Automate systematic literature reviews with Google Drive, GPT-4, Gemini, Qdrant and Airtable

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1. Workflow Overview

How it works Automates systematic literature review by downloading papers from Google Drive, extracting text, and evaluating them against strict inclusion/exclusion criteria using LLM agents Routes...

Best for

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

Tools used

n8n-nodes-base.googledrive, n8n-nodes-base.extractfromfile, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.embeddingsgooglegemini, n8n-nodes-base.splitinbatches, n8n-nodes-base.manualtrigger, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.lmchatgooglegemini

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Automate systematic literature reviews with Google Drive, GPT-4, Gemini, Qdrant and Airtable
Workflow name: Automate systematic literature reviews with Google Drive, GPT-4, Gemini, Qdrant and Airtable

How it works

Automates systematic literature review by downloading papers from Google Drive, extracting text, and evaluating them against strict inclusion/exclusion criteria using LLM agents
Routes included papers to Qdrant vector stores with Gemini embeddings for semantic search, and excluded papers to a separate folder
Logs all decisions to Airtable with PRISMA-compliant justification for complete audit trails

Set up steps

Connect Google Drive credentials to access your paper folder
Configure Airtable base and table for decision logging
Add OpenAI (GPT-4) and Google Gemini API credentials for LLM evaluation and embeddings
Set up Qdrant instances for vector storage (supports up to 3 collections)
Keep detailed descriptions of your inclusion/exclusion criteria in the sticky notes inside your workflow

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 - Download PDF

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

Block 2 - Extract PDF Text

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

Block 3 - Default Data Loader

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

Block 4 - Default Data Loader1

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

Block 5 - Default Data Loader2

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

Block 6 - Embeddings Google Gemini

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

Block 7 - Embeddings Google Gemini1

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

Block 8 - Embeddings Google Gemini2

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

Block 9 - Loop Over Items

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

Block 10 - When clicking ‘Execute workflow’

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

Block 11 - Structured Output Parser

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

Block 12 - Google Gemini Chat Model1

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

Block 13 - If

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

Block 14 - Structured Output Parser1

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

Block 15 - Google Gemini Chat Model3

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

Block 16 - Search files and folders

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

Block 17 - OpenAI Chat Model

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

Block 18 - OpenAI Chat Model1

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

Block 19 - 📋 Overview: SLR Paper Review Agent

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

Block 20 - Stage 1: Paper Input & Extraction

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

Block 21 - Stage 2: LLM-Based SLR Review

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

Block 22 - Stage 3: Decision Routing & Logging

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

Block 23 - Stage 4: Vector Store & File Organization

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

Block 24 - Log Excluded Paper

Type / Role: n8n-nodes-base.airtable - airtable
Config choices: Version 2.1

Showing the first 24 of 36 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Automate systematic literature reviews with Google Drive, GPT-4, Gemini, Qdrant and Airtable
Complexity	advanced
Nodes	36
Categories	Document Extraction, AI RAG
Author	Jannik Hiller
Published	19 Jan 2026

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/12827/12827.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 Automate systematic literature reviews with Google Drive, GPT-4, Gemini, Qdrant and Airtable 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.

Jannik Hiller

4 workflows

Nodes

n8n-nodes-base.googledrive n8n-nodes-base.extractfromfile @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.embeddingsgooglegemini n8n-nodes-base.splitinbatches n8n-nodes-base.manualtrigger @n8n/n8n-nodes-langchain.outputparserstructured @n8n/n8n-nodes-langchain.lmchatgooglegemini

Complexity

advanced

Published 19 Jan 2026

Likes 0

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Install path: /data/workflows/12827/12827.json

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Automate systematic literature reviews with Google Drive, GPT-4, Gemini, Qdrant and Airtable

Workflow preview

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Download PDF

Block 2 - Extract PDF Text

Block 3 - Default Data Loader

Block 4 - Default Data Loader1

Block 5 - Default Data Loader2

Block 6 - Embeddings Google Gemini

Block 7 - Embeddings Google Gemini1

Block 8 - Embeddings Google Gemini2

Block 9 - Loop Over Items

Block 10 - When clicking ‘Execute workflow’

Block 11 - Structured Output Parser

Block 12 - Google Gemini Chat Model1

Block 13 - If

Block 14 - Structured Output Parser1

Block 15 - Google Gemini Chat Model3

Block 16 - Search files and folders

Block 17 - OpenAI Chat Model

Block 18 - OpenAI Chat Model1

Block 19 - 📋 Overview: SLR Paper Review Agent

Block 20 - Stage 1: Paper Input & Extraction

Block 21 - Stage 2: LLM-Based SLR Review

Block 22 - Stage 3: Decision Routing & Logging

Block 23 - Stage 4: Vector Store & File Organization

Block 24 - Log Excluded Paper

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