# Query Data Source Block

{% embed url="<https://www.youtube.com/embed/flwFevdkruI>" %}

The **Query Data Source Block** allows you to retrieve relevant information from a [Data Source](/docs/building-ai-agents/data-sources.md) within your workflow. This block is essential for integrating custom data into your AI workflows through Retrieval Augmented Generation (RAG).

***

## **Configurations**

### **Data Source**

Select the Data Source you want to query. This is the collection of documents you have uploaded and configured in the **Data Sources Folder**.

#### **Options**

* Use the dropdown menu to select an existing Data Source.
* Click **New\...** to create a new Data Source if none are currently available.

### **Variable Name**

Creates a variable where the query results will be stored. Enter a <mark style="color:red;">`variable_name`</mark> to store the result of the query for later use in the workflow.

### **Max Results**

Define the number of results the block will retrieve from the Data Source. Each result will return a different chunk of retrieved text from the Data Source.

### **Query Template**

Enter the query prompt that instructs the AI on what information to retrieve. You can include <mark style="color:red;">`{{variables}}`</mark> to make the query dynamic and context-aware.

[Learn about variables →](/docs/building-ai-agents/variables.md)

***

## **Writing Effective Queries**

Crafting effective queries is crucial for retrieving the most relevant and accurate information from your Data Sources. A well-written query ensures that your AI can efficiently locate and use the data needed for your workflow.

#### **Understand Your Data Source**

Familiarize yourself with the content of your Data Source. Knowing the structure, topics, and focus of the documents helps you write more precise queries.

**Example:**

If your Data Source contains product manuals, your queries should explicitly reference product names or sections like "warranty" or "setup instructions."

#### **Be Clear and Specific**

Write concise and focused queries to ensure the AI retrieves the most relevant results. Avoid overly broad or ambiguous prompts.

**Examples**:

* **Broad Query**: `Tell me about this product.`
* **Specific Query**: `What are the warranty terms for the {{productName}}?`

#### **Use Contextual Language**

Include specific instructions or context in your query to guide the retrieval process.

**Examples**:

```markdown
Find troubleshooting steps for {{deviceName}} related to connectivity issues.
```

```markdown
Retrieve all safety precautions mentioned in the {{documentType}}.
```

#### **Limit Query Scope**

Tailor the query to focus on a specific part of the Data Source to improve accuracy. For large Data Sources, specifying a topic or section can yield better results.

**Example**:

```
Search the "user guide" section for instructions on resetting the {{productName}}.
```

#### **Include Actionable Keywords**

Use actionable keywords like "retrieve," "explain," "summarize," or "list" to make the purpose of the query clear.

**Examples**:

```markdown
"Summarize the maintenance steps for the {{productName}}."
```

```markdown
"List all recommended accessories for {{productName}}."
```

#### **Test and Refine**

Test your query with the [Query Tester](/docs/building-ai-agents/data-sources.md#query-tester) to ensure it retrieves the intended results. Adjust the wording, variables, or focus as necessary.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://university.mindstudio.ai/docs/building-ai-agents/blocks-reference/query-data-source-block.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.