1647 Perplexity F64e488a

Perplexity

Overview

Perplexity provides AI models with integrated web search capabilities, enabling access to up-to-date information and real-time data. Their Sonar models combine language understanding with live web search for current, factual responses.

Supported Capabilities:

Official Documentation: https://docs.perplexity.ai

Prerequisites

Account Requirements

• Perplexity account (sign up at https://www.perplexity.ai)
• API key with credits or billing enabled

Getting API Keys

1. Visit https://www.perplexity.ai/settings/api
2. Generate a new API key
3. Copy and store the key securely

Environment Variables

# Perplexity API key (required)
PERPLEXITY_API_KEY="pplx-..."

Variable Priority:

1. Direct parameter in code (api_key="...")
2. Environment variable (PERPLEXITY_API_KEY)

Quick Start

Via Factory (Recommended)

from esperanto.factory import AIFactory

# Create Perplexity model
model = AIFactory.create_language("perplexity", "llama-3.1-sonar-large-128k-online")

# Ask a question with real-time web search
messages = [{"role": "user", "content": "What are the latest AI developments?"}]
response = model.chat_complete(messages)
print(response.choices[0].message.content)

Direct Instantiation

from esperanto.providers.llm.perplexity import PerplexityLanguageModel

# Create model instance
model = PerplexityLanguageModel(
    api_key="your-api-key",
    model_name="llama-3.1-sonar-large-128k-online"
)

# Use the model
messages = [{"role": "user", "content": "Current Bitcoin price?"}]
response = model.chat_complete(messages)
print(response.choices[0].message.content)

Capabilities

Language Models (LLM)

Available Models:

Configuration:

from esperanto.factory import AIFactory

model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={
        "temperature": 0.7,                      # Randomness (0.0 - 1.0)
        "max_tokens": 1000,                      # Maximum response length
        "top_p": 0.9,                            # Nucleus sampling
        "streaming": True,                       # Enable streaming
        "search_domain_filter": ["news.com"],   # Limit search domains
        "return_images": True,                   # Include images in results
        "return_related_questions": True,        # Get related questions
        "search_recency_filter": "week"          # Filter by time period
    }
)

Example - Basic Web Search:

from esperanto.factory import AIFactory

# Create Perplexity model with web search
model = AIFactory.create_language("perplexity", "llama-3.1-sonar-large-128k-online")

# Ask about current events
messages = [{"role": "user", "content": "What are today's top tech news?"}]

response = model.chat_complete(messages)
print(response.choices[0].message.content)

Example - With Search Domain Filter:

# Filter search to specific domains
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={
        "search_domain_filter": ["techcrunch.com", "theverge.com", "arstechnica.com"]
    }
)

messages = [{"role": "user", "content": "Latest AI breakthroughs?"}]
response = model.chat_complete(messages)
print(response.choices[0].message.content)

Example - Exclude Domains:

# Exclude specific domains (prefix with -)
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={
        "search_domain_filter": ["-spam.com", "-unreliable.org"]
    }
)

messages = [{"role": "user", "content": "Climate change research"}]
response = model.chat_complete(messages)

Example - Search Recency Filter:

# Get only recent information
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={
        "search_recency_filter": "day"  # Options: "day", "week", "month", "year"
    }
)

messages = [{"role": "user", "content": "Today's stock market performance"}]
response = model.chat_complete(messages)

Example - With Related Questions:

# Request related questions
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={"return_related_questions": True}
)

messages = [{"role": "user", "content": "How does quantum computing work?"}]
response = model.chat_complete(messages)
print(response.choices[0].message.content)
# Related questions will be included in the response

Example - Include Images:

# Request image results
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={"return_images": True}
)

messages = [{"role": "user", "content": "Show me the latest SpaceX launches"}]
response = model.chat_complete(messages)
# Images will be included in the response

Example - Streaming:

model = AIFactory.create_language("perplexity", "llama-3.1-sonar-large-128k-online")

messages = [{"role": "user", "content": "Explain recent developments in AI"}]

# Synchronous streaming
for chunk in model.chat_complete(messages, stream=True):
    print(chunk.choices[0].delta.content, end="", flush=True)

# Async streaming
async for chunk in model.achat_complete(messages, stream=True):
    print(chunk.choices[0].delta.content, end="", flush=True)

Example - Multi-turn Conversation:

# Build conversation with context
messages = [
    {"role": "user", "content": "What are the latest AI models released this month?"},
    {"role": "assistant", "content": "This month saw releases of..."},
    {"role": "user", "content": "Which one is best for coding?"}
]

response = model.chat_complete(messages)
print(response.choices[0].message.content)

Example - Async Chat:

async def research_async():
    model = AIFactory.create_language("perplexity", "llama-3.1-sonar-large-128k-online")

    messages = [{"role": "user", "content": "Latest findings on renewable energy"}]
    response = await model.achat_complete(messages)
    print(response.choices[0].message.content)

# Run async
# await research_async()

Example - JSON Mode:

# Enable JSON output
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={"structured": {"type": "json"}}
)

messages = [{
    "role": "user",
    "content": "List the top 5 tech companies by market cap as JSON with name and value"
}]

response = model.chat_complete(messages)
print(response.choices[0].message.content)
# Response will be valid JSON

Advanced Features

Search Domain Filtering

Control which domains are searched:

# Include specific domains
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={
        "search_domain_filter": [
            "arxiv.org",           # Include research papers
            "github.com",          # Include code repositories
            "stackoverflow.com"    # Include developer Q&A
        ]
    }
)

# Exclude unreliable sources
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={
        "search_domain_filter": [
            "-tabloid.com",        # Exclude tabloids
            "-spam.org",           # Exclude spam sites
            "news.com",            # Include trusted news
            "research.edu"         # Include research sites
        ]
    }
)

Search Recency Control

Filter results by time period:

# Get very recent information (last 24 hours)
day_model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={"search_recency_filter": "day"}
)

# Get recent information (last 7 days)
week_model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={"search_recency_filter": "week"}
)

# Get information from last month
month_model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={"search_recency_filter": "month"}
)

# Get information from last year
year_model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={"search_recency_filter": "year"}
)

Web Search Options

Control search context and behavior:

model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={
        "web_search_options": {
            "context_size": "large",  # Amount of context to use
            "num_results": 10          # Number of search results
        }
    }
)

Enhanced Results

Get additional information with responses:

# Get images and related questions
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={
        "return_images": True,
        "return_related_questions": True
    }
)

messages = [{"role": "user", "content": "Latest Mars rover discoveries"}]
response = model.chat_complete(messages)
# Response includes images and suggested follow-up questions

LangChain Integration

from esperanto.factory import AIFactory

model = AIFactory.create_language("perplexity", "llama-3.1-sonar-large-128k-online")
langchain_model = model.to_langchain()

# Use with LangChain
from langchain.chains import ConversationChain
chain = ConversationChain(llm=langchain_model)

Model Selection Guide

Sonar Small (Recommended for Speed)

Best for: Quick answers, high-volume queries

• Fastest response times
• Most cost-effective
• Still provides accurate, current information
• Good for simple questions

model = AIFactory.create_language("perplexity", "llama-3.1-sonar-small-128k-online")

Sonar Large (Recommended for Quality)

Best for: Balanced performance, comprehensive answers

• Excellent balance of speed and quality
• More detailed responses
• Better reasoning capabilities
• Good for most use cases

model = AIFactory.create_language("perplexity", "llama-3.1-sonar-large-128k-online")

Sonar Huge (Best Quality)

Best for: Complex research, detailed analysis

• Most comprehensive responses
• Best reasoning and analysis
• Highest quality answers
• Use when quality is paramount

model = AIFactory.create_language("perplexity", "llama-3.1-sonar-huge-128k-online")

Use Cases

Real-Time Information

# Current events
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={"search_recency_filter": "day"}
)

messages = [{"role": "user", "content": "What happened in tech today?"}]

Research and Analysis

# Academic research
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-huge-128k-online",
    config={
        "search_domain_filter": ["arxiv.org", "scholar.google.com"],
        "return_related_questions": True
    }
)

messages = [{"role": "user", "content": "Latest quantum computing research"}]

Market Intelligence

# Financial information
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={
        "search_domain_filter": ["bloomberg.com", "reuters.com", "wsj.com"],
        "search_recency_filter": "day"
    }
)

messages = [{"role": "user", "content": "Today's market analysis for tech stocks"}]

Content Verification

# Fact-checking with credible sources
model = AIFactory.create_language(
    "perplexity",
    "llama-3.1-sonar-large-128k-online",
    config={
        "search_domain_filter": [
            "reuters.com",
            "apnews.com",
            "factcheck.org",
            "-tabloid.com",
            "-conspiracy.org"
        ]
    }
)

messages = [{"role": "user", "content": "Verify this claim: [claim to verify]"}]

Troubleshooting

Common Errors

Authentication Error:

Error: Invalid API key

Solution: Verify your API key is correct and active at https://www.perplexity.ai/settings/api

Rate Limit Error:

Error: Rate limit exceeded

Solution: Implement retry logic with exponential backoff or upgrade your plan

Context Length Exceeded:

Error: Request exceeds token limit

Solution: Reduce message history or content length (max 128K tokens)

Invalid Domain Filter:

Error: Invalid search_domain_filter

Solution: Check domain format (e.g., "domain.com" not "https://domain.com")

Invalid Recency Filter:

Error: Invalid search_recency_filter

Solution: Use valid values: "day", "week", "month", or "year"

Best Practices

1. Use Domain Filtering: Always filter domains for focused, reliable results

2. Set Recency Appropriately: Use "day" for current events, "month" or "year" for historical

3. Choose Right Model: Small for speed, Large for balance, Huge for quality

4. Enable Related Questions: Helps users explore topics more deeply

5. Stream for UX: Use streaming for better user experience with longer responses

6. Exclude Unreliable Sources: Use "-domain.com" to exclude low-quality sites

Performance Characteristics

Response Times

• Small: 1-3 seconds (with web search)
• Large: 2-5 seconds (with web search)
• Huge: 3-8 seconds (with web search)

Context Window

All Sonar models support 128K token context:

• Approximately 96,000 words
• Extensive conversation history
• Long document processing

Web Search Integration

• Real-time web search included
• Automatic citation of sources
• Up-to-date information (as recent as today)
• Configurable search parameters

See Also

• Language Models Guide
• OpenAI Provider
• Anthropic Provider
• Google Provider