Build a support knowledge base from historical Gmail emails with OpenAI and PostgreSQL

KB Builder — Historical Emails n8n Workflow Template

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📘 Description

This workflow automates the process of building a structured Knowledge Base (KB) from your existing Gmail inbox by processing historical email threads, extracting customer-support conversation pairs, classifying them with AI, generating vector embeddings, and storing everything in a PostgreSQL database — ready to power AI-assisted draft generation in downstream workflows.

The workflow is triggered manually and fetches the last N emails from your connected Gmail account. Each email is parsed, filtered, and deduplicated before being processed. A thread-level fetch retrieves the full conversation context, separating customer messages from support replies. An AI classification step categorises each thread into a defined scenario type. Vector embeddings are generated for semantic similarity search. Qualified threads are inserted into three database tables — kbdata for clean Q&A pairs, scenariopatterns for reusable handling logic, and corrections for diverse real-world examples — with strict deduplication to keep the KB clean and non-redundant.

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⚙️ What This Workflow Does (Step-by-Step)

📥 Manual Trigger — Run on demand whenever you want to import a batch of historical emails into the KB. No scheduling required.