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AI & researchAgentAggregateChat TriggerExecute Workflow TriggerHTTP Request

receive chat events and acuity support search api and results to items

Configured with Agent, Aggregate, Chat Trigger, Execute Workflow Trigger, and additional nodes. Review the connections and setup before adapting this template.

Workflow preview
TriggerWhen chat messagereceivedChat TriggerAI & modelsOpenAI Chat ModelOpenAI Chat ModelData storeSimple MemoryMemory Buffer WindowWeb / APIAcuity Support SearchAPIHTTP RequestTransformExtract Relevant FieldsEdit FieldsTransformResults to ItemsSplit OutRoutingHas Results?IfTransformEmpty ResponseEdit FieldsTransformAggregate ResponseAggregateSubworkflowKnowledgebase ToolTool WorkflowAI & modelsAcuitySchedulingSupport ChatbotAgentTriggerKnowledgeBase ToolSubworkflowExecute Workflow Trigger

Node categories in this workflow

  • Trigger
  • AI & models
  • Data store
  • Web / API
  • Transform
  • Routing
  • Subworkflow

Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.

Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.

How it works

The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.

Workflow node names, types, and operations
NodeCategoryTypeOperation
When chat message receivedTriggerChat TriggerSee node parameters
OpenAI Chat ModelAI & modelsOpenAI Chat ModelSee node parameters
Simple MemoryData storeMemory Buffer WindowSee node parameters
Acuity Support Search APIWeb / APIHTTP RequestPOST
Extract Relevant FieldsTransformEdit FieldsSee node parameters
Results to ItemsTransformSplit OutSee node parameters
Has Results?RoutingIfSee node parameters
Empty ResponseTransformEdit FieldsSee node parameters
Aggregate ResponseTransformAggregateSee node parameters
Knowledgebase ToolSubworkflowTool WorkflowSee node parameters
AcuityScheduling Support ChatbotAI & modelsAgentSee node parameters
KnowledgeBase Tool SubworkflowTriggerExecute Workflow TriggerSee node parameters

Setup

  1. Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
  2. Connect your own credentials through n8n’s credential store. Replace REPLACE_WITH_* values with your resource identifiers.
  3. Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
  4. Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.

Requirements

An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.

Credential types declared in the source

  • openAiApi

Workflow notes

1. Simple Chatbot with Knowledgebase Tool

1. Simple Chatbot with Knowledgebase Tool

Learn more about AI agents

The AI agent node is the simplest and recommended way to create user-friendly chatbots in n8n. Here, we'll define a support agent which can answer AcuityScheduling.com questions. To ensure the answers are accurate and up-to-date, we'll connect it to the support knowledgebase via a custom workflow tool.

2. Use your Existing Help Portal Search

2. Use your Existing Help Portal Search

Read more about the HTTP request tool

The concept of RAG need to be synonymous with vector stores! In truth, many companies with a decent enough support website are able to leverage this existing knowledgebase for support agents. This saves time, money and effort and additional avoids maintenance of a vector store where syncs and updates are common.

3. Clean up the Results to Optimise Tokens

3. Clean up the Results to Optimise Tokens

Read more about the aggregate node

Of course, the results are intended for the website format but by using the custom workflow tool, we can edit it down to suit our chat scenario and save LLM costs (in terms of tokens) whilst we're at it.

Try it Out!

Try it Out!

This n8n template demonstrates how you can leverage existing support site search to power your Support Chatbots and agents.

Building a support chatbot need not be complicated! If building and indexing vector stores or duplicating data isn't necessarily your thing, an alternative implementation of the RAG approach is to leverage existing knowledge-bases such as support portals.

How it works

  • A simple AI agent is connected with chat trigger to receive user queries.
  • The AI agent is instructed to fetch information from the knowledge-base via the attached custom workflow tool (aka "knowledgebase tool").
  • There is no step to replicate the entire support articles database into a vector store. You may choose not too because of time, cost and maintainence involved.
  • Instead, the tool leverages the existing support portal's search API to retrieve knowledge-base articles.
  • Finally, the search results are formatted before sending an aggregated response back to the agent.

How to use?

  • Customise the subworkflow to work with your own support portal API and format accordingly.
  • Try the following queries
    • How do I connect my icloud to acuityScheduling?
    • How do I download past invoices for my Acuity account?

Requirements

  • OpenAI for LLM.
  • If your organisation's APIs require authorisation, you may need to add custom credentials as necessary.

Customising this workflow

  • Add additional tools to reach other parts of your internal knowledgebase.
  • Not using OpenAI? Feel free to swap but ensure the LLM has tools/function calling support.

Need Help?

Join the Discord or ask in the Forum!

Happy Hacking!

receive chat events and acuity support search api and results to items

receive chat events and acuity support search api and results to items

Purpose and structure

This folder contains the receive chat events and acuity support search api and results to items workflow. Its executable nodes use agent, aggregate, chatTrigger, executeWorkflowTrigger, httpRequest, if, lmChatOpenAi, memoryBufferWindow, set, splitOut, toolWorkflow. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

When chat message received, KnowledgeBase Tool Subworkflow

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Splitout/0840_Splitout_HTTP_Send_Webhook.json MIT; preserve source notices.

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