Naver APInaver.com ↗

"""
Naver Search API Client

This module provides a Python client for the Naver Search API, allowing you to search
across multiple content types and get autocomplete suggestions.

Get your API key from: https://parse.bot/settings
"""

import os
import json
import requests
from typing import Optional


class ParseClient:
    """Client for interacting with the Parse API (Naver Search)."""

    def __init__(self, api_key: Optional[str] = None):
        """
        Initialize the Parse API client.

        Args:
            api_key: API key for authentication. If not provided, reads from PARSE_API_KEY env var.
        """
        self.base_url = "https://api.parse.bot"
        self.scraper_id = "59b25417-91af-4955-8c05-f24339274634"
        self.api_key = api_key or os.getenv("PARSE_API_KEY")

        if not self.api_key:
            raise ValueError("API key must be provided or set in PARSE_API_KEY environment variable")

    def _call(self, endpoint: str, method: str = "POST", **params) -> dict:
        """
        Make a request to the Parse API.

        Args:
            endpoint: The API endpoint to call (e.g., 'search', 'autocomplete')
            method: HTTP method ('GET' or 'POST')
            **params: Query parameters or request body

        Returns:
            Response data as a dictionary
        """
        url = f"{self.base_url}/scraper/{self.scraper_id}/{endpoint}"
        headers = {"X-API-Key": self.api_key, "Content-Type": "application/json"}

        if method.upper() == "GET":
            response = requests.get(url, headers=headers, params=params)
        else:  # POST
            response = requests.post(url, headers=headers, json=params)

        response.raise_for_status()
        return response.json()

    def search(
        self,
        query: str,
        where: str = "nexearch",
        page: int = 1,
    ) -> dict:
        """
        Search Naver for content across multiple types.

        Args:
            query: Search query string
            where: Content type ('nexearch', 'blog', 'news', 'cafe', 'image', 'video', 'shop')
            page: Page number for pagination (starts at 1)

        Returns:
            Dictionary with search results and metadata
        """
        return self._call(
            "search",
            method="GET",
            query=query,
            where=where,
            page=page,
        )

    def autocomplete(self, query: str) -> dict:
        """
        Get autocomplete suggestions for a search query.

        Args:
            query: Query prefix to get suggestions for

        Returns:
            Dictionary with suggestions list
        """
        return self._call(
            "autocomplete",
            method="GET",
            query=query,
        )


def main():
    """Demonstrate practical usage of the Naver Search API client."""
    # Initialize the client
    client = ParseClient()

    # Workflow: Search for a topic, then get suggestions for related searches
    search_topic = "machine learning"

    print("=" * 60)
    print(f"NAVER SEARCH WORKFLOW: Exploring '{search_topic}'")
    print("=" * 60)

    # Step 1: Get autocomplete suggestions for the topic
    print(f"\n1. Getting autocomplete suggestions for '{search_topic}'...")
    autocomplete_response = client.autocomplete(search_topic)

    if autocomplete_response.get("status") == "success":
        suggestions = autocomplete_response.get("data", {}).get("suggestions", [])
        print(f"   Found {len(suggestions)} suggestions:")
        for i, suggestion in enumerate(suggestions[:5], 1):
            print(f"   {i}. {suggestion}")
    else:
        print("   Error getting suggestions")

    # Step 2: Search for blog posts about the topic
    print(f"\n2. Searching for blog posts about '{search_topic}'...")
    blog_search = client.search(query=search_topic, where="blog", page=1)

    if blog_search.get("status") == "success":
        search_data = blog_search.get("data", {})
        results = search_data.get("results", [])
        total_results = search_data.get("total_results", 0)

        print(f"   Found {total_results} blog posts (showing first {len(results)}):")
        for i, result in enumerate(results[:3], 1):
            print(f"\n   Blog Post {i}:")
            print(f"   Title: {result.get('title', 'N/A')}")
            print(f"   Source: {result.get('source', 'N/A')}")
            description = result.get("description", "N/A")
            if len(description) > 100:
                description = description[:100] + "..."
            print(f"   Description: {description}")
    else:
        print("   Error searching blog posts")

    # Step 3: Search for news articles
    print(f"\n3. Searching for news articles about '{search_topic}'...")
    news_search = client.search(query=search_topic, where="news", page=1)

    if news_search.get("status") == "success":
        search_data = news_search.get("data", {})
        results = search_data.get("results", [])

        print(f"   Found news articles (showing up to 3):")
        for i, result in enumerate(results[:3], 1):
            print(f"\n   News {i}:")
            print(f"   Title: {result.get('title', 'N/A')}")
            print(f"   Source: {result.get('source', 'N/A')}")
    else:
        print("   Error searching news")

    # Step 4: Use a suggestion from autocomplete for a new search
    if autocomplete_response.get("status") == "success":
        suggestions = autocomplete_response.get("data", {}).get("suggestions", [])
        if len(suggestions) > 2:
            related_query = suggestions[2]  # Use third suggestion
            print(f"\n4. Searching web results for related topic: '{related_query}'...")

            web_search = client.search(query=related_query, where="nexearch", page=1)

            if web_search.get("status") == "success":
                search_data = web_search.get("data", {})
                results = search_data.get("results", [])
                total_results = search_data.get("total_results", 0)

                print(f"   Found {total_results} web results (showing first 2):")
                for i, result in enumerate(results[:2], 1):
                    print(f"\n   Result {i}:")
                    print(f"   Title: {result.get('title', 'N/A')}")
                    print(f"   Link: {result.get('link', 'N/A')}")
            else:
                print("   Error searching web")

    print("\n" + "=" * 60)
    print("WORKFLOW COMPLETE")
    print("=" * 60)


if __name__ == "__main__":
    main()