> ## Documentation Index
> Fetch the complete documentation index at: https://docs.obiguard.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Perplexity AI

Obiguard provides a robust and secure gateway to facilitate the integration of various Large Language Models (LLMs) into your applications, including [Perplexity AI APIs](https://docs.perplexity.ai/reference/post%5Fchat%5Fcompletions).

With Obiguard, you can take advantage of features like fast AI gateway access, observability, prompt management, and more, all while ensuring the secure management of your LLM API keys through a [virtual key](/virtual-keys) system.

<Note>
  Provider Slug. `perplexity-ai`
</Note>

## Obiguard SDK Integration with Perplexity AI Models

Obiguard provides a consistent API to interact with models from various providers. To integrate Perplexity AI with Obiguard:

### 1. Install the Obiguard SDK

Add the Obiguard SDK to your application to interact with Perplexity AI's API through Obiguard's gateway.

<Tabs>
  <Tab title="Python SDK">
    ```sh theme={null}
    pip install obiguard
    ```
  </Tab>
</Tabs>

### 2. Initialize Obiguard with the Virtual Key

To use Perplexity AI with Obiguard, [get your API key from here,](https://www.perplexity.ai/settings/api) then add it to Obiguard to create the virtual key.

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    from obiguard import Obiguard

    client = Obiguard(
      obiguard_api_key="vk-obg***",  # Your Obiguard virtual key
    )
    ```
  </Tab>
</Tabs>

### 3. Invoke Chat Completions with Perplexity AI

Use the Obiguard instance to send requests to Perplexity AI. You can also override the virtual key directly in the API call if needed.

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    completion = client.chat.completions.create(
      messages= [{"role": 'user', "content": 'Say this is a test'}],
      model= 'pplx-70b-chat'
    )

    print(completion)
    ```
  </Tab>
</Tabs>

## Fetching citations

If you need to obtain citations in the response, you can disable [strict open ai compliance](/gateway/strict-open-ai-compliance)

## Perplexity-Specific Features

Perplexity AI offers several unique features that can be accessed through additional parameters in your requests:

### Search Domain Filter (Beta)

You can limit citations to specific domains using the `search_domain_filter` parameter.
This feature is currently in closed beta and limited to 3 domains for whitelisting or blacklisting.

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    completion = client.chat.completions.create(
      messages=[{"role": "user", "content": "Tell me about electric cars"}],
      model="pplx-70b-chat",
      search_domain_filter=["tesla.com", "ford.com", "-competitors.com"] # Use '-' prefix for blacklisting
    )
    ```
  </Tab>
</Tabs>

### Image Results (Beta)

Enable image results in responses from online models using the `return_images` parameter:

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    completion = client.chat.completions.create(
      messages=[{"role": "user", "content": "Show me pictures of electric cars"}],
      model="pplx-70b-chat",
      return_images=True # Feature in closed beta
    )
    ```
  </Tab>
</Tabs>

### Related Questions (Beta)

Get related questions in the response using the `return_related_questions` parameter:

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    completion = client.chat.completions.create(
      messages=[{"role": "user", "content": "Tell me about electric cars"}],
      model="pplx-70b-chat",
      return_related_questions=True # Feature in closed beta
    )
    ```
  </Tab>
</Tabs>

### Search Recency Filter

Filter search results based on time intervals using the `search_recency_filter` parameter:

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    completion = client.chat.completions.create(
      messages=[{"role": "user", "content": "What are the latest developments in electric cars?"}],
      model="pplx-70b-chat",
      search_recency_filter="week" # Options: month, week, day, hour
    )
    ```
  </Tab>
</Tabs>

### Web Search Options

Determines how much search context is retrieved for the model.
Options are:

* `low`: minimizes context for cost savings but less comprehensive answers.
* `medium`: balanced approach suitable for most queries.
* `high`: maximizes context for comprehensive answers but at higher cost.

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    completion = client.chat.completions.create(
      messages=[{"role": "user", "content": "What are the latest developments in electric cars?"}],
      model="sonar",
      web_search_options={
        "search_context_size": "high"
      }
    )
    ```
  </Tab>
</Tabs>

### Search Recency Filter

Filters search results based on time (e.g., 'week', 'day').

<Tabs>
  <Tab title="Python SDK">
    ```python theme={null}
    completion = client.chat.completions.create(
      messages=[{"role": "user", "content": "What are the latest developments in electric cars?"}],
      model="sonar",
      search_recency_filter="<string>",
    )
    ```
  </Tab>
</Tabs>

## Next Steps

The complete list of features supported in the SDK are available on the link below.

<Card title="Obiguard SDK Client" icon="code" href="/api-reference/sdk/python">
  Learn more about the Obiguard SDK Client
</Card>