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https://github.com/dayanacavalcante/ai_agent_rag
RAG AI Agent in n8n
https://github.com/dayanacavalcante/ai_agent_rag
n8n openai postgresql supabase
Last synced: 6 days ago
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RAG AI Agent in n8n
- Host: GitHub
- URL: https://github.com/dayanacavalcante/ai_agent_rag
- Owner: dayanacavalcante
- Created: 2025-02-07T19:04:28.000Z (7 days ago)
- Default Branch: master
- Last Pushed: 2025-02-07T19:30:46.000Z (7 days ago)
- Last Synced: 2025-02-07T20:28:35.474Z (7 days ago)
- Topics: n8n, openai, postgresql, supabase
- Homepage:
- Size: 327 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚀 Creating an RAG AI Agent in n8n
This repository contains a detailed tutorial for building an RAG (Retrieval-Augmented Generation) AI Agent in n8n, capable of answering questions based on documents stored in a vector database.
## 📌 Objective
Create an AI agent that can answer questions based on stored documents using **GPT-4o mini** from OpenAI, **PostgreSQL** for memory, and **Supabase Vector Store** for vector storage.
## 🛠️ Technologies Used
- **n8n** - Automation platform
- **OpenAI GPT-4o mini** - AI model
- **PostgreSQL** - AI agent memory
- **Supabase Vector Store** - Vector storage
- **Google Drive** - Document upload
## 🔗 Integration of Tools
The AI agent workflow involves:
1. Connecting **OpenAI's API** to **PostgreSQL** and **Supabase Vector Store**
2. Configuring the AI agent's memory in **PostgreSQL**
3. Storing documents in **Supabase Vector Store**
4. Creating a **Google Drive trigger** to capture new documents
5. Processing files and generating embeddings with **OpenAI**
6. Using **n8n** to manage the data flow
The test documents contain fictional questions and answers for a virtual Customer Support assistant, generated by **ChatGPT**.
---
## 📖 Step-by-Step Guide
### 1️⃣ Creating the AI Agent in n8n
- Add a **message trigger**
- Use the node **"Advanced AI / AI Agent"**
- The AI agent will respond and decide when to consult the database
- Configure the **"AI Agent"**
- **Chat Model**: Create OpenAI credentials
- **Memory**: Choose PostgreSQL (using Supabase)
### 2️⃣ Setting Up Supabase
- Create a new project in **Supabase** (e.g., `n8n AI Agent - Date - DEMO Z`)
- Set a password for the database
- Ensure the project is active (**Check Project Status**)
- Go to **Project Settings > Database > Connection Parameters**
### 3️⃣ Configuring PostgreSQL in n8n
- Create a new **PostgreSQL credential** in n8n
- Configure the parameters:
- **Host, Port, User, Password** (obtained from Supabase)
### 4️⃣ Setting Up the Vector Store Tool
- Add the **vector storage tool**
- Name and description
- Select **Supabase Vector Store**
- Connect to the Supabase account (**Project Settings > API**)
- Copy **URL and secret key** into n8n
- Create the **storage table**
- In **Supabase SQL Editor**, run the script from n8n documentation (**Quickstart for setting up your vector store**)
- Choose **Table Name**: `documents`
- Add option: `match_documents`
### 5️⃣ Uploading and Processing Documents
- Create a folder in **Google Drive**
- In **n8n**, add a trigger **"Google Drive - On Changes on Specific Folder"**
- Select event: **When a document is created**
- Upload documents to the **Google Drive folder**
- Run **"Fetch Test Event"** in n8n to verify document detection
### 6️⃣ Processing the Files
- Convert the document to a processable format
- **Google File Conversion**: TXT to CSV
- Download the document
- **Google Drive - Download File by ID**
- Transform the file
- **Data Transformation**: Extract from text file
- Ensure no duplicates in **Supabase**
- Delete previous records in **Supabase Vector Store**
- Set a filter to prevent duplication:
```sql
metadata->>file_id=like.*{{$json.id}}*
```
### 7️⃣ Creating Embeddings in Supabase Vector Store
- Add **OpenAI embeddings**
- Define **chunk size and overlap** for document splitting
- Use the **"Recursive Character Test"** model
- Save settings and test agent queries
---
## 📌 Final Considerations
Your **AI agent** is now ready to answer questions based on stored documents! 🚀
It can retrieve information from the **vector database** and generate intelligent responses using the **GPT-4o mini** model.
If you need assistance or want to contribute, feel free to **open an issue** or **submit a pull request**!