Byte-Sized n8n AI Series: Knowledge Injection

Description:

Stop hallucination and start grounding your AI in reality. An LLM is only as smart as the data it can access.

In this 90-minute session, you will learn how to perform Knowledge Injection—the process of feeding your own private documents and live web data into an AI’s reasoning process. We will explore Vector Foundations to store and retrieve information efficiently. You’ll learn how to "clean" the internet using “FireCrawl” to turn messy websites into LLM-ready Markdown and how to force your agent to speak in Structured JSON, ensuring your automations never break due to "chatty" AI responses.

Duration:

90 minutes

Course Code: BDT 536

Learning Objectives:

After this course, you will be able to:

1. Set up and query a local or cloud-based Vector Store within n8n
2. Build a pipeline to ingest and "chunk" PDFs and live websites
3. Implement "Structured Outputs" to ensure 100% reliable downstream data mapping

AI developers, data analysts, and automation engineers who want to move beyond general LLM knowledge and ground their agents in private, proprietary, or real-time web data.

Experience with n8n and local LLMs. Basic understanding of what a PDF and a URL are. No prior experience with Vector Databases is required.

Course Outline:

1. Vector Foundations: The Agent’s Long-Term Memory
   1. What is a Vector Store? Moving from keyword search to semantic "meaning" search
   2. Embeddings Explained: How text becomes math (5k dimensions of meaning)
   3. In-Memory or Disk Store: Choosing the right storage for you stack
   4. Lab: Initializing a Vector Store node and "Seeding" it with a text file

2. The Extraction Pipeline: Turning Noise into Signal
   1. The PDF Problem: Handling multi-column layouts, tables, and "messy" text extraction
   2. Web Scraping with “Fire Crawl”: Turning any URL into clean, LLM-friendly Markdown
   3. Chunking Strategies: Why size matters—balancing context window vs. retrieval precision
   4. Lab: Building a "Website-to-Knowledge" pipeline in n8n

3. Structured Outputs: Reliable Automation
   1. Prompting JSON: Moving from "Write a summary" to "Return a JSON object."
   2. Output Parsers: Using n8n nodes to enforce strict data schemas
   3. Error Handling: What to do when the LLM returns invalid code
   4. Lab: Creating a Document Intelligence agent that extracts 5 specific data points from a PDF into a JSON format

Training material provided: Yes (Digital format)

Hands-on Lab: Students will be provided with docker compose file and n8n workflow JSON.