Perplexity AI

Perplexity AI provides search-augmented LLMs with real-time web access. Unlike standard LLMs, Perplexity's Sonar models automatically search the web and include citations in their responses — making them ideal for research, news monitoring, and fact-checking tasks.

Install

pip install synapsekit[openai]

Perplexity AI uses the OpenAI-compatible API, so it requires the openai package.

Basic usage

from synapsekit import LLMConfig
from synapsekit.llm.perplexity import PerplexityLLM

llm = PerplexityLLM(LLMConfig(
    model="sonar-pro",
    api_key="pplx-...",
))

response = await llm.generate("What are the latest developments in AI safety research?")
print(response)
# Recent AI safety research has focused on... [cites sources]

Streaming

from synapsekit import LLMConfig
from synapsekit.llm.perplexity import PerplexityLLM

llm = PerplexityLLM(LLMConfig(
    model="sonar",
    api_key="pplx-...",
))

async for token in llm.stream("What happened in AI this week?"):
    print(token, end="", flush=True)
# This week in AI: OpenAI announced... Anthropic released... [with citations]

Available Sonar models

Model	Context	Notes
sonar	128K	Fast, real-time web search
sonar-pro	200K	Higher quality search, deeper research
sonar-reasoning	128K	Web search + chain-of-thought reasoning
sonar-reasoning-pro	200K	Best quality for complex research questions
sonar-deep-research	128K	Multi-step research synthesis

See the full list at docs.perplexity.ai.

Real-time web search

Sonar models automatically search the web for every request. The response includes citations you can access:

from synapsekit import LLMConfig
from synapsekit.llm.perplexity import PerplexityLLM

llm = PerplexityLLM(LLMConfig(model="sonar-pro", api_key="pplx-..."))

# Ask about current events — Perplexity fetches live data
response = await llm.generate("What is the current price of Bitcoin?")
print(response)
# As of March 2026, Bitcoin is trading at approximately $X,XXX...

# Access citations from the raw response
raw = await llm.generate_raw("Latest Python 3.14 release notes")
if hasattr(raw, "citations"):
    for cite in raw.citations:
        print(f"  - {cite}")

caution

Perplexity models are not suitable for tasks requiring deterministic, reproducible outputs. Because they search the web at inference time, the same prompt may return different answers on different days as news changes.

Research with Sonar Reasoning

For complex research questions, sonar-reasoning combines web search with step-by-step thinking:

llm = PerplexityLLM(LLMConfig(
    model="sonar-reasoning",
    api_key="pplx-...",
))

response = await llm.generate(
    "Compare the performance benchmarks of the latest open-source LLMs released in 2026. "
    "Which ones perform best on coding tasks?"
)
print(response)
# <think>
# Let me search for recent LLM benchmarks...
# </think>
# Based on current benchmarks, the top-performing models for coding in 2026 are...

Combining with RAG

Use Perplexity for freshness and a local vector store for private data:

from synapsekit import RAG
from synapsekit.llm.perplexity import PerplexityLLM
from synapsekit import LLMConfig

# Use Sonar to answer questions that need up-to-date web knowledge
web_llm = PerplexityLLM(LLMConfig(model="sonar-pro", api_key="pplx-..."))

# Use a standard RAG pipeline for your private documents
from synapsekit import RAGPipeline, LLMConfig as RagConfig
private_rag = RAGPipeline(LLMConfig(model="gpt-4o-mini", api_key="sk-..."))
private_rag.add_file("internal_report.pdf")

# Route queries: private docs → RAG, current events → Perplexity
query = "What are the latest regulations for our industry?"
if "latest" in query or "current" in query or "recent" in query:
    answer = await web_llm.generate(query)
else:
    answer = await private_rag.ask(query)

Custom base URL

llm = PerplexityLLM(
    LLMConfig(model="sonar-pro", api_key="pplx-..."),
    base_url="http://localhost:8000/v1",
)

Parameters reference

Parameter	Description
model	Perplexity model ID (e.g. sonar-pro)
api_key	Your Perplexity API key (starts with pplx-)
temperature	Sampling temperature (lower = more factual)
max_tokens	Maximum output tokens
base_url	Custom API base URL (default: https://api.perplexity.ai)

Error handling

from synapsekit.exceptions import LLMError, RateLimitError, AuthenticationError

try:
    response = await llm.generate("What's new in machine learning?")
except AuthenticationError:
    print("Invalid API key — get one at perplexity.ai/settings/api")
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after}s")
except LLMError as e:
    print(f"Perplexity error: {e}")

tip

Use sonar for fast lookups and sonar-pro for deeper research. If your application needs to verify facts or track current events, Perplexity's search-augmented models are more reliable than asking a static LLM about recent information.