Perigon integration

src/oss/python/integrations/providers/all_providers.mdx

@@ -3012,4 +3012,12 @@ title: "All providers"
         cta="View provider guide"
     />
 
+    <Card
+        title="Perigon"
+        icon="link"
+        href="/oss/integrations/providers/perigon_search"
+        arrow="true"
+        cta="View provider guide"
+    />
+
 </Columns>

src/oss/python/integrations/providers/index.mdx

@@ -135,6 +135,7 @@ These providers have standalone `langchain-{provider}` packages for improved ver
 | [MCP Toolbox](/oss/integrations/providers/toolbox-langchain/) | [toolbox-langchain](https://pypi.org/project/toolbox-langchain/) | ![Downloads](https://static.pepy.tech/badge/toolbox-langchain/month) | ![PyPI - Version](https://img.shields.io/pypi/v/toolbox-langchain?style=flat-square&label=%20&color=orange) | ❌ |
 | [Scrapeless](/oss/integrations/providers/scrapeless/) | [langchain-scrapeless](https://pypi.org/project/langchain-scrapeless/) | ![Downloads](https://static.pepy.tech/badge/langchain-scrapeless/month) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-scrapeless?style=flat-square&label=%20&color=orange) | ❌ |
 | [ZeusDB](/oss/integrations/providers/zeusdb/) | [langchain-zeusdb](https://pypi.org/project/langchain-zeusdb/) | ![Downloads](https://static.pepy.tech/badge/langchain-zeusdb/month) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-zeusdb?style=flat-square&label=%20&color=orange) | ❌ |
+| [Perigon](/oss/integrations/providers/perigon_search/) | [langchain-perigon](https://pypi.org/project/langchain-perigon/) | ![Downloads](https://static.pepy.tech/badge/langchain-perigon/month) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-perigon?style=flat-square&label=%20&color=orange) | ❌ |
 
 ## All Providers
 

src/oss/python/integrations/providers/perigon.mdx

@@ -0,0 +1,80 @@
+---
+title: Perigon
+---
+
+>[Perigon](https://perigon.io/) is a comprehensive news API that provides access to real-time contextual information in news articles, stories, metadata and wikipedia pages from thousands of sources worldwide.
+>
+
+## Installation and Setup
+
+`Perigon` integration exists in its own [partner package](https://pypi.org/project/langchain-perigon/). You can install it with:
+
+```python
+%pip install -qU langchain-perigon
+```
+
+In order to use the package, you will also need to set the `PERIGON_API_KEY` environment variable to your Perigon API key.
+
+## Retrievers
+
+Perigon provides two retrievers:
+
+### ArticlesRetriever
+
+This retriever retrieves articles based on a given query and optional filters.
+
+See a [full usage example](/oss/integrations/retrievers/perigon#using-articlesretriever).
+
+```python
+# Make sure PERIGON_API_KEY environment variable is set to your Perigon API key
+from langchain_perigon import ArticlesRetriever, ArticlesFilter
+
+# Create retriever with specific number of results
+retriever = ArticlesRetriever(k=12)
+
+# Configure filter options to exclude reprints and focus on US articles
+options: ArticlesFilter = {
+    "showReprints": False,  # Exclude duplicate/reprint articles
+    "filter": {"country": "us"},  # Only US-based news
+}
+
+try:
+    documents = retriever.invoke("Recent big tech layoffs", options=options)
+
+    # Check if we got results before accessing
+    if documents:
+        print(f"First document: {documents[0].page_content[:200]}...")
+    else:
+        print("No articles found for the given query.")
+except Exception as e:
+    print(f"Error retrieving articles: {e}")
+```
+
+You can use the `ArticlesRetriever` in a standard retrieval pipeline:
+
+### WikipediaRetriever
+
+This retriever retrieves wikipedia pages based on a given query and optional filters.
+
+See a [full usage example](/oss/integrations/retrievers/perigon#using-wikipediaretriever).
+
+```python
+# Make sure PERIGON_API_KEY environment variable is set to your Perigon API key
+from langchain_perigon import WikipediaRetriever
+
+# Create retriever with specific number of results
+retriever = WikipediaRetriever(k=12)
+
+try:
+    documents = retriever.invoke("machine learning")
+
+    # Safely access results with error handling
+    if documents:
+        print(f"First document: {documents[0].page_content[:200]}...")
+    else:
+        print("No Wikipedia articles found for the given query.")
+except Exception as e:
+    print(f"Error retrieving Wikipedia articles: {e}")
+```
+
+You can use the `WikipediaRetriever` in a standard retrieval pipeline: