Google Sheets AI Q&A Assistant

Overview

This workflow implements a Google Sheets-based AI assistant, designed to answer user questions strictly using information from a provided Google Sheet. The system ensures reliable, document-grounded responses and avoids introducing any information not present in the supplied sheet. The workflow is modular, scalable, and suitable for automating document-based question answering and support tasks, making it useful for knowledge management, customer support, and internal document querying.

Workflow Structure

The workflow consists of the following main components:

The edges in the flow connect these nodes, ensuring that data moves smoothly from user input, through document retrieval and AI reasoning, to user output.

Step-by-Step Logic

1. Welcome Message & Input Capture

   • When a user opens the assistant, a Message Widget displays a customizable welcome message, instructing them to upload or select a document and ask questions.
   • The Chat Input node captures user queries (text or file uploads) and passes them into the workflow.

2. Document Retrieval

   • The Google Sheets Retriever node allows the user (or the system) to select a specific Google Sheet (and sheet tab) to use as the information source. The node fetches the content and exposes a “tool” interface for the AI agent.

3. Chat History Context

   • The Chat History node records the last 5 messages (up to 800 tokens) for context, enabling the AI to provide more coherent and context-aware answers in multi-turn conversations.

4. AI Reasoning with Restrictions

   • The Tool Calling Agent is the core AI component. It:
     • Receives the user’s question and the relevant chat history.
     • Has access to the Google Sheet as its only tool/knowledge source.
     • Is governed by a detailed system prompt that enforces:
       • Answers must be strictly based on the sheet content.
       • No external knowledge or assumptions are allowed.
       • If the answer is not present, the agent must say so.
       • Quoting the relevant section is encouraged for transparency.

5. Presenting the Answer

   • The AI agent’s response is shown to the user via a Chat Output node.

Flow Diagram (Conceptual)

[User Input] 
   ↓
[Welcome Message] → [Chat Output (welcome)]
   ↓
[Chat Input] 
   ↓
[Tool Calling Agent] ← [Chat History]
        ↑
        [Google Sheets Retriever]
   ↓
[Chat Output (answer)]

Key Features and Benefits

• Strict Document Grounding:
  Ensures that all AI outputs are traceable to the provided Google Sheet, which is critical for compliance, reliability, and transparency.

• Scalable and Reusable:
  The modular design allows you to plug in different documents, adjust retrieval settings, or swap the AI model as needed.

• Automated Knowledge Delivery:
  Users can self-serve answers to document-based queries without manual intervention, reducing support workload and scaling information access.

• Context-Aware Responses:
  By leveraging recent chat history, the system can handle follow-up questions and multi-turn exchanges more effectively.

• Error Handling:
  If the answer is not found in the document, the AI clearly communicates this, preventing misinformation.

Example Use Cases

• Internal Knowledge Base Q&A:
  Employees can query policy documents, price lists, or project records stored in Google Sheets.
• Customer Support:
  Customers receive precise answers based on official documentation, such as product specs or FAQs.
• Automated Data Extraction:
  Structured answers to ad-hoc data inquiries from operational sheets.

Conclusion

This workflow automates the process of answering questions based only on the content of a selected Google Sheet. Its strict adherence to document-based knowledge and robust, scalable design make it a valuable tool for organizations seeking accurate, auditable, and efficient information delivery.