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

# Quickstart

> Make your first Andi AI Search API call in minutes.

<Info>You need an API key to follow this guide. Get one from the [API Console](https://console.andiai.com).</Info>

<Steps>
  <Step title="Get your API key">
    Sign in to the [API Console](https://console.andiai.com) and create an API key. Copy it — you'll need it for the next step.
  </Step>

  <Step title="Make your first request">
    Send a search query using the `x-api-key` header:

    <CodeGroup>
      ```bash curl theme={null}
      curl "https://search-api.andisearch.com/api/v1/search?q=what+is+RAG" \
        -H "x-api-key: YOUR_API_KEY"
      ```

      ```python Python theme={null}
      import requests

      response = requests.get(
          "https://search-api.andisearch.com/api/v1/search",
          params={"q": "what is RAG"},
          headers={"x-api-key": "YOUR_API_KEY"}
      )

      data = response.json()
      for result in data["results"]:
          print(result["title"], result["link"])
      ```

      ```javascript JavaScript theme={null}
      const response = await fetch(
        "https://search-api.andisearch.com/api/v1/search?q=what+is+RAG",
        { headers: { "x-api-key": "YOUR_API_KEY" } }
      );

      const data = await response.json();
      data.results.forEach(r => console.log(r.title, r.link));
      ```
    </CodeGroup>
  </Step>

  <Step title="Read the response">
    The API returns a JSON object with top-level fields and a `results` array. Each result includes a `title`, `link`, `desc`, and `source`:

    ```json theme={null}
    {
      "results_type": "search",
      "answer": "",
      "type": "search",
      "title": "what is RAG",
      "results": [
        {
          "title": "Retrieval-Augmented Generation (RAG) Explained",
          "link": "https://example.com/rag-explained",
          "desc": "RAG combines a retrieval system with a language model to generate responses grounded in retrieved documents.",
          "source": "example.com",
          "type": "website"
        }
      ],
      "metrics": {
        "query": "what is RAG",
        "intent": "FallbackSearchIntent",
        "duration": 234,
        "results_returned": 10,
        "total_results_found": 42
      }
    }
    ```
  </Step>
</Steps>

## Next steps

<CardGroup cols={2}>
  <Card title="Fast search" icon="bolt" href="/search/fast-search">
    Default search mode for real-time applications.
  </Card>

  <Card title="Deep search" icon="microscope" href="/search/deep-search">
    Enhanced search with spell correction and more sources.
  </Card>

  <Card title="Query parameters" icon="sliders" href="/features/query-parameters">
    Full parameter reference for the search endpoint.
  </Card>

  <Card title="Authentication" icon="key" href="/getting-started/authentication">
    API key management and security practices.
  </Card>
</CardGroup>