Tool Calling Agent
Agent that answers user questions using Google Search, with a detailed system prompt to guide the LLM's behavior.
AI-Powered Google Answer Chatbot
An AI chatbot that provides instant, up-to-date answers to any question by searching Google and retrieving relevant website content, always including source links. Ideal for anyone seeking fast, reliable information across any topic.
Flows
How the AI Flow works
- User Starts Chat.
- The user opens the chat interface and is greeted with example questions to get started.
- User Submits Question.
- The user enters a question or selects from pre-set options to request information.
- AI Agent Searches Google.
- The AI agent uses Google Search to find relevant and up-to-date information for the user's query.
- Content Retrieved from URLs.
- Relevant website content is retrieved and processed to ensure the answer is accurate and sourced.
- Answer Presented with Sources.
- The chatbot displays a concise answer with direct links to the original sources for transparency.
Prompts used in this flow
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.
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.
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.
Button Widget
The Button Widget component in FlowHunt transforms text or input into interactive, clickable buttons within your workflow. Perfect for creating dynamic user interfaces, collecting user choices, and improving engagement in AI-driven chatbots or automated processes.
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.
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.
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.
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.
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.
Flow description
Purpose and benefits
The Google Answer Generator workflow is designed as a general AI chatbot with integrated Google search capabilities. Its primary function is to allow users to ask any question and receive up-to-date, concise, and well-referenced answers, complete with links to credible sources.
Workflow Overview
When a user opens the chat interface, a friendly welcome message is displayed, inviting them to ask questions. The interface also provides example buttons, such as “Give me the latest news” and “What’s the square root of Pi?”, to help guide the user on possible queries.
Once the user inputs a question (either by typing or selecting a preset button), the workflow utilizes an AI agent that is equipped with Google search tools and URL content extraction functionalities. The agent processes the question, searches Google for relevant information, retrieves content from the top URLs, and formulates a brief, source-supported answer. The final response is displayed back to the user in the chat interface.
Step-by-Step Breakdown
| Step | Component | Functionality |
|---|---|---|
| 1 | Chat Opened Trigger | Initiates workflow when user opens the chat, displaying a welcome message and example buttons. |
| 2 | Button Widgets | Provide users with example questions they can click to see how the chatbot works. |
| 3 | Chat Input | Receives user-typed questions. |
| 4 | Chat History | Maintains a memory of previous chat messages for contextual understanding. |
| 5 | Google Search Tool | Searches Google for the most relevant and up-to-date information related to the user’s query. |
| 6 | URL Retriever | Fetches and extracts content from the URLs returned by Google Search. |
| 7 | Tool Calling Agent | The central AI component that orchestrates search, content retrieval, and answer generation. |
| 8 | Chat Output | Displays the AI-generated answer with links directly in the chat interface for the user. |
Key Features and Design Choices
- User Experience: The chatbot welcomes users and provides example queries, making it accessible even to first-time users.
- Always Current: By integrating Google search, the bot delivers information that is as up-to-date as possible.
- Cited Sources: Answers always include links to the information sources, improving credibility and transparency.
- Efficient Communication: The AI agent is instructed to avoid generic introductions or conclusions, focusing only on concise, factual answers.
- Context Awareness: Chat history is used to maintain context, enabling more coherent and relevant responses in ongoing conversations.
- Scalable and Automated: The modular structure allows for easy scaling—handling multiple users or being extended with new tools and integrations.
Why This Workflow Is Useful
This workflow is ideal for automating the process of question answering in scenarios where real-time, accurate, and source-backed information is needed. It can be deployed as a customer support assistant, research helper, or general Q&A bot on websites and applications. By combining the power of AI and live web search, it delivers a scalable solution for providing users with reliable answers without human intervention.
Example Use Cases
- Customer Support: Instantly answer customer queries with the latest information from across the web.
- Research Assistance: Help users find quick, referenced answers for academic or professional research.
- Personal Knowledge Assistant: Provide users with an always-available assistant to answer any question with up-to-date information.
This workflow automates and scales the task of information retrieval and Q&A, reducing the need for manual research and response, and ensuring users always receive current, well-cited answers.
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