Hey everyone! 👋 I've been lurking for a while, learning so much from all your pipeline war stories. Seriously, thank you. My lab PI decided we should try Iris.ai for literature review and knowledge mapping, and guess who got tasked with the "light integration" into our existing data workflow? Yep, me. I'm documenting my first-time setup here, partly for my own notes and partly in case it helps anyone else who's feeling a bit lost.
Our existing stack is mostly Python scripts that scrape PubMed/arXiv, load abstracts into a small Postgres instance, and then we have some basic keyword tagging. The goal was to use Iris.ai to get smarter about connections between papers and auto-generate some research topic maps. The main hurdles I hit:
* **Authentication & API Quotas:** The initial API key setup was straightforward in the Iris dashboard, but I immediately got rate-limited on my first script run. 😅 Their docs mention it, but I missed the default free-tier limits. Had to add some aggressive `time.sleep()` calls in my loop.
* **Data Formatting for the "Workspace":** To upload our own PDFs and existing paper metadata, you need a specific JSON structure. My first few attempts failed because my author field was a string, but their schema expects a list for multi-author papers. The error messages got me there eventually.
* **The "Researcher" vs. "Workspace" Workflow:** This confused me for a day. I initially built everything around the "Researcher" API endpoints (for searching their global database), but to process *our* internal documents, you need to create a "Workspace" first, upload there, and then use *those* endpoints. Once I mapped that out, it clicked.
Here’s a rough sketch of the pipeline flow I ended up with:
1. Our existing scraper dumps new paper metadata (title, abstract, DOI) into a `staging_papers` table.
2. A Python script runs daily (via a simple cron, we're not on Airflow yet... baby steps) to check for new entries.
3. It formats the data into Iris's required JSON, uploads batches to a specific Workspace via their API, and polls for processing completion.
4. Another script later fetches the extracted concepts and suggested related papers from that Workspace, which we then dump into our own DB for visualization.
The cool part is now we can cross-reference Iris's suggested connections with our own tagging. The painful part was the back-and-forth to get the data shapes right. I'm still not sure I'm using the "Focus" feature optimally for narrowing down topic maps.
Has anyone else done something similar? I'd love to compare notes, especially on:
* Handling large batches of PDFs (we have a few thousand legacy papers we'd like to process).
* Whether you built any intermediate data validation or if you just let the API errors guide you (like I did).
* If you connected the output to something like a Neo4j graph or just kept it in a relational DB.
The tool seems powerful, but the learning curve for integrating it into a custom pipeline felt a bit steep for a newbie like me. Any pro-tips would be massively appreciated!
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Oh, the rate limiting bit is so relatable! I also got tripped up by that when I was first testing their search API endpoints. My script would run fine for a few dozen calls and then just hang - turned out I burned through the minute quota in about 30 seconds flat.
> specific JSON structure
This was the biggest time-sink for me, too. Their example schema is minimal. I found you need to be very precise with the `document_source` field - if it's not one of their predefined types, the upload just silently fails. I ended up writing a small validation function to check my JSON against their API's error responses before any batch upload.
Are you planning to use their Focus feature to narrow down topics? I found that step really changed the quality of the auto-generated maps, but feeding it the right seed documents is key.
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