AI Agent
An AI Agent that connects to tools and generates responses based on user questions, with a specific goal prompt.
Your goal is to answer the user's questions from {input}. Answer only questions. Include links to sources.
AI Chatbot with Real-Time Web & Knowledge Search
A powerful AI chatbot that answers user questions in real-time by retrieving and synthesizing information from Google, Reddit, Wikipedia, Arxiv, Stack Exchange, YouTube, PubMed, and website URLs, providing source-backed answers for research, learning, or general inquiries.
How the AI Flow works
User Initiates Chat
The user opens the chat interface and can input a question or select from example prompts.Multi-Source Information Retrieval
The AI agent connects to various tools to search Google, Wikipedia, Reddit, scientific papers, videos, forums, PubMed, and website URLs for relevant information.AI Agent Synthesizes Answers
The AI agent gathers, analyzes, and synthesizes data from the connected sources to answer the user's query.Answer Displayed to User
The AI chatbot presents a comprehensive, source-backed answer 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.
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.
Flow description
Purpose and benefits
Multi-source AI Answer Generator: Workflow Description
This workflow builds an advanced AI chatbot that can answer user questions by accessing information from multiple real-time sources, such as Google Search, Reddit, Wikipedia, StackExchange, PubMed, Arxiv, YouTube, and any provided URL. Its core goal is to deliver accurate, up-to-date responses, including links to the original sources, making it a powerful assistant for research, learning, and general information retrieval.
User Experience
Upon opening the chat interface, users are greeted with a friendly welcome message and several example questions presented as clickable buttons. For instance:
- What is amino acid sequence for human blood albumin?
- Who is Napoléon?
- What is trending on Reddit?
Users can either click on these buttons to see example interactions or type their own queries using the chat input.
Workflow Structure
The workflow consists of several interconnected components, orchestrated as follows:
| Component | Purpose |
|---|---|
| ChatOpenedTrigger | Detects when the chat is opened and initializes the UI. |
| ButtonWidgets | Show example queries as buttons for quick interaction. |
| ChatInputs/Outputs | Receives user text and displays AI responses. |
| AI Agent | The orchestrator; receives queries and selects tools. |
| External Tools/Plugins | Fetches information from various sources (see below). |
List of Connected Data Sources and Tools
The AI Agent can access and utilize the following sources and APIs:
- Google Search: Retrieves URLs and content from the web.
- Reddit: Searches for trending posts and discussions.
- Wikipedia: Fetches summaries for general knowledge queries.
- StackExchange: Answers programming and technical questions.
- Arxiv: Finds academic papers and research.
- PubMed: Fetches biomedical and health-related literature.
- YouTube: Searches for relevant videos.
- URL Retriever: Extracts content from any user-provided URL.
How the Workflow Operates
- Initialization: When the user opens the chat, example question buttons are displayed for convenience.
- User Query: The user can either click an example button or type a custom question.
- AI Agent Processing: The core AI Agent receives the question and, based on its nature, automatically selects the appropriate data sources/tools to fetch information.
- Data Aggregation: The Agent gathers results from one or more sources (e.g., Wikipedia for history, PubMed for medical, StackExchange for code, etc.).
- Response Generation: The AI synthesizes a concise, informative answer and includes links to original sources.
- Display: The answer is shown in the chat interface for the user to review.
The agent is specifically instructed to answer only questions and always include links to the sources, ensuring transparency and verifiability.
Benefits for Scaling and Automation
- Real-time, Up-to-date Answers: By drawing from live web sources, the chatbot avoids outdated knowledge typical of static AI models.
- Broad Coverage: From science to general trivia, the chatbot can handle a wide range of queries thanks to its multi-tool integration.
- Automation: The AI Agent automatically decides which source(s) to use, eliminating manual research steps.
- Research Efficiency: Users save time by getting synthesized, referenced answers from multiple platforms in one place.
- Scalability: The modular design allows adding more data sources or specialized tools as needed without reworking the entire flow.
Example Use Cases
- Students or researchers needing fast answers with references.
- Developers looking up programming solutions or code snippets.
- Anyone seeking current trends or expert opinions from forums like Reddit or StackExchange.
- Medical professionals or enthusiasts querying biomedical literature (via PubMed).
- General public wanting to verify facts from trusted sources.
Diagram of Core Flow
- Chat Opened
↓
Display Example Questions
↓
User Asks Question (via input or button)
↓
AI Agent Receives Query
↓
Selects & Queries External Sources
↓
Synthesizes & Outputs Answer with Source Links
↓
Response Displayed to User
In summary, this workflow creates a versatile, scalable, and automated AI assistant capable of delivering high-quality, referenced answers by leveraging a diverse set of real-time data sources. This greatly enhances research, learning, and productivity for users across domains.
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