Build a PDF Q&A system with LlamaIndex, OpenAI embeddings & Pinecone vector DB
This automated pipeline streamlines the creation of a Retrieval-Augmented Generation (RAG) system by processing PDFs directly from Google Drive. It utilizes LlamaIndex for high-fidelity parsing and OpenAI to generate embeddings, which are then indexed in Pinecone for efficient semantic retrieval. The end result is a sophisticated chat interface capable of answering complex questions based on your private document library.
Run this with your team's AIWhat This Recipe Does
Transform your unstructured PDF documents into a high-performance knowledge base for AI applications. This automation eliminates the manual labor associated with processing large volumes of documents by automatically monitoring your Google Drive for new uploads. Once a PDF is detected, the system parses the text, normalizes the formatting, and extracts key data points to ensure consistency. It then converts this information into vector embeddings and stores them directly in Pinecone. By automating the data ingestion pipeline, your business can maintain an up-to-date Retrieval-Augmented Generation (RAG) system, allowing your AI chatbots and internal tools to provide accurate, context-aware answers based on your latest company documentation. This workflow ensures that your proprietary data is organized, searchable, and ready to power advanced AI interactions without requiring a manual data entry team.
What your team gets
Forms and dashboards, so it is not a script only one person understands
Runs on your schedule in the cloud, so it does not stop when a laptop closes
Endpoints, so the rest of your stack can trigger the same work
Google Drive connected for the team, not per person
How It Works
- 1
Open the recipe and connect your accounts
Connect Google Drive once, in your team cloud, and nobody has to do it again on their own machine
- 2
Tell your own agent what is different about your process
Claude, ChatGPT, Cursor, whichever your team already uses. It adapts the recipe to how you actually work
- 3
Run it, then leave it running
It lives in your team cloud, so it keeps going after you close the laptop and every teammate's AI can use it
Who Uses This
- Customer Support leads who need to sync the latest product manuals and troubleshooting guides with their AI support bots.
- Legal and Compliance teams who want to make vast libraries of regulatory documents searchable for instant internal queries.
- Operations managers who need to process and store technical specifications or standard operating procedures for automated staff training tools.
Frequently Asked Questions
Do I need to manually trigger the workflow for every new document?
No. The automation uses a Google Drive trigger that monitors your selected folders and begins processing as soon as a new PDF is uploaded.
Can I customize how the text is cleaned or normalized?
Yes. The workflow includes a normalization step that can be adjusted to handle specific document formats or remove unwanted characters and headers.
Is this compatible with other cloud storage services?
While this specific recipe is built for Google Drive, the core logic can be adapted to work with OneDrive, Dropbox, or local file systems.
What is the end result of this automation?
You get a fully indexed Pinecone database where your document content is stored as searchable vectors, ready to be used by any AI model or chatbot.
Coming from n8n?
This recipe uses nodes like GoogleDriveTrigger, GoogleDrive, Langchain.documentDefaultDataLoader, StickyNote and 7 more. On Runwork, you don't need to learn n8n's workflow syntax. Describe what you want to your own AI agent in plain English.
Based on n8n community workflow. View original
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Run this with the AI your team already uses
Your agent adapts it, your team cloud keeps it running, and everyone's AI can find it.
Open this recipe in Runwork