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sidebar_position: 3
slug: /retrieval_component
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---
# Retrieval component

A component that retrieves information from specified datasets.

## Scenarios

A **Retrieval** component is essential in most RAG scenarios, where information is extracted from designated datasets 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 dataset(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 depends 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 dataset(s) to query

You can specify one or multiple datasets to retrieve data from. If selecting multiple, 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 for retrieval. If a rerank model is selected, a combination of weighted keyword similarity and weighted reranking score will be used instead.

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.
:::

### 5. Enable cross-language search

If your user query is different from the languages of the datasets, 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 **Run** button on the top of canvas to test the retrieval results.

### 7. Choose the next component

When necessary, click the **+** button on the **Retrieval** component to choose the next component in the workflow from the dropdown list.


## Configurations

### Query variables

*Mandatory*

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 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.

### Retrieval from

Select the dataset(s) and memory to retrieve data from.

- If no dataset is selected, meaning conversations with the agent will not be based on any dataset, ensure that the **Empty response** field is left blank to avoid an error.
- If you select multiple datasets, you must ensure that the datasets you select use the same embedding model; otherwise, an error message would occur.

![](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/retrieve_from_memory.PNG)

### Similarity threshold

RAGFlow employs a combination of weighted keyword similarity and weighted vector cosine similarity during retrieval. This parameter sets the threshold for similarities between the user query and chunks stored in the datasets. Any chunk with a similarity score below this threshold will be excluded from the results.

Defaults to 0.2.

### Vector similarity weight

This parameter sets the weight of vector similarity in the composite similarity score. The total of the two weights must equal 1.0. Its default value is 0.3, which means the weight of keyword similarity in a combined search is 1 - 0.3 = 0.7.

### Top N

This parameter selects the "Top N" chunks from retrieved ones and feed them to the LLM.

Defaults to 8.


### Rerank model

*Optional*

If a rerank model is selected, a combination of weighted keyword similarity and weighted reranking score will be used for retrieval.

:::caution WARNING
Using a rerank model will *significantly* increase the system's response time.
:::

### Empty response

- Set this as a response if no results are retrieved from the dataset(s) for your query, or 
- Leave this field blank to allow the chat model to improvise when nothing is found.

:::caution WARNING
If you do not specify a dataset, you must leave this field blank; otherwise, an error would occur.
:::

### Cross-language search

Select one or more languages for cross‑language search. If no language is selected, the system searches with the original query.

### Use knowledge graph

:::caution IMPORTANT
Before enabling this feature, ensure you have properly [constructed a knowledge graph from each target dataset](../../dataset/construct_knowledge_graph.md).
:::

Whether to use knowledge graph(s) in the specified dataset(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.

### PageIndex

Whether to use the page index structure generated by the large model to enhance retrieval. This approach mimics human information-searching behavior in books.

### 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 to disable rerank.
- Disable **Use knowledge graph**.
