/hello-socket-mode")
def hello_command(ack, body):
user_id = body["user_id"]
ack(f"Hi, <@{user_id}>!")
@app.event("app_mention")
def event_test(body, say, event):Frequently Asked Questions
How can the JIRA JQL Generator Slack Bot improve our team's productivity?
The JIRA JQL Generator Slack Bot can significantly enhance your team's productivity by streamlining the process of creating and executing Jira queries. Instead of switching between Slack and Jira, team members can generate JQL queries directly within Slack conversations. This saves time and reduces context switching, allowing for quicker decision-making and issue tracking. The bot's ability to translate natural language into JQL also makes it accessible to team members who may not be familiar with JQL syntax, thus democratizing access to Jira data.
Can the JIRA JQL Generator Slack Bot be customized for specific project needs?
Yes, the JIRA JQL Generator Slack Bot can be customized to fit specific project needs. While the current implementation uses a general prompt for JQL generation, you can modify the llm_prompt function call to include project-specific instructions or constraints. For example, you could add predefined filters for certain projects or issue types, or include custom fields in the generated queries. This customization allows the bot to align more closely with your team's workflow and Jira setup.
How does the JIRA JQL Generator Slack Bot ensure data security when accessing Jira?
The JIRA JQL Generator Slack Bot prioritizes data security in several ways. First, it uses environment variables to store sensitive information like API tokens and server URLs, preventing these credentials from being exposed in the code. Second, the bot uses Jira's API with basic authentication, ensuring secure communication between the bot and your Jira instance. Lastly, the bot only retrieves and shares issue links rather than full issue details, minimizing the amount of potentially sensitive data transmitted through Slack.
How can I modify the JIRA JQL Generator Slack Bot to support additional Slack commands?
You can easily extend the JIRA JQL Generator Slack Bot to support additional Slack commands by adding new function decorators. Here's an example of how you could add a command to list recent issues:
python
@app.command("/recent-issues")
def list_recent_issues(ack, say, command):
ack()
try:
jira = JIRA(server=os.environ["JIRA_SERVER_URL"], basic_auth=(os.environ["JIRA_EMAIL"], os.environ["JIRA_API_TOKEN"]))
issues = jira.search_issues('order by created DESC', maxResults=5)
issue_links = "\n".join([f"<{issue.permalink()}|{issue.key}>" for issue in issues])
say(f"Recent Issues:\n{issue_links}")
except Exception as e:
say(f"Error fetching recent issues: {str(e)}")
This new command would allow users to type /recent-issues in Slack to get a list of the 5 most recently created issues.
How can I integrate the JIRA JQL Generator Slack Bot with other AI models for JQL generation?
The JIRA JQL Generator Slack Bot is designed to be flexible in terms of the AI model used for JQL generation. Currently, it uses the llm_prompt function, which is likely an abstraction for an API call to an AI model. To integrate with a different AI model, you would need to modify this function or replace it with a call to your preferred AI service. Here's an example of how you might modify the code to use a hypothetical AI service:
```python from custom_ai_service import generate_jql
# ... (rest of the code remains the same)
@app.event("app_mention") def event_test(body, say, event): # ... (earlier part of the function remains the same) jql = generate_jql(user_message) # ... (rest of the function remains the same) ```
In this example, generate_jql would be a function that interfaces with your chosen AI service to generate JQL from natural language input. This allows you to easily swap out the AI backend while keeping the rest of the bot's functionality intact.
Created: | Last Updated:
Introduction to the JIRA JQL Generator Slack Bot Template
Welcome to the step-by-step guide on how to set up and use the JIRA JQL Generator Slack Bot template on the Lazy platform. This template is designed to help you generate Jira Query Language (JQL) queries directly from Slack. When you mention the app in a Slack message, it will create a JQL based on your message and ask if you want to run the query. If you agree, it will execute the query on Jira and return the results in the same Slack thread.
To get started with this template, simply click on "Start with this Template" on the Lazy platform.
Setting Environment Secrets
Before you can use the JIRA JQL Generator Slack Bot, you need to set up a few environment secrets. These are necessary for the app to communicate with Slack and Jira. Here's what you need to do:
- SLACK_BOT_TOKEN: This token allows your app to work with Slack. You can obtain it by creating a new app in your Slack workspace, adding the bot scope, and installing the app in the workspace.
- SLACK_APP_TOKEN: This token is used for Socket Mode in Slack. You can generate it by enabling Socket Mode for your app in the Slack API settings and creating an App-Level token.
- JIRA_API_TOKEN and JIRA_EMAIL: These credentials are used to authenticate with Jira. You can generate them from your Jira account settings.
- JIRA_SERVER_URL: This is the URL of your Jira server where the queries will be run.
Enter these details in the Environment Secrets tab within the Lazy Builder interface.
External Integrations
Before using the template, you need to set up integrations with Slack and Jira:
- Create a new app in your Slack workspace and install it.
- Enable Socket Mode for your Slack app and generate the necessary tokens.
- Generate an API token from your Jira account and note down your Jira email.
These steps are crucial for the app to function correctly and to ensure that it can communicate with both Slack and Jira.
Using the Test Button
Once you have set up the environment secrets, you can use the Test button to deploy the app. The Lazy CLI will prompt you for any required user input after you use the Test button. If the app requires additional input, provide it through the Lazy CLI as prompted.
If the app uses an API, Lazy will provide you with a dedicated server link to use the API. In the case of FastAPI, you will also receive a docs link to interact with the app.
How to Use the Interface
After deploying the app using the Test button, you can interact with the Slack bot directly within your Slack workspace. Mention the bot in a message to generate a JQL query. The bot will respond in the thread, asking if you want to run the generated query. If you choose to run it, the bot will execute the query on Jira and return the results in the Slack thread.
Here's a sample interaction with the bot:
User: @JiraBot Find all open bugs in the project ABC
Bot: Generating a JQL, please wait...
Bot: Generated JQL: `project \= 'ABC' AND issuetype \= 'Bug' AND status \= 'Open'`. Do you want to run this query? [View JQL](JiraLink)
Clicking "Yes" will run the query and the bot will post the results in the thread. Clicking "No" will cancel the operation.
By following these steps, you can easily set up and use the JIRA JQL Generator Slack Bot on the Lazy platform to streamline your workflow and enhance your productivity within Slack.
Template Benefits
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Streamlined Jira Query Generation: This template enables users to generate complex Jira queries directly from Slack, eliminating the need to switch between applications and reducing the time spent on query formulation.
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Improved Cross-Team Collaboration: By making Jira data easily accessible within Slack, this bot facilitates better communication and collaboration between teams, especially those who may not be as familiar with Jira's query language.
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Increased Productivity: The automatic translation of natural language to JQL significantly reduces the learning curve for non-technical team members, allowing them to quickly access the Jira data they need without extensive training.
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Enhanced Decision Making: With real-time access to Jira data in Slack, teams can make faster, more informed decisions based on up-to-date project information, improving overall project management and efficiency.
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Customizable and Scalable Solution: This template provides a foundation that can be easily customized to fit specific organizational needs and scaled to accommodate multiple teams or projects, offering a flexible solution for businesses of all sizes.
Technologies
Streamline Slack Workflows with Lazy AI: Automate Notifications, API Integrations and More
Optimize Jira Workflows with Lazy AI: Automate Tasks, Reports, Collaboration and More
