RAG Workflow For Company Documents stored in Google Drive

For Google Drive, this automated workflow efficiently manages company documents by integrating LangChain and AI capabilities. It enables real-time updates and retrieval of information, ensuring employees can quickly access relevant company policies and documents. The system automatically processes new or updated files, enhancing knowledge sharing and support within the organization.

7/8/2025
18 nodes
Complex
manualcomplexlangchaingoogle drivesticky notegoogledrivetriggeradvanced
Categories:
Complex WorkflowManual TriggeredCloud Storage & File Management
Integrations:
LangChainGoogle DriveSticky NoteGoogleDriveTrigger

Target Audience

Target Audience


- HR Professionals: This workflow is ideal for HR teams looking to automate responses to employee queries using internal documents stored in Google Drive.
- Document Managers: Individuals responsible for managing company documents can benefit from streamlined document updates and retrieval processes.
- IT Administrators: Those managing integrations between various tools like Google Drive, LangChain, and Pinecone will find this workflow useful for maintaining data flow and accuracy.
- Data Analysts: Analysts needing quick access to company policies and documents for reporting or analysis can leverage this automation to retrieve information efficiently.

Problem Solved

Problem Solved


This workflow addresses the challenge of efficiently retrieving and utilizing company documents stored in Google Drive to answer employee inquiries. By automating the process of document updates, embeddings, and retrieval, it significantly reduces response times and improves the accuracy of information provided to employees, ensuring that they receive timely and relevant answers based on the latest company policies.

Workflow Steps

Workflow Steps


1. Trigger Events: The workflow begins with the manual trigger or automatically when files are created or updated in a specific Google Drive folder.
2. File Download: When a file is created or updated, it is downloaded from Google Drive.
3. Text Splitting: The downloaded document is processed using a Recursive Character Text Splitter to break it into manageable chunks for better embedding.
4. Generate Embeddings: The text chunks are then converted into embeddings using Google Gemini, which helps in understanding the content better.
5. Insert into Vector Store: The generated embeddings are inserted into a Pinecone Vector Store for efficient retrieval later.
6. AI Agent Interaction: An AI agent is set up to respond to employee queries, utilizing the vector store to fetch relevant information when a chat message is received.
7. Response Generation: The AI agent uses the Google Gemini chat model to generate responses based on the retrieved information, ensuring accurate and helpful answers are provided to employees.

Customization Guide

Customization Guide


- Adjust Folder Settings: Users can modify the folder ID in the Google Drive Trigger nodes to watch a different folder for document changes.
- Change Vector Store Index: Update the Pinecone Vector Store nodes to use a different index if needed, ensuring it aligns with your organizational structure.
- Modify AI Agent Behavior: Customize the AI agent's system message to adjust its tone or focus, depending on the type of inquiries expected from employees.
- Embedding Model Selection: Users can choose different models for embeddings and chat responses by updating the model names in the relevant nodes, allowing for tailored performance based on specific needs.