If you’re a freelancer working with AI or automation, you know how crucial PDFs are for many businesses. But extracting data from unstructured PDFs? That’s a pain—until now. With LlamaParse, you can build smarter automation flows that make handling complex PDFs simple, efficient, and highly valuable to your clients. Why You Should Care • Next-Level Parsing: LlamaParse is far superior to the default parse node in N8N, handling even the most complex, unstructured PDF documents. • Client-Ready Automation: Working with invoices, contracts, or reports? This guide will help you create a robust PDF parsing flow that your clients will love—and pay for. • Win More Projects: Offer tailored, scalable solutions that save your clients hours of manual work and position you as the go-to automation expert. What You’ll Learn 1️⃣ How to set up LlamaParse in N8N for powerful PDF parsing. 2️⃣ Extract and structure data from even the messiest PDFs with ease. 3️⃣ Build a complete automation flow to simplify your clients’ processes, including data organization and storage in Airtable. 🎥 Check out the video tutorial for a step-by-step walkthrough and start building smarter solutions for your clients today!

I have a workflow which writes out to a Google Sheet to log messages, but it waits for that node to complete before it sends the reply to the user. I'm wondering is if I can have a workflow that can run nodes asynchronously, so it doesn't need to wait for that one to complete before it moves on to the next. Is that possible?

Hi :) I am new here, very happy to find this group. What can I do to feed two types of searches in RAG system. 1) We all have it now, the vector search and it works fine. 2) keyword search related to the prompt, so if we use product number like WXZ2432 , it will know it need to share all the chunks it has this unique word. how can we do it?

I'm trying to build a workflow where an AI agent can use context from a document uploaded in that session by the user. I tried the chat trigger node with upload files enabled, but it requires the document to be sent at every interaction. Has anyone ever tried to build something like this?

The default data extractor node will not consider that information. (or I am doing it wrong) We can create a custom node to extract that data and preserve it with the chunk as metadata, but hoping n8n or the community has already solved this.