Article 1 Title: Why Solr Is No Longer Enough for Modern Commerce Search
Introduction I had the opportunity to create a search engine using Retrieval-Augmented Generation (RAG) with OpenAI and Supabase. To say that I was impressed is an understatement. I realized that every developer now has the power to build advanced search capabilities tailored to their organization’s needs without relying on legacy keyword search engines.
For over a decade, Apache Solr has been the backbone of search functionality in SAP Commerce (Hybris). It excels at keyword indexing, faceted navigation, and performance at scale. But in the age of AI-driven customer experiences, Solr is showing its limitations. Today's customers expect search to understand their intent, not just match their keywords.
This article is the first in a multi-part series dedicated to helping SAP Commerce teams modernize their search stack using Retrieval-Augmented Generation (RAG) with OpenAI and Supabase. We'll explore why traditional search is no longer enough, how to build a modern AI-enhanced layer, and how to gradually evolve your platform without disrupting your existing infrastructure.
In this first article, we explore why Solr, while still reliable, is no longer sufficient for delivering intelligent, personalized product discovery—and why RAG powered by OpenAI and Supabase is emerging as a next-gen alternative.
The Solr Era: What It Got Right
- High performance full-text search: Solr is fast and scalable.
- Faceted filtering: Category, price, brand filters are tightly integrated.
- Integration with SAP Commerce: Native indexing, SmartEdit support, and OCC APIs.
But while Solr is structured and reliable, it lacks "understanding."
Where Solr Falls Short in 2025
- Keyword dependence: Solr only matches tokens. No understanding of meaning or context.
- Lack of semantic understanding: Cannot handle queries like "best vacuum for pet hair and allergies."
- No personalization: Solr doesn’t adapt to individual user behavior or preferences.
- Hard to maintain: Boosting and synonyms require manual tuning and merchandising.
RAG with OpenAI + Supabase: A New Paradigm Retrieval-Augmented Generation (RAG) combines two powerful ideas:
- Semantic retrieval of the most relevant content (via vector search)
- Generative answering using a large language model (e.g., GPT-4)
With Supabase as your vector store (via pgvector) and OpenAI for embeddings + response generation, you can:
- Understand the intent behind search queries
- Return answers, not just results
- Create intelligent assistants that interact with your product catalog
Example: Solr vs RAG in Action User Query: "What's a good air purifier for someone with pet allergies?"
- Solr Result: Products with the exact words "air," "purifier," "pet," "allergies"
- RAG Result: A GPT-powered answer citing a HEPA purifier with pet dander filters and linking to product SKUs
Why SAP Commerce "Hybirs" Architects Should Pay Attention
- Composable Commerce is gaining momentum; RAG fits this model
- Solr is hardwired, but not irreplaceable
- You can run RAG alongside Solr for hybrid search
- RAG enables voice, chat, and natural Q&A interfaces
Coming Up Next in the Series
- Article 2: "How to Build a Supabase + OpenAI RAG Layer for SAP Commerce"
- Article 3: "Co-Existence Strategy: Running Solr and RAG Together"
- Article 4: "Replacing Solr Faceted Search with pgvector Filtering"
- Article 5: "Measuring the Business Impact of AI-Powered Search"
