> ## Documentation Index
> Fetch the complete documentation index at: https://portkey-docs-add-third-party-integration-issues-fixes.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Perplexity AI
Portkey 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 Portkey, 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](/product/ai-gateway/virtual-keys) system.
Provider Slug. `perplexity-ai`
## Portkey SDK Integration with Perplexity AI Models
Portkey provides a consistent API to interact with models from various providers. To integrate Perplexity AI with Portkey:
### 1. Install the Portkey SDK
Add the Portkey SDK to your application to interact with Perplexity AI's API through Portkey's gateway.
```sh theme={null}
npm install --save portkey-ai
```
```sh theme={null}
pip install portkey-ai
```
### 2. Initialize Portkey with the Virtual Key
To use Perplexity AI with Portkey, [get your API key from here,](https://www.perplexity.ai/settings/api) then add it to Portkey to create the virtual key.
```js theme={null}
import Portkey from 'portkey-ai'
const portkey = new Portkey({
apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
virtualKey: "VIRTUAL_KEY" // Your Perplexity AI Virtual Key
})
```
```python theme={null}
from portkey_ai import Portkey
portkey = Portkey(
api_key="PORTKEY_API_KEY", # Replace with your Portkey API key
virtual_key="VIRTUAL_KEY" # Replace with your virtual key for Perplexity AI
)
```
### **3. Invoke Chat Completions with** Perplexity AI
Use the Portkey instance to send requests to Perplexity AI. You can also override the virtual key directly in the API call if needed.
```js theme={null}
const chatCompletion = await portkey.chat.completions.create({
messages: [{ role: 'user', content: 'Say this is a test' }],
model: 'pplx-70b-chat',
});
console.log(chatCompletion.choices);
```
```python theme={null}
completion = portkey.chat.completions.create(
messages= [{ "role": 'user', "content": 'Say this is a test' }],
model= 'pplx-70b-chat'
)
print(completion)
```
## Fetching citations
If you need to obtain citations in the response, you can disable [strict open ai compliance](/product/ai-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.
```python theme={null}
completion = portkey.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
)
```
```js theme={null}
const completion = await portkey.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
});
```
### Image Results (Beta)
Enable image results in responses from online models using the `return_images` parameter:
```python theme={null}
completion = portkey.chat.completions.create(
messages=[{"role": "user", "content": "Show me pictures of electric cars"}],
model="pplx-70b-chat",
return_images=True # Feature in closed beta
)
```
```js theme={null}
const completion = await portkey.chat.completions.create({
messages: [{ role: "user", content: "Show me pictures of electric cars" }],
model: "pplx-70b-chat",
return_images: true // Feature in closed beta
});
```
### Related Questions (Beta)
Get related questions in the response using the `return_related_questions` parameter:
```python theme={null}
completion = portkey.chat.completions.create(
messages=[{"role": "user", "content": "Tell me about electric cars"}],
model="pplx-70b-chat",
return_related_questions=True # Feature in closed beta
)
```
```js theme={null}
const completion = await portkey.chat.completions.create({
messages: [{ role: "user", content: "Tell me about electric cars" }],
model: "pplx-70b-chat",
return_related_questions: true // Feature in closed beta
});
```
### Search Recency Filter
Filter search results based on time intervals using the `search_recency_filter` parameter:
```python theme={null}
completion = portkey.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
)
```
```js theme={null}
const completion = await portkey.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
});
```
### 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.
```python theme={null}
completion = portkey.chat.completions.create(
messages=[{"role": "user", "content": "What are the latest developments in electric cars?"}],
model="sonar",
web_search_options={
"search_context_size": "high"
}
)
```
```js theme={null}
const completion = await portkey.chat.completions.create({
messages: [{ role: "user", content: "What are the latest developments in electric cars?" }],
model: "sonar",
web_search_options: {
"search_context_size": "high"
}
});
```
### Search Recency Filter
Filters search results based on time (e.g., 'week', 'day').
```python theme={null}
completion = portkey.chat.completions.create(
messages=[{"role": "user", "content": "What are the latest developments in electric cars?"}],
model="sonar",
"search_recency_filter": "",
)
```
```js theme={null}
const completion = await portkey.chat.completions.create({
messages: [{ role: "user", content: "What are the latest developments in electric cars?" }],
model: "sonar",
search_recency_filter: "",
});
```
## Managing Perplexity AI Prompts
You can manage all prompts to Perplexity AI in the [Prompt Library](/product/prompt-library). All the current models of Perplexity AI are supported and you can easily start testing different prompts.
Once you're ready with your prompt, you can use the `portkey.prompts.completions.create` interface to use the prompt in your application.
## Next Steps
The complete list of features supported in the SDK are available on the link below.
You'll find more information in the relevant sections:
1. [Add metadata to your requests](/product/observability/metadata)
2. [Add gateway configs to your Perplexity AI](/product/ai-gateway/configs)[ requests](/product/ai-gateway/configs)
3. [Tracing Perplexity AI requests](/product/observability/traces)
4. [Setup a fallback from OpenAI to Perplexity AI APIs](/product/ai-gateway/fallbacks)