How to Integrate Lovable with Perplexity

Start by navigating to your Lovable project. In the left sidebar, click the Settings icon — it looks like a gear and sits near the bottom of the sidebar. In the Settings panel that opens, look for the Connectors section in the left navigation and click it. You will land on the Shared connectors page, which lists all 17 connectors available for your workspace. Scroll down the connector list until you see Perplexity with its distinctive teal logo. Click the Perplexity card to expand it. You will see a toggle switch and a description of what the connector enables. Click the toggle to turn it on. Lovable will immediately prompt you to provide your API key — this is the secure handoff step where your credential moves into Cloud Secrets rather than project code. Have your Perplexity API key copied from your Perplexity account dashboard (perplexity.ai/api → API Keys) before proceeding. Note: the Shared connectors page is workspace-level, so activating Perplexity here makes it available across all projects in your workspace, not just the current one.

After toggling the connector on, Lovable presents a secure key entry form directly in the Connectors panel — this is the 'Add API Key' flow. Type or paste your Perplexity API key into the field provided. The key format starts with 'pplx-' followed by a long string of characters. Do not paste the key anywhere in Lovable's chat window, in any code file, or in any text field other than this secure form or the Cloud Secrets panel — on the free tier, chat history is publicly visible, and hardcoded keys are recoverable from commit history. Lovable encrypts the key and stores it as PERPLEXITY_API_KEY in your project's Cloud Secrets. To verify it was stored, click the '+' icon next to Preview in the top toolbar to open the Cloud tab, then click Secrets. You should see PERPLEXITY_API_KEY listed with a masked value. If you need to update the key later — for example, if you rotate keys for security — come back to this Secrets panel, click the key name, and paste the new value. The Edge Functions Lovable generates will reference this key via Deno.env.get('PERPLEXITY_API_KEY') automatically — you never need to touch that code directly.

Now open Lovable's chat prompt (bottom-left of the editor) and describe the search feature you want. Be specific about what users will search, what kind of results they should see, and where in your app the feature should appear. Lovable's AI agent, now aware of the active Perplexity connector, will automatically generate a Supabase Edge Function that calls the Perplexity API using your stored key, a React component with a search input and results display, and the wiring between them. The generated Edge Function will POST to Perplexity's /chat/completions endpoint with a system prompt scoped to your use case and the user's query as the user message. Results — including cited sources — will flow back to your frontend component. You do not need to write any of this code manually. After Lovable finishes generating, click the preview to test the feature. Type a question into the search input and confirm that a real, web-backed answer appears with source links.

By default, Lovable instructs Perplexity to answer general questions using the full web. In most apps, you will want to constrain the search to a specific domain or topic — for example, limit results to recent news about your industry, answers relevant to your product category, or research within a particular date range. The easiest way to do this is through a follow-up prompt in Lovable's chat. You can also specify which Perplexity model to use: the sonar model family offers different speed and capability tradeoffs. The llama-3.1-sonar-small-128k-online model is fastest and cheapest, suitable for simple factual queries. The llama-3.1-sonar-large-128k-online model provides deeper synthesis and handles nuanced questions better. The llama-3.1-sonar-huge-128k-online model is the most capable but slowest. For most founder use cases — internal research tools, competitive intelligence panels, FAQ assistants — the large model hits the right balance. Tell Lovable which model to use in your follow-up prompt and it will update the Edge Function accordingly. You can also ask Lovable to add a filter for recency (e.g., 'only return results from the past 30 days') or to format the answer differently — as bullet points, a structured summary, or a plain paragraph.

Perplexity integration — like all connector-based integrations — must be tested in your deployed app, not just in Lovable's preview iframe. The preview iframe runs in a sandboxed context that can behave differently from production, particularly for Edge Function calls. To deploy, click the Publish icon in the top-right corner of the Lovable editor. On the Publish panel, click Update (or Publish if this is your first deploy). Wait for the deploy to complete — typically 30 to 60 seconds. Then open your live app URL in a new browser tab. Test the search feature by typing several different questions: a factual query (e.g., 'What is the current funding landscape for B2B SaaS startups?'), a recent-events question (e.g., 'What AI tools launched in Q1 2026?'), and a question in your scoped domain if you customized the prompt in step 4. For each query, verify that the answer is substantive and that source citation links appear and resolve to real web pages. If results look incomplete or citations are missing, check Cloud → Logs for Edge Function execution details. A successful Perplexity API response always includes a citations array alongside the answer content — if your UI is not showing citations, ask Lovable to update the component to render the citations field from the API response.