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Revert "change to json formatted text and reformat all .txt to .json & fix error json format"

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Enes Cingöz
2025-07-05 22:28:12 +01:00
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HR_and_Recruitment/BambooHR AI-Powered Company Policies and Benefits Chatbot.json
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HR_and_Recruitment/BambooHR AI-Powered Company Policies and Benefits Chatbot.txt Normal file
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HR_and_Recruitment/CV Screening with OpenAI.json
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@@ -1,273 +0,0 @@
{
"meta": {
"instanceId": "6a2a7715680b8313f7cb4676321c5baa46680adfb913072f089f2766f42e43bd"
},
"nodes": [
{
"id": "0f3b39af-2802-462c-ac54-a7bccf5b78c5",
"name": "Extract Document PDF",
"type": "n8n-nodes-base.extractFromFile",
"position": [
520,
400
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1,
"alwaysOutputData": false
},
{
"id": "6f76e3a6-a3be-4f9f-a0db-3f002eafc2ad",
"name": "Download File",
"type": "n8n-nodes-base.httpRequest",
"position": [
340,
400
],
"parameters": {
"url": "={{ $json.file_url }}",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "2c4e0b0f-28c7-48f5-b051-6e909ac878d2",
"name": "When clicking ‘Test workflow’",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-20,
400
],
"parameters": {},
"typeVersion": 1
},
{
"id": "a70d972b-ceb4-4f4d-8737-f0be624d6234",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
120,
280
],
"parameters": {
"width": 187.37066290133808,
"height": 80,
"content": "**Add direct link to CV and Job description**"
},
"typeVersion": 1
},
{
"id": "9fdff1be-14cf-4167-af2d-7c5e60943831",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-800,
140
],
"parameters": {
"color": 7,
"width": 280.2462120317618,
"height": 438.5821431288714,
"content": "### Setup\n\n1. **Download File**: Fetch the CV using its direct URL.\n2. **Extract Data**: Use N8N’s PDF or text extraction nodes to retrieve text from the CV.\n3. **Send to OpenAI**:\n - **URL**: POST to OpenAI’s API for analysis.\n - **Parameters**:\n - Include the extracted CV data and job description.\n - Use JSON Schema to structure the response.\n4. **Save Results**:\n - Store the extracted data and OpenAI's analysis in Supabase for further use."
},
"typeVersion": 1
},
{
"id": "b1ce4a61-270f-480b-a716-6618e6034581",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-800,
-500
],
"parameters": {
"color": 7,
"width": 636.2128494576581,
"height": 598.6675280064023,
"content": "![5min Logo](https://cflobdhpqwnoisuctsoc.supabase.co/storage/v1/object/public/my_storage/Untitled%20(1500%20x%20300%20px).png)\n## CV Screening with OpenAI\n**Made by [Mark Shcherbakov](https://www.linkedin.com/in/marklowcoding/) from community [5minAI](https://www.skool.com/5minai-2861)**\n\nThis workflow is ideal for recruitment agencies, HR professionals, and hiring managers looking to automate the initial screening of CVs. It is especially useful for organizations handling large volumes of applications and seeking to streamline their recruitment process.\n\nThis workflow automates the resume screening process using OpenAI for analysis and Supabase for structured data storage. It provides a matching score, a summary of candidate suitability, and key insights into why the candidate fits (or doesn’t fit) the job. \n\n1. **Retrieve Resume**: The workflow downloads CVs from a direct link (e.g., Supabase storage or Dropbox).\n2. **Extract Data**: Extracts text data from PDF or DOC files for analysis.\n3. **Analyze with OpenAI**: Sends the extracted data and job description to OpenAI to:\n - Generate a matching score.\n - Summarize candidate strengths and weaknesses.\n - Provide actionable insights into their suitability for the job.\n4. **Store Results in Supabase**: Saves the analysis and raw data in a structured format for further processing or integration into other tools.\n"
},
"typeVersion": 1
},
{
"id": "747591cd-76b1-417e-ab9d-0a3935d3db03",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-500,
140
],
"parameters": {
"color": 7,
"width": 330.5152611046425,
"height": 240.6839895136402,
"content": "### ... or watch set up video [8 min]\n[![Youtube Thumbnail](https://cflobdhpqwnoisuctsoc.supabase.co/storage/v1/object/public/my_storage/11.png)](https://youtu.be/TWuI3dOcn0E)\n"
},
"typeVersion": 1
},
{
"id": "051d8cb0-2557-4e35-9045-c769ec5a34f9",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
660,
280
],
"parameters": {
"width": 187.37066290133808,
"height": 80,
"content": "**Replace OpenAI connection**"
},
"typeVersion": 1
},
{
"id": "865f4f69-e13d-49c1-8bb4-9f98facbf75c",
"name": "OpenAI - Analyze CV",
"type": "n8n-nodes-base.httpRequest",
"position": [
700,
400
],
"parameters": {
"url": "=https://api.openai.com/v1/chat/completions",
"method": "POST",
"options": {},
"jsonBody": "={\n \"model\": \"gpt-4o-mini\",\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"{{ $('Set Variables').item.json.prompt }}\"\n },\n {\n \"role\": \"user\",\n \"content\": {{ JSON.stringify(encodeURIComponent($json.text))}}\n }\n ],\n \"response_format\":{ \"type\": \"json_schema\", \"json_schema\": {{ $('Set Variables').item.json.json_schema }}\n\n }\n }",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "SphXAX7rlwRLkiox",
"name": "Test club key"
}
},
"typeVersion": 4.2
},
{
"id": "68b7fc08-506d-4816-9a8f-db7ab89e4589",
"name": "Set Variables",
"type": "n8n-nodes-base.set",
"position": [
160,
400
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "83274f6f-c73e-4d5e-946f-c6dfdf7ed1c4",
"name": "file_url",
"type": "string",
"value": "https://cflobdhpqwnoisuctsoc.supabase.co/storage/v1/object/public/my_storage/software_engineer_resume_example.pdf"
},
{
"id": "6e44f3e5-a0df-4337-9f7e-7cfa91b3cc37",
"name": "job_description",
"type": "string",
"value": "Melange is a venture-backed startup building a brand new search infrastructure for the patent system. Leveraging recent and ongoing advancements in machine learning and natural language processing, we are building systems to conduct patent search faster and more accurately than any human currently can. We are a small team with a friendly, mostly-remote culture\\n\\nAbout the team\\nMelange is currently made up of 9 people. We are remote but headquartered in Brooklyn, NY. We look for people who are curious and earnest.\\n\\nAbout the role\\nJoin the team at Melange, a startup with a focus on revolutionizing patent search through advanced technology. As a software engineer in this role, you will be responsible for developing conversation graphs, integrating grammar processes, and maintaining a robust codebase. The ideal candidate will have experience shipping products, working with cloud platforms, and have familiarity with containerization tools. Additionally, experience with prompting tools, NLP packages, and cybersecurity is a plus.\\n\\nCandidate location - the US. Strong preference if they're in NYC, Boston or SF but open to anywhere else but needs to be rockstar\\n\\nYou will \\n\\n* Ship high-quality products.\\n* Utilize prompting libraries such as Langchain and Langgraph to develop conversation graphs and evaluation flows.\\n* Collaborate with linguists to integrate our in-house grammar and entity mapping processes into an iterable patent search algorithm piloted by AI patent agents.\\n* Steward the codebase, ensuring that it remains robust as it scales.\\n\\n\\nCandidate requirements\\nMinimum requirements a candidate must meet\\nHad ownership over aspects of product development in both small and large organizations at differing points in your career.\\n\\nHave used Langchain, LangGraph, or other prompting tools in production or for personal projects.\\n\\nFamiliarity with NLP packages such as Spacy, Stanza, PyTorch, and/or Tensorflow.\\n\\nShipped a working product to users, either as part of a team or on your own. \\nThis means you have: \\nproficiency with one of AWS, Azure, or Google Cloud, \\nfamiliarity with containerization and orchestration tools like Docker and Kubernetes, and \\nbuilt and maintained CI/CD pipelines.\\n5+ years of experience as a software engineer\\n\\nNice-to-haves\\nWhat could make your candidate stand out\\nExperience with cybersecurity.\\n\\nIdeal companies\\nSuccessful b2b growth stage startups that have a strong emphasis on product and design. Orgs with competent management where talent is dense and protected.\\n\\nRamp, Rippling, Brex, Carta, Toast, Asana, Airtable, Benchling, Figma, Gusto, Stripe, Plaid, Monday.com, Smartsheet, Bill.com, Freshworks, Intercom, Sprout Social, Sisense, InsightSquared, DocuSign, Dropbox, Slack, Trello, Qualtrics, Datadog, HubSpot, Shopify, Zendesk, SurveyMonkey, Squarespace, Mixpanel, Github, Atlassian, Zapier, PagerDuty, Box, Snowflake, Greenhouse, Lever, Pendo, Lucidchart, Asana, New Relic, Kajabi, Veeva Systems, Adyen, Twilio, Workday, ServiceNow, Confluent.\\n"
},
{
"id": "c597c502-9a3c-48e6-a5f5-8a2a8be7282c",
"name": "prompt",
"type": "string",
"value": "You are the recruiter in recruiting agency, you are strict and you pay extra attention on details in a resume. You work with companies and find talents for their jobs. You asses any resume really attentively and critically. If the candidate is a jumper, you notice that and say us. You need to say if the candidate from out base is suitable for this job. Return 4 things: 1. Percentage (10% step) of matching candidate resume with job. 2. Short summary - should use simple language and be short. Provide final decision on candidate based on matching percentage and candidate skills vs job requirements. 3. Summary why this candidate suits this jobs. 4. Summary why this candidate doesn't suit this jobs."
},
{
"id": "1884eed1-9111-4ce1-8d07-ed176611f2d8",
"name": "json_schema",
"type": "string",
"value": "{ \"name\": \"candidate_evaluation\", \"description\": \"Structured data for evaluating a candidate based on experience and fit\", \"strict\": true, \"schema\": { \"type\": \"object\", \"properties\": { \"percentage\": { \"type\": \"integer\", \"description\": \"Overall suitability percentage score for the candidate\" }, \"summary\": { \"type\": \"string\", \"description\": \"A brief summary of the candidate's experience, personality, and any notable strengths or concerns\" }, \"reasons-suit\": { \"type\": \"array\", \"items\": { \"type\": \"object\", \"properties\": { \"name\": { \"type\": \"string\", \"description\": \"Title of the strength or reason for suitability\" }, \"text\": { \"type\": \"string\", \"description\": \"Description of how this experience or skill matches the job requirements\" } }, \"required\": [\"name\", \"text\"], \"additionalProperties\": false }, \"description\": \"List of reasons why the candidate is suitable for the position\" }, \"reasons-notsuit\": { \"type\": \"array\", \"items\": { \"type\": \"object\", \"properties\": { \"name\": { \"type\": \"string\", \"description\": \"Title of the concern or reason for unsuitability\" }, \"text\": { \"type\": \"string\", \"description\": \"Description of how this factor may not align with the job requirements\" } }, \"required\": [\"name\", \"text\"], \"additionalProperties\": false }, \"description\": \"List of reasons why the candidate may not be suitable for the position\" } }, \"required\": [\"percentage\", \"summary\", \"reasons-suit\", \"reasons-notsuit\"], \"additionalProperties\": false } }"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "22dedac7-c44b-430f-b9c7-57d0c55328fa",
"name": "Parsed JSON",
"type": "n8n-nodes-base.set",
"position": [
880,
400
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "83274f6f-c73e-4d5e-946f-c6dfdf7ed1c4",
"name": "json_parsed",
"type": "object",
"value": "={{ JSON.parse($json.choices[0].message.content) }}"
}
]
}
},
"typeVersion": 3.4
}
],
"pinData": {},
"connections": {
"Download File": {
"main": [
[
{
"node": "Extract Document PDF",
"type": "main",
"index": 0
}
]
]
},
"Set Variables": {
"main": [
[
{
"node": "Download File",
"type": "main",
"index": 0
}
]
]
},
"OpenAI - Analyze CV": {
"main": [
[
{
"node": "Parsed JSON",
"type": "main",
"index": 0
}
]
]
},
"Extract Document PDF": {
"main": [
[
{
"node": "OpenAI - Analyze CV",
"type": "main",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "Set Variables",
"type": "main",
"index": 0
}
]
]
}
}
}

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HR_and_Recruitment/CV Screening with OpenAI.txt Normal file
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@@ -0,0 +1,273 @@
{
"meta": {
"instanceId": "6a2a7715680b8313f7cb4676321c5baa46680adfb913072f089f2766f42e43bd"
},
"nodes": [
{
"id": "0f3b39af-2802-462c-ac54-a7bccf5b78c5",
"name": "Extract Document PDF",
"type": "n8n-nodes-base.extractFromFile",
"position": [
520,
400
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1,
"alwaysOutputData": false
},
{
"id": "6f76e3a6-a3be-4f9f-a0db-3f002eafc2ad",
"name": "Download File",
"type": "n8n-nodes-base.httpRequest",
"position": [
340,
400
],
"parameters": {
"url": "={{ $json.file_url }}",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "2c4e0b0f-28c7-48f5-b051-6e909ac878d2",
"name": "When clicking ‘Test workflow’",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-20,
400
],
"parameters": {},
"typeVersion": 1
},
{
"id": "a70d972b-ceb4-4f4d-8737-f0be624d6234",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
120,
280
],
"parameters": {
"width": 187.37066290133808,
"height": 80,
"content": "**Add direct link to CV and Job description**"
},
"typeVersion": 1
},
{
"id": "9fdff1be-14cf-4167-af2d-7c5e60943831",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-800,
140
],
"parameters": {
"color": 7,
"width": 280.2462120317618,
"height": 438.5821431288714,
"content": "### Setup\n\n1. **Download File**: Fetch the CV using its direct URL.\n2. **Extract Data**: Use N8N’s PDF or text extraction nodes to retrieve text from the CV.\n3. **Send to OpenAI**:\n - **URL**: POST to OpenAI’s API for analysis.\n - **Parameters**:\n - Include the extracted CV data and job description.\n - Use JSON Schema to structure the response.\n4. **Save Results**:\n - Store the extracted data and OpenAI's analysis in Supabase for further use."
},
"typeVersion": 1
},
{
"id": "b1ce4a61-270f-480b-a716-6618e6034581",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-800,
-500
],
"parameters": {
"color": 7,
"width": 636.2128494576581,
"height": 598.6675280064023,
"content": "![5min Logo](https://cflobdhpqwnoisuctsoc.supabase.co/storage/v1/object/public/my_storage/Untitled%20(1500%20x%20300%20px).png)\n## CV Screening with OpenAI\n**Made by [Mark Shcherbakov](https://www.linkedin.com/in/marklowcoding/) from community [5minAI](https://www.skool.com/5minai-2861)**\n\nThis workflow is ideal for recruitment agencies, HR professionals, and hiring managers looking to automate the initial screening of CVs. It is especially useful for organizations handling large volumes of applications and seeking to streamline their recruitment process.\n\nThis workflow automates the resume screening process using OpenAI for analysis and Supabase for structured data storage. It provides a matching score, a summary of candidate suitability, and key insights into why the candidate fits (or doesn’t fit) the job. \n\n1. **Retrieve Resume**: The workflow downloads CVs from a direct link (e.g., Supabase storage or Dropbox).\n2. **Extract Data**: Extracts text data from PDF or DOC files for analysis.\n3. **Analyze with OpenAI**: Sends the extracted data and job description to OpenAI to:\n - Generate a matching score.\n - Summarize candidate strengths and weaknesses.\n - Provide actionable insights into their suitability for the job.\n4. **Store Results in Supabase**: Saves the analysis and raw data in a structured format for further processing or integration into other tools.\n"
},
"typeVersion": 1
},
{
"id": "747591cd-76b1-417e-ab9d-0a3935d3db03",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-500,
140
],
"parameters": {
"color": 7,
"width": 330.5152611046425,
"height": 240.6839895136402,
"content": "### ... or watch set up video [8 min]\n[![Youtube Thumbnail](https://cflobdhpqwnoisuctsoc.supabase.co/storage/v1/object/public/my_storage/11.png)](https://youtu.be/TWuI3dOcn0E)\n"
},
"typeVersion": 1
},
{
"id": "051d8cb0-2557-4e35-9045-c769ec5a34f9",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
660,
280
],
"parameters": {
"width": 187.37066290133808,
"height": 80,
"content": "**Replace OpenAI connection**"
},
"typeVersion": 1
},
{
"id": "865f4f69-e13d-49c1-8bb4-9f98facbf75c",
"name": "OpenAI - Analyze CV",
"type": "n8n-nodes-base.httpRequest",
"position": [
700,
400
],
"parameters": {
"url": "=https://api.openai.com/v1/chat/completions",
"method": "POST",
"options": {},
"jsonBody": "={\n \"model\": \"gpt-4o-mini\",\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"{{ $('Set Variables').item.json.prompt }}\"\n },\n {\n \"role\": \"user\",\n \"content\": {{ JSON.stringify(encodeURIComponent($json.text))}}\n }\n ],\n \"response_format\":{ \"type\": \"json_schema\", \"json_schema\": {{ $('Set Variables').item.json.json_schema }}\n\n }\n }",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "SphXAX7rlwRLkiox",
"name": "Test club key"
}
},
"typeVersion": 4.2
},
{
"id": "68b7fc08-506d-4816-9a8f-db7ab89e4589",
"name": "Set Variables",
"type": "n8n-nodes-base.set",
"position": [
160,
400
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "83274f6f-c73e-4d5e-946f-c6dfdf7ed1c4",
"name": "file_url",
"type": "string",
"value": "https://cflobdhpqwnoisuctsoc.supabase.co/storage/v1/object/public/my_storage/software_engineer_resume_example.pdf"
},
{
"id": "6e44f3e5-a0df-4337-9f7e-7cfa91b3cc37",
"name": "job_description",
"type": "string",
"value": "Melange is a venture-backed startup building a brand new search infrastructure for the patent system. Leveraging recent and ongoing advancements in machine learning and natural language processing, we are building systems to conduct patent search faster and more accurately than any human currently can. We are a small team with a friendly, mostly-remote culture\\n\\nAbout the team\\nMelange is currently made up of 9 people. We are remote but headquartered in Brooklyn, NY. We look for people who are curious and earnest.\\n\\nAbout the role\\nJoin the team at Melange, a startup with a focus on revolutionizing patent search through advanced technology. As a software engineer in this role, you will be responsible for developing conversation graphs, integrating grammar processes, and maintaining a robust codebase. The ideal candidate will have experience shipping products, working with cloud platforms, and have familiarity with containerization tools. Additionally, experience with prompting tools, NLP packages, and cybersecurity is a plus.\\n\\nCandidate location - the US. Strong preference if they're in NYC, Boston or SF but open to anywhere else but needs to be rockstar\\n\\nYou will \\n\\n* Ship high-quality products.\\n* Utilize prompting libraries such as Langchain and Langgraph to develop conversation graphs and evaluation flows.\\n* Collaborate with linguists to integrate our in-house grammar and entity mapping processes into an iterable patent search algorithm piloted by AI patent agents.\\n* Steward the codebase, ensuring that it remains robust as it scales.\\n\\n\\nCandidate requirements\\nMinimum requirements a candidate must meet\\nHad ownership over aspects of product development in both small and large organizations at differing points in your career.\\n\\nHave used Langchain, LangGraph, or other prompting tools in production or for personal projects.\\n\\nFamiliarity with NLP packages such as Spacy, Stanza, PyTorch, and/or Tensorflow.\\n\\nShipped a working product to users, either as part of a team or on your own. \\nThis means you have: \\nproficiency with one of AWS, Azure, or Google Cloud, \\nfamiliarity with containerization and orchestration tools like Docker and Kubernetes, and \\nbuilt and maintained CI/CD pipelines.\\n5+ years of experience as a software engineer\\n\\nNice-to-haves\\nWhat could make your candidate stand out\\nExperience with cybersecurity.\\n\\nIdeal companies\\nSuccessful b2b growth stage startups that have a strong emphasis on product and design. Orgs with competent management where talent is dense and protected.\\n\\nRamp, Rippling, Brex, Carta, Toast, Asana, Airtable, Benchling, Figma, Gusto, Stripe, Plaid, Monday.com, Smartsheet, Bill.com, Freshworks, Intercom, Sprout Social, Sisense, InsightSquared, DocuSign, Dropbox, Slack, Trello, Qualtrics, Datadog, HubSpot, Shopify, Zendesk, SurveyMonkey, Squarespace, Mixpanel, Github, Atlassian, Zapier, PagerDuty, Box, Snowflake, Greenhouse, Lever, Pendo, Lucidchart, Asana, New Relic, Kajabi, Veeva Systems, Adyen, Twilio, Workday, ServiceNow, Confluent.\\n"
},
{
"id": "c597c502-9a3c-48e6-a5f5-8a2a8be7282c",
"name": "prompt",
"type": "string",
"value": "You are the recruiter in recruiting agency, you are strict and you pay extra attention on details in a resume. You work with companies and find talents for their jobs. You asses any resume really attentively and critically. If the candidate is a jumper, you notice that and say us. You need to say if the candidate from out base is suitable for this job. Return 4 things: 1. Percentage (10% step) of matching candidate resume with job. 2. Short summary - should use simple language and be short. Provide final decision on candidate based on matching percentage and candidate skills vs job requirements. 3. Summary why this candidate suits this jobs. 4. Summary why this candidate doesn't suit this jobs."
},
{
"id": "1884eed1-9111-4ce1-8d07-ed176611f2d8",
"name": "json_schema",
"type": "string",
"value": "{ \"name\": \"candidate_evaluation\", \"description\": \"Structured data for evaluating a candidate based on experience and fit\", \"strict\": true, \"schema\": { \"type\": \"object\", \"properties\": { \"percentage\": { \"type\": \"integer\", \"description\": \"Overall suitability percentage score for the candidate\" }, \"summary\": { \"type\": \"string\", \"description\": \"A brief summary of the candidate's experience, personality, and any notable strengths or concerns\" }, \"reasons-suit\": { \"type\": \"array\", \"items\": { \"type\": \"object\", \"properties\": { \"name\": { \"type\": \"string\", \"description\": \"Title of the strength or reason for suitability\" }, \"text\": { \"type\": \"string\", \"description\": \"Description of how this experience or skill matches the job requirements\" } }, \"required\": [\"name\", \"text\"], \"additionalProperties\": false }, \"description\": \"List of reasons why the candidate is suitable for the position\" }, \"reasons-notsuit\": { \"type\": \"array\", \"items\": { \"type\": \"object\", \"properties\": { \"name\": { \"type\": \"string\", \"description\": \"Title of the concern or reason for unsuitability\" }, \"text\": { \"type\": \"string\", \"description\": \"Description of how this factor may not align with the job requirements\" } }, \"required\": [\"name\", \"text\"], \"additionalProperties\": false }, \"description\": \"List of reasons why the candidate may not be suitable for the position\" } }, \"required\": [\"percentage\", \"summary\", \"reasons-suit\", \"reasons-notsuit\"], \"additionalProperties\": false } }"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "22dedac7-c44b-430f-b9c7-57d0c55328fa",
"name": "Parsed JSON",
"type": "n8n-nodes-base.set",
"position": [
880,
400
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "83274f6f-c73e-4d5e-946f-c6dfdf7ed1c4",
"name": "json_parsed",
"type": "object",
"value": "={{ JSON.parse($json.choices[0].message.content) }}"
}
]
}
},
"typeVersion": 3.4
}
],
"pinData": {},
"connections": {
"Download File": {
"main": [
[
{
"node": "Extract Document PDF",
"type": "main",
"index": 0
}
]
]
},
"Set Variables": {
"main": [
[
{
"node": "Download File",
"type": "main",
"index": 0
}
]
]
},
"OpenAI - Analyze CV": {
"main": [
[
{
"node": "Parsed JSON",
"type": "main",
"index": 0
}
]
]
},
"Extract Document PDF": {
"main": [
[
{
"node": "OpenAI - Analyze CV",
"type": "main",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "Set Variables",
"type": "main",
"index": 0
}
]
]
}
}
}

760
HR_and_Recruitment/HR & IT Helpdesk Chatbot with Audio Transcription.json
View File

@@ -1,760 +0,0 @@
{
"id": "zmgSshZ5xESr3ozl",
"meta": {
"instanceId": "1fedaf0aa3a5d200ffa1bbc98554b56cac895dd5d001907cb6f1c7a3c0a78215",
"templateCredsSetupCompleted": true
},
"name": "HR & IT Helpdesk Chatbot with Audio Transcription",
"tags": [],
"nodes": [
{
"id": "c6cb921e-97ac-48f6-9d79-133993dd6ef7",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-300,
-280
],
"parameters": {
"color": 7,
"width": 780,
"height": 460,
"content": "## 1. Download & Extract Internal Policy Documents\n[Read more about the HTTP Request Tool](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest)\n\nBegin by importing the PDF documents that contain your internal policies and FAQs—these will become the knowledge base for your Internal Helpdesk Assistant. For example, you can store a company handbook or IT/HR policy PDFs on a shared drive or cloud storage and reference a direct download link here.\n\nIn this demonstration, we'll use the **HTTP Request node** to fetch the PDF file from a given URL and then parse its text contents using the **Extract from File node**. Once extracted, these text chunks will be used to build the vector store that underpins your helpdesk chatbot’s responses.\n\n[Example Employee Handbook with Policies](https://s3.amazonaws.com/scschoolfiles/656/employee_handbook_print_1.pdf)"
},
"typeVersion": 1
},
{
"id": "450a254c-eec3-41ea-a11d-eb87b62ee4f4",
"name": "When clicking ‘Test workflow’",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-80,
20
],
"parameters": {},
"typeVersion": 1
},
{
"id": "0972f31c-1f62-430c-8beb-bef8976cd0eb",
"name": "HTTP Request",
"type": "n8n-nodes-base.httpRequest",
"position": [
100,
20
],
"parameters": {
"url": "https://s3.amazonaws.com/scschoolfiles/656/employee_handbook_print_1.pdf",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "bf523255-39f5-410a-beb7-6331139c5f9b",
"name": "Extract from File",
"type": "n8n-nodes-base.extractFromFile",
"position": [
280,
20
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "88901c7c-e747-44c7-87d9-e14ac99a93db",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
540,
-280
],
"parameters": {
"color": 7,
"width": 780,
"height": 1020,
"content": "## 2. Create Internal Policy Vector Store\n[Read more about the In-Memory Vector Store](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreinmemory/)\n\nVector stores power the retrieval process by matching a user's natural language questions to relevant chunks of text. We'll transform your extracted internal policy text into vector embeddings and store them in a database-like structure.\n\nWe will be using PostgreSQL which has production ready vector support.\n\n**How it works** \n1. The text extracted in Step 1 is split into manageable segments (chunks). \n2. An embedding model transforms these segments into numerical vectors. \n3. These vectors, along with metadata, are stored in PostgreSQL. \n4. When users ask a question, their query is embedded and matched to the most relevant vectors, improving the accuracy of the chatbot's response."
},
"typeVersion": 1
},
{
"id": "8d6472ab-dcff-4d24-a320-109787bce52a",
"name": "Create HR Policies",
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
"position": [
620,
100
],
"parameters": {
"mode": "insert",
"options": {}
},
"credentials": {
"postgres": {
"id": "wQK6JXyS5y1icHw3",
"name": "Postgres account"
}
},
"typeVersion": 1
},
{
"id": "e669b3fb-aaf1-4df8-855b-d3142215b308",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
600,
320
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "e25418af-65bb-4628-9b26-ec59cae7b2b4",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
760,
340
],
"parameters": {
"options": {},
"jsonData": "={{ $('Extract from File').item.json.text }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "a4538deb-8406-4a5b-9b1e-4e2f859943c8",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
860,
560
],
"parameters": {
"options": {},
"chunkSize": 2000
},
"typeVersion": 1
},
{
"id": "7ee0e861-1576-4b0c-b2ef-3fc023371907",
"name": "Telegram Trigger",
"type": "n8n-nodes-base.telegramTrigger",
"position": [
1420,
240
],
"webhookId": "65f501de-3c14-4089-9b9d-8956676bebf3",
"parameters": {
"updates": [
"message"
],
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.1
},
{
"id": "bcf1e82e-0e83-4783-a59f-857a6d1528b6",
"name": "Verify Message Type",
"type": "n8n-nodes-base.switch",
"position": [
1620,
240
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "Text",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "array",
"operation": "contains",
"rightType": "any"
},
"leftValue": "={{ $json.message.keys()}}",
"rightValue": "text"
}
]
},
"renameOutput": true
},
{
"outputKey": "Audio",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "d16eb899-cccb-41b6-921e-172c525ff92c",
"operator": {
"type": "array",
"operation": "contains",
"rightType": "any"
},
"leftValue": "={{ $json.message.keys()}}",
"rightValue": "voice"
}
]
},
"renameOutput": true
}
]
},
"options": {
"fallbackOutput": "extra"
}
},
"typeVersion": 3.2,
"alwaysOutputData": false
},
{
"id": "d403f864-c781-48fc-a62b-de0c8bfedf06",
"name": "OpenAI",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
2340,
380
],
"parameters": {
"options": {},
"resource": "audio",
"operation": "transcribe",
"binaryPropertyName": "=data"
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.8
},
{
"id": "5b17c8f1-4bee-4f2a-abcb-74fe72d4cdfd",
"name": "Telegram1",
"type": "n8n-nodes-base.telegram",
"position": [
2120,
380
],
"parameters": {
"fileId": "={{ $json.message.voice.file_id }}",
"resource": "file"
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.2
},
{
"id": "cc6862cb-acfc-465b-b142-dd5fdc12fb13",
"name": "Unsupported Message Type",
"type": "n8n-nodes-base.telegram",
"position": [
2200,
560
],
"parameters": {
"text": "I'm not able to process this message type.",
"chatId": "={{ $json.message.chat.id }}",
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.2
},
{
"id": "8b97aaa1-ea0d-4b11-89c9-9ac6376c0760",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
2860,
400
],
"parameters": {
"text": "={{ $json.text }}",
"options": {
"systemMessage": "You are a helpful assistant for HR and employee policies"
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "e0d5416e-a799-46a2-83e3-fa6919ec0e36",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
2800,
840
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.1
},
{
"id": "9149f41d-692e-49bc-ad70-848492d2c345",
"name": "Postgres Chat Memory",
"type": "@n8n/n8n-nodes-langchain.memoryPostgresChat",
"position": [
3060,
840
],
"parameters": {
"sessionKey": "={{ $('Telegram Trigger').item.json.message.chat.id }}",
"sessionIdType": "customKey"
},
"credentials": {
"postgres": {
"id": "wQK6JXyS5y1icHw3",
"name": "Postgres account"
}
},
"typeVersion": 1.3
},
{
"id": "a1f68887-da44-4bff-86fc-f607a5bd0ab6",
"name": "Answer questions with a vector store",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
3360,
580
],
"parameters": {
"name": "hr_employee_policies",
"description": "data for HR and employee policies"
},
"typeVersion": 1
},
{
"id": "76220fe4-2448-4b32-92d8-68c564cc702d",
"name": "Postgres PGVector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
"position": [
3220,
780
],
"parameters": {
"options": {}
},
"credentials": {
"postgres": {
"id": "wQK6JXyS5y1icHw3",
"name": "Postgres account"
}
},
"typeVersion": 1
},
{
"id": "055fd294-7483-45ce-b58a-c90075199f5f",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
3640,
780
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.1
},
{
"id": "cc13eac7-8163-45bf-8d8a-9cf72659e357",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
3300,
920
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "d46e415e-75ff-46b8-b382-cdcda216b1ed",
"name": "Telegram",
"type": "n8n-nodes-base.telegram",
"position": [
4200,
420
],
"parameters": {
"text": "={{ $json.output }}",
"chatId": "={{ $('Telegram Trigger').first().json.message.chat.id }}",
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.2
},
{
"id": "ddf623a1-0a5e-48c9-b897-6a339895a891",
"name": "Edit Fields",
"type": "n8n-nodes-base.set",
"position": [
2120,
200
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "403b336f-87ce-4bef-a5f2-1640425f8198",
"name": "text",
"type": "string",
"value": "={{ $json.message.text }}"
}
]
},
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "4ae84e17-cfc1-425c-930d-949da7308b78",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1340,
-280
],
"parameters": {
"color": 4,
"width": 1300,
"height": 1020,
"content": "## 3. Handling Messages with Fallback Support\n\nThis workflow processes Telegram messages to handle **text** and **voice** inputs, with a fallback for unsupported message types. Here’s how it works:\n\n1. **Trigger Node**:\n - The workflow starts with a Telegram trigger that listens for incoming messages.\n\n2. **Message Type Check**:\n - The workflow verifies the type of message received:\n - **Text Message**: If the message contains `$json.message.text`, it is sent directly to the agent.\n - **Voice Message**: If the message contains `$json.message.voice`, the audio is transcribed into text using a transcription service, and the result is sent to the agent.\n\n3. **Fallback Path**:\n - If the message is neither text nor voice, a fallback response is returned:\n `\"Sorry, I couldn’t process your message. Please try again.\"`\n\n4. **Unified Output**:\n - Both text messages and transcribed voice messages are converted into the same format before sending to the agent, ensuring consistency in handling.\n"
},
"typeVersion": 1
},
{
"id": "86ad4e08-ef2d-405e-8861-bff38e1db651",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
220,
220
],
"parameters": {
"width": 260,
"height": 80,
"content": "The setup needs to be run at the start or when data is changed"
},
"typeVersion": 1
},
{
"id": "b05c4437-00fb-40f6-87fa-8dc564b16005",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
2680,
-280
],
"parameters": {
"color": 4,
"width": 1180,
"height": 1420,
"content": "## 4. HR & IT AI Agent Provides Helpdesk Support \nn8n's AI agents allow you to create intelligent and interactive workflows that can access and retrieve data from internal knowledgebases. In this workflow, the AI agent is configured to provide answers for HR and IT queries by performing Retrieval-Augmented Generation (RAG) on internal documents.\n\n### How It Works:\n- **Internal Knowledgebase Access**: A **Vector store tool** is used to connect the agent to the HR & IT knowledgebase built earlier in the workflow. This enables the agent to fetch accurate and specific answers for employee queries.\n- **Chat Memory**: A **Chat memory subnode** tracks the conversation, allowing the agent to maintain context across multiple queries from the same user, creating a personalized and cohesive experience.\n- **Dynamic Query Responses**: Whether employees ask about policies, leave balances, or technical troubleshooting, the agent retrieves relevant data from the vector store and crafts a natural language response.\n\nBy integrating the AI agent with a vector store and chat memory, this workflow empowers your HR & IT helpdesk chatbot to provide quick, accurate, and conversational support to employees. \n\nPostgrSQL is used for all steps to simplify development in production."
},
"typeVersion": 1
},
{
"id": "b266ca42-de62-4341-9aff-33ee0ac68045",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
3900,
300
],
"parameters": {
"color": 4,
"width": 540,
"height": 280,
"content": "## 5. Send Message\n\nThe simplest and most important part :)"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "7b1d11ca-9b56-4c5f-9189-26d536c24b76",
"connections": {
"OpenAI": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "Telegram",
"type": "main",
"index": 0
}
]
]
},
"Telegram1": {
"main": [
[
{
"node": "OpenAI",
"type": "main",
"index": 0
}
]
]
},
"Edit Fields": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"HTTP Request": {
"main": [
[
{
"node": "Extract from File",
"type": "main",
"index": 0
}
]
]
},
"Telegram Trigger": {
"main": [
[
{
"node": "Verify Message Type",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Create HR Policies",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Extract from File": {
"main": [
[
{
"node": "Create HR Policies",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Postgres PGVector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model1": {
"ai_languageModel": [
[
{
"node": "Answer questions with a vector store",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Create HR Policies",
"type": "ai_document",
"index": 0
}
]
]
},
"Verify Message Type": {
"main": [
[
{
"node": "Edit Fields",
"type": "main",
"index": 0
}
],
[
{
"node": "Telegram1",
"type": "main",
"index": 0
}
],
[
{
"node": "Unsupported Message Type",
"type": "main",
"index": 0
}
]
]
},
"Postgres Chat Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Postgres PGVector Store": {
"ai_vectorStore": [
[
{
"node": "Answer questions with a vector store",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "HTTP Request",
"type": "main",
"index": 0
}
]
]
},
"Answer questions with a vector store": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
}
}

760
HR_and_Recruitment/HR & IT Helpdesk Chatbot with Audio Transcription.txt Normal file
View File

@@ -0,0 +1,760 @@
{
"id": "zmgSshZ5xESr3ozl",
"meta": {
"instanceId": "1fedaf0aa3a5d200ffa1bbc98554b56cac895dd5d001907cb6f1c7a3c0a78215",
"templateCredsSetupCompleted": true
},
"name": "HR & IT Helpdesk Chatbot with Audio Transcription",
"tags": [],
"nodes": [
{
"id": "c6cb921e-97ac-48f6-9d79-133993dd6ef7",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-300,
-280
],
"parameters": {
"color": 7,
"width": 780,
"height": 460,
"content": "## 1. Download & Extract Internal Policy Documents\n[Read more about the HTTP Request Tool](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest)\n\nBegin by importing the PDF documents that contain your internal policies and FAQs—these will become the knowledge base for your Internal Helpdesk Assistant. For example, you can store a company handbook or IT/HR policy PDFs on a shared drive or cloud storage and reference a direct download link here.\n\nIn this demonstration, we'll use the **HTTP Request node** to fetch the PDF file from a given URL and then parse its text contents using the **Extract from File node**. Once extracted, these text chunks will be used to build the vector store that underpins your helpdesk chatbot’s responses.\n\n[Example Employee Handbook with Policies](https://s3.amazonaws.com/scschoolfiles/656/employee_handbook_print_1.pdf)"
},
"typeVersion": 1
},
{
"id": "450a254c-eec3-41ea-a11d-eb87b62ee4f4",
"name": "When clicking ‘Test workflow’",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-80,
20
],
"parameters": {},
"typeVersion": 1
},
{
"id": "0972f31c-1f62-430c-8beb-bef8976cd0eb",
"name": "HTTP Request",
"type": "n8n-nodes-base.httpRequest",
"position": [
100,
20
],
"parameters": {
"url": "https://s3.amazonaws.com/scschoolfiles/656/employee_handbook_print_1.pdf",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "bf523255-39f5-410a-beb7-6331139c5f9b",
"name": "Extract from File",
"type": "n8n-nodes-base.extractFromFile",
"position": [
280,
20
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "88901c7c-e747-44c7-87d9-e14ac99a93db",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
540,
-280
],
"parameters": {
"color": 7,
"width": 780,
"height": 1020,
"content": "## 2. Create Internal Policy Vector Store\n[Read more about the In-Memory Vector Store](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreinmemory/)\n\nVector stores power the retrieval process by matching a user's natural language questions to relevant chunks of text. We'll transform your extracted internal policy text into vector embeddings and store them in a database-like structure.\n\nWe will be using PostgreSQL which has production ready vector support.\n\n**How it works** \n1. The text extracted in Step 1 is split into manageable segments (chunks). \n2. An embedding model transforms these segments into numerical vectors. \n3. These vectors, along with metadata, are stored in PostgreSQL. \n4. When users ask a question, their query is embedded and matched to the most relevant vectors, improving the accuracy of the chatbot's response."
},
"typeVersion": 1
},
{
"id": "8d6472ab-dcff-4d24-a320-109787bce52a",
"name": "Create HR Policies",
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
"position": [
620,
100
],
"parameters": {
"mode": "insert",
"options": {}
},
"credentials": {
"postgres": {
"id": "wQK6JXyS5y1icHw3",
"name": "Postgres account"
}
},
"typeVersion": 1
},
{
"id": "e669b3fb-aaf1-4df8-855b-d3142215b308",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
600,
320
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "e25418af-65bb-4628-9b26-ec59cae7b2b4",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
760,
340
],
"parameters": {
"options": {},
"jsonData": "={{ $('Extract from File').item.json.text }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "a4538deb-8406-4a5b-9b1e-4e2f859943c8",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
860,
560
],
"parameters": {
"options": {},
"chunkSize": 2000
},
"typeVersion": 1
},
{
"id": "7ee0e861-1576-4b0c-b2ef-3fc023371907",
"name": "Telegram Trigger",
"type": "n8n-nodes-base.telegramTrigger",
"position": [
1420,
240
],
"webhookId": "65f501de-3c14-4089-9b9d-8956676bebf3",
"parameters": {
"updates": [
"message"
],
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.1
},
{
"id": "bcf1e82e-0e83-4783-a59f-857a6d1528b6",
"name": "Verify Message Type",
"type": "n8n-nodes-base.switch",
"position": [
1620,
240
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "Text",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "array",
"operation": "contains",
"rightType": "any"
},
"leftValue": "={{ $json.message.keys()}}",
"rightValue": "text"
}
]
},
"renameOutput": true
},
{
"outputKey": "Audio",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "d16eb899-cccb-41b6-921e-172c525ff92c",
"operator": {
"type": "array",
"operation": "contains",
"rightType": "any"
},
"leftValue": "={{ $json.message.keys()}}",
"rightValue": "voice"
}
]
},
"renameOutput": true
}
]
},
"options": {
"fallbackOutput": "extra"
}
},
"typeVersion": 3.2,
"alwaysOutputData": false
},
{
"id": "d403f864-c781-48fc-a62b-de0c8bfedf06",
"name": "OpenAI",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
2340,
380
],
"parameters": {
"options": {},
"resource": "audio",
"operation": "transcribe",
"binaryPropertyName": "=data"
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.8
},
{
"id": "5b17c8f1-4bee-4f2a-abcb-74fe72d4cdfd",
"name": "Telegram1",
"type": "n8n-nodes-base.telegram",
"position": [
2120,
380
],
"parameters": {
"fileId": "={{ $json.message.voice.file_id }}",
"resource": "file"
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.2
},
{
"id": "cc6862cb-acfc-465b-b142-dd5fdc12fb13",
"name": "Unsupported Message Type",
"type": "n8n-nodes-base.telegram",
"position": [
2200,
560
],
"parameters": {
"text": "I'm not able to process this message type.",
"chatId": "={{ $json.message.chat.id }}",
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.2
},
{
"id": "8b97aaa1-ea0d-4b11-89c9-9ac6376c0760",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
2860,
400
],
"parameters": {
"text": "={{ $json.text }}",
"options": {
"systemMessage": "You are a helpful assistant for HR and employee policies"
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "e0d5416e-a799-46a2-83e3-fa6919ec0e36",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
2800,
840
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.1
},
{
"id": "9149f41d-692e-49bc-ad70-848492d2c345",
"name": "Postgres Chat Memory",
"type": "@n8n/n8n-nodes-langchain.memoryPostgresChat",
"position": [
3060,
840
],
"parameters": {
"sessionKey": "={{ $('Telegram Trigger').item.json.message.chat.id }}",
"sessionIdType": "customKey"
},
"credentials": {
"postgres": {
"id": "wQK6JXyS5y1icHw3",
"name": "Postgres account"
}
},
"typeVersion": 1.3
},
{
"id": "a1f68887-da44-4bff-86fc-f607a5bd0ab6",
"name": "Answer questions with a vector store",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
3360,
580
],
"parameters": {
"name": "hr_employee_policies",
"description": "data for HR and employee policies"
},
"typeVersion": 1
},
{
"id": "76220fe4-2448-4b32-92d8-68c564cc702d",
"name": "Postgres PGVector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
"position": [
3220,
780
],
"parameters": {
"options": {}
},
"credentials": {
"postgres": {
"id": "wQK6JXyS5y1icHw3",
"name": "Postgres account"
}
},
"typeVersion": 1
},
{
"id": "055fd294-7483-45ce-b58a-c90075199f5f",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
3640,
780
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.1
},
{
"id": "cc13eac7-8163-45bf-8d8a-9cf72659e357",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
3300,
920
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "J2D6m1evHLUJOMhO",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "d46e415e-75ff-46b8-b382-cdcda216b1ed",
"name": "Telegram",
"type": "n8n-nodes-base.telegram",
"position": [
4200,
420
],
"parameters": {
"text": "={{ $json.output }}",
"chatId": "={{ $('Telegram Trigger').first().json.message.chat.id }}",
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "jSdrxiRKb8yfG6Ty",
"name": "Telegram account"
}
},
"typeVersion": 1.2
},
{
"id": "ddf623a1-0a5e-48c9-b897-6a339895a891",
"name": "Edit Fields",
"type": "n8n-nodes-base.set",
"position": [
2120,
200
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "403b336f-87ce-4bef-a5f2-1640425f8198",
"name": "text",
"type": "string",
"value": "={{ $json.message.text }}"
}
]
},
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "4ae84e17-cfc1-425c-930d-949da7308b78",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1340,
-280
],
"parameters": {
"color": 4,
"width": 1300,
"height": 1020,
"content": "## 3. Handling Messages with Fallback Support\n\nThis workflow processes Telegram messages to handle **text** and **voice** inputs, with a fallback for unsupported message types. Here’s how it works:\n\n1. **Trigger Node**:\n - The workflow starts with a Telegram trigger that listens for incoming messages.\n\n2. **Message Type Check**:\n - The workflow verifies the type of message received:\n - **Text Message**: If the message contains `$json.message.text`, it is sent directly to the agent.\n - **Voice Message**: If the message contains `$json.message.voice`, the audio is transcribed into text using a transcription service, and the result is sent to the agent.\n\n3. **Fallback Path**:\n - If the message is neither text nor voice, a fallback response is returned:\n `\"Sorry, I couldn’t process your message. Please try again.\"`\n\n4. **Unified Output**:\n - Both text messages and transcribed voice messages are converted into the same format before sending to the agent, ensuring consistency in handling.\n"
},
"typeVersion": 1
},
{
"id": "86ad4e08-ef2d-405e-8861-bff38e1db651",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
220,
220
],
"parameters": {
"width": 260,
"height": 80,
"content": "The setup needs to be run at the start or when data is changed"
},
"typeVersion": 1
},
{
"id": "b05c4437-00fb-40f6-87fa-8dc564b16005",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
2680,
-280
],
"parameters": {
"color": 4,
"width": 1180,
"height": 1420,
"content": "## 4. HR & IT AI Agent Provides Helpdesk Support \nn8n's AI agents allow you to create intelligent and interactive workflows that can access and retrieve data from internal knowledgebases. In this workflow, the AI agent is configured to provide answers for HR and IT queries by performing Retrieval-Augmented Generation (RAG) on internal documents.\n\n### How It Works:\n- **Internal Knowledgebase Access**: A **Vector store tool** is used to connect the agent to the HR & IT knowledgebase built earlier in the workflow. This enables the agent to fetch accurate and specific answers for employee queries.\n- **Chat Memory**: A **Chat memory subnode** tracks the conversation, allowing the agent to maintain context across multiple queries from the same user, creating a personalized and cohesive experience.\n- **Dynamic Query Responses**: Whether employees ask about policies, leave balances, or technical troubleshooting, the agent retrieves relevant data from the vector store and crafts a natural language response.\n\nBy integrating the AI agent with a vector store and chat memory, this workflow empowers your HR & IT helpdesk chatbot to provide quick, accurate, and conversational support to employees. \n\nPostgrSQL is used for all steps to simplify development in production."
},
"typeVersion": 1
},
{
"id": "b266ca42-de62-4341-9aff-33ee0ac68045",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
3900,
300
],
"parameters": {
"color": 4,
"width": 540,
"height": 280,
"content": "## 5. Send Message\n\nThe simplest and most important part :)"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "7b1d11ca-9b56-4c5f-9189-26d536c24b76",
"connections": {
"OpenAI": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "Telegram",
"type": "main",
"index": 0
}
]
]
},
"Telegram1": {
"main": [
[
{
"node": "OpenAI",
"type": "main",
"index": 0
}
]
]
},
"Edit Fields": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"HTTP Request": {
"main": [
[
{
"node": "Extract from File",
"type": "main",
"index": 0
}
]
]
},
"Telegram Trigger": {
"main": [
[
{
"node": "Verify Message Type",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Create HR Policies",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Extract from File": {
"main": [
[
{
"node": "Create HR Policies",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Postgres PGVector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model1": {
"ai_languageModel": [
[
{
"node": "Answer questions with a vector store",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Create HR Policies",
"type": "ai_document",
"index": 0
}
]
]
},
"Verify Message Type": {
"main": [
[
{
"node": "Edit Fields",
"type": "main",
"index": 0
}
],
[
{
"node": "Telegram1",
"type": "main",
"index": 0
}
],
[
{
"node": "Unsupported Message Type",
"type": "main",
"index": 0
}
]
]
},
"Postgres Chat Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Postgres PGVector Store": {
"ai_vectorStore": [
[
{
"node": "Answer questions with a vector store",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "HTTP Request",
"type": "main",
"index": 0
}
]
]
},
"Answer questions with a vector store": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
}
}

3069
HR_and_Recruitment/HR Job Posting and Evaluation with AI.json
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3069
HR_and_Recruitment/HR Job Posting and Evaluation with AI.txt Normal file
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