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AI chatbot for Max Messenger with voice recognition (GigaChat +SaluteSpeech)

KonstantinKonstantin
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2/3/2026
AssistantOpenAIData ExtractionUtilityAPI
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Name: AI Chatbot for Max Messenger with Voice Recognition (GigaChat + Sber)

Description:

How it works

This workflow powers an intelligent, conversational AI bot for Max messenger that can understand and respond to both text and voice messages. The bot uses GigaChat AI with built-in memory, allowing it to remember the conversation history for each unique user and answer follow-up questions. Voice messages are transcribed using Sber SmartSpeech. It's a complete solution for creating an engaging, automated assistant within your Max bot, using Russian AI services.

Step-by-step

  • Max Trigger: The workflow starts when the Max Trigger node receives a new message sent to your Max bot.
  • Access Control: The Check User node verifies the sender's user ID against an allowed list. This prevents unauthorized users from accessing your bot.
  • Access Denied Response: If the user is not authorized, the Access Denied node sends a polite rejection message.
  • Message Type Routing: The Text/Attachment (Switch) node checks if the message contains plain text or has attachments (voice, photo, file).
  • Attachment Processing: If an attachment is detected, the Download Attachment (HTTP Request) node retrieves it, and the Attachment Router (Switch) node determines its type (voice, photo, or file).
  • Voice Transcription: For voice messages, the workflow gets a Sber access token via Get Access Token (HTTP Request), merges it with the audio file, and sends it to Get Response (HTTP Request) which uses Sber SmartSpeech API to transcribe the audio to text.
  • Input Unification: The Voice to Prompt node converts transcribed text into a prompt, while Text to Prompt does the same for plain text messages. Both paths merge at the Combine node.
  • AI Agent Processing: The unified prompt is passed to the AI Agent, powered by GigaChat Model and using Simple Memory to retain the last 10 messages per user (using Max user_id as the session key).
  • Response Delivery: The AI-generated response is sent back to the user via the Send Message node.

Set up steps

Estimated set up time: 15 minutes

  1. Get Max bot credentials: Visit https://business.max.ru/ to create a bot and obtain API credentials. Add these credentials to Max Trigger, Send Message, and Access Denied nodes.
  2. Add GigaChat credentials: Register for GigaChat API access and add your credentials to the GigaChat Model node.
  3. Add Sber credentials: Obtain Sber SmartSpeech API credentials and add them to Get Access Token and Get Response nodes (HTTP Header Auth).
  4. Configure access control: Open the Check User node and change the user_id value (currently 50488534) to your own Max user ID. This ensures only you can use the bot during testing.
  5. Customize bot personality: Open the AI Agent node and edit the system message to change the bot's name, behavior, and add your own contact information or links.
  6. Test the bot: Activate the workflow and send a text or voice message to your Max bot to verify it responds correctly.

Notes

This workflow is specifically designed for Russian-speaking users and uses Russian AI services (GigaChat and Sber SmartSpeech) as alternatives to OpenAI. Make sure you have valid API access to both services before setting up this workflow.

n8n AI Chatbot with Voice Recognition and GigaChat

This n8n workflow creates a sophisticated AI chatbot that integrates with a messenger service (likely Max Messenger, given the directory name, though the JSON doesn't explicitly state it) and leverages GigaChat for conversational AI. It includes voice recognition capabilities and a "salute speech" feature, making the interaction more dynamic and user-friendly.

The workflow is designed to process incoming messages, determine if they contain voice data, transcribe the voice, interact with an AI agent for a response, and then potentially convert the AI's text response back into speech.

What it does

  1. Receives Incoming Messages: The workflow is triggered by incoming messages, likely from a messenger platform.
  2. Checks for Voice Data: It uses a conditional If node to determine if the incoming message contains voice data (e.g., an audio file).
  3. Transcribes Voice (if present): If voice data is detected, an HTTP Request node is used to send the audio to a voice recognition service (like GigaChat's speech-to-text API or a similar service) for transcription.
  4. Prepares Text for AI: The transcribed text (or the original text message if no voice was present) is prepared for the AI agent.
  5. Manages Conversation History: A Simple Memory node is used to maintain the conversational context, allowing the AI to remember previous turns in the conversation.
  6. Generates AI Response: An AI Agent (powered by LangChain) processes the message and the conversation history to generate a relevant response using GigaChat.
  7. Prepares AI Response for Messenger: The AI's text response is formatted for delivery back to the messenger.
  8. Synthesizes Speech (Optional/Implied): While not explicitly shown with a dedicated text-to-speech node in this JSON snippet, the "salute speech" in the directory name implies that the AI's text response might be converted back into speech before being sent to the user.
  9. Sends Response: The final response (text or speech) is sent back to the user via the messenger.

Prerequisites/Requirements

  • n8n Instance: A running n8n instance.
  • Messenger Integration: Access and credentials for the messenger platform (e.g., Max Messenger) to send and receive messages. This typically involves a webhook trigger and an HTTP Request node for sending responses.
  • GigaChat API Key: An API key for GigaChat or a similar large language model service to power the AI Agent.
  • Voice Recognition Service: Access to a speech-to-text API (e.g., GigaChat's voice recognition, Google Cloud Speech-to-Text, or another compatible service) for transcribing audio messages.
  • Text-to-Speech Service (Optional): If "salute speech" implies voice output, a text-to-speech API would be required.

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Messenger Trigger: Set up the webhook trigger (not shown in this JSON, but implied by the incoming message processing) to receive messages from your chosen messenger platform.
  3. Configure GigaChat/LLM Credentials: Update the AI Agent node with your GigaChat API key or credentials for your chosen Large Language Model.
  4. Configure Voice Recognition HTTP Request:
    • Modify the HTTP Request node (ID 19) to point to your preferred speech-to-text API endpoint.
    • Ensure the request body and headers are correctly configured to send the audio data and receive the transcription.
    • Add any necessary authentication for the speech-to-text service.
  5. Configure Messenger Response: Set up the final HTTP Request node (or a dedicated messenger node, if available) to send the AI's response back to the user in the messenger.
  6. Activate the Workflow: Once configured, activate the workflow to start processing messages.

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