{
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{
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"name": "When clicking ‘Test workflow’",
"type": "n8n-nodes-base.manualTrigger",
"position": [
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],
"parameters": {},
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},
{
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"name": "Web Search For API Schema",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueRegularOutput",
"position": [
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],
"parameters": {
"url": "https://api.apify.com/v2/acts/serping~fast-google-search-results-scraper/run-sync-get-dataset-items",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "genericCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "searchTerms",
"value": "={{\n[\n `site:${$json.data.url.replace(/^http[s]:\\/\\//, '').replace(/\\/$/, '').replace('www.', '')} \"${$json.data.service}\" api developer (intext:reference OR intext:resource) (-inurl:support OR -inurl:help) (inurl:api OR intitle:api) -filetype:pdf`\n]\n}}"
},
{
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"value": "={{ 10 }}"
}
]
},
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{
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"name": "Scrape Webpage Contents",
"type": "n8n-nodes-base.httpRequest",
"position": [
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],
"parameters": {
"url": "https://api.apify.com/v2/acts/apify~web-scraper/run-sync-get-dataset-items",
"options": {
"batching": {
"batch": {
"batchSize": 2,
"batchInterval": 30000
}
}
},
"jsonBody": "={\n \"startUrls\": [\n {\n \"url\": \"{{ $json.source.link }}\",\n \"method\": \"GET\"\n }\n ],\n \"breakpointLocation\": \"NONE\",\n \"browserLog\": false,\n \"closeCookieModals\": false,\n \"debugLog\": false,\n \"downloadCss\": false,\n \"downloadMedia\": false,\n \"excludes\": [\n {\n \"glob\": \"/**/*.{png,jpg,jpeg,pdf}\"\n }\n ],\n \"headless\": true,\n \"ignoreCorsAndCsp\": false,\n \"ignoreSslErrors\": false,\n \n \"injectJQuery\": true,\n \"keepUrlFragments\": false,\n \"linkSelector\": \"a[href]\",\n \"maxCrawlingDepth\": 1,\n \"maxPagesPerCrawl\": 1,\n \"maxRequestRetries\": 1,\n \"maxResultsPerCrawl\": 1,\n \"pageFunction\": \"// The function accepts a single argument: the \\\"context\\\" object.\\n// For a complete list of its properties and functions,\\n// see https://apify.com/apify/web-scraper#page-function \\nasync function pageFunction(context) {\\n\\n await new Promise(res => { setTimeout(res, 6000) });\\n // This statement works as a breakpoint when you're trying to debug your code. Works only with Run mode: DEVELOPMENT!\\n // debugger; \\n\\n // jQuery is handy for finding DOM elements and extracting data from them.\\n // To use it, make sure to enable the \\\"Inject jQuery\\\" option.\\n const $ = context.jQuery;\\n const title = $('title').first().text();\\n\\n // Clone the body to avoid modifying the original content\\n const bodyClone = $('body').clone();\\n bodyClone.find('iframe, img, script, style, object, embed, noscript, svg, video, audio').remove();\\n const body = bodyClone.html();\\n\\n // Return an object with the data extracted from the page.\\n // It will be stored to the resulting dataset.\\n return {\\n url: context.request.url,\\n title,\\n body\\n };\\n}\",\n \"postNavigationHooks\": \"// We need to return array of (possibly async) functions here.\\n// The functions accept a single argument: the \\\"crawlingContext\\\" object.\\n[\\n async (crawlingContext) => {\\n // ...\\n },\\n]\",\n \"preNavigationHooks\": \"// We need to return array of (possibly async) functions here.\\n// The functions accept two arguments: the \\\"crawlingContext\\\" object\\n// and \\\"gotoOptions\\\".\\n[\\n async (crawlingContext, gotoOptions) => {\\n // ...\\n },\\n]\\n\",\n \"proxyConfiguration\": {\n \"useApifyProxy\": true\n },\n \"runMode\": \"PRODUCTION\",\n \n \"useChrome\": false,\n \"waitUntil\": [\n \"domcontentloaded\"\n ],\n \"globs\": [],\n \"pseudoUrls\": [],\n \"proxyRotation\": \"RECOMMENDED\",\n \"maxConcurrency\": 50,\n \"pageLoadTimeoutSecs\": 60,\n \"pageFunctionTimeoutSecs\": 60,\n \"maxScrollHeightPixels\": 5000,\n \"customData\": {}\n}",
"sendBody": true,
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{
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],
"parameters": {
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"fieldToSplitOut": "origin_search.results"
},
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},
{
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"name": "Recursive Character Text Splitter1",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
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],
"parameters": {
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"chunkSize": 4000
},
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},
{
"id": "e2a8137b-7da3-4032-bca2-c14465356f02",
"name": "Content Chunking @ 50k Chars",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
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"assignments": {
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{
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"name": "=data",
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"value": "={{ new Array(Math.round($json.content.length / Math.min($json.content.length, 50000))).fill('').map((_,idx) => $json.content.substring(idx * 50000, idx * 50000 + 50000)) }}"
},
{
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"type": "string",
"value": "={{ $json.service }}"
},
{
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}
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{
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"name": "Split Out Chunks",
"type": "n8n-nodes-base.splitOut",
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],
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},
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},
{
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"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
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],
"parameters": {
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"metadata": {
"metadataValues": [
{
"name": "service",
"value": "={{ $json.service }}"
},
{
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"value": "={{ $json.url }}"
}
]
}
},
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},
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},
{
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],
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},
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"name": "service",
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"value": "={{ $('EventRouter').first().json.data.service }}"
},
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}
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],
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},
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"name": "Execution Data",
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],
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"value": "={{ $json.eventType }}"
},
{
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"value": "={{ $json.executedById }}"
},
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],
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},
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},
"leftValue": "={{ $json.eventType }}",
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}
]
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{
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},
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"name": "Google Gemini Chat Model",
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],
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{
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],
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{
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],
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},
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},
{
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"name": "Embeddings Google Gemini",
"type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
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],
"parameters": {
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},
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},
{
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"name": "Has API Documentation?",
"type": "@n8n/n8n-nodes-langchain.textClassifier",
"position": [
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3580
],
"parameters": {
"options": {
"fallback": "discard"
},
"inputText": "={{\n$json.body\n .replaceAll('\\n', '')\n .substring(0, 40000)\n}}",
"categories": {
"categories": [
{
"category": "contains_api_schema_documentation",
"description": "True if this document contains REST API schema documentation or definitions"
}
]
}
},
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},
{
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"name": "Store Document Embeddings",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
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5700,
3740
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "={{ $('EventRouter').first().json.data.collection }}"
}
},
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},
{
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"name": "Embeddings Google Gemini1",
"type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
"position": [
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4520
],
"parameters": {
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},
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},
{
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"name": "Google Gemini Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
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],
"parameters": {
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"modelName": "models/gemini-1.5-pro-002"
},
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},
{
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"name": "Extract API Operations",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
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],
"parameters": {
"text": "={{ $json.documents }}",
"options": {
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},
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},
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},
{
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"name": "Search in Relevant Docs",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
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4380
],
"parameters": {
"mode": "load",
"topK": 5,
"prompt": "={{ $json.query }}",
"options": {
"searchFilterJson": "={{\n{\n \"must\": [\n {\n \"key\": \"metadata.service\",\n \"match\": {\n \"value\": $('EventRouter').first().json.data.service\n }\n }\n ]\n}\n}}"
},
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"mode": "id",
"value": "={{ $('EventRouter').first().json.data.collection }}"
}
},
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},
{
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],
"webhookId": "e9ad3ef0-7403-4e65-b0a4-4afdfb0cbc6d",
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},
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},
{
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"name": "Remove Dupes",
"type": "n8n-nodes-base.removeDuplicates",
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],
"parameters": {
"compare": "selectedFields",
"options": {},
"fieldsToCompare": "source.link"
},
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},
{
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},
"leftValue": "={{ $json.type }}",
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}
]
}
},
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},
{
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"name": "Research",
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],
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}
},
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},
{
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"singleValue": true
},
"leftValue": "={{ $jmespath($json, 'origin_search.results') }}",
"rightValue": ""
}
]
}
},
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},
{
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],
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{
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}
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{
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}
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{
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"name": "Combine Docs",
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4380
],
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"aggregate": "aggregateAllItemData"
},
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},
{
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"name": "Template to List",
"type": "n8n-nodes-base.splitOut",
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],
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},
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},
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},
{
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],
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"value": "=[\n\"What are the core functionalities, essential features, or primary use cases of {{ $json.data.service }}?\",\n\"Is there an API overview or API categories for {{ $json.data.service }}? What main APIs are listed or mentioned?\",\n\"What industry does {{ $json.data.service }} operate in? What is the most important of the services in the industry? Return the important service as the function.\",\n\"What REST apis (GET, POST, DELETE, PATCH) and/or operations can you identify for {{ $json.data.service }}?\",\n\"Does {{ $json.data.service }} have any CURL examples? If you can, identify one such example and explain what it does.\"\n]"
}
]
}
},
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},
{
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"name": "Google Gemini Chat Model2",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
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4240
],
"parameters": {
"options": {},
"modelName": "models/gemini-1.5-pro-002"
},
"typeVersion": 1
},
{
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"name": "For Each Template...",
"type": "n8n-nodes-base.splitInBatches",
"position": [
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4200
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
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{
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],
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{
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{
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],
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{
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],
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],
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],
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}
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{
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],
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],
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"content": "## Stage 1 - Research for API Documentation\n- Fetch a list of services pending research from Database (Google Sheet)\n- Uses a search engine (Google) to find API Documentation for each service\n- Uses Webscraper (Apify) to read the contents of search results to filter irrelevant pages\n- Stores webpage contents and metadata into Vector Store (Qdrant)"
},
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"content": "## Stage 2 - Extract API Operations From Documentation\n- Fetch a list of services pending extraction from Database (Google Sheet)\n- Query Vector store (Qdrant) to figure out service's products, solutions and offerings\n- Query Vector store (Qdrant) again for API documentation relevant to these products, solutions and offerings\n- Extract any API operations found in the API documentation results using LLM (Gemini)\n- Store extracted API operations into Database (Google Sheet)"
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"content": "## Stage 3 - Generate Custom Schema From API Operations\n- Fetch a list of services pending generation from Database (Google Sheet)\n- Fetch all API operations for each service from Database (Google Sheet)\n- Use Code node to combine and group all API operations for a service and convert to a custom schema\n- Upload the resulting custom schema to file storage (Google Drive)"
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},
"Recursive Character Text Splitter1": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
}
}
}