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.