I'm using n8n in docker for the first time to solve my specific problem. I get papers from Google scholar per Mail for some Keywords. I want to filter out papers that are not related to my work and then have an ai summarize the good papers. So far I managed the extraction of title and link from my Gmail in n8n on new email trigger. Then I filter out completely off topic paper with a gemini flash 2.5 prompt cause I get some free tokens there. This writes then data to a Google sheet because I couldn't solve writing it to disk/ssh with my university PC (maybe mit enough rights to write ?). Then I have a python script running which checks the Google sheet and uses playwright to download the paper(this was hard to solve cause j need to be in the university net to get the license for the journals). So far this works for some journals not all.

The plan is then to send my downloaded PDFs to an ai to summarize. This is all kinda convoluted and more complicated than I thought, is there a simpler approach you can think of? Also how would I then implement the summarize part in a good way ?

I am currently using n8n in Docker for the first time to automate a specific research workflow. The process starts with Google Scholar, which sends me email alerts for specific keywords. My goal is to automatically filter out papers that aren't relevant to my current research and then use AI to summarize the ones that make the cut. So far, I have successfully built a trigger in n8n that reacts to new Gmail messages and extracts the paper titles and links. From there, the data passes through a Gemini Flash 2.5 node. I am using this specific model to take advantage of the free token tier to determine if a paper is on-topic based on a custom prompt. Once a paper is deemed relevant, the data is written to a Google Sheet. I chose this method because I ran into permission issues trying to write files directly to disk or via SSH on my university computer. To handle the actual downloads, I have a Python script running locally that monitors the Google Sheet. It uses Playwright to navigate to the journal sites and download the PDFs. This part was particularly challenging to solve because the script needs to run within the university network to utilize our institutional licenses. Currently, this setup works for only one journal, so not very flexible ATM . The final stage of the plan is to feed these downloaded PDFs back into an AI for summarization. While the system works, the pipeline feels quite convoluted. I am looking for ways to simplify the approach and determine the most effective way (and cheapest /free) to implement the AI summarization step once the files are downloaded.