Automated Interview Scheduler with RAG Agent

Name: Automated Interview Scheduler with RAG Agent
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Author: Free N8N

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AutomationAILangchainRAGWeaviateautomationinterviewschedulingwebhook

This n8n workflow automates interview scheduling by leveraging a Webhook Trigger, Langchain's RAG Agent, and Weaviate for vector storage. It processes incoming requests, retrieves relevant context, and logs the outcome to a Google Sheet.

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About This Workflow

Overview

This n8n workflow is designed to automate the process of scheduling interviews. It utilizes a RAG (Retrieval-Augmented Generation) Agent powered by Langchain to process incoming interview requests. The workflow starts with a Webhook Trigger that receives interview details. These details are then processed through a Text Splitter and Embeddings node before being stored in Weaviate, a vector database. When a new request comes in, the Weaviate Query and Vector Tool nodes retrieve relevant historical interview data. The RAG Agent then uses this retrieved context, along with Window Memory and a Chat Model, to generate an appropriate response or schedule the interview. Finally, the Append Sheet node logs the status to a Google Sheet, and a Slack Alert node is used for error notifications.

Key Features

Automated Interview Scheduling: Streamlines the interview booking process.
RAG Agent Integration: Utilizes Langchain's RAG capabilities for context-aware responses.
Vector Database Integration: Employs Weaviate to store and retrieve interview-related data for better context.
Webhook Trigger: Enables external systems to initiate the scheduling process.
Google Sheets Logging: Automatically logs interview statuses for record-keeping.
Slack Error Alerts: Notifies the team of any issues during the process.

How To Use

Set up Credentials: Configure your OpenAI API and Weaviate API credentials within n8n.
Configure Google Sheets: Ensure your Google Sheet is set up with a 'Log' sheet name and provide the correct SHEET_ID.
Set up Webhook: Activate the Webhook Trigger and use the provided URL to send interview request data.
Adjust Text Splitter and Embeddings: Fine-tune the Text Splitter parameters (chunk size, overlap) and select the appropriate embedding model if needed.
Define Weaviate Index: Ensure the indexName in Weaviate Insert and Weaviate Query nodes matches your Weaviate index.
Customize RAG Agent Prompt: Modify the RAG Agent node's text parameter and systemMessage to suit your specific interview scheduling needs.
Test the Workflow: Send a test request to the webhook URL to verify the entire process from trigger to logging.

Apps Used

Langchain

RAG

Weaviate

automation

interview

scheduling

webhook

Workflow JSON

{
  "id": "fb7afb16-c26a-4fa0-95ed-b16bfc6b7e23",
  "name": "Automated Interview Scheduler with RAG Agent",
  "nodes": 0,
  "category": "Automation",
  "status": "active",
  "version": "1.0.0"
}

Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.