Skip to main content Implement Perplexity PII handling, data retention, and GDPR/CCPA compliance patterns.
Use when handling sensitive data, implementing data redaction, configuring retention policies,
or ensuring compliance with privacy regulations for Perplexity integrations.
Trigger with phrases like "perplexity data", "perplexity PII",
"perplexity GDPR", "perplexity data retention", "perplexity privacy", "perplexity CCPA".
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill perplexity-data-handling ai automation claude-code devops mcp ai-agents
Perplexity Data Handling
Overview
Manage data flowing through Perplexity Sonar API. Critical concern: queries are sent to Perplexity for web search, so any PII in queries is exposed to external infrastructure. Responses contain citations (third-party URLs) that must be validated before displaying to users.
Data Flow
User Input → Query Sanitization → Perplexity API → Response Parsing
│
┌─────────────┼──────────────┐
│ │ │
Answer Text Citations Search Results
│ │ │
Format & Validate & Store for
Display Deduplicate Analytics
Prerequisites
Perplexity API key configured
Understanding of PII regulations (GDPR/CCPA)
Cache storage (Redis or in-memory)
Instructions
Step 1: Query Sanitization
function sanitizeQuery(query: string): { clean: string; redacted: boolean } {
let clean = query;
let redacted = false;
const patterns: Array<[RegExp, string]> = [
[/\b[\w.+-]+@[\w-]+\.[\w.]+\b/g, "[email]"],
[/\b\d{3}[-.]?\d{3}[-.]?\d{4}\b/g, "[phone]"],
[/\b\d{3}-\d{2}-\d{4}\b/g, "[ssn]"],
[/\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b/g, "[card]"],
[/\b(pplx-|sk-|pk_|sk_live_)\w{20,}\b/g, "[token]"],
[/\b(user|customer|account)\s*#?\s*\d+\b/gi, "[id]"],
];
for (const [pattern, replacement] of patterns) {
if (pattern.test(clean)) {
clean = clean.replace(pattern, replacement);
redacted = true;
}
}
return { clean, redacted };
}
async function safeSearch(rawQuery: string) {
const { clean, redacted } = sanitizeQuery(rawQuery);
if (redacted) {
console.warn("[Data] PII redacted from Perplexity query");
}
return perplexity.chat.completions.create({
model: "sonar",
messages: [{ role: "user", content: clean }],
});
}
Step 2: Citation Validation interface ValidatedCitation {
url: string;
domain: string;
valid: boolean;
index: number;
}
function validateCitations(citations: string[]): ValidatedCitation[] {
return citations.map((url, i) => {
try {
const parsed = new URL(url);
return {
url: url.replace(/[.,;:]+$/, ""),
domain: parsed.hostname,
valid: ["http:", "https:"].includes(parsed.protocol),
index: i + 1,
};
} catch {
return { url, domain: "unknown", valid: false, index: i + 1 };
}
});
}
function deduplicateCitations(citations: ValidatedCitation[]): ValidatedCitation[] {
const seen = new Set<string>();
return citations.filter((c) => {
const normalized = c.url.split("?")[0].replace(/\/$/, "");
if (seen.has(normalized)) return false;
seen.add(normalized);
return true;
});
}
// Replace [1] markers with linked citations
function renderCitations(answer: string, citations: ValidatedCitation[]): string {
let rendered = answer;
for (const c of citations.filter((c) => c.valid)) {
rendered = rendered.replaceAll(`[${c.index}]`, `${c.index}`);
}
return rendered;
}
Step 3: Result Caching with Freshness Policy import { LRUCache } from "lru-cache";
import { createHash } from "crypto";
interface CachedResult {
answer: string;
citations: ValidatedCitation[];
cachedAt: number;
model: string;
}
const CACHE_TTL: Record<string, number> = {
news: 30 * 60_000, // 30 min for breaking/current events
research: 4 * 3600_000, // 4 hours for research topics
factual: 24 * 3600_000, // 24 hours for stable facts
default: 1 * 3600_000, // 1 hour default
};
const resultCache = new LRUCache<string, CachedResult>({ max: 500 });
function detectQueryType(query: string): keyof typeof CACHE_TTL {
if (/\b(latest|today|breaking|recent|this week)\b/i.test(query)) return "news";
if (/\b(research|study|paper|analysis|compare)\b/i.test(query)) return "research";
if (/\b(what is|define|how does|who is)\b/i.test(query)) return "factual";
return "default";
}
async function cachedSearch(query: string, model = "sonar") {
const hash = createHash("sha256")
.update(`${model}:${query.toLowerCase().trim()}`)
.digest("hex");
const cached = resultCache.get(hash);
if (cached) return { ...cached, fromCache: true };
const response = await safeSearch(query);
const rawCitations = (response as any).citations || [];
const citations = deduplicateCitations(validateCitations(rawCitations));
const queryType = detectQueryType(query);
const entry: CachedResult = {
answer: response.choices[0].message.content || "",
citations,
cachedAt: Date.now(),
model: response.model,
};
resultCache.set(hash, entry, { ttl: CACHE_TTL[queryType] });
return { ...entry, fromCache: false };
}
Step 4: Conversation Context Management import OpenAI from "openai";
type Message = OpenAI.ChatCompletionMessageParam;
class SearchContext {
private messages: Message[] = [];
private readonly maxMessages = 10;
private readonly maxEstimatedTokens = 8000;
constructor(systemPrompt?: string) {
if (systemPrompt) {
this.messages.push({ role: "system", content: systemPrompt });
}
}
addUserMessage(content: string) {
this.messages.push({ role: "user", content });
this.trim();
}
addAssistantMessage(content: string) {
this.messages.push({ role: "assistant", content });
this.trim();
}
getMessages(): Message[] {
return [...this.messages];
}
private trim() {
// Keep system prompt + last N messages
while (this.messages.length > this.maxMessages) {
const systemIdx = this.messages[0].role === "system" ? 1 : 0;
this.messages.splice(systemIdx, 1);
}
// Trim if estimated tokens too high
while (this.estimateTokens() > this.maxEstimatedTokens && this.messages.length > 2) {
const systemIdx = this.messages[0].role === "system" ? 1 : 0;
this.messages.splice(systemIdx, 1);
}
}
private estimateTokens(): number {
return this.messages.reduce(
(sum, m) => sum + Math.ceil(String(m.content).length / 4),
0
);
}
clear() {
const system = this.messages.find((m) => m.role === "system");
this.messages = system ? [system] : [];
}
}
Error Handling Issue Cause Solution PII in search query User entered personal data Apply sanitizeQuery before API call Broken citation URLs Source page moved/deleted Validate URLs, filter invalid ones Stale cached results TTL too long for news Use query-type-aware TTL Context overflow Too many conversation turns Automatic trimming in SearchContext Duplicate citations Same source cited multiple times Deduplicate by normalized URL
Output
Query sanitization stripping PII before API calls
Citation validation and deduplication
Cache with query-type-aware TTL
Conversation context with automatic trimming
Resources
Next Steps For access control, see perplexity-enterprise-rbac.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
