Perplexity AI¶

What it is¶

Perplexity is an AI-powered conversational search engine. It provides real-time information and citations from across the web to answer complex questions.

What problem it solves¶

Combines the depth of AI-generated answers with the currency of web search, providing cited, up-to-date responses that go beyond what a static LLM knowledge cutoff can offer.

Where it fits in the stack¶

AI & Knowledge — used as a research and information retrieval tool when up-to-date, cited web information is needed.

Typical use cases¶

Researching technical topics with source citations
Getting up-to-date answers that require current web information
Comparing tools, libraries, or approaches with cited references

Strengths¶

Provides citations and sources for its answers
Access to real-time web information beyond LLM training data
Conversational interface allows follow-up questions to refine results

Limitations¶

Cloud-based service; queries and data are sent to external servers
Free tier has usage limits; Pro subscription required for heavy use
Cannot process private or local data

When to use it¶

When you need current, cited information from the web
When researching topics where accuracy and source verification matter

When not to use it¶

When working with private or sensitive data that should not leave the local network
When offline access is required

Getting started¶

Installation¶

Perplexity provides an OpenAI-compatible API. You can use the standard OpenAI Python client to interact with it.

pip install openai

Minimal Python Example¶

To use the Perplexity API, you need a valid API key from the Perplexity API Settings.

from openai import OpenAI

client = OpenAI(api_key="YOUR_PPLX_API_KEY", base_url="https://api.perplexity.ai")

response = client.chat.completions.create(
    model="sonar-reasoning-pro",
    messages=[{"role": "user", "content": "What are the current top 3 Python frameworks for web development in 2026?"}],
)
print(response.choices[0].message.content)

CLI examples¶

1. Basic Search Query (curl)¶

curl -X POST https://api.perplexity.ai/chat/completions \
  -H "Authorization: Bearer $PPLX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sonar-reasoning-pro",
    "messages": [
      {"role": "user", "content": "What is the latest stable version of Kubernetes?"}
    ]
  }'

2. Precise and Concise Output¶

curl -X POST https://api.perplexity.ai/chat/completions \
  -H "Authorization: Bearer $PPLX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sonar-reasoning-pro",
    "messages": [
      {"role": "system", "content": "Be precise and concise."},
      {"role": "user", "content": "Current price of Bitcoin in USD."}
    ]
  }'

3. List Supported Models¶

# Perplexity uses a specific set of Sonar models. Refer to docs for the latest list.
curl https://api.perplexity.ai/models \
  -H "Authorization: Bearer $PPLX_API_KEY"

API examples¶

Python (OpenAI SDK)¶

from openai import OpenAI

YOUR_API_KEY = "pplx-xxxxxxxx"

client = OpenAI(api_key=YOUR_API_KEY, base_url="https://api.perplexity.ai")

# Chat completion with reasoning and citations
response = client.chat.completions.create(
    model="sonar-reasoning-pro",
    messages=[
        {
            "role": "system",
            "content": "You are a professional researcher.",
        },
        {
            "role": "user",
            "content": "Analyze the impact of Model Context Protocol (MCP) on the AI agent ecosystem.",
        },
    ],
)
print(response.choices[0].message.content)

Sources / references¶

Contribution Metadata¶

Last reviewed: 2026-05-28
Confidence: high