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Extract & Summarize Wikipedia Data with Bright Data and Gemini AI

12 nodes · manual, lmChatGoogleGemini, httpRequest, chainLlm, chainSummarization

Engine
n8n
Nodes
12
Trigger
scheduled/triggered
Source file
1979_Manual_Stickynote_Automation_Webhook.json

Workflow flow

IntegrationsManualLmChatGoogleGeminiHTTPChainLlmChainSummarization@n8n/n8n-nodes-langchain.lmChatGoogleGemini@n8n/n8n-nodes-langchain.chainLlm@n8n/n8n-nodes-langchain.chainSummarizationSet
100%
TRIGGER

When clicking ‘Test workflow’

Manual

Google Gemini Chat Model For Summarization

@n8n/n8n-nodes-langchain.lmChatGoogleGemini

Google Gemini Chat Model2

@n8n/n8n-nodes-langchain.lmChatGoogleGemini

Set Wikipedia URL with Bright Data Zone

Set

Wikipedia Web Request

HTTP

LLM Data Extractor

@n8n/n8n-nodes-langchain.chainLlm

Concise Summary Generator

@n8n/n8n-nodes-langchain.chainSummarization

Summary Webhook Notifier

HTTP

8 steps · flows top to bottom, branches left to right

Browse by integration

manuallmChatGoogleGeminihttpRequestchainLlmchainSummarization
Browse all integrations →
Download n8n JSONDownload sanitized JSONDownload flow graph

Import into n8n

  1. Download the n8n JSON file (full or sanitized).
  2. In n8n: Workflows → Import from file.
  3. Open each node and reconnect credentials to your accounts.
  4. Test with manual execution before enabling triggers.

Sanitized JSON removes credential IDs — safer for sharing. Flow graph JSON is OpenRuna's lightweight format for diagrams and other tools, not n8n import.

View source corpus ↗

Overview

Extract & Summarize Wikipedia Data with Bright Data and Gemini AI (12-node n8n automation, manual, lmChatGoogleGemini, httpRequest, chainLlm, chainSummarization) is free on OpenRuna — view the graph and export the JSON to import into n8n.

What this workflow does

Built in n8n, "Extract & Summarize Wikipedia Data with Bright Data and Gemini AI" chains 12 nodes to move data between manual, lmChatGoogleGemini, httpRequest, chainLlm, chainSummarization. 12 nodes · manual, lmChatGoogleGemini, httpRequest, chainLlm, chainSummarization A scheduled/triggered entry point kicks it off, and each node passes data to the next until the run completes. On OpenRuna you get the visual graph, the integration list, and a downloadable JSON ready to import into n8n. Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.

Use cases

  • Study how data moves from manual to lmChatGoogleGemini before building your own variant.
  • Share it with your team as a documented reference for scheduled/triggered automations in the manual stack.
  • Import "Extract & Summarize Wikipedia Data with Bright Data and Gemini AI" into n8n, reconnect credentials for manual, lmChatGoogleGemini, httpRequest, chainLlm, chainSummarization, and run the same automation in your own workspace.
  • Use it as a starting template when wiring manual, lmChatGoogleGemini, httpRequest, chainLlm, chainSummarization — adjust node parameters to your accounts and record IDs.

Example output

Imported into n8n, "Extract & Summarize Wikipedia Data with Bright Data and Gemini AI" exposes 12 configured nodes. Trigger type: scheduled/triggered. Integrations: manual, lmChatGoogleGemini, httpRequest, chainLlm, chainSummarization. Set your credentials on the manual node, then verify each downstream node picks them up. Run it once in test mode to verify any branch logic (Switch/IF nodes) before enabling production triggers.

Tips by platform

Claude

Ask Claude to turn the "Extract & Summarize Wikipedia Data with Bright Data and Gemini AI" JSON into a plain-English runbook, node by node. It can flag where manual rate limits or missing retries might bite before you go live.

ChatGPT

Drop the workflow JSON or this OpenRuna page URL into ChatGPT and ask for a step-by-step runbook: triggers, branches, and which fields to customise for manual, lmChatGoogleGemini, httpRequest, chainLlm, chainSummarization. Handy for onboarding teammates new to n8n.

Cursor

In Cursor, ask Agent mode to parameterise "Extract & Summarize Wikipedia Data with Bright Data and Gemini AI" — extract manual IDs and secrets into environment variables without changing the 12-node structure.

Frequently asked questions

How do I import "Extract & Summarize Wikipedia Data with Bright Data and Gemini AI" into n8n?
Click Download n8n JSON on this page, then in n8n choose Workflows → Import from file and select the downloaded file. Reconnect credentials on each node — the export carries credential IDs from the original author's instance, not working secrets.
Which integrations does "Extract & Summarize Wikipedia Data with Bright Data and Gemini AI" use?
It uses: manual, lmChatGoogleGemini, httpRequest, chainLlm, chainSummarization. The flow diagram above shows how each service connects across the 12 nodes. Filter OpenRuna by integration to find related workflows and tools.
Can I import a sanitized export without credentials?
Yes. Use "Download sanitized JSON" to strip credential references before sharing internally. You will still add your own manual and lmChatGoogleGemini credentials in n8n after import.
What trigger type does "Extract & Summarize Wikipedia Data with Bright Data and Gemini AI" use?
Trigger type: scheduled/triggered. After import, confirm the trigger node (webhook, schedule, or app event) matches your environment, then activate the workflow when you are ready to run it.
Is "Extract & Summarize Wikipedia Data with Bright Data and Gemini AI" free to use?
OpenRuna catalogs community n8n workflows under open licenses. Check the source corpus link for the original license; commercial use may require verifying the third-party API terms for manual, lmChatGoogleGemini, httpRequest, chainLlm, chainSummarization.

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