Chat with PDF, CSV, and JSON documents using Google Gemini RAG

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1. Workflow Overview

Overview Turn documents into an AI powered knowledge base. Upload PDF, CSV, or JSON files and ask natural language questions about their content using a Retrieval Augmented Generation (RAG) workflo...

Best for

Document Extraction automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.formtrigger, n8n-nodes-base.set, @n8n/n8n-nodes-langchain.vectorstoreinmemory, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.memorybufferwindow, @n8n/n8n-nodes-langchain.embeddingsgooglegemini

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Chat with PDF, CSV, and JSON documents using Google Gemini RAG
Workflow name: Chat with PDF, CSV, and JSON documents using Google Gemini RAG

Overview

Turn documents into an AI-powered knowledge base.

Upload PDF, CSV, or JSON files and ask natural-language questions about their content using a Retrieval-Augmented Generation (RAG) workflow powered by Google Gemini. The workflow extracts, embeds, and semantically searches document data to generate accurate, source-grounded answers.

Designed as a simple and extensible starting point for building AI document assistants.

Key Features

Upload and analyze PDF, CSV, and JSON
AI chatbot with semantic document search
Retrieval-Augmented Generation (RAG) architecture
Answers grounded in uploaded documents
Beginner-friendly workflow with clear documentation
Easy to extend for production use

How It Works

Upload a document via form trigger
Content is split into searchable chunks
Gemini generates embeddings
Data is stored in a vector store
The chatbot retrieves context and answers questions

Requirements

Google Gemini API credentials

Notes

Uses an in-memory vector store (data resets on restart)
Can be replaced with Pinecone, Supabase, Weaviate, or other persistent databases
Gemini API usage may incur costs depending on document size and query volume

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 - Document Upload Form

Type / Role: n8n-nodes-base.formTrigger - formTrigger
Config choices: Version 2.3

Block 2 - Add Metadata

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 3 - Vector Store Insert

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

Block 4 - Document Loader

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

Block 5 - Chatbot Trigger

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.4

Block 6 - Knowledge Base Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3

Block 7 - Chat Memory

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

Block 8 - Embeddings Google Gemini

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

Block 9 - Google Gemini Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
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 - Vector Store Retrieve

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

Block 13 - Token Splitter

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

Block 14 - Sticky Note2

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

Block 15 - Sticky Note3

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

Block 16 - Sticky Note4

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

Block 17 - Sticky Note5

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

3. Summary Table

Workflow	Chat with PDF, CSV, and JSON documents using Google Gemini RAG
Complexity	advanced
Nodes	17
Categories	Document Extraction, AI RAG
Author	Md Khalid Ali
Published	21 Feb 2026

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/13575/13575.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 Document Extraction workflows AI RAG workflows

Frequently asked questions

What does Chat with PDF, CSV, and JSON documents using Google Gemini RAG 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 Document Extraction, AI RAG use case.

Md Khalid Ali

1 workflow

Nodes

n8n-nodes-base.formtrigger n8n-nodes-base.set @n8n/n8n-nodes-langchain.vectorstoreinmemory @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.memorybufferwindow @n8n/n8n-nodes-langchain.embeddingsgooglegemini

Complexity

advanced

Published 21 Feb 2026

Likes 0

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Install path: /data/workflows/13575/13575.json

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Chat with PDF, CSV, and JSON documents using Google Gemini RAG

Workflow preview

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Overview

Key Features

How It Works

Requirements

Notes

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Document Upload Form

Block 2 - Add Metadata

Block 3 - Vector Store Insert

Block 4 - Document Loader

Block 5 - Chatbot Trigger

Block 6 - Knowledge Base Agent

Block 7 - Chat Memory

Block 8 - Embeddings Google Gemini

Block 9 - Google Gemini Chat Model

Block 10 - Sticky Note

Block 11 - Sticky Note1

Block 12 - Vector Store Retrieve

Block 13 - Token Splitter

Block 14 - Sticky Note2

Block 15 - Sticky Note3

Block 16 - Sticky Note4

Block 17 - Sticky Note5

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