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UI updates (#9836)
### What problem does this PR solve? ### Type of change - [x] Documentation Update
This commit is contained in:
writinwaters
@ -9,12 +9,12 @@ Key concepts, basic operations, a quick view of the agent editor.
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---
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## Key concepts
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:::danger DEPRECATED!
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A new version is coming soon.
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:::
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## Key concepts
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Agents and RAG are complementary techniques, each enhancing the other’s capabilities in business applications. RAGFlow v0.8.0 introduces an agent mechanism, featuring a no-code workflow editor on the front end and a comprehensive graph-based task orchestration framework on the back end. This mechanism is built on top of RAGFlow's existing RAG solutions and aims to orchestrate search technologies such as query intent classification, conversation leading, and query rewriting to:
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- Provide higher retrievals and,
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@ -33,55 +33,19 @@ Before proceeding, ensure that:
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Click the **Agent** tab in the middle top of the page to show the **Agent** page. As shown in the screenshot below, the cards on this page represent the created agents, which you can continue to edit.
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We also provide templates catered to different business scenarios. You can either generate your agent from one of our agent templates or create one from scratch:
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1. Click **+ Create agent** to show the **agent template** page:
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2. To create an agent from scratch, click the **Blank** card. Alternatively, to create an agent from one of our templates, hover over the desired card, such as **General-purpose chatbot**, click **Use this template**, name your agent in the pop-up dialogue, and click **OK** to confirm.
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2. To create an agent from scratch, click **Create Agent**. Alternatively, to create an agent from one of our templates, click the desired card, such as **Deep Research**, name your agent in the pop-up dialogue, and click **OK** to confirm.
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*You are now taken to the **no-code workflow editor** page. The left panel lists the components (operators): Above the dividing line are the RAG-specific components; below the line are tools. We are still working to expand the component list.*
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*You are now taken to the **no-code workflow editor** page.*
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3. General speaking, now you can do the following:
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- Drag and drop a desired component to your workflow,
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- Select the knowledge base to use,
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- Update settings of specific components,
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- Update LLM settings
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- Sets the input and output for a specific component, and more.
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4. Click **Save** to apply changes to your agent and **Run** to test it.
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## Components
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Please review the flowing description of the RAG-specific components before you proceed:
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| Component | Description |
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|----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| **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. |
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| **Generate** | A component that prompts the LLM to generate responses. You must ensure the prompt is set correctly. |
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| **Interact** | A component that serves as the interface between human and the bot, receiving user inputs and displaying the agent's responses. |
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| **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. |
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| **Message** | A component that sends out a static message. If multiple messages are supplied, it randomly selects one to send. Ensure its downstream is **Interact**, the interface component. |
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| **Rewrite** | A component that rewrites a user query from the **Interact** component, based on the context of previous dialogues. |
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| **Keyword** | A component that extracts keywords from a user query, with TopN specifying the number of keywords to extract. |
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:::caution NOTE
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- Ensure **Rewrite**'s upstream component is **Relevant** and downstream component is **Retrieval**.
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- Ensure the downstream component of **Message** is **Interact**.
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- The downstream component of **Begin** is always **Interact**.
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:::
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## Basic operations
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| Operation | Description |
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|---------------------------|------------------------------------------------------------------------------------------------------------------------------------------|
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| Add a component | Drag and drop the desired component from the left panel onto the canvas. |
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| Delete a component | On the canvas, hover over the three dots (...) of the component to display the delete option, then select it to remove the component. |
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| Copy a component | On the canvas, hover over the three dots (...) of the component to display the copy option, then select it to make a copy the component. |
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| Update component settings | On the canvas, click the desired component to display the component settings. |
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3. Click the **+** button on the **Begin** component to select the desired components in your workflow.
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4. Click **Save** to apply changes to your agent.
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@ -10,4 +10,6 @@ You can use iframe to embed an agent into a third-party webpage.
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1. Before proceeding, you must [acquire an API key](../models/llm_api_key_setup.md); otherwise, an error message would appear.
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2. On the **Agent** page, click an intended agent to access its editing page.
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3. Click **Management > Embed into webpage** on the top right corner of the canvas to show the **iframe** window:
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4. Copy the iframe and embed it into a specific location on your webpage.
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4. Copy the iframe and embed it into your webpage.
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@ -1,109 +0,0 @@
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---
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sidebar_position: 2
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slug: /general_purpose_chatbot
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---
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# Create chatbot
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Create a general-purpose chatbot.
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---
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:::danger DEPRECATED!
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A new version is coming soon.
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:::
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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.
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This chatbot closely resembles the chatbot introduced in [Start an AI chat](../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.
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This document provides guides on creating such a chatbot using our chatbot template.
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## Prerequisites
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1. Ensure you have properly set the LLM to use. See the guides on [Configure your API key](../models/llm_api_key_setup.md) or [Deploy a local LLM](../models/deploy_local_llm.mdx) for more information.
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2. Ensure you have a knowledge base configured and the corresponding files properly parsed. See the guide on [Configure a knowledge base](../dataset/configure_knowledge_base.md) for more information.
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3. Make sure you have read the [Introduction to Agentic RAG](./agent_introduction.md).
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## Create a chatbot agent from template
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To create a general-purpose chatbot agent using our template:
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1. Click the **Agent** tab in the middle top of the page to show the **Agent** page.
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2. Click **+ Create agent** on the top right of the page to show the **agent template** page.
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3. On the **agent template** page, hover over the card on **General-purpose chatbot** and click **Use this template**.
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*You are now directed to the **no-code workflow editor** page.*
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:::tip NOTE
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RAGFlow's no-code editor spares you the trouble of coding, making agent development effortless.
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:::
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## Understand each component in the template
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Here’s a breakdown of each component and its role and requirements in the chatbot template:
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- **Begin**
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- Function: Sets an opening greeting for users.
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- Purpose: Establishes a welcoming atmosphere and prepares the user for interaction.
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- **Interact**
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- Function: Serves as the interface between human and the bot.
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- Role: Acts as the downstream component of **Begin**.
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- **Retrieval**
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- Function: Retrieves information from specified knowledge base(s).
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- Requirement: Must have `knowledgebases` set up to function.
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- **Relevant**
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- Function: Assesses the relevance of the retrieved information from the **Retrieval** component to the user query.
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- Process:
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- If relevant, it directs the data to the **Generate** component for final response generation.
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- Otherwise, it triggers the **Rewrite** component to refine the user query and redo the retrival process.
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- **Generate**
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- Function: Prompts the LLM to generate responses based on the retrieved information.
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- 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.
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- **Rewrite**:
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- Function: Refines a user query when no relevant information from the knowledge base is retrieved.
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- Usage: Often used in conjunction with **Relevant** and **Retrieval** to create a reflective/feedback loop.
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## Configure your chatbot agent
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1. Click **Begin** to set an opening greeting:
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2. Click **Retrieval** to select the right knowledge base(s) and make any necessary adjustments:
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3. Click **Generate** to configure the LLM's summarization behavior:
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3.1. Confirm the model.
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3.2. Review the prompt settings. If there are variables, ensure they match the correct component IDs:
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4. Click **Relevant** to review or change its settings:
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*You may retain the current settings, but feel free to experiment with changes to understand how the agent operates.*
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5. Click **Rewrite** to select a different model for query rewriting or update the maximum loop times for query rewriting:
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:::danger NOTE
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Increasing the maximum loop times may significantly extend the time required to receive the final response.
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:::
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1. Update your workflow where you see necessary.
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2. Click to **Save** to apply your changes.
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*Your agent appears as one of the agent cards on the **Agent** page.*
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## Test your chatbot agent
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1. Find your chatbot agent on the **Agent** page:
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2. Experiment with your questions to verify if this chatbot functions as intended:
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@ -11,7 +11,9 @@ Conduct an AI search.
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An AI search is a single-turn AI conversation using a predefined retrieval strategy (a hybrid search of weighted keyword similarity and weighted vector similarity) and the system's default chat model. It does not involve advanced RAG strategies like knowledge graph, auto-keyword, or auto-question. The related chunks are listed below the chat model's response in descending order based on their similarity scores.
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:::tip NOTE
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When debugging your chat assistant, you can use AI search as a reference to verify your model settings and retrieval strategy.
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@ -22,10 +24,8 @@ When debugging your chat assistant, you can use AI search as a reference to veri
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- Ensure that you have configured the system's default models on the **Model providers** page.
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- Ensure that the intended knowledge bases are properly configured and the intended documents have finished file parsing.
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## Frequently asked questions
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### Key difference between an AI search and an AI chat?
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A chat is a multi-turn AI conversation where you can define your retrieval strategy (a weighted reranking score can be used to replace the weighted vector similarity in a hybrid search) and choose your chat model. In an AI chat, you can configure advanced RAG strategies, such as knowledge graphs, auto-keyword, and auto-question, for your specific case. Retrieved chunks are not displayed along with the answer.
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@ -15,13 +15,13 @@ From v0.17.0 onward, RAGFlow supports integrating agentic reasoning in an AI cha
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To activate this feature:
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1. Enable the **Reasoning** toggle under the **Prompt engine** tab of your chat assistant dialogue.
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1. Enable the **Reasoning** toggle in **Chat setting**.
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2. Enter the correct Tavily API key under the **Assistant settings** tab of your chat assistant dialogue to leverage Tavily-based web search
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2. Enter the correct Tavily API key to leverage Tavily-based web search:
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*The following is a screenshot of a conversation that integrates Deep Research:*
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@ -9,7 +9,7 @@ Set variables to be used together with the system prompt for your LLM.
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---
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When configuring the system prompt for a chat model, variables play an important role in enhancing flexibility and reusability. With variables, you can dynamically adjust the system prompt to be sent to your model. In the context of RAGFlow, if you have defined variables in the **Chat Configuration** dialogue, except for the system's reserved variable `{knowledge}`, you are required to pass in values for them from RAGFlow's [HTTP API](../../references/http_api_reference.md#converse-with-chat-assistant) or through its [Python SDK](../../references/python_api_reference.md#converse-with-chat-assistant).
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When configuring the system prompt for a chat model, variables play an important role in enhancing flexibility and reusability. With variables, you can dynamically adjust the system prompt to be sent to your model. In the context of RAGFlow, if you have defined variables in **Chat setting**, except for the system's reserved variable `{knowledge}`, you are required to pass in values for them from RAGFlow's [HTTP API](../../references/http_api_reference.md#converse-with-chat-assistant) or through its [Python SDK](../../references/python_api_reference.md#converse-with-chat-assistant).
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:::danger IMPORTANT
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In RAGFlow, variables are closely linked with the system prompt. When you add a variable in the **Variable** section, include it in the system prompt. Conversely, when deleting a variable, ensure it is removed from the system prompt; otherwise, an error would occur.
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@ -17,9 +17,7 @@ In RAGFlow, variables are closely linked with the system prompt. When you add a
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## Where to set variables
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Hover your mouse over your chat assistant, click **Edit** to open its **Chat Configuration** dialogue, then click the **Prompt engine** tab. Here, you can work on your variables in the **System prompt** field and the **Variable** section:
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## 1. Manage variables
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@ -42,8 +40,6 @@ Besides `{knowledge}`, you can also define your own variables to pair with the s
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- **Disabled** (Default): The variable is mandatory and must be provided.
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- **Enabled**: The variable is optional and can be omitted if not needed.
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## 2. Update system prompt
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After you add or remove variables in the **Variable** section, ensure your changes are reflected in the system prompt to avoid inconsistencies or errors. Here's an example:
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@ -77,28 +77,24 @@ You start an AI conversation by creating an assistant.
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5. Now, let's start the show:
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:::tip NOTE
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1. Click the light bulb icon above the answer to view the expanded system prompt:
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*The light bulb icon is available only for the current dialogue.*
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2. Scroll down the expanded prompt to view the time consumed for each task:
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:::
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## Update settings of an existing chat assistant
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Hover over an intended chat assistant **>** **Edit** to show the chat configuration dialogue:
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## Integrate chat capabilities into your application or webpage
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@ -113,6 +109,8 @@ You can use iframe to embed the created chat assistant into a third-party webpag
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1. Before proceeding, you must [acquire an API key](../models/llm_api_key_setup.md); otherwise, an error message would appear.
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2. Hover over an intended chat assistant **>** **Edit** to show the **iframe** window:
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3. Copy the iframe and embed it into a specific location on your webpage.
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3. Copy the iframe and embed it into your webpage.
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@ -16,7 +16,7 @@ Knowledge base, hallucination-free chat, and file management are the three pilla
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With multiple knowledge bases, you can build more flexible, diversified question answering. To create your first knowledge base:
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_Each time a knowledge base is created, a folder with the same name is generated in the **root/.knowledgebase** directory._
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@ -24,7 +24,7 @@ _Each time a knowledge base is created, a folder with the same name is generated
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The following screenshot shows the configuration page of a knowledge base. A proper configuration of your knowledge base is crucial for future AI chats. For example, choosing the wrong embedding model or chunking method would cause unexpected semantic loss or mismatched answers in chats.
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This section covers the following topics:
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@ -56,7 +56,7 @@ RAGFlow offers multiple chunking template to facilitate chunking files of differ
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You can also change a file's chunking method on the **Datasets** page.
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### Select embedding model
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@ -82,10 +82,8 @@ While uploading files directly to a knowledge base seems more convenient, we *hi
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File parsing is a crucial topic in knowledge base configuration. The meaning of file parsing in RAGFlow is twofold: chunking files based on file layout and building embedding and full-text (keyword) indexes on these chunks. After having selected the chunking method and embedding model, you can start parsing a file:
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- Click the play button next to **UNSTART** to start file parsing.
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- Click the red-cross icon and then refresh, if your file parsing stalls for a long time.
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- As shown above, RAGFlow allows you to use a different chunking method for a particular file, offering flexibility beyond the default method.
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- As shown above, RAGFlow allows you to enable or disable individual files, offering finer control over knowledge base-based AI chats.
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@ -97,13 +95,13 @@ RAGFlow features visibility and explainability, allowing you to view the chunkin
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_You are taken to the **Chunk** page:_
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2. Hover over each snapshot for a quick view of each chunk.
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3. Double-click the chunked texts to add keywords or make *manual* changes where necessary:
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3. Double-click the chunked texts to add keywords, questions, tags, or make *manual* changes where necessary:
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:::caution NOTE
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You can add keywords to a file chunk to increase its ranking for queries containing those keywords. This action increases its keyword weight and can improve its position in search list.
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@ -113,7 +111,7 @@ You can add keywords to a file chunk to increase its ranking for queries contain
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_As you can tell from the following, RAGFlow responds with truthful citations._
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### Run retrieval testing
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@ -124,13 +122,11 @@ RAGFlow uses multiple recall of both full-text search and vector search in its c
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See [Run retrieval test](./run_retrieval_test.md) for details.
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## Search for knowledge base
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As of RAGFlow v0.20.4, the search feature is still in a rudimentary form, supporting only knowledge base search by name.
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## Delete knowledge base
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@ -139,4 +135,4 @@ You are allowed to delete a knowledge base. Hover your mouse over the three dot
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- The files uploaded directly to the knowledge base are gone;
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- The file references, which you created from within **File Management**, are gone, but the associated files still exist in **File Management**.
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@ -31,7 +31,7 @@ RAPTOR (Recursive Abstractive Processing for Tree Organized Retrieval) can also
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The system's default chat model is used to generate knowledge graph. Before proceeding, ensure that you have a chat model properly configured:
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||||
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||||
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## Configurations
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@ -74,7 +74,7 @@ In a knowledge graph, a community is a cluster of entities linked by relationshi
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3. Click **Knowledge graph** to view the details of the generated graph.
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4. To use the created knowledge graph, do either of the following:
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- In your **Chat Configuration** dialogue, click the **Assistant settings** tab to add the corresponding knowledge base(s) and click the **Prompt engine** tab to switch on the **Use knowledge graph** toggle.
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- In the **Chat setting** panel of your chat app, switch on the **Use knowledge graph** toggle.
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- If you are using an agent, click the **Retrieval** agent component to specify the knowledge base(s) and switch on the **Use knowledge graph** toggle.
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||||
## Frequently asked questions
|
||||
|
||||
@ -39,7 +39,7 @@ Knowledge graphs can also be used for multi-hop question-answering tasks. See [C
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||||
|
||||
The system's default chat model is used to summarize clustered content. Before proceeding, ensure that you have a chat model properly configured:
|
||||
|
||||
|
||||
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||||
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||||
## Configurations
|
||||
|
||||
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||||
@ -13,13 +13,13 @@ On the **Dataset** page of your knowledge base, you can add metadata to any uplo
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||||
For example, if you have a dataset of HTML files and want the LLM to cite the source URL when responding to your query, add a `"url"` parameter to each file's metadata.
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||||
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||||
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||||
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||||
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||||
:::tip NOTE
|
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Ensure that your metadata is in JSON format; otherwise, your updates will not be applied.
|
||||
:::
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||||
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||||
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||||
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||||
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||||
## Frequently asked questions
|
||||
|
||||
|
||||
@ -42,13 +42,7 @@ After logging into RAGFlow, configuring your model API key through the **service
|
||||
After logging into RAGFlow, you can *only* configure API Key on the **Model providers** page:
|
||||
|
||||
1. Click on your logo on the top right of the page **>** **Model providers**.
|
||||
2. Find your model card under **Models to be added** and click **Add the model**:
|
||||
|
||||
2. Find your model card under **Models to be added** and click **Add the model**.
|
||||
3. Paste your model API key.
|
||||
4. Fill in your base URL if you use a proxy to connect to the remote service.
|
||||
5. Click **OK** to confirm your changes.
|
||||
|
||||
:::note
|
||||
To update an existing model API key:
|
||||
|
||||
:::
|
||||
5. Click **OK** to confirm your changes.
|
||||
@ -26,20 +26,12 @@ You cannot invite users to a team unless you are its owner.
|
||||
|
||||
## Accept or decline team invite
|
||||
|
||||
1. You will be notified when you receive an invitation to join a team:
|
||||
|
||||
|
||||
1. You will be notified on the top right corner of your system page when you receive an invitation to join a team.
|
||||
|
||||
2. Click on your avatar in the top right corner of the page, then select **Team** in the left-hand panel to access the **Team** page.
|
||||
|
||||
|
||||
|
||||
_On the **Team** page, you can view the information about members of your team and the teams you have joined._
|
||||
|
||||
|
||||
|
||||
_After accepting the team invite, you should be able to view and update the team owner's knowledge bases whose **Permissions** is set to **Team**._
|
||||
|
||||
## Leave a joined team
|
||||
|
||||
|
||||
## Leave a joined team
|
||||
@ -29,14 +29,14 @@ By default, each RAGFlow user is assigned a single team named after their name.
|
||||
|
||||
Click on your avatar in the top right corner of the page, then select **Team** in the left-hand panel to access the **Team** page.
|
||||
|
||||
|
||||
|
||||
|
||||
_On the **Team** page, you can view the information about members of your team and the teams you have joined._
|
||||
|
||||
You are, by default, the owner of your own team and the only person permitted to invite users to join your team or remove team members.
|
||||
|
||||
|
||||
|
||||
|
||||
## Remove team members
|
||||
|
||||
|
||||
|
||||
@ -12,12 +12,8 @@ Share an Agent with your team members.
|
||||
When ready, you may share your Agents with your team members so that they can use them. Please note that your Agents are not shared automatically; you must manually enable sharing by selecting the corresponding **Permissions** radio button:
|
||||
|
||||
1. Click the intended Agent to open its editing canvas.
|
||||
2. Click **Settings** to show the **Agent settings** dialogue.
|
||||
2. Click **Management** > **Settings** to show the **Agent settings** dialogue.
|
||||
3. Change **Permissions** from **Only me** to **Team**.
|
||||
4. Click **Save** to apply your changes.
|
||||
|
||||
|
||||
|
||||
*When completed, your team members will see your shared Agents like this:*
|
||||
|
||||
|
||||
*When completed, your team members will see your shared Agents.*
|
||||
@ -15,8 +15,4 @@ When ready, you may share your knowledge bases with your team members so that th
|
||||
2. Change **Permissions** from **Only me** to **Team**.
|
||||
3. Click **Save** to apply your changes.
|
||||
|
||||
|
||||
|
||||
*Once completed, your team members will see your shared knowledge bases like this:*
|
||||
|
||||
|
||||
*Once completed, your team members will see your shared knowledge bases.*
|
||||
Reference in New Issue
Block a user