Real-Time Domain-Specific RAG Chatbot

A real-time chatbot that uses Google Search restricted to your own domain, retrieves relevant web content, and leverages OpenAI LLM to answer user queries with up-to-date information. Ideal for providing accurate, domain-specific responses in customer support or information portals.

Flows

How the AI Flow works

User Query Input.: Captures user questions via chat input or pre-defined buttons.
Query Expansion.: Paraphrases and expands the user query to improve retrieval accuracy.
Domain-Specific Google Search.: Performs a Google Search limited to the specified domain using the expanded queries.
Web Content Retrieval.: Fetches the content from the top relevant URLs returned by the search.
LLM Response Generation.: Uses OpenAI LLM to generate a final, context-enriched answer displayed to the user.

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.

Prompt

Create a prompt template with dynamic variables for LLM, supporting fields such as {input}, {human_input}, {context}, {chat_history}, {system_message}.

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.

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

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.

See Button Widget

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

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

Prompt Component in FlowHunt

Learn how FlowHunt's Prompt component lets you define your AI bot’s role and behavior, ensuring relevant, personalized responses. Customize prompts and templates for effective, context-aware chatbot flows.

See Prompt Component in FlowHunt

Generator

Explore the Generator component in FlowHunt—powerful AI-driven text generation using your chosen LLM model. Effortlessly create dynamic chatbot responses by combining prompts, optional system instructions, and even images as input, making it a core tool for building intelligent, conversational workflows.

See Generator

LLM OpenAI

FlowHunt supports dozens of text generation models, including models by OpenAI. Here's how to use ChatGPT in your AI tools and chatbots.

See LLM OpenAI

Query Expansion

Query Expansion in FlowHunt enhances chatbot understanding by finding synonyms, fixing spelling errors, and ensuring consistent, accurate responses for user queries.

See Query Expansion

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

Flow description

Purpose and benefits

Overview

This workflow implements a simple Retrieval-Augmented Generation (RAG) chatbot that leverages real-time Google Search to retrieve up-to-date information from the internet—specifically, it can be customized to restrict all searches to a particular domain. The main goal is to create a chatbot that can answer user queries using the most relevant and recent content found online, making it highly valuable for scenarios where static knowledge bases are insufficient.

Key Components and Flow

The workflow is composed of several modular blocks, each representing a specific capability. Below is a breakdown of the workflow’s structure and functionality:

Component	Role
Chat Input	Receives user queries and chat messages.
Chat History	Maintains conversation history for context-aware responses.
Query Expansion	Paraphrases user input into multiple alternative queries to improve search coverage.
Google Search	Executes searches on Google, restricted by a customizable domain prefix.
URL Retriever	Extracts content from the URLs returned by Google Search.
Prompt Template	Structures context, user input, and history for the language model.
OpenAI LLM	Generates responses using a language model (e.g., GPT-3/4).
Generator	Invokes the LLM with the prompt and context to produce the answer.
Chat Output	Displays chatbot responses to the user.
Button Widgets	Provides quick example queries for users to try with a single click.
Chat Opened Trigger	Initializes the conversation and populates quick-start buttons.

How the Workflow Operates

When a user opens the chat, the Chat Opened Trigger activates. This initializes the chat interface and presents several Button Widgets with example queries (e.g., “what dinosaur has 500 teeth?”). When a user clicks a button or enters a custom message via Chat Input, the workflow proceeds as follows:

Query Expansion: The user’s input is paraphrased into multiple versions to maximize the likelihood of retrieving relevant search results.
Google Search: The expanded queries are sent to Google Search. By default, the search is limited to a specific domain (set by the query_prefix field, e.g., site: www.YOURDOMAIN.com), allowing you to focus the chatbot’s knowledge on your own website or any trusted source.
URL Retriever: The workflow retrieves the content of the top search results (URLs) as full documents.
Prompt Assembly: The retrieved content, user input, and chat history are combined using the Prompt Template component to provide rich context for the answer.
Language Model Generation: The prompt is sent to the OpenAI LLM, which generates a coherent and contextually relevant response.
Response Output: The generated answer is displayed to the user via the Chat Output.

Example Use Case Flow

User opens chat: Welcome message and three example question buttons appear.
User clicks “when is mother’s day 2024?”: The question is immediately shown in the chat output (for instant feedback).
The workflow runs the query through expansion, search, retrieval, prompt assembly, and LLM generation, then displays the answer.

Why This Workflow is Useful

Real-time Knowledge: The chatbot can answer questions using the latest information available on the internet or your chosen domain.
Domain Restriction: By customizing the query_prefix, you can ensure the chatbot sources information only from your trusted website or knowledge base, improving the reliability of answers.
Context Awareness: By including chat history and retrieved content in the prompt, responses can be tailored and contextually relevant for multi-turn conversations.
Scalability and Automation: The modular design allows the workflow to be easily extended or adapted for various domains, supporting large-scale deployment across different topics or websites.
User Experience: Quick-start buttons and instant feedback make the chatbot approachable for end-users.

Workflow Summary Table

Step	Description
User Input	User types a question or clicks a quick-start button
Query Expansion	Input is paraphrased for broader search coverage
Google Search	Searches are performed on Google, restricted to a given domain
URL Content Retrieval	Top search result contents are fetched
Prompt Construction	User input, search results, and chat history are compiled into a prompt
LLM Generation	OpenAI LLM generates a response using the full context
Output	Response is shown to the user

Customization

To focus the chatbot on your own domain, modify the query_prefix field in the Google Search component (e.g., site: www.YOURDOMAIN.com).
Add or change example queries using the Button Widget components for a more tailored user experience.

Ideal Use Cases

Customer support bots that always give answers based on your up-to-date documentation or web content.
Internal knowledge assistants limited to your company’s intranet or support portal.
Any chatbot that must always cite or rely on external, authoritative sources (e.g., for compliance or accuracy).

By automating the search, retrieval, and answer generation process, this workflow saves manual research time and ensures users always get the most current and relevant information available.

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