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Web Search

AI search lets a project reach current information from the web, so answers reflect the latest data rather than only the model's training data and the documents you have indexed locally. Every answer that used search is annotated with the sites it drew from.

A conversation answering a product question, with a REFERENCES section below the answer listing the three source sites used

Getting Started​

AI search is powered by the Brave Search API. Brave's free tier includes 1,000 queries per month, which is enough for regular day-to-day use.

  1. Sign up at brave.com/search/api.
  2. Subscribe to the free plan.
  3. Copy your API key.

Connecting your key​

Your Brave key is stored securely in your account. When you're logged in, the client app makes web searches through the centralized proxy using that key.

You can configure web search from the Settings page in the app, or centrally at app.smartloop.ai/settings/websearch.

Web search config

Combining your data​

Search is most useful alongside your own data rather than instead of it. Because a project already holds your documents, one prompt can draw on both — your documents for context, the web for what is current.

Product research is a good example. With your existing gear, specifications, or past purchases in the project, you can ask what to buy next: the assistant reads your documents to understand what you already own and what would fit, searches for current models, pricing, and reviews, and then recommends one — with the sites behind the recommendation listed underneath.

An answer whose REFERENCES block cites a local text file alongside two websites, showing a document and web sources used together

Annotated sources​

When an answer draws on search results, the sources used are listed in a REFERENCES block beneath it. Web sources link out to the original page, so you can confirm where a claim came from and read further. Documents from your own project appear in the same block, labelled by file type and openable directly — so it is clear at a glance which parts of an answer came from your files and which came from the web.

How it works​

  1. Your prompt is normalized into a search query — conversational filler such as "can you tell me" is stripped so the query matches the actual subject rather than the phrasing.
  2. The query goes to Brave, which returns result links.
  3. Up to ten candidate pages are fetched and converted to text (HTML and PDF are both handled).
  4. The candidates are reranked by relevance to your question, and the top three are kept as sources for the answer.

If search is unavailable for any reason — turned off for the project, a rejected key, or no results — it is skipped silently and the model answers from what it already knows. You will see an answer without a REFERENCES block rather than an error.

Summary​

AI search keeps a project current without giving up local execution: the model and your documents stay on your device, and only the search query leaves it. The annotated references make it clear which parts of an answer came from the web.