# Response Fields

## **Top-Level Fields**

<table data-full-width="false"><thead><tr><th width="185.6666259765625">Field</th><th width="306">Description</th><th>Example</th></tr></thead><tbody><tr><td><strong><code>traceID</code></strong></td><td>Unique identifier for the request. Used for debugging, support, and internal tracking.</td><td><p><code>"traceID": "71f9e0b2-3a77-4ad0-</code></p><p><code>9a61-23b7e1bfa8d1"</code></p></td></tr><tr><td><strong><code>timestamp</code></strong></td><td>ISO-8601 timestamp indicating when the request was processed.</td><td><p><code>"timestamp": "2025-12-08</code></p><p><code>T11:39:19.269Z"</code></p></td></tr><tr><td><strong><code>request_duration</code></strong></td><td>End-to-end request duration in seconds (from request received to response returned).</td><td><code>"request_duration": 6.3</code></td></tr><tr><td><strong><code>process_duration</code></strong></td><td>Total internal processing time in seconds. This includes the full request lifecycle.</td><td><code>"process_duration": 6.4</code></td></tr><tr><td><strong><code>scraper</code></strong></td><td>Scraper name by LLM Engine</td><td><code>"scraper": "Perplexity"</code></td></tr><tr><td><strong><code>llm_model</code></strong></td><td>LLM Model used by provider</td><td><code>"llm_model": "gpt-5.4"</code></td></tr></tbody></table>

**Note**\
`request_duration` is included within `process_duration`.\
For example, if `request_duration` is `6.3` and `process_duration` is `6.4`, the overall time experienced by the client is **6.4 seconds**.

***

## **Response Object**

The main parsed output from the LLM.

***

### `prompt`

The original prompt sent to the LLM for this request.\
This field is always returned as part of the response for traceability and debugging.

**Example**

```json
"prompt": "what is the best seo tips in 2025"
```

***

### `text_markdown`

Markdown-formatted version of the response, preserving structure such as headings, lists, emphasis, and inline citations.

This field represents the **primary readable output** of the LLM, including formatting and citation markers as returned by the model.

***

#### Example

```json
"text_markdown": "Here are the leading SEO tips for 2026:\n\n- Focus on semantic, intent-driven content\n- Emphasize E-E-A-T and content quality\n- Invest in entity-based SEO and knowledge graphs"
```

***

### **`text` (plain text)**

Plain-text version of the response with formatting removed.\
Useful for storage, indexing, or systems that don’t support Markdown.

**Example**

```json
"text": "Here are the leading SEO tips for 2026 include focusing on content quality and user experience."
```

***

### **`citations_found`**

Boolean indicating whether the model output included citations.

**Example**

```json
"citations_found": true
```

***

### **`citations`**

Flat list of citation objects extracted from Perplexity.

Each citation includes:

* `id`
* `title`
* `url`
* `section`: `"citations"` or `"more"`

**Example**

```json
"citations": [
  {
    "id": 1,
    "title": "Best Headphones of 2025",
    "url": "https://example.com/best-headphones-2025",
    "section": "citations"
  }
]
```

***

### `inline_citations`

Structured citations extracted directly from the main response text.

Each inline citation represents a **specific source tied to an exact text span (anchor)** in the generated answer.

Unlike `citations`, which are a flat list of sources, `inline_citations` show **where and how each source is used inside the content**.

Each inline citation includes:

* `id`
* `title`
* `url`
* `text_anchor`

**Example**

```json
"inline_citations": [
  {
    "id": 1,
    "url": "https://example.com/ai-productivity-report",
    "title": "AI Productivity Trends 2025",
    "text_anchor": "AI tools have increased developer productivity by up to 40% in enterprise environments."
  },
  {
    "id": 2,
    "url": "https://example.com/startup-ai-adoption",
    "title": "Startup Adoption of AI Tools",
    "text_anchor": "Startups are adopting AI solutions faster than enterprises due to fewer legacy constraints."
  },
  {
    "id": 3,
    "url": "https://example.com/ai-productivity-report",
    "title": "AI Productivity Trends 2025",
    "text_anchor": "AI tools have increased developer productivity by up to 40% in enterprise environments."
  }
]
```

Notes

* A single text span may have **multiple inline citations**
* `id` corresponds to the same source in the `citations` array

***

#### `follow_ups`

Suggested follow-up questions generated based on the original query and response.

These represent **related queries a user might ask next**, helping to explore the topic further or refine the results.

Each follow-up is returned as a simple string.

***

Example

```
"follow_ups": [
  "Top SEO trends 2026 to watch for",
  "How to optimize for search intent in 2026",
  "Technical SEO checklist for 2026 sites",
  "EAT impact on rankings in 2026 strategies",
  "AI-assisted SEO tools for 2026 optimization"
]
```

***

### `media_items`

Structured media elements extracted from the response, such as images and videos displayed alongside the answer.

These items represent **visual content returned by Perplexity**, including top images, video results, and additional media related to the query.

Each media item includes:

* `type`
* `title`
* `url`
* `source_url`

***

Example

```
"media_items": [
  {
    "type": "image",
    "url": "https://images.example.com/smartphone1.jpg",
    "title": "Top Smartphones of 2025",
    "source_url": "https://example.com/best-smartphones-2025"
  },
  {
    "type": "image",
    "url": "https://images.example.com/smartphone2.jpg",
    "title": "Best Phones Ranked in 2025",
    "source_url": "https://example.com/phone-ranking"
  },
  {
    "type": "video",
    "url": "https://www.youtube.com/watch?v=example",
    "title": "Top 5 Smartphones of 2025",
    "source_url": "https://www.youtube.com/watch?v=example"
  }
]
```