Build a Website Customer Support Chatbot with Groq AI and Google Sheets Knowledge Base

Build a Website Customer Support Chatbot with Groq AI and Google Sheets Knowledge Base

1. Workflow Overview

This workflow implements a website customer support chatbot powered by Groq AI, utilizing a Google Sheets document as its knowledge base. It is designed for organizations seeking to provide automated, AI-driven customer support by integrating conversational AI with structured knowledge stored in spreadsheets. The workflow logically divides into the following blocks:

  • 1.1 Input Reception: Captures incoming chat messages from website users.
  • 1.2 Contextual Memory Management: Maintains conversational context across multiple exchanges.
  • 1.3 AI Processing: Routes the chat input through a configurable language model agent enhanced with memory and knowledge base tools.
  • 1.4 Knowledge Base Integration: Provides the AI agent access to a Google Sheets-based knowledge base to inform its responses.
  • 1.5 Language Model Selection: Allows selection of the preferred Large Language Model (LLM) to customize AI response behavior.

2. Block-by-Block Analysis

2.1 Input Reception

  • Overview:
    This block receives chat messages initiated by website users, serving as the workflow’s entry point.

  • Nodes Involved:

    • When chat message received
  • Node Details:

    • When chat message received
      • Type: @n8n/n8n-nodes-langchain.chatTrigger
      • Role: Webhook listener node that triggers the workflow upon receiving a chat message from the website interface.
      • Configuration: Uses a webhook ID to receive real-time chat messages; no additional parameters configured.
      • Inputs: External HTTP request (chat message)
      • Outputs: Sends chat message data to the AI Agent node
      • Version-specific: Uses version 1.1 of the chatTrigger node.
      • Potential Failures: Webhook connectivity issues, malformed incoming data, or message payload timeouts.

2.2 Contextual Memory Management

  • Overview:
    Manages chat history to maintain conversation context and continuity during interactions.

  • Nodes Involved:

    • Chat History
  • Node Details:

    • Chat History
      • Type: @n8n/n8n-nodes-langchain.memoryBufferWindow
      • Role: Holds a sliding window of recent chat messages to provide context to the AI Agent.
      • Configuration: Default buffer window size (not explicitly set) to maintain recent chat history.
      • Inputs: Receives AI Agent output for updating memory.
      • Outputs: Provides memory context back to AI Agent for enriched responses.
      • Version-specific: Version 1.3 of memoryBufferWindow node.
      • Edge Cases: Memory overflow if buffer window too small or too large; potential stale context if conversations are very long.

2.3 AI Processing

  • Overview:
    Central logic for processing user input, generating AI responses with memory and knowledge base support.

  • Nodes Involved:

    • AI Agent
  • Node Details:

    • AI Agent
      • Type: @n8n/n8n-nodes-langchain.agent
      • Role: Core AI conversational agent that integrates memory (chat history), language model, and knowledge base tools.
      • Configuration: No explicit parameters; connects inputs from chat trigger, memory, selected language model, and knowledge base nodes.
      • Inputs: Receives chat messages, memory context, language model selection, and tools (Google Sheets knowledge base).
      • Outputs: Generates AI responses forwarded back to the initiating chat interface.
      • Version-specific: Version 1.7 of the agent node.
      • Edge Cases: Possible AI model timeouts, API rate limits, errors in tool integration, or malformed input data.

2.4 Knowledge Base Integration

  • Overview:
    Enables the AI Agent to query and retrieve information from a Google Sheets document serving as the knowledge base.

  • Nodes Involved:

    • Select your Knowledge Base
  • Node Details:

    • Select your Knowledge Base
      • Type: n8n-nodes-base.googleSheetsTool
      • Role: Provides read/write access to a Google Sheets spreadsheet that contains customer support information.
      • Configuration: Connected as an AI tool input to the AI Agent; credentials and spreadsheet ID/configuration must be set.
      • Inputs: None (triggered via AI Agent)
      • Outputs: Supplies data to AI Agent for informed responses.
      • Version-specific: Version 4.5 of the Google Sheets Tool node.
      • Edge Cases: Authentication failures, invalid spreadsheet ID, API quota exhaustion, spreadsheet format changes.

2.5 Language Model Selection

  • Overview:
    Allows selection of the preferred Large Language Model for generating AI responses, here specifically using Groq AI.

  • Nodes Involved:

    • Choose your prefered LLM
  • Node Details:

    • Choose your prefered LLM
      • Type: @n8n/n8n-nodes-langchain.lmChatGroq
      • Role: Supplies the AI Agent with the Groq AI language model as the processing backend.
      • Configuration: No extra parameters explicitly set; credentials for Groq AI must be configured externally.
      • Inputs: None (acts as a language model resource)
      • Outputs: Provides language model interface to AI Agent.
      • Version-specific: Version 1.0 (inferred from context) of lmChatGroq node.
      • Edge Cases: Authentication errors, API limits, network timeouts.

3. Summary Table

Node Name Node Type Functional Role Input Node(s) Output Node(s) Sticky Note
When chat message received @n8n/n8n-nodes-langchain.chatTrigger Entry point, receives chat messages (Webhook) AI Agent
AI Agent @n8n/n8n-nodes-langchain.agent Core AI processing agent When chat message received, Chat History, Choose your prefered LLM, Select your Knowledge Base Chat History
Chat History @n8n/n8n-nodes-langchain.memoryBufferWindow Maintains conversational context AI Agent (ai_memory) AI Agent (ai_memory)
Choose your prefered LLM @n8n/n8n-nodes-langchain.lmChatGroq Supplies Groq AI language model None AI Agent (ai_languageModel)
Select your Knowledge Base n8n-nodes-base.googleSheetsTool Provides Google Sheets knowledge base None AI Agent (ai_tool)
Sticky Note1 n8n-nodes-base.stickyNote (No content) None None
Sticky Note2 n8n-nodes-base.stickyNote (No content) None None
Sticky Note3 n8n-nodes-base.stickyNote (No content) None None
Sticky Note n8n-nodes-base.stickyNote (No content) None None

4. Reproducing the Workflow from Scratch

  1. Create the “When chat message received” node

    • Type: @n8n/n8n-nodes-langchain.chatTrigger
    • Configuration: Set up a webhook to receive incoming chat messages from your website’s chat interface. No additional parameters required.
    • Position: Start of the workflow.
  2. Create the “AI Agent” node

    • Type: @n8n/n8n-nodes-langchain.agent
    • Configuration: Leave default parameters. Connect incoming data from the chat trigger node. This node orchestrates AI responses integrating memory, language model, and tools.
    • Position: Central node, directly connected from “When chat message received”.
  3. Create the “Chat History” node

    • Type: @n8n/n8n-nodes-langchain.memoryBufferWindow
    • Configuration: Use default sliding window memory buffer settings to store recent conversation history.
    • Position: Connect output “ai_memory” from AI Agent to this node’s input, and output “ai_memory” back to AI Agent.
  4. Create the “Choose your prefered LLM” node

    • Type: @n8n/n8n-nodes-langchain.lmChatGroq
    • Configuration: Ensure Groq AI credentials are configured in n8n. No other parameters required.
    • Position: Connect output “ai_languageModel” from this node into the AI Agent’s language model input.
  5. Create the “Select your Knowledge Base” node

    • Type: n8n-nodes-base.googleSheetsTool
    • Configuration: Configure Google Sheets credentials and specify the spreadsheet ID containing your customer support knowledge base.
    • Position: Connect output “ai_tool” from this node into the AI Agent’s tool input.
  6. Wire the connections:

    • “When chat message received” → AI Agent (main input)
    • AI Agent (ai_memory output) → Chat History (input)
    • Chat History (ai_memory output) → AI Agent (ai_memory input)
    • “Choose your prefered LLM” → AI Agent (ai_languageModel input)
    • “Select your Knowledge Base” → AI Agent (ai_tool input)
  7. Credential Setup:

    • Add credentials for Groq AI in n8n to enable the language model node.
    • Add Google OAuth2 credentials scoped for Google Sheets API access to enable the knowledge base node.
  8. Test:

    • Trigger the webhook by sending a chat message from the website interface to verify end-to-end functionality.
    • Adjust memory buffer size or knowledge base if needed to optimize performance.

5. General Notes & Resources

Note Content Context or Link
This workflow leverages the powerful Groq AI LLM combined with a Google Sheets knowledge base to provide dynamic, context-aware customer support. Workflow purpose summary
Ensure Google Sheets API credentials have sufficient permissions to read the knowledge base spreadsheet. Credential setup note
The chatTrigger node requires a public webhook URL exposed to the website chat frontend. Deployment consideration
Refer to n8n official documentation for setting up LangChain nodes and integrations: https://docs.n8n.io/integrations/builtin/n8n-nodes-langchain/ Official n8n LangChain node documentation
Groq AI usage may require account setup and API key management per Groq AI policies. Groq AI platform details

Disclaimer: The text provided derives exclusively from an automation workflow created with n8n, an integration and automation tool. This processing strictly respects applicable content policies and contains no illegal, offensive, or protected elements. All data handled is legal and public.

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