{
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"meta": {
"instanceId": "cba4a4a2eb5d7683330e2944837278938831ed3c042e20da6f5049c07ad14798",
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"name": "AI-Powered Research with Jina AI Deep Search",
"tags": [],
"nodes": [
{
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"content": "\n# **\ud83d\ude80 Developed by Leonard van Hemert** \n\nThank you for using **FREE: Open Deep Research 2.0**! \ud83c\udf89 \n\nThis workflow was created to **democratize AI-powered research** and make advanced **automated knowledge discovery** available to **everyone**, without **API restrictions** or **cost barriers**. \n\nIf you find this useful, feel free to **connect with me on LinkedIn** and stay updated on my latest AI & automation projects! \n\n\ud83d\udd17 **Follow me on LinkedIn**: [Leonard van Hemert](https:\/\/www.linkedin.com\/in\/leonard-van-hemert\/) \n\nI truly appreciate the support from the **n8n community**, and I can\u2019t wait to see how you use and improve this workflow! \ud83d\ude80 \n\nHappy researching, \n**Leonard van Hemert** \ud83d\udca1"
},
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{
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"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
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"content": "## \ud83d\ude80 **FREE: Open Deep Research 2.0** \nFully automated **AI-powered research workflow** using **Jina AI\u2019s DeepSearch** to generate structured, fact-based reports\u2014**no API key required!** "
},
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{
"id": "dbe1cc91-34b4-4e5b-b404-dd86f47d1ebf",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
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"parameters": {
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"content": "## \ud83e\udde0 **How This Workflow Works** \n\nThis workflow automates **deep research and report generation** using **Jina AI's DeepSearch API**, making **advanced knowledge discovery accessible for free**. \n\n1\ufe0f\u20e3 **User Input \u2192 AI Research** \n- A user **enters a research query** via chat. \n- The workflow **sends the query** to **Jina AI\u2019s DeepSearch API** for **in-depth analysis**. \n\n2\ufe0f\u20e3 **AI-Powered Insights** \n- DeepSearch **retrieves** and **analyzes** relevant information. \n- The response includes **key insights, structured analysis, and sources**. \n\n3\ufe0f\u20e3 **Markdown Formatting & Cleanup** \n- The response **passes through a Code Node** that extracts, cleans, and **formats** the AI-generated insights into **readable Markdown output**. \n- URLs are properly formatted, footnotes are structured, and the report is easy to read. \n\n4\ufe0f\u20e3 **Final Output** \n- The final, **well-structured research report** is ready for use, **fully automated and free of charge!** "
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{
"id": "42fd2f04-7d83-44c9-a41b-48860efbcf79",
"name": "Jina AI DeepSearch Request",
"type": "n8n-nodes-base.httpRequest",
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"parameters": {
"url": "https:\/\/deepsearch.jina.ai\/v1\/chat\/completions",
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"jsonBody": "={\n \"model\": \"jina-deepsearch-v1\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"You are an advanced AI researcher that provides precise, well-structured, and insightful reports based on deep analysis. Your responses are factual, concise, and highly relevant.\"\n },\n {\n \"role\": \"assistant\",\n \"content\": \"Hi, how can I help you?\"\n },\n {\n \"role\": \"user\",\n \"content\": \"Provide a deep and insightful analysis on: \\\"{{ $json.chatInput }}\\\". Ensure the response is well-structured, fact-based, and directly relevant to the topic, with no unnecessary information.\"\n }\n ],\n \"stream\": true,\n \"reasoning_effort\": \"low\"\n}",
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"type": "@n8n\/n8n-nodes-langchain.chatTrigger",
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"webhookId": "8a4b05af-cd63-4692-9924-e35aaed5f077",
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{
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"name": "Format & Clean AI Response",
"type": "n8n-nodes-base.code",
"position": [
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"parameters": {
"jsCode": "function extractAndFormatMarkdown(input) {\n let extractedContent = [];\n\n \/\/ Extract raw data string from n8n input\n let rawData = input.first().json.data;\n\n \/\/ Split into individual JSON strings\n let jsonStrings = rawData.split(\"\\n\\ndata: \").map(s => s.replace(\/^data: \/, ''));\n\n let lastContent = \"\";\n \n \/\/ Reverse loop to find the last \"content\" field\n for (let i = jsonStrings.length - 1; i >= 0; i--) {\n try {\n let parsedChunk = JSON.parse(jsonStrings[i]);\n\n if (parsedChunk.choices && parsedChunk.choices.length > 0) {\n for (let j = parsedChunk.choices.length - 1; j >= 0; j--) {\n let choice = parsedChunk.choices[j];\n\n if (choice.delta && choice.delta.content) {\n lastContent = choice.delta.content.trim();\n break;\n }\n }\n }\n\n if (lastContent) break; \/\/ Stop once the last content is found\n } catch (error) {\n console.error(\"Failed to parse JSON string:\", jsonStrings[i], error);\n }\n }\n\n \/\/ Clean and format Markdown\n lastContent = lastContent.replace(\/\\[\\^(\\d+)\\]: (.*?)\\n\/g, \"[$1]: $2\\n\"); \/\/ Format footnotes\n lastContent = lastContent.replace(\/\\[\\^(\\d+)\\]\/g, \"[^$1]\"); \/\/ Inline footnotes\n lastContent = lastContent.replace(\/(https?:\\\/\\\/[^\\s]+)(?=[^]]*\\])\/g, \"<$1>\"); \/\/ Format links\n\n \/\/ Return formatted content as an array of objects (n8n expects this format)\n return [{ text: lastContent.trim() }];\n}\n\n\/\/ Execute function and return formatted output\nreturn extractAndFormatMarkdown($input);\n"
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"typeVersion": 2
}
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