> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/has-url.md).

# Has Url

**Objective**\
This metric checks if the text contains a valid URL, ensuring proper handling of web links.

**Interpretation**\
A higher score indicates the presence of a valid URL in the response. A lower (or zero) score indicates no valid URL was found.

**Code Execution**

```python
metrics = [
    {
        "name": "Has Url",
        "config": {
            "model": "gpt-4o-mini",
            "provider": "openai"
        },
        "column_name": "your-column-identifier",
        "schema_mapping": schema_mapping
    }
]
```

**Example**

* **Prompt**: “Can you provide a link to your product page?”
* **Context**: “We only allow official links.”
* **Response**: “Visit <http://example.com> for more details.”
* **Metric Output**: `{"score": 1, "reason": "Response contains a valid URL."}`


---

# Agent Instructions
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## 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://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/guardrails/has-url.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.