Local News API quickstart guide

pip install requests

import requests
import json

# Configuration
API_KEY = "YOUR_API_KEY_HERE"  # Replace with your actual API key
URL = "https://local-news.newscatcherapi.com/api/search"
HEADERS = {"x-api-token": API_KEY, "Content-Type": "application/json"}
PAYLOAD = {
    "q": "*",
    "associated_towns": [{"name": "San Francisco, California"}],
    "theme": "Tech",
    "lang": "en",
    "from_": "7 days ago",
}

try:
    # Fetch articles using POST method
    response = requests.post(URL, headers=HEADERS, json=PAYLOAD)
    response.raise_for_status()  # Check if the request was successful

    # Print the raw JSON response
    print(json.dumps(response.json(), indent=2))

except requests.exceptions.RequestException as e:
    print(f"Failed to fetch articles: {e}")

python local_news_quickstart.py

{
"status": "ok",
"total_hits": 51,
"page": 1,
"total_pages": 1,
"page_size": 100,
"articles": [
    {
    "id": "d156bb26af2b39ed33bd96b8428b4b21",
    "associated_town": [
        {
        "ai_validated": true,
        "name": "San Francisco, California",
        "description": ["HYPERLOCAL_SOURCES_INCLUDE_QUERY"]
        }
    ],
    "title": "Researchers say an AI-powered transcription tool used in hospitals invents things no one ever said",
    "link": "https://www.sfchronicle.com/business/article/researchers-say-an-ai-powered-transcription-tool-19864411.php",
    "published_date": "2024-10-26 04:15:41",
    "domain_url": "sfchronicle.com",
    "nlp": {
        "theme": ["Tech", "Science"],
        "summary": "Whisper, an AI-powered transcription tool, is prone to making up chunks of text or entire sentences. It's being used in industries worldwide to translate and transcribe interviews, generate text in popular consumer technologies and create subtitles for videos.",
        "sentiment": {
          "title": 0.8877,
          "content": -0.9958
        },
        // other NLP fields
      }
    }
    // ... additional articles
],
"user_input": {
    "q": "*",
    "associated_towns": [
      {
        "name": "San Francisco, California"
      }
    ],
    "theme": "Tech",
    "lang": "en",
    "from_": "7 days ago"
  }
}

import requests

# Configuration
API_KEY = "YOUR_API_KEY_HERE"  # Replace with your actual API key
URL = "https://local-news.newscatcherapi.com/api/search"
HEADERS = {"x-api-token": API_KEY, "Content-Type": "application/json"}
PAYLOAD = {
    "q": "expansion OR investment OR startup OR headquarters",
    "associated_towns": [
        {"name": "San Francisco, California"},
        {"name": "San Jose, California"},
        {"name": "Palo Alto, California"},
    ],
    "theme": "Tech",
    "lang": "en",
    "from_": "7 days ago",
    "clustering": True,
}

# Association method descriptions
ASSOCIATION_METHODS = {
    "HYPERLOCAL_SOURCES_EXCLUDE_QUERY": "Hyper-local source",
    "HYPERLOCAL_SOURCES_INCLUDE_QUERY": "Exact city match",
    "LOCAL_SOURCES_EXCLUDE_QUERY": "State-level source",
    "CITY_STATE_COUNTY_QUERY": "Matches 'City, State' format",
    "NEAR_CITY_STATE_QUERY": "Proximity match",
}

try:
    # Fetch articles using POST method
    response = requests.post(URL, headers=HEADERS, json=PAYLOAD)
    response.raise_for_status()

    # Parse and display the articles
    data = response.json()
    clusters = data.get("clusters", {})

    print(f"Found {data['clusters_count']} major tech stories")
    print(f"Total articles: {data['total_hits']}")
    print("---")

    for cluster_id, cluster in clusters.items():
        # Get the main article from each cluster
        article = cluster["articles"][0]

        # Extract key information
        title = article["title"]

        # Process all associated locations
        locations = []
        for town in article["associated_town"]:
            # Get human-readable association methods
            methods = [
                ASSOCIATION_METHODS.get(method, method)
                for method in town["description"]
            ]
            # Create location string with validation and methods
            validation = "AI validated" if town.get("ai_validated") else "Not validated"
            loc_str = f"{town['name']} ({validation}, {', '.join(methods)})"
            locations.append(loc_str)

        summary = article["nlp"]["summary"]
        cluster_size = cluster["cluster_size"]
        sentiment = (
            "Positive" if article["nlp"]["sentiment"]["content"] > 0 else "Negative"
        )

        # Print story details
        print(f"TRENDING STORY ({cluster_size} related articles):")
        print(f"Title: {title}")
        print("Locations:")
        for loc in locations:
            print(f"  - {loc}")
        print(f"Sentiment: {sentiment}")
        print(f"Summary: {summary}")
        print("---")

except requests.exceptions.RequestException as e:
    print(f"Failed to fetch articles: {e}")

Found 20 major tech stories
Total articles: 28
---
TRENDING STORY (3 related articles):
Title: Persona Positioned Highest for Ability to Execute in the
Inaugural Gartner Magic Quadrant™ for Identity Verification
Locations:
- San Francisco, California (AI validated, Exact city match)
Sentiment: Positive
Summary: Persona ranked first across all Use Cases: Know Your Customer (KYC),
Fraud Detection, Account Recovery, and Sensitive or Regulated scenarios
in the Gartner Critical Capabilities report. Persona's advanced identity
verification platform stands as a critical defense that provides granular
fraud and risk controls to help organizations stay ahead of digital fraud
and safeguard both their business and customers.
---