ragflow/docs/guides/agentic_rag/general_purpose_chatbot.md

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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.*

:::tip 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.

• Interact

  • Function: Serves as the interface between human and the bot.
  • Role: Acts as the downstream component of Begin.
    

• 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:
   

:::danger 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: