Perigon API suite는 글로벌 뉴스와 이벤트에 대한 빠르고 구조화된 액세스를 제공하여 실시간 데이터 기반 제품을 구축할 수 있도록 도와줍니다. 새로운 위험을 추적하거나, 관련 기사를 표시하거나, 핵심 인사이트를 발견하는 등 Perigon은 이를 프로그래밍 방식으로 수행할 수 있는 도구를 제공합니다. 기존의 키워드 기반 검색과 달리 Perigon의 semantic search 기능은 쿼리를 맥락적으로 이해하고 관련 문서를 반환할 수 있습니다. 이 노트북은 뉴스 기사와 Wikipedia 콘텐츠 모두에 대해 LangChain과 함께 Perigon의 retriever를 사용하는 방법을 보여줍니다.

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Setup

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Installation

LangChain Perigon integration package를 설치합니다: 
pip install -qU langchain-perigon

# and some deps for this notebook
pip install -qU langchain langchain-openai langchain-community

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Credentials

이 integration을 사용하려면 Perigon API key가 필요합니다. Perigon.io에서 가입하여 API key를 받으세요. 
import getpass
import os

if not os.environ.get("PERIGON_API_KEY"):
    os.environ["PERIGON_API_KEY"] = getpass.getpass("Perigon API key:\n")

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ArticlesRetriever 사용하기

ArticlesRetriever를 사용하면 semantic search 기능을 사용하여 뉴스 기사를 검색할 수 있습니다:

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기본 사용법

from langchain_perigon import ArticlesRetriever

# Create a new instance of the ArticlesRetriever
# PERIGON_API_KEY is automatically read from environment variables
retriever = ArticlesRetriever()

try:
    # Search for articles using semantic search
    documents = retriever.invoke("artificial intelligence developments")

    # Check if we got results
    if not documents:
        print("No articles found for the given query.")
    else:
        print(f"Found {len(documents)} articles")

        # Display first 3 results with metadata
        for doc in documents[:3]:
            # Safely extract metadata with fallbacks
            print(f"Title: {doc.metadata.get('title', 'N/A')}")
            print(f"URL: {doc.metadata.get('url', 'N/A')}")
            print(f"Published: {doc.metadata.get('publishedAt', 'N/A')}")
            print(f"Content: {doc.page_content[:200]}...")
            print("-" * 80)
except Exception as e:
    print(f"Error retrieving articles: {e}")

​
필터링을 사용한 고급 기능

고급 필터링 옵션을 사용하여 검색 결과를 좁힐 수 있습니다: 
from langchain_perigon import ArticlesRetriever, ArticlesFilter

# Create retriever with custom parameters
# PERIGON_API_KEY is automatically read from environment variables
retriever = ArticlesRetriever(
    k=10  # Number of results to return
)

# Define advanced filter options
options: ArticlesFilter = {
    "size": 10,
    "showReprints": False,  # Exclude reprints
    "filter": {
        "country": "us",  # Only US articles
        "category": "tech",  # Technology category
        "source": ["techcrunch.com", "wired.com"]  # Specific sources
    }
}

try:
    # Search with advanced filters applied
    documents = retriever.invoke("machine learning breakthroughs", options=options)

    if not documents:
        print("No articles found matching the filter criteria.")
    else:
        print(f"Found {len(documents)} filtered articles")

        # Display results with relevant metadata
        for doc in documents[:3]:
            print(f"Title: {doc.metadata.get('title', 'N/A')}")
            print(f"Source: {doc.metadata.get('source', 'N/A')}")
            print(f"Category: {doc.metadata.get('category', 'N/A')}")
            print(f"Content: {doc.page_content[:150]}...")
            print("-" * 80)

except Exception as e:
    print(f"Error retrieving filtered articles: {e}")

​
위치 기반 필터링

지리적 관련성으로 기사를 필터링할 수 있습니다: 
from langchain_perigon.types import ArticlesFilter
from langchain_perigon import ArticlesRetriever

retriever = ArticlesRetriever()

# Filter by location
location_options: ArticlesFilter = {
    "size": 5,
    "filter": {"country": "us", "state": "CA", "city": "San Francisco"},
}

documents = retriever.invoke("startup funding rounds", options=location_options)

print(f"Found {len(documents)} San Francisco startup articles")
for doc in documents:
    print(f"Title: {doc.metadata.get('title', 'N/A')}")
    print("-" * 60)

​
WikipediaRetriever 사용하기

WikipediaRetriever는 풍부한 메타데이터와 함께 Wikipedia 콘텐츠에 대한 semantic search 기능을 제공합니다:

​
기본 사용법

from langchain_perigon import WikipediaRetriever

# Create a new instance of the WikipediaRetriever
# PERIGON_API_KEY is automatically read from environment variables
wiki_retriever = WikipediaRetriever()

try:
    # Search for Wikipedia articles using semantic search
    documents = wiki_retriever.invoke("quantum computing")

    # Validate results before processing
    if not documents:
        print("No Wikipedia articles found for the given query.")
    else:
        print(f"Found {len(documents)} Wikipedia articles")

        # Display first 3 results with rich metadata
        for doc in documents[:3]:
            # Extract Wikipedia-specific metadata safely
            print(f"Title: {doc.metadata.get('title', 'N/A')}")
            print(f"Pageviews: {doc.metadata.get('pageviews', 'N/A')}")
            print(f"Wikidata ID: {doc.metadata.get('wikidataId', 'N/A')}")
            print(f"Content: {doc.page_content[:200]}...")
            print("-" * 80)
except Exception as e:
    print(f"Error retrieving Wikipedia articles: {e}")

​
고급 Wikipedia 검색

인기도, 카테고리 및 기타 메타데이터로 Wikipedia 결과를 필터링할 수 있습니다: 
from langchain_perigon import WikipediaRetriever, WikipediaOptions

# Create retriever with custom parameters
# PERIGON_API_KEY is automatically read from environment variables
wiki_retriever = WikipediaRetriever(k=5)

# Define advanced filter options
wiki_options: WikipediaOptions = {
    "size": 5,
    "pageviewsFrom": 100,  # Only popular pages with 100+ daily views
    "filter": {
        "wikidataInstanceOfLabel": ["academic discipline"],
        "category": ["Computer science", "Physics"],
    },
}

# Search with filters
documents = wiki_retriever.invoke("machine learning", options=wiki_options)

print(f"Found {len(documents)} academic Wikipedia articles")
for doc in documents:
    print(f"Title: {doc.metadata.get('title', 'N/A')}")
    print(f"Daily pageviews: {doc.metadata.get('pageviews', 'N/A')}")
    print(f"Instance of: {doc.metadata.get('wikidataInstanceOf', 'N/A')}")
    print(f"Wiki code: {doc.metadata.get('wikiCode', 'N/A')}")
    print("-" * 80)

​
시간 기반 Wikipedia 필터링

수정 날짜로 Wikipedia 기사를 필터링합니다: 
from langchain_perigon import WikipediaRetriever, WikipediaOptions

wiki_retriever = WikipediaRetriever()

# Filter by recent revisions
recent_options: WikipediaOptions = {
    "size": 10,
    "wiki_revision_from": "2025-09-22T00:00:00.000",  # Recently updated articles
    "filter": {"with_pageviews": True},  # Only articles with pageview data
}

documents = wiki_retriever.invoke("artificial intelligence", options=recent_options)

print(f"Found {len(documents)} recently updated AI articles")
for doc in documents:
    print(f"Title: {doc.metadata.get('title', 'N/A')}")
    print(f"Last revision: {doc.metadata.get('wikiRevisionTs', 'N/A')}")
    print(f"Pageviews: {doc.metadata.get('pageviews', 'N/A')}")
    print("-" * 60)

​
Async 사용법

두 retriever 모두 더 나은 성능을 위해 비동기 작업을 지원합니다: 
import asyncio
from langchain_perigon import (
    ArticlesRetriever,
    WikipediaRetriever,
    ArticlesFilter,
    WikipediaOptions,
)


async def search_both():
    """Perform concurrent searches across news articles and Wikipedia.

    Returns:
        tuple: (news_articles, wikipedia_docs) - Results from both retrievers

    Raises:
        Exception: If either retriever fails or API errors occur
    """
    # Initialize retrievers with automatic API key detection
    articles_retriever = ArticlesRetriever()
    wiki_retriever = WikipediaRetriever()

    # Configure search options for targeted results
    articles_options: ArticlesFilter = {
        "size": 3,  # Limit to 3 articles for faster response
        "filter": {
            "country": "us",      # US-based news sources
            "category": "tech",   # Technology category only
        },
    }

    # Filter Wikipedia results by popularity (pageviews)
    wiki_options: WikipediaOptions = {
        "size": 3,           # Limit to 3 articles
        "pageviewsFrom": 50  # Only articles with 50+ daily views
    }

    try:
        # Perform concurrent async searches for better performance
        articles_task = articles_retriever.ainvoke(
            "climate change", options=articles_options
        )
        wiki_task = wiki_retriever.ainvoke(
            "climate change", options=wiki_options
        )

        # Wait for both searches to complete simultaneously
        articles, wiki_docs = await asyncio.gather(
            articles_task, wiki_task, return_exceptions=True
        )

        # Handle potential exceptions from either retriever
        if isinstance(articles, Exception):
            print(f"Articles retrieval failed: {articles}")
            articles = []
        if isinstance(wiki_docs, Exception):
            print(f"Wikipedia retrieval failed: {wiki_docs}")
            wiki_docs = []

        return articles, wiki_docs

    except Exception as e:
        print(f"Error in concurrent search: {e}")
        return [], []


# Run async search with error handling
try:
    articles, wiki_docs = asyncio.run(search_both())

    # Display results summary
    print(f"Found {len(articles)} news articles and {len(wiki_docs)} Wikipedia articles")

    # Show sample results if available
    if articles:
        print(f"Sample article: {articles[0].metadata.get('title', 'N/A')}")
    if wiki_docs:
        print(f"Sample Wikipedia: {wiki_docs[0].metadata.get('title', 'N/A')}")

except Exception as e:
    print(f"Async search failed: {e}")

​
API reference

모든 Perigon API 기능 및 구성에 대한 자세한 문서는 Perigon API documentation을 참조하세요.
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