Chat with PDF docs using AI (quoting sources)

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

This workflow allows you to ask questions about a PDF document. The answers are provided by an AI model of your choice, and the answer includes a citation pointing to the information it used. You c...

Best for

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

Tools used

n8n-nodes-base.manualtrigger, n8n-nodes-base.stickynote, @n8n/n8n-nodes-langchain.vectorstorepinecone, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.set, n8n-nodes-base.googledrive, @n8n/n8n-nodes-langchain.documentdefaultdataloader

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Chat with PDF docs using AI (quoting sources)
Workflow name: Chat with PDF docs using AI (quoting sources)

This workflow allows you to ask questions about a PDF document. The answers are provided by an AI model of your choice, and the answer includes a citation pointing to the information it used.

You can use n8n’s built-in chat interface to ask the questions, or you could customise this workflow to use another one (e.g. Slack, Teams, etc.)

Example

The workflow is set up with the Bitcoin whitepaper. So you could ask things like:

Question: “Which email provider does the creator of Bitcoin use?“ Answer: “GMX [Bitcoin whitepaper.pdf, lines 1-35]”

Requirements

A Pinecone account (they have a free tier at the time of writing that is easily enough for this workflow)
Access to a large language model (e.g. an OpenAI account)

Customizing this workflow

The workflow only reads in one document, but you could customise it to read in all the documents in a folder (or more).

The workflow is set up to use GPT 3.5, but you could swap that out for any other model (including self-hosted ones).

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 - When clicking "Execute Workflow"

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

Block 2 - Sticky Note

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

Block 3 - Sticky Note2

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

Block 4 - Sticky Note1

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

Block 5 - Sticky Note4

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

Block 6 - Pinecone Vector Store

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

Block 7 - When chat message received

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

Block 8 - OpenAI Chat Model1

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

Block 9 - Set file URL in Google Drive

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

Block 10 - Download file

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

Block 11 - Default Data Loader

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

Block 12 - Embeddings OpenAI2

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

Block 13 - Embeddings OpenAI

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

Block 14 - Recursive Character Text Splitter

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

Block 15 - Set max chunks to send to model

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

Block 16 - Get top chunks matching query

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

Block 17 - Prepare chunks

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 18 - Answer the query based on chunks

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

Block 19 - Compose citations

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

Block 20 - Generate response

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

3. Summary Table

Workflow	Chat with PDF docs using AI (quoting sources)
Complexity	advanced
Nodes	20
Categories	Document Extraction, AI RAG
Author	David Roberts
Published	05 Mar 2024

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/2165/2165.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 docs using AI (quoting sources) 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.

David Roberts

18 workflows

Nodes

n8n-nodes-base.manualtrigger n8n-nodes-base.stickynote @n8n/n8n-nodes-langchain.vectorstorepinecone @n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.set n8n-nodes-base.googledrive @n8n/n8n-nodes-langchain.documentdefaultdataloader

Complexity

advanced

Published 05 Mar 2024

Likes 0

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Install path: /data/workflows/2165/2165.json

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Chat with PDF docs using AI (quoting sources)

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Example

Requirements

Customizing this workflow

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When clicking "Execute Workflow"

Block 2 - Sticky Note

Block 3 - Sticky Note2

Block 4 - Sticky Note1

Block 5 - Sticky Note4

Block 6 - Pinecone Vector Store

Block 7 - When chat message received

Block 8 - OpenAI Chat Model1

Block 9 - Set file URL in Google Drive

Block 10 - Download file

Block 11 - Default Data Loader

Block 12 - Embeddings OpenAI2

Block 13 - Embeddings OpenAI

Block 14 - Recursive Character Text Splitter

Block 15 - Set max chunks to send to model

Block 16 - Get top chunks matching query

Block 17 - Prepare chunks

Block 18 - Answer the query based on chunks

Block 19 - Compose citations

Block 20 - Generate response

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