Build Better AI Applications With Google Drive And Vectorize

Managing mountains of data in Google Drive can feel overwhelming, especially when trying to integrate it into AI workflows like retrieval-augmented generation (RAG) pipelines. Enter Vectorize’s new Google Drive Connector—a smarter way for teams to tap into the power of their stored files for AI applications.

In just a few clicks, you can create a RAG pipeline that automatically pulls and processes files from Google Drive based on the criteria you set. Whether it’s all files in a shared drive, or Google Documents in a specific folder and its subfolders, you’re in charge.

Vectorize helps you customize searches by file type—Google Docs, PDFs, and more—before transforming your data into vector search indexes stored in your database. Once the pipeline is up and running, it continuously checks for new files, ensuring your LLM always has the latest information.

In addition to Google Drive, Vectorize allows you to integrate multiple sources into a single RAG pipeline. For instance, if you’re building a chatbot that needs to retrieve documents from Google Docs while also accessing data from Dropbox or Confluence, you can easily configure your pipeline to include all those sources.

Vectorize’s real-time RAG pipelines handle the heavy lifting—creating vector search indexes from your unstructured data, loading them into your vector database, and keeping them up-to-date. By automating your RAG pipeline, you’re freed up to focus on building accurate, high-performance AI applications.

Get Started with Google Drive and Vectorize

Ready to start importing documents from Google Drive into your RAG pipelines? Sign up for the Vectorize platform today!