Perplexity

Perplexity API

Build AI applications with real-time web search and grounded responses.

Quick Navigation

• Models & pricing: references/models.md
• Search API patterns: references/search-api.md
• Chat completions guide: references/chat-completions.md
• Browser sessions API: references/browser.md
• Embeddings API: references/embeddings.md
• Structured outputs: references/structured-outputs.md
• Filters (domain/language/date/location): references/filters.md
• Media (images/videos/attachments): references/media.md
• Pro Search: references/pro-search.md
• Prompting best practices: references/prompting.md

When to Use

• Need AI responses grounded in current web data
• Building search-powered applications
• Research tools requiring citations
• Real-time Q&A with source verification
• Document/image analysis with web context

Installation

Install: pip install perplexityai (Python) or npm install @perplexityai/perplexity (TypeScript/JavaScript).

Authentication

# macOS/Linux
export PERPLEXITY_API_KEY="your_api_key_here"

# Windows
setx PERPLEXITY_API_KEY "your_api_key_here"

SDK auto-reads PERPLEXITY_API_KEY environment variable.

Quick Start — Chat Completion

from perplexity import Perplexity

client = Perplexity()

completion = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "What is the latest news on AI?"}]
)

print(completion.choices[0].message.content)

Note (v0.28.0): The Python client includes a custom JSON encoder to support additional types in request payloads.

Quick Start — Search API

from perplexity import Perplexity

client = Perplexity()

search = client.search.create(
    query="artificial intelligence trends 2024",
    max_results=5
)

for result in search.results:
    print(f"{result.title}: {result.url}")

Release Highlights (0.34.1 -> 0.38.0)

• Streaming: responses.create now yields named SSE events and discriminates the ResponseStreamEvent union, which matters for typed stream consumers.
• Search context: search_context_size was briefly exposed on search.create, removed in 0.35.1, then reintroduced in 0.37.0 for both the Search API and the web_search tool to control retrieved context size.
• Background responses: the SDK adds background-task support and responses.retrieve, so long-running response workflows can be polled instead of only streamed inline.
• Reasoning effort: xhigh is available where the API supports reasoning-effort controls.
• Sandbox tool: 0.36.0 adds the Responses API sandbox built-in tool; 0.38.0 adds a files subresource for retrieving sandbox-produced files. Gate both like other executable/tooling surfaces.

Model Selection Guide

| Model | Use Case | Cost | | --------------------- | ------------------------------ | ------- | | sonar | Quick facts, simple Q&A | Lowest | | sonar-pro | Complex queries, research | Medium | | sonar-reasoning-pro | Multi-step reasoning, analysis | Medium | | sonar-deep-research | Exhaustive research, reports | Highest |

Key Patterns

Streaming Responses

stream = client.chat.completions.create(
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    model="sonar",
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Multi-Turn Conversation

messages = [
    {"role": "system", "content": "You are a research assistant."},
    {"role": "user", "content": "What causes climate change?"},
    {"role": "assistant", "content": "Climate change is caused by..."},
    {"role": "user", "content": "What are the solutions?"}
]

completion = client.chat.completions.create(messages=messages, model="sonar")

Web Search Options

completion = client.chat.completions.create(
    messages=[{"role": "user", "content": "Latest renewable energy news"}],
    model="sonar",
    web_search_options={
        "search_recency_filter": "week",
        "search_domain_filter": ["energy.gov", "iea.org"]
    }
)

Pro Search (Multi-Step Research)

# REQUIRES stream=True
completion = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "Research solar panel ROI"}],
    search_type="pro",
    stream=True
)

for chunk in completion:
    print(chunk.choices[0].delta.content or "", end="")

Image Attachment

completion = client.chat.completions.create(
    model="sonar-pro",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this image"},
            {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
        ]
    }]
)

File Attachment (PDF Analysis)

completion = client.chat.completions.create(
    model="sonar-pro",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Summarize this document"},
            {"type": "file_url", "file_url": {"url": "https://example.com/report.pdf"}}
        ]
    }]
)

Return Images in Response

completion = client.chat.completions.create(
    model="sonar",
    messages=[{"role": "user", "content": "Mount Everest photos"}],
    return_images=True,
    image_format_filter=["jpg", "png"]
)

Domain Filtering (Search API)

# Allowlist: include only these domains
search = client.search.create(
    query="climate research",
    search_domain_filter=["science.org", "nature.com"]
)

# Denylist: exclude these domains
search = client.search.create(
    query="tech news",
    search_domain_filter=["-reddit.com", "-pinterest.com"]
)

Multi-Query Search

search = client.search.create(
    query=[
        "AI trends 2024",
        "machine learning healthcare",
        "neural networks applications"
    ],
    max_results=5
)

for i, query_results in enumerate(search.results):
    print(f"Query {i+1} results:")
    for result in query_results:
        print(f"  {result.title}")

Structured Outputs (JSON Schema)

from pydantic import BaseModel

class ContactInfo(BaseModel):
    email: str
    phone: str

completion = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "Find contact for Tesla IR"}],
    response_format={
        "type": "json_schema",
        "json_schema": {"schema": ContactInfo.model_json_schema()}
    }
)

contact = ContactInfo.model_validate_json(completion.choices[0].message.content)

Async Operations

import asyncio
from perplexity import AsyncPerplexity

async def main():
    async with AsyncPerplexity() as client:
        tasks = [
            client.search.create(query="AI news"),
            client.search.create(query="tech trends")
        ]
        results = await asyncio.gather(*tasks)

asyncio.run(main())

Rate Limit Handling

import time
from perplexity import RateLimitError

def search_with_retry(client, query, max_retries=3):
    for attempt in range(max_retries):
        try:
            return client.search.create(query=query)
        except RateLimitError:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)
            else:
                raise

Response Parameters

| Parameter | Default | Description | | ------------------- | ------- | ------------------------------- | | temperature | 0.7 | Creativity (0-2) | | max_tokens | varies | Response length limit | | top_p | 0.9 | Nucleus sampling | | presence_penalty | 0 | Reduce repetition (-2 to 2) | | frequency_penalty | 0 | Reduce word frequency (-2 to 2) |

Search API Parameters

| Parameter | Description | | ------------------------ | --------------------------------------- | | max_results | 1-20 results per query | | max_tokens_per_page | Content extraction depth (default 2048) | | country | ISO country code for regional results | | search_domain_filter | Domain allowlist/denylist (max 20) | | search_language_filter | ISO 639-1 language codes (max 10) |

Pricing Quick Reference

Search API: $5/1K requests (no token costs)

Sonar Models (per 1M tokens): | Model | Input | Output | |-------|-------|--------| | sonar | $1 | $1 | | sonar-pro | $3 | $15 | | sonar-reasoning-pro | $2 | $8 |

Request fees (per 1K requests): $5-$14 depending on search context size.

Critical Prohibitions

• Do NOT request links/URLs in prompts (use citations field instead — model will hallucinate URLs)
• Do NOT use recursive JSON schemas (not supported)
• Do NOT use dict[str, Any] in Pydantic models for structured outputs
• Do NOT mix allowlist and denylist in search_domain_filter
• Do NOT exceed 5 queries in multi-query search
• Do NOT expect first request with new JSON schema to be fast (10-30s warmup)
• Do NOT use Pro Search without stream=True (will fail)
• Do NOT send images to sonar-deep-research (not supported)
• Do NOT include data: prefix for file attachments base64 (only for images)
• Do NOT try to control search via prompts (use API parameters instead)

Error Handling

import perplexity

try:
    completion = client.chat.completions.create(...)
except perplexity.BadRequestError as e:
    print(f"Invalid parameters: {e}")
except perplexity.RateLimitError:
    print("Rate limited, retry later")
except perplexity.APIStatusError as e:
    print(f"API error: {e.status_code}")

OpenAI SDK Compatibility

Perplexity supports OpenAI Chat Completions format. Use OpenAI client by pointing to Perplexity endpoint.

Links

• Documentation
• Releases
• GitHub
• Python SDK (PyPI)
• TypeScript SDK (npm)
• API Portal

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: itechmeat
• Source: itechmeat/llm-code
• License: MIT

Install and usage instructions live in the source repository linked above.