Docs: Refactored Retrieval component reference (#9862)

### What problem does this PR solve?

### Type of change

- [x] Documentation Update

docs/guides/agent/agent_component_reference/retrieval.mdx

@@ -9,19 +9,70 @@ A component that retrieves information from specified datasets.
 
 ## Scenarios
 
-A **Retrieval** component is essential in most RAG scenarios, where information is extracted from designated knowledge bases before being sent to the LLM for content generation. As of v0.20.4, a **Retrieval** component can operate either as a workflow component or as a tool of an **Agent**, enabling the Agent to control its invocation and search queries.
+A **Retrieval** component is essential in most RAG scenarios, where information is extracted from designated knowledge bases before being sent to the LLM for content generation. A **Retrieval** component can operate either as a standalone workflow module or as a tool for an **Agent** component. In the latter role, the **Agent** component has autonomous control over when to invoke it for query and retrieval.
+
+The following screenshot shows a reference design using the **Retrieval** component, where the component serves as a tool for an **Agent** component. You can find it from the **Report Agent Using Knowledge Base** Agent template.
+
+![retrieval_reference_design](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/retrieval_reference_design.jpg)
+
+## Prerequisites
+
+Ensure you [have properly configured your target knowledge base(s)](../../dataset/configure_knowledge_base.md).
+
+## Quickstart
+
+### 1. Click on a **Retrieval** component to show its configuration panel  
+
+The corresponding configuration panel appears to the right of the canvas. Use this panel to define and fine-tune the **Retrieval** component's search behavior.
+
+### 2. Input query variable(s)
+
+The **Retrieval** component relies on query variables to specify its queries. 
+
+:::caution IMPORTANT
+- If you use the **Retrieval** component as a standalone workflow module, input query variables in the **Input Variables** text box.
+- If it is used as a tool for an **Agent** component, input the query variables in the **Agent** component's **User prompt** field.
+:::
+
+By default, you can use `sys.query`, which is the user query and the default output of the **Begin** component. All global variables defined before the **Retrieval** component can also be used as query statements. Use the `(x)` button or type `/` to show all the available query variables.
+
+### 3. Select knowledge base(s) to query
+
+You can specify one or multiple knowledge bases to retrieve data from. If selecting mutiple, ensure they use the same embedding model.
+
+### 4. Expand **Advanced Settings** to configure the retrieval method
+
+By default, a combination of weighted keyword similarity and weighted vector cosine similarity is used during retrieval. If a rerank model is selected, a combination of weighted keyword similarity and weighted reranking score will be used for retrieval.
+
+As a starter, you can skip this step to stay with the default retrieval method.
+
+:::caution WARNING
+Using a rerank model will *significantly* increase the system's response time. If you must use a rerank model, ensure you use a SaaS reranker; if you prefer a locally deployed rerank model, ensure you start RAGFlow with **docker-compose-gpu.yml**.
+:::
+
+### 5. Enable cross-language search
+
+If your user query is different from the languages of the knowledge bases, you can select the target languages in the **Cross-language search** dropdown menu. The model will then translates queries to ensure accurate matching of semantic meaning across languages.
+
+
+### 6. Test retrieval results
+
+Click the triangle button on the top of canvas to test the retrieval results.
+
+### 6. Choose the next component
+
+When necessary, click the **+** button on the **Retrieval** component to choose the next component in the worflow from the dropdown list.
+
 
 ## Configurations
 
-Click on a **Retrieval** component to open its configuration window.
-
 ### Query variables
 
 *Mandatory*
 
-Select the query source for retrieval.
+Select the query source for retrieval. Defaults to `sys.query`, which is the default output of the **Begin** component.
 
-The **Retrieval** component relies on query variables to specify its data inputs (queries). All global variables defined before the **Retrieval** component are available in the dropdown list.  
+The **Retrieval** component relies on query variables to specify its queries. All global variables defined before the **Retrieval** component can also be used as queries. Use the `(x)` button or type `/` to show all the available query variables.
 
 ### Knowledge bases 
 
@@ -72,8 +123,23 @@ Select one or more languages for cross‑language search. If no language is sele
 
 ### Use knowledge graph
 
+:::caution IMPORTANT
+Before enabling this feature, ensure you have properly [constructed a knowledge graph from each target knowledge base](../../dataset/construct_knowledge_graph.md).
+:::
+
 Whether to use knowledge graph(s) in the specified knowledge base(s) during retrieval for multi-hop question answering. When enabled, this would involve iterative searches across entity, relationship, and community report chunks, greatly increasing retrieval time.
 
 ### Output
 
 The global variable name for the output of the **Retrieval** component, which can be referenced by other components in the workflow.
+
+
+## Frequently asked questions
+
+### How to reduce response time?
+
+Go through the checklist below for best performance:
+
+- Leave the **Rerank model** field empty.
+- If you must use a rerank model, ensure you use a SaaS reranker; if you prefer a locally deployed rerank model, ensure you start RAGFlow with **docker-compose-gpu.yml**.
+- Disable **Use knowledge graph**.

docs/quickstart.mdx

@@ -267,7 +267,7 @@ RAGFlow also supports deploying LLMs locally using Ollama, Xinference, or LocalA
 
 To add and configure an LLM: 
 
-1. Click on your logo on the top right of the page **>** **Model providers**。
+1. Click on your logo on the top right of the page **>** **Model providers**.
 
 2. Click on the desired LLM and update the API key accordingly.
 
@@ -286,7 +286,7 @@ You are allowed to upload files to a knowledge base in RAGFlow and parse them in
 
 To create your first knowledge base:
 
-1. Click the **Knowledge Base** tab in the top middle of the page **>** **Create knowledge base**.
+1. Click the **Dataset** tab in the top middle of the page **>** **Create dataset**.
 
 2. Input the name of your knowledge base and click **OK** to confirm your changes.
 
@@ -330,7 +330,7 @@ RAGFlow features visibility and explainability, allowing you to view the chunkin
    ![update chunk](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/add_keyword_question.jpg)
 
 :::caution NOTE
-You can add keywords to a file chunk to improve its ranking for queries containing those keywords. This action increases its keyword weight and can improve its position in search list.
+You can add keywords or questions to a file chunk to improve its ranking for queries containing those keywords. This action increases its keyword weight and can improve its position in search list.
 :::
 
 4. In Retrieval testing, ask a quick question in **Test text** to double check if your configurations work: