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Struggling with subpar Retrieval Augmented Generation (RAG) results? In this video, discover how to properly ingest and query data with N8N or a custom code-based approach - so your AI searches actually deliver relevant, high-quality answers. You’ll learn to optimize chunking, metadata, and vector databases for a robust RAG pipeline.
RAG Ingestion Repos & Deployment Scripts ⬇️
Workflow Download: https://gettingautomated.com/youre-doing-rag-wrong-with-n8n-how-to-fix-it
GitHub (RAG Code Based Example): https://github.com/Getting-Automated/n8n-and-code-rag
Pandoc AWS Lambda Converter: https://github.com/Getting-Automated/pandoc-lambda-python
🔥 What You’ll Learn
• Why N8N isn’t ideal for large-scale RAG ingestion (and what it’s great at instead)
• Code-Based Ingestion: How Python + LangChain can handle advanced chunking, metadata, and big data workflows
• Metadata Best Practices: Filter out irrelevant docs before you even search
• Hybrid RAG Model: Use code for ingestion and N8N for retrieval
• Practical OCR & Table Extraction: Get the entire document into your vector database, not just half
🛠️ Core Components
• N8N Workflow: Automatic ingestion for smaller data sets + retrieving documents on demand
• Python + LangChain: Document loaders, advanced chunking, parallel processing, and version control
• Pandoc on AWS Lambda: Convert tricky Word/Markdown files to plain text
• Superbase (PG Vector): Store embeddings with robust metadata for more accurate referencing
💻 Tutorial / Process Overview
• Data Extraction & Parsing: Use specialized loaders for PDF, DOCX, JSON, or even Slack exports.
• Chunking & Overlaps: Split documents into smaller, context-aware slices to improve search.
• Metadata Strategy: Tag each chunk with owner, folder path, version, and permissions.
• Vector DB Integration: Insert embeddings into Pinecone, Superbase, or any vector DB.
• Retrieval Workflows in N8N: Query your vector DB for relevant chunks, then feed results to GPT.
• Hybrid Setup: Combine advanced code ingestion with N8N’s orchestration for the best of both worlds.
💼 Get Expert Guidance
• Book a Call: https://calendly.com/workflowsy/30-minute-connect
• Automation Consulting: https://workflowsy.io/
• Email: hunter@gettingautomated.com
Enjoyed this video? Like, subscribe, and let us know in the comments how you’re tackling RAG!
Stay tuned for more AI & automation solutions that work at scale.
Timestamps:
00:00 – Introduction & Why Common RAG Approaches Fail
01:21 – N8N for RAG at Scale? Not So Fast…
03:06 – What To Expect
03:37 – RAG Core Concepts
07:17 – Where RAG Goes Wrong
08:55 – Specialized Tooling for RAG
10:24 – Great Metadata: What It Looks Like
11:23 – DEMO: n8n & Code Based RAG Demo
23:43 – What I Would Do: The Hybrid Model
24:37 – Join the Getting Automated Community
25:50 – Wrap-Up & Final Thoughts
#RAG #AI #Automation #N8N #LangChain #VectorDatabases #Python