RAG Workflow for Company Documents Stored in Google Drive
This workflow builds an intelligent question-and-answer system based on company documents stored in Google Drive, utilizing a vector database and large language models to achieve rapid information retrieval and natural language interaction. By automatically synchronizing document updates, employees can obtain concise and accurate answers related to policies and processes in real time, thereby enhancing knowledge management efficiency, optimizing the self-service experience, and addressing the issues of traditional document fragmentation and retrieval difficulties. It is applicable to various scenarios, including internal knowledge bases, HR policy inquiries, and intelligent retrieval of legal compliance documents.
Workflow Name
RAG Workflow for Company Documents Stored in Google Drive
Key Features and Highlights
This workflow implements an intelligent Q&A system based on company documents stored in Google Drive. Leveraging advanced vector database technology (Pinecone) and Google Gemini (PaLM) large language models, it automatically synchronizes document updates and constructs semantic vector indexes to achieve efficient and precise information retrieval and natural language interaction. The system can respond in real-time to employee queries related to company policies, providing concise and accurate answers, significantly enhancing internal knowledge management and employee self-service experience.
Core Problems Addressed
Traditional company documents are scattered and difficult to retrieve quickly, causing employees to spend excessive time searching for policies, procedures, and other information. This workflow solves issues of poor document retrievability, slow Q&A response, and low knowledge utilization by automating document synchronization, vectorized storage, and semantic search, enabling intelligent document management and interaction.
Application Scenarios
- Internal corporate knowledge base management and Q&A
- Automated HR policy and procedure query assistant
- Intelligent retrieval of legal and compliance documents
- Rapid onboarding for new employees to access company information
- Any business scenario requiring intelligent Q&A based on document content
Main Process Steps
- Monitor Designated Google Drive Folder: Capture document creation and update events in real-time via “Google Drive File Created” and “Google Drive File Updated” triggers.
- Download Files: Automatically download the latest triggered files.
- Text Preprocessing: Use the “Recursive Character Text Splitter” to chunk text, ensuring contextual integrity for vectorization.
- Generate Text Vectors: Invoke Google Gemini’s text embedding model to convert document content into vector representations.
- Vector Storage: Insert vector data into the Pinecone vector database to establish efficient indexing.
- Chat Trigger: Activate chat node when employees send query messages.
- Semantic Retrieval: Retrieve relevant document vectors from Pinecone based on the query.
- AI Q&A Generation: Utilize Google Gemini chat model with contextual memory combined with retrieval results to generate accurate and concise responses.
- Respond to Employees: Return answers to employees, enabling natural language interaction.
Involved Systems or Services
- Google Drive: Document storage and monitoring
- Pinecone: Vector database for semantic search
- Google Gemini (PaLM) API: Text embedding generation and conversational language model
- n8n Automation Platform: Workflow orchestration and node execution
Target Users and Value
- Enterprise IT and digital transformation teams building intelligent document Q&A systems
- Human Resources departments enabling employee self-service policy queries
- Legal and compliance teams for rapid document content retrieval
- Any organization requiring intelligent management and interaction of large volumes of documents
- Improves employee productivity, reduces repetitive inquiries, and optimizes knowledge asset utilization
By seamlessly integrating leading AI technologies and cloud services, this workflow builds an efficient, intelligent, and easy-to-maintain company document knowledge base Q&A solution, greatly promoting internal information flow and modernization of knowledge management within enterprises.