Automated Unpaid Invoice Reminder System

This workflow automates the process of managing unpaid invoices by integrating webhooks, natural language processing, vector search, and notifications. When an invoice is identified as unpaid, the workflow triggers via a webhook, processes invoice data through text splitting and embedding, and stores or retrieves information in a vector database. It uses a retrieval-augmented generation (RAG) approach with a language model to understand and generate context-specific reminders or messages. The workflow also logs each interaction to Google Sheets and sends error alerts via Slack. This automation ensures timely follow-up on unpaid invoices, reducing manual effort and improving cash flow management.

Step-by-step, the process begins with receiving a POST request through a webhook. The message content is then split into manageable parts using a text splitter. The split data is embedded using Cohere’s embedding model, which is stored in Weaviate’s vector database for efficient similarity searches. When a query related to unpaid invoices is received, the system fetches relevant information from Weaviate, utilizes a vector tool for context, and engages a language model for generating appropriate reminders or responses.

The RAG agent orchestrates the retrieval of contextual data and generates customized messages for unpaid invoice follow-ups. These messages are logged into a Google Sheet for record-keeping. If any errors occur during the process, a Slack notification alerts the team immediately, ensuring prompt handling. Practically, this workflow is useful for businesses that want to automate their collections process, maintain organized records, and ensure consistent communication with clients about unpaid invoices.