Auto-update knowledge base with Drive, LlamaIndex & Azure OpenAI embeddings

This Workflow auto-ingests Google Drive documents, parses them with LlamaIndex, and stores Azure OpenAI embeddings in an in-memory vector store—cutting manual update time from ~30 minutes to under 2 minutes per doc.

Why Use This Workflow? Cost Reduction: Eliminates pays monthly fee on cloud just for store knowledge

Ideal For

- Knowledge Managers / Documentation Teams: Automatically keep product docs and SOPs in sync when source files change on Google Drive. - Support Teams: Ensure the searchable KB is always up-to-date after doc edits, speeding agent onboarding and resolution time. - Developer / AI Teams: Populate an in-memory vector store for experiments, rapid prototyping, or local RAG demos.

How It Works

1. Trigger: Google Drive Trigger watches a specific document or folder for updates. 2. Data Collection: The updated file is downloaded from Google Drive. 3. Processing: The file is uploaded to LlamaIndex cloud via an HTTP Request to create a parsing job. 4. Intelligence Layer: Workflow polls LlamaIndex job status (Wait + Monitor loop). If parsing status equals SUCCESS, the result is retrieved as markdown. 5. Output & Delivery: Parsed markdown is loaded into LangChain's Default Data Loader, passed to Azure OpenAI embeddings (deployment "3small"), then inserted into an in-memory vector store. 6. Storage & Logging: Vector store holds embeddings in memory (good for prototyping). Optionally persist to an external vector DB for production.

Setup Guide

Prerequisites