Why Use This This skill provides specialized capabilities for jeremylongshore's codebase.
Use Cases Developing new features in the jeremylongshore repository Refactoring existing code to follow jeremylongshore standards Understanding and working with jeremylongshore's codebase structure
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Updated At Mar 11, 2026, 05:33 AM
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License MIT
---
name: perplexity-reliability-patterns
description: |
Implement Perplexity reliability patterns including circuit breakers, idempotency, and graceful degradation.
Use when building fault-tolerant Perplexity integrations, implementing retry strategies,
or adding resilience to production Perplexity services.
Trigger with phrases like "perplexity reliability", "perplexity circuit breaker",
"perplexity idempotent", "perplexity resilience", "perplexity fallback", "perplexity bulkhead".
allowed-tools: Read, Write, Edit
version: 1.0.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
compatible-with: claude-code, codex, openclaw
---
# Perplexity Reliability Patterns
## Overview
Production reliability patterns for Perplexity Sonar API integrations. Perplexity performs live web searches per request, making response times variable and dependent on search complexity -- unlike static LLM inference.
## Prerequisites
- Perplexity API key configured
- Caching layer (Redis recommended)
- Understanding of search-augmented generation latency
## Instructions
### Step 1: Cache Identical Queries
Perplexity's web search is expensive per call. Cache results for repeated queries within a time window.
```python
import hashlib, json
class PerplexityCache:
def __init__(self, redis_client, ttl=600): # 600: timeout: 10 minutes
self.r = redis_client
self.ttl = ttl
def get_or_search(self, client, messages, model="sonar", **kwargs):
key = self._cache_key(messages, model, **kwargs)
cached = self.r.get(key)
if cached:
return json.loads(cached)
result = client.chat.completions.create(
model=model, messages=messages, **kwargs
)
self.r.setex(key, self.ttl, json.dumps(result.to_dict()))
return result
def _cache_key(self, messages, model, **kwargs):
data = json.dumps({"m": messages, "model": model, **kwargs}, sort_keys=True)
return f"pplx:{hashlib.sha256(data.encode()).hexdigest()}"
```
### Step 2: Model Tier Fallback
If `sonar-pro` times out or errors, fall back to `sonar` for a faster but shallower response.
```python
def resilient_search(client, messages, timeout=30):
try:
return client.chat.completions.create(
model="sonar-pro", messages=messages, timeout=timeout
)
except Exception:
return client.chat.completions.create(
model="sonar", messages=messages, timeout=15
)
```
### Step 3: Streaming with Timeout Protection
Perplexity streams can stall on complex searches. Set per-chunk timeouts.
```python
import time
def stream_with_timeout(client, messages, chunk_timeout=10):
stream = client.chat.completions.create(
model="sonar", messages=messages, stream=True
)
last_chunk = time.time()
full_response = ""
citations = []
for chunk in stream:
if time.time() - last_chunk > chunk_timeout:
raise TimeoutError("Stream stalled")
last_chunk = time.time()
delta = chunk.choices[0].delta.content or ""
full_response += delta
if hasattr(chunk, 'citations'):
citations = chunk.citations
yield delta
return full_response, citations
```
### Step 4: Citation Validation
Verify cited URLs are accessible before presenting to users.
```python
import aiohttp
async def validate_citations(citations: list[str]) -> list[dict]:
validated = []
async with aiohttp.ClientSession() as session:
for url in citations[:5]: # limit to top 5
try:
async with session.head(url, timeout=aiohttp.ClientTimeout(total=5)) as r:
validated.append({"url": url, "status": r.status, "valid": r.status < 400}) # HTTP 400 Bad Request
except:
validated.append({"url": url, "status": 0, "valid": False})
return validated
```
## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| Slow responses (>15s) | Complex search query | Use sonar instead of sonar-pro |
| Stream stalls | Search taking too long | Per-chunk timeout detection |
| Stale results | Cached data too old | Reduce TTL for time-sensitive queries |
| Broken citation links | Source pages moved | Validate URLs before displaying |
## Examples
**Basic usage**: Apply perplexity reliability patterns to a standard project setup with default configuration options.
**Advanced scenario**: Customize perplexity reliability patterns for production environments with multiple constraints and team-specific requirements.
## Resources
- [Perplexity API Docs](https://docs.perplexity.ai)
## Output
- Configuration files or code changes applied to the project
- Validation report confirming correct implementation
- Summary of changes made and their rationale