Answer HR policy and benefits questions with BambooHR, OpenAI and Supabase
Needs changesWhat this workflow does
This AI workflow has 39 nodes and runs manually, when you click “Execute workflow” (plus 2 other triggers). Along the way it connects Embeddings OpenAI, OpenAI Chat Model, Vector Store Question Answer and BambooHR and 1 more.
How it runs
Trigger: Manually, when you click “Execute workflow” (plus 2 other triggers).
- GET all files (BambooHR): get many (file)
- HR AI Agent (AI Agent): runs an AI agent, with model: OpenAI Chat Model; 2 tools (Vector Store Tool, Employee Lookup Tool); memory: Window Buffer Memory
- Text Classifier: classifies text into categories with AI, with model: OpenAI Chat Model
- Filter: keeps only the items that match a condition
- GET all employees (BambooHR): get many
- GET all employees (second path) (BambooHR): get many
- Split Out: splits a list into separate items
- Filter: keeps only the items that match a condition
- Extract departments (Aggregate): combines items into one
- Filter: keeps only the items that match a condition
…plus 10 more steps. See the full node list below.
Services it uses
Before you upgrade to n8n 3.0
1 thing in this workflow is removed in n8n 3.0. Fix it before you upgrade:
- AI Agent v1 (old agent modes) in HR AI Agent (v1.7): replace with Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent.
Rather not rewrite it? Many of these flows can be rebuilt in Make (free plan).
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) HR AI Agent (v1.7) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (22)
- Manual Trigger
n8n-nodes-base.manualTrigger - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Vector Store Question Answer Tool
@n8n/n8n-nodes-langchain.toolVectorStore - Call n8n Workflow Tool
@n8n/n8n-nodes-langchain.toolWorkflow - Auto-fixing Output Parser
@n8n/n8n-nodes-langchain.outputParserAutofixing - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - BambooHR
n8n-nodes-base.bambooHr - Filter
n8n-nodes-base.filter - Split Out
n8n-nodes-base.splitOut - Supabase Vector Store
@n8n/n8n-nodes-langchain.vectorStoreSupabase - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Execute Workflow Trigger
n8n-nodes-base.executeWorkflowTrigger - Text Classifier
@n8n/n8n-nodes-langchain.textClassifier - Edit Fields (Set)
n8n-nodes-base.set - Aggregate
n8n-nodes-base.aggregate - Information Extractor
@n8n/n8n-nodes-langchain.informationExtractor - Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm - AI Agent
@n8n/n8n-nodes-langchain.agent
From the author
Quick Overview This workflow ingests company policy PDFs from BambooHR into a Supabase vector database and provides an employee chat interface that answers policy and benefits questions using OpenAI, with an additional employee/department lookup powered by…