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Initial draft of Create a General-purpose chatbot (#2411)

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docs/guides/agentic_rag/agentic_rag_introduction.md
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@ -51,9 +51,9 @@ Please review the flowing description of the RAG-specific components before you
| Component | Description |
| -------------- | ------------------------------------------------------------ |
| **Retrieval** | A component that retrieves information from a specified knowledge base and returns 'Empty response' if no information is found. Ensure the correct knowledge base is selected. |
| **Retrieval** | A component that retrieves information from specified knowledge bases and returns 'Empty response' if no information is found. Ensure the correct knowledge bases are selected. |
| **Generate** | A component that prompts the LLM to generate responses. You must ensure the prompt is set correctly. |
| **Answer** | A component that serves as the interface between human and bot, receiving user inputs and displaying the agent's responses. |
| **Answer** | A component that serves as the interface between human and the bot, receiving user inputs and displaying the agent's responses. |
| **Categorize** | A component that uses the LLM to classify user inputs into predefined categories. Ensure you specify the name, description, and examples for each category, along with the corresponding next component. |
| **Message** | A component that sends out a static message. If multiple messages are supplied, it randomly selects one to send. Ensure its downstream is **Answer**, the interface component. |
| **Relevant** | A component that uses the LLM to assess whether the upstream output is relevant to the user's latest query. Ensure you specify the next component for each judge result. |

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docs/guides/agentic_rag/general_purpose_chatbot.md Normal file
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---
sidebar_position: 2
slug: /general_purpose_chatbot
---
# Create and configure a general-purpose chatbot agent
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](../start_chat.md), 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](../llm_api_key_setup.md) or [Deploy a local LLM](../deploy_local_llm.mdx) 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](../configure_knowledge_base.md) for more information.
3. Make sure you have read the [Introduction to Agentic RAG](./agentic_rag_introduction.md).
## 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.*
![workflow_editor](https://github.com/user-attachments/assets/9fc6891c-7784-43b8-ab4a-3b08a9e551c4)
:::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.
- **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:
![opener](https://github.com/user-attachments/assets/4416bc16-2a84-4f24-a19b-6dc8b1de0908)
2. Click **Retrieval** to select the right knowledge base(s) and make any necessary adjustments:
![setting_knowledge_bases](https://github.com/user-attachments/assets/5f694820-5651-45bc-afd6-cf580ca0228d)
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:
![prompt_settings](https://github.com/user-attachments/assets/19e94ea7-7f62-4b73-b526-32fcfa62f1e9)
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.*
![relevant_settings](https://github.com/user-attachments/assets/f582cc1c-0dd5-499c-813a-294dbfb941dd)
5. Click **Rewrite** to select a different model for query rewriting or update the maximum loop times for query rewriting:
![choose_model](https://github.com/user-attachments/assets/2bac1d6c-c4f1-42ac-997b-102858c3f550)
![loop_time](https://github.com/user-attachments/assets/09a4ce34-7aac-496f-aa59-d8aa33bf0b1f)
:::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:
![find_chatbot](https://github.com/user-attachments/assets/6e6382c6-9a86-4190-9fdd-e363b7f64ba9)
2. Experiment with your questions to verify if this chatbot functions as intended:
![test_chatbot](https://github.com/user-attachments/assets/c074d3bd-4c39-4b05-a68b-1fd361f256b3)