> ## Documentation Index
> Fetch the complete documentation index at: https://productlasso.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Product search

> Find products in Lasso's database using natural language queries.

Lasso Search lets you find products using natural language. Describe what you're looking for and Lasso returns structured, typed product data matching your schema.

## How it works

1. **Describe what you need** -- Write a natural language query like "Sony noise cancelling headphones under \$200".
2. **Lasso searches its database** -- Products matching your query are discovered and ranked.
3. **Results are structured** -- Each product is extracted into your schema with typed fields, source URLs, and confidence scores.

## Schema options

You control the output shape with three options:

* **Use an existing schema** -- Pass a `schema_id` from a schema you've already created via `POST /v1/schemas`.
* **Define columns inline** -- Pass a `columns` array directly in the request.
* **Use the default** -- Omit both and Lasso uses a standard product schema (name, brand, price, description, category, image\_url, url, features).

## Sync vs async

By default, search is synchronous — you send a request and get results back in the same call. For large or slow queries, pass a `webhook_url` to switch to async mode: Lasso returns `202` immediately and delivers results via webhook when ready.

## Example

<CodeGroup>
  ```typescript TypeScript theme={null}
  const results = await client.search({
    query: "organic protein powder chocolate flavor",
    columns: [
      { key: "name", label: "Product Name", type: "text" },
      { key: "brand", label: "Brand", type: "text" },
      { key: "price", label: "Price", type: "number" },
      { key: "protein_per_serving", label: "Protein (g)", type: "number" },
    ],
    max_results: 10,
  });

  console.log(`Found ${results.total_results} products`);
  for (const p of results.results) {
    console.log(`${p.data.name} by ${p.data.brand} — $${p.data.price}`);
  }
  ```

  ```python Python theme={null}
  results = client.search(
      query="organic protein powder chocolate flavor",
      columns=[
          {"key": "name", "label": "Product Name", "type": "text"},
          {"key": "brand", "label": "Brand", "type": "text"},
          {"key": "price", "label": "Price", "type": "number"},
          {"key": "protein_per_serving", "label": "Protein (g)", "type": "number"},
      ],
      max_results=10,
  )

  print(f"Found {results['total_results']} products")
  for p in results["results"]:
      print(f"{p['data']['name']} by {p['data']['brand']} — ${p['data']['price']}")
  ```
</CodeGroup>

## Credits

Each search costs **5 credits**, regardless of how many results are returned.

## Next steps

* [Search API reference](/api-reference/search) -- Full parameter and response documentation.
* [Enrich](/learn/enrich) -- Take search results and enrich them with additional data.
* [Schemas](/learn/schemas) -- Create reusable schemas for consistent output.