Build Document RAG System with Kimi-K2, Gemini Embeddings & Qdrant - n8n Workflow | Neura Market
Build Document RAG System with Kimi-K2, Gemini Embeddings & Qdrant
This n8n workflow builds a Retrieval-Augmented Generation (RAG) system for large documents, generating contextual page summaries with Kimi-K2, embedding them via Gemini, and storing in Qdrant for efficient retrieval.
This advanced n8n workflow constructs a complete RAG pipeline for processing large documents, such as the UK Highway Code or educational materials. It imports documents via HTTP, extracts text, and processes each page individually by generating contextual summaries using the surrounding pages (previous and next) with the Kimi-K2 Instruct LLM from Moonshot AI. This approach ensures summaries capture broader context, improving retrieval accuracy over raw text embeddings.
The workflow then convert
Platform
n8n
Category
Education
Price
$24.99
Creator
Maxim Luong
RAG
Embeddings
Qdrant
Gemini
Kimi-K2
Document Processing
Summarization
Vector Database
AI Automation
Data Extraction
How to import this workflow into n8n
1Purchase or download the workflow to get the n8n workflow JSON file.
2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
3Open each node marked with a credential warning and connect your own accounts and API keys.
4Run the workflow once manually to verify the data flow, then toggle it to Active.