Search is how users find content, and to reap the benefits of your investment in Adobe Experience Manager (AEM), you need the ability to retrieve content for various purposes, including Retrieval Augmented Generation (RAG).? Integrating AEM with a vector database like Coveo, Qdrant, Elasticsearch, OpenSearch, or Vespa can significantly improve search functionality.? The initial method you could consider doing this might be a web crawler might be the first and only method you consider to this but there are better methods to address the content indexing.
Challenges of Web Crawling AEM Sites
Web crawling an AEM site might seem like a straightforward approach to indexing content, but it presents several challenges:
- Dynamic Content and Personalization: AEM sites often deliver personalized content based on user profiles, behaviors, or preferences. Web crawlers may not access all content variations, leading to incomplete indexing and suboptimal search results.
- Access Control and Permissions: AEM implements robust access control lists (ACLs) to manage user permissions. Web crawlers lack the ability to retrieve these and crawl, at best, with a single permission, resulting in gaps in the indexed data. This can be mitigated by multiple crawls but that has the burden of additional traffic on your site and additional documents in your index for effectively redundant content.
- Content Structure and Metadata: AEM’s content repository is rich with metadata and structured content that web crawlers may not fully interpret. This limitation can lead to inadequate indexing and retrieval of content. Utilizing AEM’s native indexing capabilities ensures that the content structure and metadata are accurately represented in search results3.
- Performance and Load Considerations: Web crawling can impose significant load on the server, potentially affecting site performance. AEM’s built-in indexing mechanisms are optimized to handle content efficiently without overloading the system.
- Latency of changes: Web crawling is a ‘pull method’.? It’s difficult to understand changes and require recrawls.? Further complicating this are things like changes to metadata and security which may not result in changes that a web crawler can easily detect.? The work around is full recrawls which further escalate points 3 and 4.
Integrating AEM with a Vector Database
To address these challenges, integrating AEM with a vector database through a dedicated connector is advisable. Such connectors are designed to interact directly with AEM’s content repository and can receive event record changes ensuring comprehensive and accurate indexing while respecting access controls and content structures. This approach leads to more reliable and efficient search functionality compared to traditional web crawling methods.?
(A company I work for has just such a connector.? Check it out at: https://mcplusa.com/technology/connectors/)?
Benefits of Using a Vector Database
Search and retrieval aren’t typically what draws folks to AEM and going back to the DayCMS days, you didn’t really want to look at or breath on the CMS for fear of crashing it.? While that has changed, what hasn’t changed is that Vector DBs are built with retrieval in mind and do this efficiently and are easy to scale.? By connecting AEM to a vector database, organizations can leverage the following benefits:
- Enhanced Search Accuracy: Vector databases can handle complex queries and provide more accurate search results by considering the context and relationships between different pieces of content.
- Scalability: Vector databases are designed to handle large volumes of data, making them suitable for organizations with extensive content repositories.
- Real-Time Updates: Changes in the content repository can be reflected in the search index in real-time, ensuring that users always have access to the most up-to-date information.
