Automatisez le Résumé de Vos Emails avec A.I. et Messagerie
Ce workflow n8n vous permet d'automatiser la gestion de vos emails en utilisant l'intelligence artificielle pour résume...
Documentation Complète
📋 Optimisation des Processus d'Extraction de Schémas API
💡 Description
Ce workflow n8n automatisé permet aux entreprises de rationaliser la recherche, l'extraction et la génération de schémas d'API à partir de documentations disponibles en ligne. En utilisant des outils puissants comme Apify pour le scraping web et Google Sheets pour le stockage des données, ce processus garantit une collecte rapide et précise des informations d'API critiques, optimisant ainsi les efforts de développement et améliorant l'efficacité opérationnelle.
📈 Impact & ROI: En automatisant la découverte et l'extraction des schémas API, ce workflow peut réduire considérablement le temps passé par les développeurs sur ces tâches répétitives, permettant un retour sur investissement rapide grâce à une efficacité accrue et une réduction des erreurs.
🚀 Fonctionnalités Clés
- ✅ Recherche automatisée de documentations API - Améliore l'efficacité
- ✅ Extraction précise des opérations API - Réduit les erreurs manuelles
- ✅ Génération de schémas personnalisés - Facilite l'intégration
- ✅ Stockage structuré dans Google Sheets - Accès simplifié aux données
📊 Architecture Technique
88
Nodes
Nodes
75
Connexions
Connexions
4
Services
Services
🔌 Services Intégrés
ApifyGoogle SheetsGoogle DriveQdrant
🔧 Composition du Workflow
| Node | Type | Description |
|---|---|---|
| When clicking ‘Test workflow’ | manualTrigger | Traitement des données |
| Web Search For API Schema | httpRequest | Requête HTTP vers une API externe |
| Scrape Webpage Contents | httpRequest | Requête HTTP vers une API externe |
| Results to List | splitOut | Division des données en plusieurs branches |
| Recursive Character Text Splitter1 | @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter | Division des données en plusieurs branches |
| Content Chunking @ 50k Chars | set | Traitement des données |
| Split Out Chunks | splitOut | Division des données en plusieurs branches |
| Default Data Loader | @n8n/n8n-nodes-langchain.documentDefaultDataLoader | Traitement des données |
| Set Embedding Variables | set | Traitement des données |
| Execute Workflow Trigger | executeWorkflowTrigger | Traitement des données |
| Execution Data | executionData | Traitement des données |
| EventRouter | switch | Traitement des données |
| Google Gemini Chat Model | @n8n/n8n-nodes-langchain.lmChatGoogleGemini | Traitement des données |
| Successful Runs | filter | Traitement des données |
| For Each Document... | splitInBatches | Division des données en plusieurs branches |
| Embeddings Google Gemini | @n8n/n8n-nodes-langchain.embeddingsGoogleGemini | Traitement des données |
| Has API Documentation? | @n8n/n8n-nodes-langchain.textClassifier | Condition logique pour router le flux |
| Store Document Embeddings | @n8n/n8n-nodes-langchain.vectorStoreQdrant | Traitement des données |
| Embeddings Google Gemini1 | @n8n/n8n-nodes-langchain.embeddingsGoogleGemini | Traitement des données |
| Google Gemini Chat Model1 | @n8n/n8n-nodes-langchain.lmChatGoogleGemini | Traitement des données |
| Extract API Operations | @n8n/n8n-nodes-langchain.informationExtractor | Traitement des données |
| Search in Relevant Docs | @n8n/n8n-nodes-langchain.vectorStoreQdrant | Traitement des données |
| Wait | wait | Traitement des données |
| Remove Dupes | removeDuplicates | Traitement des données |
| Filter Results | filter | Traitement des données |
| Research | executeWorkflow | Traitement des données |
| Has Results? | if | Condition logique pour router le flux |
| Response Empty | set | Traitement des données |
| Response OK | set | Traitement des données |
| Combine Docs | aggregate | Traitement des données |
| Template to List | splitOut | Division des données en plusieurs branches |
| Query Templates | set | Traitement des données |
| Google Gemini Chat Model2 | @n8n/n8n-nodes-langchain.lmChatGoogleGemini | Traitement des données |
| For Each Template... | splitInBatches | Division des données en plusieurs branches |
| Query & Docs | set | Traitement des données |
| Identify Service Products | @n8n/n8n-nodes-langchain.informationExtractor | Traitement des données |
| Extract API Templates | set | Traitement des données |
| Embeddings Google Gemini2 | @n8n/n8n-nodes-langchain.embeddingsGoogleGemini | Traitement des données |
| Search in Relevant Docs1 | @n8n/n8n-nodes-langchain.vectorStoreQdrant | Traitement des données |
| Combine Docs1 | aggregate | Traitement des données |
| Query & Docs1 | set | Traitement des données |
| For Each Template...1 | splitInBatches | Division des données en plusieurs branches |
| Merge Lists | code | Traitement des données |
| Remove Duplicates | removeDuplicates | Traitement des données |
| Append Row | googleSheets | Traitement des données |
| Response OK1 | set | Traitement des données |
| Has Operations? | if | Condition logique pour router le flux |
| Response Empty1 | set | Traitement des données |
| Research Pending | googleSheets | Traitement des données |
| Research Result | googleSheets | Traitement des données |
| Research Error | googleSheets | Traitement des données |
| Extract Pending | googleSheets | Traitement des données |
| Research Event | set | Traitement des données |
| Extract Event | set | Traitement des données |
| Extract | executeWorkflow | Traitement des données |
| Extract Result | googleSheets | Traitement des données |
| Extract Error | googleSheets | Traitement des données |
| Get API Operations | googleSheets | Traitement des données |
| Contruct JSON Schema | code | Traitement des données |
| Upload to Drive | googleDrive | Traitement des données |
| Set Upload Fields | set | Traitement des données |
| Response OK2 | set | Traitement des données |
| Generate Event | set | Traitement des données |
| Generate Pending | googleSheets | Traitement des données |
| Generate | executeWorkflow | Traitement des données |
| Generate Error | googleSheets | Traitement des données |
| Generate Result | googleSheets | Traitement des données |
| Get All Extract | googleSheets | Traitement des données |
| Get All Research | googleSheets | Traitement des données |
| For Each Research... | splitInBatches | Division des données en plusieurs branches |
| For Each Extract... | splitInBatches | Division des données en plusieurs branches |
| Wait1 | wait | Traitement des données |
| All Research Done? | if | Condition logique pour router le flux |
| All Extract Done? | if | Condition logique pour router le flux |
| Get All Generate | googleSheets | Traitement des données |
| All Generate Done? | if | Condition logique pour router le flux |
| For Each Generate... | splitInBatches | Division des données en plusieurs branches |
| Wait2 | wait | Traitement des données |
| Has Results?1 | if | Condition logique pour router le flux |
| Response Scrape Error | set | Traitement des données |
| Has Results?3 | if | Condition logique pour router le flux |
| Response No API Docs | set | Traitement des données |
| Sticky Note | stickyNote | Traitement des données |
| Sticky Note1 | stickyNote | Traitement des données |
| Sticky Note2 | stickyNote | Traitement des données |
| Sticky Note3 | stickyNote | Traitement des données |
| Sticky Note4 | stickyNote | Traitement des données |
| Sticky Note5 | stickyNote | Traitement des données |
📖 Guide d'Implémentation
- Import du workflow: Téléchargez le fichier JSON et importez-le dans votre instance n8n
- Configuration des credentials: Configurez les accès pour chaque service utilisé
- Personnalisation: Adaptez les paramètres selon vos besoins spécifiques
- Test: Exécutez le workflow en mode test pour vérifier le bon fonctionnement
- Activation: Activez le workflow pour une exécution automatique
🏷️ Tags
Structure JSON
Voir le code JSON complet
{
"nodes": [
{
"id": "2498bb93-176f-458c-acee-f541859df770",
"name": "When clicking ‘Test workflow’",
"type": "n8n-nodes-base.manualTrigger",
"position": [
2460,
2820
],
"parameters": [],
"typeVersion": 1
},
{
"id": "c08bcf84-9336-44f9-b452-0c9469f18f48",
"name": "Web Search For API Schema",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueRegularOutput",
"position": [
3100,
3820
],
"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}}"
},
{
"name": "resultsPerPage",
"value": "={{ 10 }}"
}
]
},
"genericAuthType": "httpHeaderAuth"
},
"typeVersion": 4.2
},
{
"id": "d5b19e3a-acd0-4b06-8d77-42de1f797dba",
"name": "Scrape Webpage Contents",
"type": "n8n-nodes-base.httpRequest",
"position": [
3940,
3720
],
"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,
"sendQuery": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpQueryAuth",
"queryParameters": {
"parameters": [
{
"name": "memory",
"value": "2048"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "5853ba7e-4068-4792-be5c-b8cf81ee89cb",
"name": "Results to List",
"type": "n8n-nodes-base.splitOut",
"position": [
3460,
3720
],
"parameters": {
"options": [],
"fieldToSplitOut": "origin_search.results"
},
"typeVersion": 1
},
{
"id": "8ed2e8ec-b2e3-474b-b19d-f38b518f274b",
"name": "Recursive Character Text Splitter1",
"type": "@n8n\/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
5800,
4020
],
"parameters": {
"options": [],
"chunkSize": 4000
},
"typeVersion": 1
},
{
"id": "e2a8137b-7da3-4032-bca2-c14465356f02",
"name": "Content Chunking @ 50k Chars",
"type": "n8n-nodes-base.set",
"position": [
5380,
3740
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "7753a4f4-3ec2-4c05-81df-3d5e8979a478",
"name": "=data",
"type": "array",
"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)) }}"
},
{
"id": "7973bcb4-f239-4619-85fc-c76e20386375",
"name": "service",
"type": "string",
"value": "={{ $json.service }}"
},
{
"id": "b46e44bc-ad01-4cf0-8b07-25eeb1fb5874",
"name": "url",
"type": "string",
"value": "={{ $json.url }}"
}
]
}
},
"typeVersion": 3.3
},
{
"id": "6ef5866a-d992-4472-9221-27efbec8e7be",
"name": "Split Out Chunks",
"type": "n8n-nodes-base.splitOut",
"position": [
5540,
3740
],
"parameters": {
"include": "allOtherFields",
"options": [],
"fieldToSplitOut": "data"
},
"typeVersion": 1
},
{
"id": "5e43b4d8-cebf-43ed-866d-0b4cb2997853",
"name": "Default Data Loader",
"type": "@n8n\/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
5800,
3900
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "service",
"value": "={{ $json.service }}"
},
{
"name": "url",
"value": "={{ $json.url }}"
}
]
}
},
"jsonData": "={{ $json.data }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "d4b34767-be50-44ee-b778-18842034c276",
"name": "Set Embedding Variables",
"type": "n8n-nodes-base.set",
"position": [
4980,
3580
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "4008ae44-7998-4a6f-88c9-686f8b02e92b",
"name": "content",
"type": "string",
"value": "={{ $json.body }}"
},
{
"id": "f7381ac6-ef40-463c-ad2b-df2c31d3e828",
"name": "service",
"type": "string",
"value": "={{ $('EventRouter').first().json.data.service }}"
},
{
"id": "7eae99fd-75c7-4974-a128-641b8ada0cc2",
"name": "url",
"type": "string",
"value": "={{ $json.url }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "109b6c3a-9b16-40cc-9186-5045df387b52",
"name": "Execute Workflow Trigger",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
2420,
4200
],
"parameters": [],
"typeVersion": 1
},
{
"id": "31556ff2-6358-4bd4-8ec4-2797d993256e",
"name": "Execution Data",
"type": "n8n-nodes-base.executionData",
"position": [
2620,
4200
],
"parameters": {
"dataToSave": {
"values": [
{
"key": "eventType",
"value": "={{ $json.eventType }}"
},
{
"key": "executedById",
"value": "={{ $json.executedById }}"
},
{
"key": "service",
"value": "={{ $json.data.service }}"
}
]
}
},
"typeVersion": 1
},
{
"id": "b65b3d4d-f667-4f8f-a06f-847c3d7b83e0",
"name": "EventRouter",
"type": "n8n-nodes-base.switch",
"position": [
2800,
4200
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "research",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.eventType }}",
"rightValue": "research"
}
]
},
"renameOutput": true
},
{
"outputKey": "extraction",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "5418515e-ef6a-42e0-aeb9-8d0d35b898ca",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.eventType }}",
"rightValue": "extract"
}
]
},
"renameOutput": true
},
{
"outputKey": "generate",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "0135165e-d211-44f3-92a4-a91858a57d99",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.eventType }}",
"rightValue": "generate"
}
]
},
"renameOutput": true
}
]
},
"options": []
},
"typeVersion": 3.2
},
{
"id": "541f7d9b-c8ff-44dc-8618-8550dbf0b951",
"name": "Google Gemini Chat Model",
"type": "@n8n\/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
4460,
3740
],
"parameters": {
"options": [],
"modelName": "models\/gemini-1.5-flash-latest"
},
"typeVersion": 1
},
{
"id": "617d6139-8417-4ecb-8f7c-558cd1c38ac3",
"name": "Successful Runs",
"type": "n8n-nodes-base.filter",
"position": [
4100,
3720
],
"parameters": {
"options": [],
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "cac77cce-0a5c-469e-ba80-9fb026f04b18",
"operator": {
"type": "string",
"operation": "exists",
"singleValue": true
},
"leftValue": "={{ $json.body }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2,
"alwaysOutputData": true
},
{
"id": "1115db69-b414-46cd-a9a1-565ae98cbd91",
"name": "For Each Document...",
"type": "n8n-nodes-base.splitInBatches",
"position": [
5180,
3580
],
"parameters": {
"options": []
},
"typeVersion": 3
},
{
"id": "3f0e3764-2479-4d74-aca8-c3e830eac423",
"name": "Embeddings Google Gemini",
"type": "@n8n\/n8n-nodes-langchain.embeddingsGoogleGemini",
"position": [
5680,
3900
],
"parameters": {
"modelName": "models\/text-embedding-004"
},
"typeVersion": 1
},
{
"id": "87d42766-d1a2-406d-b01c-044fd2fc8910",
"name": "Has API Documentation?",
"type": "@n8n\/n8n-nodes-langchain.textClassifier",
"position": [
4460,
3580
],
"parameters": {
"options": {
"fallback": "discard"
},
"inputText": "={{\n$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"
}
]
}
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "55939b49-d91c-42a1-9770-48cbe4008c9a",
"name": "Store Document Embeddings",
"type": "@n8n\/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
5700,
3740
],
"parameters": {
"mode": "insert",
"options": [],
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "={{ $('EventRouter').first().json.data.collection }}"
}
},
"typeVersion": 1
},
{
"id": "3e1da749-b8b9-42cb-818b-eabf4b114abb",
"name": "Embeddings Google Gemini1",
"type": "@n8n\/n8n-nodes-langchain.embeddingsGoogleGemini",
"position": [
3700,
4520
],
"parameters": {
"modelName": "models\/text-embedding-004"
},
"typeVersion": 1
},
{
"id": "be0906d4-351f-4b3b-9f32-8e5ee68083c5",
"name": "Google Gemini Chat Model1",
"type": "@n8n\/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
4600,
4240
],
"parameters": {
"options": [],
"modelName": "models\/gemini-1.5-pro-002"
},
"typeVersion": 1
},
{
"id": "886415d5-c888-4b97-9fb5-02e6a14df4cc",
"name": "Extract API Operations",
"type": "@n8n\/n8n-nodes-langchain.informationExtractor",
"position": [
4600,
4100
],
"parameters": {
"text": "={{ $json.documents }}",
"options": {
"systemPromptTemplate": "=You have been given an extract of a webpage which should contain a list of web\/REST api operations.\nStep 1. Extract all REST (eg. GET,POST,PUT,DELETE) API operation endpoints from the page content and generate appropriate labels for the resource, operation, description, method for each.\n* \"resource\" refers to the API group, for example: \"\/v1\/api\/indicators\/list\" and \"\/v1\/api\/indicators\/create\" will both have the resource name of \"indicators\". Use the following template \"<domain>\" eg. \"entities\", \"posts\", \"credentials\".\n* \"operation\" refers to the action performed, use the following template \"<verb> <entity>\" eg. \"List entities\", \"Create post\", \"Update credentials\"\n* only use one HTTP verb for \"method\"\n* \"description\" should be limited to one sentence.\n* Examples of API urls: \"\/api\/\", \"\/api\/v1\/\", \"\/v1\/api\". API urls should not end with \"htm\" or html\".\n* Extract a maximum of 15 endpoints.\n* If the page content contains no api operations, return an empty array."
},
"schemaType": "manual",
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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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},
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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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"content": "## Stage 1 - Subworkflow"
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"content": "## Stage 2 - Subworkflow"
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"content": "## Stage 3 - Subworkflow"
},
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"service": "Formstack",
"collection": "api_schema_crawler_and_extractor",
"row_number": 2
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"createdAt": "2024-12-07T12:22:35.344-05:00",
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},
"Results to List": {
"main": [
[
{
"node": "Filter Results",
"type": "main",
"index": 0
}
]
]
},
"Successful Runs": {
"main": [
[
{
"node": "Has Results?1",
"type": "main",
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}
]
]
},
"Upload to Drive": {
"main": [
[
{
"node": "Response OK2",
"type": "main",
"index": 0
}
]
]
},
"Generate Pending": {
"main": [
[
{
"node": "Generate Event",
"type": "main",
"index": 0
}
]
]
},
"Get All Generate": {
"main": [
[
{
"node": "All Generate Done?",
"type": "main",
"index": 0
}
]
]
},
"Get All Research": {
"main": [
[
{
"node": "All Research Done?",
"type": "main",
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}
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]
},
"Research Pending": {
"main": [
[
{
"node": "Research Event",
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}
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},
"Split Out Chunks": {
"main": [
[
{
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},
"Template to List": {
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{
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},
"All Extract Done?": {
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{
"node": "Get All Generate",
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],
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{
"node": "For Each Extract...",
"type": "main",
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}
]
]
},
"Remove Duplicates": {
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[
{
"node": "Append Row",
"type": "main",
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}
]
]
},
"Set Upload Fields": {
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{
"node": "Upload to Drive",
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}
]
]
},
"All Generate Done?": {
"main": [
[],
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{
"node": "For Each Generate...",
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}
]
]
},
"All Research Done?": {
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[
{
"node": "Get All Extract",
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"index": 0
}
],
[
{
"node": "For Each Research...",
"type": "main",
"index": 0
}
]
]
},
"Get API Operations": {
"main": [
[
{
"node": "Contruct JSON Schema",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
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{
"node": "Store Document Embeddings",
"type": "ai_document",
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}
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},
"For Each Extract...": {
"main": [
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{
"node": "Get All Generate",
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],
[
{
"node": "Extract Pending",
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"index": 0
}
]
]
},
"Contruct JSON Schema": {
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[
{
"node": "Set Upload Fields",
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"index": 0
}
]
]
},
"For Each Document...": {
"main": [
[
{
"node": "Response OK",
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}
],
[
{
"node": "Content Chunking @ 50k Chars",
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}
]
]
},
"For Each Generate...": {
"main": [
[],
[
{
"node": "Generate Pending",
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]
]
},
"For Each Research...": {
"main": [
[
{
"node": "Get All Extract",
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}
],
[
{
"node": "Research Pending",
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"index": 0
}
]
]
},
"For Each Template...": {
"main": [
[
{
"node": "Identify Service Products",
"type": "main",
"index": 0
}
],
[
{
"node": "Search in Relevant Docs",
"type": "main",
"index": 0
}
]
]
},
"Extract API Templates": {
"main": [
[
{
"node": "For Each Template...1",
"type": "main",
"index": 0
}
]
]
},
"For Each Template...1": {
"main": [
[
{
"node": "Extract API Operations",
"type": "main",
"index": 0
}
],
[
{
"node": "Search in Relevant Docs1",
"type": "main",
"index": 0
}
]
]
},
"Extract API Operations": {
"main": [
[
{
"node": "Merge Lists",
"type": "main",
"index": 0
}
]
]
},
"Has API Documentation?": {
"main": [
[
{
"node": "Has Results?3",
"type": "main",
"index": 0
}
]
]
},
"Scrape Webpage Contents": {
"main": [
[
{
"node": "Successful Runs",
"type": "main",
"index": 0
}
]
]
},
"Search in Relevant Docs": {
"main": [
[
{
"node": "Combine Docs",
"type": "main",
"index": 0
}
]
]
},
"Set Embedding Variables": {
"main": [
[
{
"node": "For Each Document...",
"type": "main",
"index": 0
}
]
]
},
"Embeddings Google Gemini": {
"ai_embedding": [
[
{
"node": "Store Document Embeddings",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Execute Workflow Trigger": {
"main": [
[
{
"node": "Execution Data",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Has API Documentation?",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Search in Relevant Docs1": {
"main": [
[
{
"node": "Combine Docs1",
"type": "main",
"index": 0
}
]
]
},
"Embeddings Google Gemini1": {
"ai_embedding": [
[
{
"node": "Search in Relevant Docs",
"type": "ai_embedding",
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}
]
]
},
"Embeddings Google Gemini2": {
"ai_embedding": [
[
{
"node": "Search in Relevant Docs1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Google Gemini Chat Model1": {
"ai_languageModel": [
[
{
"node": "Extract API Operations",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Google Gemini Chat Model2": {
"ai_languageModel": [
[
{
"node": "Identify Service Products",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Identify Service Products": {
"main": [
[
{
"node": "Extract API Templates",
"type": "main",
"index": 0
}
]
]
},
"Store Document Embeddings": {
"main": [
[
{
"node": "For Each Document...",
"type": "main",
"index": 0
}
]
]
},
"Web Search For API Schema": {
"main": [
[
{
"node": "Has Results?",
"type": "main",
"index": 0
}
]
]
},
"Content Chunking @ 50k Chars": {
"main": [
[
{
"node": "Split Out Chunks",
"type": "main",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "Get All Research",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter1": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
}
}
}
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