Community Template: Unverified Data to Vector DB Chat

Name: Community Template: Unverified Data to Vector DB Chat
Rating: 1.0 (1 reviews)
Author: Free N8N

Beginner

0 nodes connected

detail.loadingPreview

Free N8N Temples

31 views

0 downloads

Community Contributedchatbotgoogle-driveopenaipineconeragunverifiedvector-database

Load data from Google Drive into Pinecone, then chat with it using OpenAI.

🚀Ready to Deploy This Workflow?

⚡Deploy on Zeabur 🎁Get $200 Credit on DigitalOcean

About This Workflow

Overview

This workflow demonstrates how to load unstructured data from Google Drive into a Pinecone vector database and then query it using an OpenAI language model. It's a practical example of building a simple RAG (Retrieval Augmented Generation) system.

Key Features

Reads documents from Google Drive.
Splits documents into manageable chunks for embedding.
Stores document embeddings in a Pinecone vector database.
Allows querying the vector database with natural language prompts.
Formulates answers based on retrieved document chunks using OpenAI.

How To Use

Setup Credentials: Configure Google Drive and OpenAI API credentials in n8n.
Configure Pinecone: Create a Pinecone index with 1536 dimensions and specify its name in the Pinecone nodes.
Set Google Drive File: Update the file_url in the 'Set Google Drive file URL' node to the URL of your document.
Load Data: Click 'Test Workflow' to load the data into Pinecone.
Chat: Click 'Chat' to send queries to the workflow and get answers.

Apps Used

chatbot

google-drive

openai

pinecone

rag

unverified

vector-database

Workflow JSON

{
  "id": "9243fe98-e533-4bce-9937-0455d3986872",
  "name": "Community Template: Unverified Data to Vector DB Chat",
  "nodes": 0,
  "category": "Community Contributed",
  "status": "active",
  "version": "1.0.0"
}

Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.