Build a RAG Platform with Hologres and n8n: A Step-by-Step Guide

Retrieval-Augmented Generation (RAG) has become essential for building intelligent applications that combine large language models with domain-specific knowledge. In this guide, you'll learn how to build a complete RAG platform using Hologres as your vector store and n8n as your workflow automation tool. This combination gives you a powerful, serverless-ready solution for enterprise-grade RAG applications.

Why Hologres + n8n for RAG?

Hologres: Alibaba Cloud's real-time data warehouse supports vector search natively, offering high-performance similarity search with SQL compatibility
n8n: An open-source workflow automation tool with a visual interface makes building complex RAG pipelines intuitive
Integration: The n8n-nodes-hologres-vectorstore community node bridges these technologies seamlessly

Let's dive into the implementation!

Prerequisites

Before starting, ensure you have:

An Alibaba Cloud account with Hologres service enabled. Follow the official documentation tocreate your Hologres service.
Node.js installed (for running n8n locally)
Basic familiarity with n8n workflows

Overview of the RAG Architecture

This guide will help you build a RAG platform that integrates three core components:

n8n: Orchestrates the entire workflow through a visual automation interface
Hologres: Serves as the vector database for efficient similarity search and storage
Model Studio (Alibaba Cloud): Provides embedding generation and LLM capabilities via API

These components work together to transform documents into actionable knowledge that can be queried through natural language.

Step 1: Set Up Your Hologres Environment

First, create the necessary Hologres resources:

Create a Hologres instance through the Alibaba Cloud console
Create a database (e.g., name it n8n)
Create a dedicated user for n8n (e.g., BASIC$n8n)

Grant permissions: Assign at least developer role to your user since it needs table creation privileges

Step 2: Install and Configure n8n

Install n8n following the official installation guide:

npm install n8n -g
n8n start

Once running, access n8n at http://localhost:5678.

Step 3: Install the Hologres Vector Store Node

The integration is powered by a community node available on npm:

In n8n, click Settings (top right)
Go to Community Nodes
Click Install a community node
Enter package name: n8n-nodes-hologres-vectorstore
Click Install

You can find the source code on GitHub.

Step 4: Import the RAG Workflow

Download the sample workflow file:

In n8n:

Click Workflows → Import from file
Select your downloaded JSON file
The workflow will appear with all nodes pre-configured

Step 5: Configure the Workflow Components

Configure Hologres Connection

In the Insert Documents into Hologres Vector Store node:

Create a new Hologres credential
Enter your connection details:
- Host: Your Hologres instance endpoint
- Port: Usually 80
- Database: n8n (or your chosen database name)
- User: BASIC$n8n (or your created user)
- Password: Your user's password

Set Vector Store Parameters

Configure the vector storage settings:

Table name: Choose a name for your vector table (e.g., rag_documents)
Vector dimension: Must match your embedding model (e.g., 1536 for text-embedding-v4)
Batch size: Adjust based on your document size (default 100 works well)

Configure Embedding Model

For embeddings, we'll use Alibaba Cloud's Model Studio service; Feel free to use any OpenAI compatible Mode service here.

In the Embeddings Model node, add credentials
Set API Key from your Model console
Set Base URL to Model Studio's endpoint
Select model: e.g., text-embedding-v4

Refer to Model Studio's documentation to get your API and Base URL.

Configure Chat Model

For the LLM component:

Reuse the same Bailian credentials
Select model: qwen3.5-plus (or your preferred model)

Step 6: Load Your Documents

Now you're ready to ingest documents:

Click Execute Workflow
In the pop-up window, upload your PDF document. In the following steps of this guide, we'll use Alibaba Group's quarterly financial report as an example, which you can download from Alibaba Group's Investor Relations page.
The workflow will automatically:
- Split the document into chunks
- Generate embeddings
- Store vectors in Hologres

Step 7: Query Your document with RAG System

Once documents are processed, you can ask questions directly through the workflow interface.

Example query:

What was the year-over-year growth rate of the Cloud Intelligence Group's revenue in the quarter ended September 30, 2025?

The system should return: Based on the search results, the Cloud Intelligence Group's revenue grew by 34% year-over-year in the quarter ended

Testing Your RAG Implementation

To validate your setup, try these question categories:

Fact Retrieval

"What was Alibaba Group's total revenue for the fiscal year ended March 31, 2025?"
"Who is the current CEO of Alibaba Group?"

Comparative Analysis

"Compare the non-GAAP net income of the first half of fiscal year 2025 with fiscal year 2024."

Strategic Understanding

"What are the two core strategic areas Alibaba is heavily investing in?"

Complex Reasoning

"Why did the non-GAAP net income drop significantly in the first half of the year despite revenue growth?"

Next Steps

Now that you have a working RAG platform:

Scale: Connect to larger document repositories
Customize: Modify chunking strategies for your domain
Deploy: Run n8n in production with proper authentication
Monitor: Track query performance and relevance metrics

With Hologres handling vector storage and n8n orchestrating the workflow, you have a flexible foundation for enterprise RAG applications that can evolve with your needs.

Happy building!

Community