AI Google Docs Research Assistant

This AI-powered workflow extracts specific information from a Google Doc and then expands on it by researching across sources like Google Search, Wikipedia, and URLs. Ideal for users who want fast, contextual answers that combine internal documents with up-to-date web knowledge.

How the AI Flow works - AI Google Docs Research Assistant

Flows

How the AI Flow works

User Initiates Research Request.
The user starts a chat and submits a query along with a Google Doc for analysis.
Extract Content from Google Doc.
The system retrieves targeted information or content from the provided Google Document.
Expand with AI Research Agent.
An AI research agent uses multiple tools—Google Search, Wikipedia, and URL content retrievers—to gather additional context and validate or enrich the extracted information.
Combine and Generate Response.
The AI agent synthesizes findings from both the Google Doc and external sources into a comprehensive answer.
Present Results to User.
The workflow delivers the researched and enriched information back to the user in the chat interface.

Prompts used in this flow

Below is a complete list of all prompts used in this flow to achieve its functionality. Prompts are the instructions given to the AI model to generate responses or perform actions. They guide the AI in understanding user intent and generating relevant outputs.

Welcome Message

Displays a welcome message and brief instructions to the user about the Google Docs Research Assistant and its capabilities.

                 Welcome to the Google Docs Research Assistant!
I’m here to retrieve specific information from your provided Google Docs 📄 using the Google Docs Retriever and then dig deeper with research using tools like Google Search, Reddit, Wikipedia, and more 🌐. Whether you're exploring a topic or verifying facts, I’ve got you covered.

Just let me know what you’re looking for—and let’s get started! 🔍✨

Components used in this flow

Below is a complete list of all components used in this flow to achieve its functionality. Components are the building blocks of every AI Flow. They allow you to create complex interactions and automate tasks by connecting various functionalities. Each component serves a specific purpose, such as handling user input, processing data, or integrating with external services.

Chat Opened Trigger

The Chat Opened Trigger component detects when a chat session starts, enabling workflows to respond instantly as soon as a user opens the chat. It initiates flows with the initial chat message, making it essential for building responsive, interactive chatbots.

See Chat Opened Trigger

ChatInput

The Chat Input component in FlowHunt initiates user interactions by capturing messages from the Playground. It serves as the starting point for flows, enabling the workflow to process both text and file-based inputs.

See ChatInput

Chat Output

Discover the Chat Output component in FlowHunt—finalize chatbot responses with flexible, multi-part outputs. Essential for seamless flow completion and creating advanced, interactive AI chatbots.

See Chat Output

Message Widget

The Message Widget component displays custom messages within your workflow. Ideal for welcoming users, providing instructions, or showing any important information, it supports Markdown formatting and can be set to appear only once per session.

See Message Widget

Chat History Component

The Chat History component in FlowHunt enables chatbots to remember previous messages, ensuring coherent conversations and improved customer experience while optimizing memory and token usage.

See Chat History Component

Google Docs Retriever

Integrate your workflows with Google Docs using the Google Docs Retriever component—seamlessly fetch document content for use in automations, chatbots, or knowledge workflows. Ideal for accessing, processing, and leveraging your Google Docs within FlowHunt flows.

See Google Docs Retriever

Tool Calling Agent

Explore the Tool Calling Agent in FlowHunt—an advanced workflow component that enables AI agents to intelligently select and use external tools to answer complex queries. Perfect for building smart AI solutions that require dynamic tool usage, iterative reasoning, and integration with multiple resources.

See Tool Calling Agent

GoogleSearch Component

FlowHunt's GoogleSearch component enhances chatbot accuracy using Retrieval-Augmented Generation (RAG) to access up-to-date knowledge from Google. Control results with options like language, country, and query prefixes for precise and relevant outputs.

See GoogleSearch Component

URL Retriever

Unlock web content in your workflows with the URL Retriever component. Effortlessly extract and process the text and metadata from any list of URLs—including web articles, documents, and more. Supports advanced options like OCR for images, selective metadata extraction, and customizable caching, making it ideal for building knowledge-rich AI flows and automations.

See URL Retriever

Wikipedia Tool

Effortlessly chat with any Wikipedia page using FlowHunt's AI Agents. Get concise summaries, source links, and turn hours of research into interactive insights.

See Wikipedia Tool

Flow description

Purpose and benefits

This workflow, titled Google Docs AI Agent Researcher, is designed to help users efficiently extract information from a Google Document and then expand, validate, or enrich that information using AI research tools. It automates the process of combining document retrieval with advanced research capabilities, making it valuable for anyone needing deeper insights from their documents or requiring rapid fact-checking and context gathering.

Overview of the Workflow

Upon launching, the workflow greets the user and waits for their query. The user provides a prompt (i.e., what they’re looking for) and optionally attaches a Google Doc. The system then retrieves the content of the Google Doc and analyzes it using an AI agent. This agent is equipped with multiple tools, including:

  • Google Docs Retriever: Extracts content from the provided Google Doc.
  • Google Search: Performs web searches to find additional relevant material.
  • URL Content Retriever: Fetches and parses content from specific URLs.
  • Wikipedia Tool: Answers general questions and provides background information.
  • Chat Memory: Maintains chat history for context-aware responses.

The AI agent combines these sources to extract the requested information, enrich it, and present the findings back to the user in the chat interface.

Step-by-Step Flow

StepActionTool/ComponentPurpose
1User opens chatChat Opened Trigger & Message WidgetDisplays a welcome message and instructions
2User submits query and/or attaches Google DocChat InputCaptures user prompt and document
3Chat history is maintainedChat HistorySupports context-aware responses
4Google Docs content is retrievedGoogle Docs RetrieverMakes document content available to the AI agent
5AI agent processes requestTool Calling AgentUses all research tools to find, analyze, and synthesize the answer
6Agent leverages external toolsGoogle Search, URL Retriever, WikipediaExpands, validates, and enriches the information
7Final answer displayed to userChat OutputPresents results in the chat interface

How the Tools Work Together

  • The Tool Calling Agent acts as the central brain, orchestrating which tools to call based on the user’s query.
  • The agent can extract a specific piece of information from the Google Doc and, if needed, perform web searches or fetch content from URLs to gather further context or cross-check facts.
  • By accessing Wikipedia, the agent can provide general background or answer broader questions related to the query.

Example Use Cases

  • Academic Research: Extract a definition, claim, or reference from a shared Google Doc and see how it is discussed or validated in academic sources and the wider web.
  • Market Analysis: Pull a company name or statistic from a report and get the latest news, Wikipedia background, and other online mentions.
  • Fact Checking: Validate claims or facts presented in a document by comparing with up-to-date web and Wikipedia data.

Benefits for Scaling and Automation

This workflow is especially useful for scaling repetitive research or document analysis tasks, as it:

  • Saves Time: Automates information extraction and research, reducing manual effort.
  • Ensures Accuracy: Cross-references document content with reliable online sources for validation.
  • Enhances Insights: Provides enriched responses by combining document, web, and encyclopedic knowledge.
  • Supports Collaboration: Ideal for teams sharing and discussing documents, as all research happens in a unified chat environment.

Summary Table of Main Components

ComponentFunctionality
Chat Input/OutputUser interface for queries and responses
Message WidgetDisplays welcome and instructional messages
Chat HistoryMaintains conversational context
Google Docs RetrieverExtracts document content
Google SearchFinds relevant web content
URL RetrieverPulls content from specific URLs
Wikipedia ToolProvides factual background and explanations
Tool Calling AgentCoordinates all tools and synthesizes the answer

By automating both the extraction and enrichment of information, this workflow empowers users to get more value from their documents—faster and with greater confidence.

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