Hey everyone! Our team built this cool system that uses AI to make Jira tickets from feedback. We’re using the web version now but want to switch to the API. We’re running into some problems though.
Right now we’re:
- Using AI to look at tons of feedback
- Making Jira tickets automatically
- Catching duplicate issues
- Making reports to track progress
But we’re hitting some walls:
- Can’t process a lot at once
- Have to manually manage stuff between talks with the AI
- Can’t keep going without someone watching
- No way to keep track of things across batches
We’ve got some questions:
- Will the API have the same Jira functions as the web version?
- How’s the API different from the web version?
- Anyone got examples of using AI for Jira stuff through the API?
- Tips for handling lots of data?
- How should we set up our system to work well with the AI?
Thanks for any help you can give! We’re excited to make this work better.
hey stella, that project is rad! the api is similar to the web version, but a bit more nimble for automations. try chunking data and async handling along with webhooks to manage loads. hope this helps, cheers.
The API should provide similar functionality to the web version, but with more flexibility for automation. Key differences include direct programmatic access and potential for higher throughput. For large-scale processing, consider implementing a queue system and handling tasks asynchronously. This can help manage high volumes without overloading your systems or hitting rate limits.
Regarding AI integration, look into Jira’s REST API documentation for endpoint details. You might want to set up a middleware layer to handle AI processing and API interactions. This can help with batching requests, error handling, and maintaining state across processing cycles.
For system architecture, consider a modular approach that separates concerns: data ingestion, AI processing, Jira interaction, and reporting. This will make your system more scalable and easier to maintain as you grow.
As someone who’s worked on similar projects, I can share some insights. The API is definitely the way to go for scaling. It’s more flexible and allows for better automation compared to the web version.
One big tip: implement a robust queueing system. This helped us handle large volumes of data without overwhelming our systems or hitting Jira’s rate limits. We used Apache Kafka, but there are other options depending on your stack.
For AI integration, we found it useful to create a separate microservice that handles the AI processing. This keeps your main application lean and allows you to scale the AI component independently.
Regarding duplicate issue detection, we implemented a fingerprinting system using a combination of issue attributes. This significantly improved our accuracy in catching duplicates across batches.
Lastly, don’t underestimate the importance of good logging and monitoring. It’s crucial for debugging and optimizing your system as you scale up. Tools like ELK stack or Prometheus can be really helpful here.
Good luck with your project!