Chat with Google Drive documents using OpenAI and Pinecone RAG search

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1. Workflow Overview

Try it out This n8n workflow template lets you chat with your Google Drive documents (.docx, .json, .md, .txt, .pdf) using OpenAI and Pinecone vector database. It retrieves relevant context from yo...

Best for

Internal Wiki automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.vectorstorepinecone, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.stickynote, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.rerankercohere

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Pinecone.

Original n8n.io source

1.1 Workflow description

Title: Chat with Google Drive documents using OpenAI and Pinecone RAG search
Workflow name: Chat with Google Drive documents using OpenAI and Pinecone RAG search

Try it out

This n8n workflow template lets you chat with your Google Drive documents (.docx, .json, .md, .txt, .pdf) using OpenAI and Pinecone vector database. It retrieves relevant context from your files in real time so you can get accurate, context-aware answers about your proprietary data—without the need to train your own LLM.

Not interested in chunking and embedding your own data or figuring out which search method to use?

Try our n8n quickstart for Pinecone Assistant here or check out the full workflow to chat with your Google Drive documents here.

Prerequisites

A Pinecone account
A GCP project with Google Drive API enabled and configured
An Open AI account and API key
A Cohere account and API key

Setup

Create a Pinecone index in the Pinecone Console here
Name your index n8n-dense-index
Select OpenAI's text-embedding-3-small
Set the Dimension to 1536
Leave everything else as default
If you use a different index name, update the related nodes to reflect this change
Use the Connect to Pinecone button to authenticate to Pinecone or if you self-host n8n, create a Pinecone credential and add your Pinecone API key directly
Setup your Google Drive OAuth2 API, Open AI, and Cohere credentials in n8n
Download these files and add them to a Drive folder named n8n-pinecone-demo in the root of your My Drive
https://docs.pinecone.io/release-notes/2022.md
https://docs.pinecone.io/release-notes/2023.md
https://docs.pinecone.io/release-notes/2024.md
https://docs.pinecone.io/release-notes/2025.md
https://docs.pinecone.io/release-notes/2026.md
Activate the workflow or test it with a manual execution to ingest the documents
Enter the chat prompts to chat with the Pinecone release notes
What support does Pinecone have for MCP?
When was fetch by metadata released?

Ideas for customizing this workflow

Use your own data and adjust the chunking strategy
Update the AI Agent System Message to reflect how the Pinecone Vector Store Tool will be used. Be sure to include info on what data can be retrieved using that tool.
Update the Pinecone Vector Store Tool Description to reflect what data you are storing in the Pinecone index

Need help?

You can find help by asking in the Pinecone Discord community or filing an issue on this repo.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Pinecone Vector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStorePinecone - vectorStorePinecone
Config choices: Version 1.3

Block 2 - Embeddings OpenAI

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1.2

Block 3 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.3

Block 4 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2.2

Block 5 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 6 - Pinecone Vector Store Tool

Type / Role: @n8n/n8n-nodes-langchain.vectorStorePinecone - vectorStorePinecone
Config choices: Version 1.3

Block 7 - Sticky Note7

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 8 - Default Data Loader

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1.1

Block 9 - Cohere Reranker

Type / Role: @n8n/n8n-nodes-langchain.rerankerCohere - rerankerCohere
Config choices: Version 1

Block 10 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 11 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 12 - Character Text Splitter - markdown

Type / Role: @n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter - textSplitterCharacterTextSplitter
Config choices: Version 1

Block 13 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 14 - Google Drive Trigger

Type / Role: n8n-nodes-base.googleDriveTrigger - googleDriveTrigger
Config choices: Version 1

Block 15 - Download file

Type / Role: n8n-nodes-base.googleDrive - googleDrive
Config choices: Version 3

3. Summary Table

Workflow	Chat with Google Drive documents using OpenAI and Pinecone RAG search
Complexity	advanced
Nodes	15
Categories	Internal Wiki, AI RAG
Author	Pinecone
Published	16 Dec 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/11870/11870.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Internal Wiki workflows AI RAG workflows

Frequently asked questions

What does Chat with Google Drive documents using OpenAI and Pinecone RAG search do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Internal Wiki, AI RAG use case.

Pinecone

4 workflows

Nodes

@n8n/n8n-nodes-langchain.vectorstorepinecone @n8n/n8n-nodes-langchain.embeddingsopenai @n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.stickynote @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.rerankercohere

Complexity

advanced

Published 16 Dec 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/11870/11870.json

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Chat with Google Drive documents using OpenAI and Pinecone RAG search

Workflow preview

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Try it out

Not interested in chunking and embedding your own data or figuring out which search method to use?

Prerequisites

Setup

Ideas for customizing this workflow

Need help?

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Pinecone Vector Store

Block 2 - Embeddings OpenAI

Block 3 - When chat message received

Block 4 - AI Agent

Block 5 - OpenAI Chat Model

Block 6 - Pinecone Vector Store Tool

Block 7 - Sticky Note7

Block 8 - Default Data Loader

Block 9 - Cohere Reranker

Block 10 - Sticky Note

Block 11 - Sticky Note1

Block 12 - Character Text Splitter - markdown

Block 13 - Sticky Note2

Block 14 - Google Drive Trigger

Block 15 - Download file

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions