Community Template: Unverified Data to Vector DB Chat
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Load data from Google Drive into Pinecone, then chat with it using OpenAI.
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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_urlin 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
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.
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ID: 9243fe98-e533...
About the Author
SaaS_Connector
Integration Guru
Connecting CRM, Notion, and Slack to automate your life.
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