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Chatbot działu pomocy technicznej HR i IT z transkrypcją audio

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Inteligentny chatbot wspierający pracowników

Inteligentny chatbot, który pomaga pracownikom, odpowiadając na typowe pytania z zakresu HR lub IT, obsługując zarówno wiadomości tekstowe, jak i głosowe. Unikalna funkcja umożliwia pracownikom wygodne zadawanie pytań za pomocą wiadomości głosowych, które są transkrybowane i przetwarzane jak zapytania tekstowe.

Jak to działa

Proces działania automatyzacji składa się z kilku kluczowych etapów:

  • Przechwytywanie wiadomości: Gdy pracownik wysyła wiadomość do chatbota w WhatsApp lub Telegram (tekst lub audio), chatbot przechwytuje wprowadzone dane.
  • Transkrypcja audio: W przypadku wiadomości głosowych chatbot transkrybuje treść na tekst przy użyciu usługi transkrypcji opartej na sztucznej inteligencji (np. Whisper, Google Cloud Speech-to-Text).
  • Przetwarzanie zapytania:
    • Transkrybowany tekst (lub bezpośrednio wprowadzony tekst) jest wysyłany do usługi AI (np. OpenAI) w celu wygenerowania osadzeń.
    • Osadzenia te są wykorzystywane do przeszukiwania bazy danych wektorowych (np. Supabase lub Qdrant) zawierającej wewnętrzną dokumentację HR i IT firmy.
    • Najbardziej odpowiednie dane są pobierane i wysyłane z powrotem do usługi AI w celu skomponowania zwięzłej i pomocnej odpowiedzi.
  • Dostarczanie odpowiedzi: Chatbot wysyła ostateczną odpowiedź z powrotem do pracownika, niezależnie od tego, czy wprowadzono tekst, czy audio.

Przykłady zastosowań

Automatyzacja ta może być wykorzystywana w różnych scenariuszach, usprawniając komunikację i dostęp do informacji w firmie. Oto kilka potencjalnych zastosowań:

  • Odpowiedzi na pytania dotyczące urlopów i dni wolnych.
  • Rozwiązywanie problemów technicznych związanych z IT.
  • Udostępnianie informacji o benefitach pracowniczych.
  • Pomoc w procesach onboardingu nowych pracowników.
  • Odpowiedzi na pytania dotyczące polityki firmy.
  • Wsparcie w zgłaszaniu problemów z sprzętem lub oprogramowaniem.
  • Automatyczne kierowanie złożonych zapytań do odpowiednich działów.

Kroki konfiguracji

Szacowany czas konfiguracji: 20–25 minut.

Wymagania wstępne:

  • Utwórz konto u dostawcy AI (np. OpenAI).
  • Połącz dane logowania WhatsApp lub Telegram w n8n.
  • Skonfiguruj usługę transkrypcji (np. Whisper lub Google Cloud Speech-to-Text).
  • Skonfiguruj bazę danych wektorowych (np. Supabase lub Qdrant) i dodaj wewnętrzną dokumentację HR i IT.
  • Zaimportuj szablon przepływu pracy do n8n i zaktualizuj zmienne środowiskowe dla swoich poświadczeń.

   Skopiuj kod szablonu   
{"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 Documentsn[Read more about the HTTP Request Tool](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest)nnBegin 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.nnIn 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.nn[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 Storen[Read more about the In-Memory Vector Store](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreinmemory/)nnVector 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.nnWe will be using PostgreSQL which has production ready vector support.nn**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":"={{ $json.message.text }}"}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"4ae84e17-cfc1-425c-930d-949da7308b78","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[1340,-280],"parameters":{"color":4,"width":1300,"height":1020,"content":"## 3. Handling Messages with Fallback SupportnnThis workflow processes Telegram messages to handle **text** and **voice** inputs, with a fallback for unsupported message types. Here’s how it works:nn1. **Trigger Node**:n - The workflow starts with a Telegram trigger that listens for incoming messages.nn2. **Message Type Check**:n - The workflow verifies the type of message received:n - **Text Message**: If the message contains `$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.nn3. **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."`nn4. **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.nn### 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.nnBy 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. nnPostgrSQL 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 MessagennThe 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}]]}}}
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