Create a general-purpose chatbot

Chatbot is one of the most common AI scenarios. However, effectively understanding user queries and responding appropriately remains a challenge. RAGFlow's general-purpose chatbot agent is our attempt to tackle this longstanding issue.

This chatbot closely resembles the chatbot introduced in Start an AI chat, but with a key difference - it introduces a reflective mechanism that allows it to improve the retrieval from the target knowledge bases by rewriting the user's query.

This document provides guides on creating such a chatbot using our chatbot template.

Prerequisites

1. Ensure you have properly set the LLM to use. See the guides on Configure your API key or Deploy a local LLM for more information.
2. Ensure you have a knowledge base configured and the corresponding files properly parsed. See the guide on Configure a knowledge base for more information.
3. Make sure you have read the Introduction to Agentic RAG.

Create a chatbot agent from template

To create a general-purpose chatbot agent using our template:

1. Click the Agent tab in the middle top of the page to show the Agent page.

2. Click + Create agent on the top right of the page to show the agent template page.

3. On the agent template page, hover over the card on General-purpose chatbot and click Use this template.
   You are now directed to the no-code workflow editor page.

   

NOTE

RAGFlow's no-code editor spares you the trouble of coding, making agent development effortless.

Understand each component in the template

Here’s a breakdown of each component and its role and requirements in the chatbot template:

• Begin

  • Function: Sets the opening greeting for the user.
  • Purpose: Establishes a welcoming atmosphere and prepares the user for interaction.

• Answer

  • Function: Serves as the interface between human and the bot.
  • Role: Acts as the downstream component of Begin.
  • Note: Though named "Answer", it does not engage with the LLM.

• Retrieval

  • Function: Retrieves information from specified knowledge base(s).
  • Requirement: Must have knowledgebases set up to function.

• Relevant

  • Function: Assesses the relevance of the retrieved information from the Retrieval component to the user query.
  • Process:
    • If relevant, it directs the data to the Generate component for final response generation.
    • Otherwise, it triggers the Rewrite component to refine the user query and redo the retrival process.

• Generate

  • Function: Prompts the LLM to generate responses based on the retrieved information.
  • Note: The prompt settings allow you to control the way in which the LLM generates responses. Be sure to review the prompts and make necessary changes.

• Rewrite:

  • Function: Refines a user query when no relevant information from the knowledge base is retrieved.
  • Usage: Often used in conjunction with Relevant and Retrieval to create a reflective/feedback loop.

Configure your chatbot agent

1. Click Begin to set an opening greeting:
   

2. Click Retrieval to select the right knowledge base(s) and make any necessary adjustments:
   

3. Click Generate to configure the LLM's summarization behavior:
   3.1. Confirm the model.
   3.2. Review the prompt settings. If there are variables, ensure they match the correct component IDs:
   

4. Click Relevant to review or change its settings:
   You may retain the current settings, but feel free to experiment with changes to understand how the agent operates.

5. Click Rewrite to select a different model for query rewriting or update the maximum loop times for query rewriting:
   

NOTE

Increasing the maximum loop times may significantly extend the time required to receive the final response.

1. Update your workflow where you see necessary.

2. Click to Save to apply your changes.
   Your agent appears as one of the agent cards on the Agent page.

Test your chatbot agent

1. Find your chatbot agent on the Agent page:
   

2. Experiment with your questions to verify if this chatbot functions as intended: