Chat with Google Drive documents using GPT, Pinecone, and RAG

📌 Short Overview

Automatically sync files from Google Drive into a searchable AI knowledge base with Pinecone, and answer user queries using GPT-4o with conversational memory.

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🛠️ Workflow Usage Steps

1. Watch Google Drive for file changes Trigger the workflow when a new file is uploaded or an existing file is updated in a specific Google Drive folder. 2. Download and process the file Retrieve the file, split it into smaller text chunks with a Recursive Character Text Splitter, and generate vector embeddings using OpenAI. 3. Store embeddings in Pinecone Save the embeddings in a Pinecone vector database to keep your knowledge base continuously updated and searchable. 4. Search context for chat queries When a user asks a question, query Pinecone for relevant context, combine results with conversational memory, and process them with GPT-4o. 5. Respond with AI-powered answers Provide a concise response (100–200 words) that blends knowledge from your documents with the conversation history.

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✅ Use Cases • Keep a live, AI-ready knowledge base from your Google Drive files. • Enable team members to query company documents instantly. • Build a personal assistant that stays up to date with your latest uploads.

⚙️ Setup Steps 1. Google Drive • Create a Google Cloud project. • Enable the Google Drive API. • Generate OAuth credentials and connect them in n8n. 2. OpenAI • Sign up at OpenAI. • Copy your API key from the dashboard. • Add it to n8n under Credentials → OpenAI API. 3. Pinecone • Create an account at Pinecone. • Create a new index (e.g., docs-embeddings). • Copy your API key and environment, then add them to n8n under Credentials → Pinecone API. 4. Workflow Configuration • Import this workflow into your n8n instance. • Select the Google Drive folder you want to monitor. • Set the Pinecone index name in the workflow. • Adjust chunk size / overlap in the text splitter if needed. 5. Test the Workflow • Upload a new document to your Google Drive folder. • Run the workflow to confirm embeddings are created and stored in Pinecone. • Ask a sample query and verify the AI returns a context-aware answer.