E-commerce Catalog RAG Template

The E-commerce Catalog RAG Template provides a pre-configured, production-ready pipeline for search & retrieval. Instead of building from scratch, you get a tested configuration that handles the common patterns and edge cases teams encounter. Template for building semantic product search by combining product catalog data with customer reviews, enabling natural language product discovery. This template has been refined based on real-world deployments across hundreds of IngestIQ users.

Use case: Building conversational product search. This is a common scenario where the E-commerce Catalog RAG Template saves significant development time by providing pre-built handling for the specific data patterns involved. Use case: Enhancing existing search with semantic understanding. This is a common scenario where the E-commerce Catalog RAG Template saves significant development time by providing pre-built handling for the specific data patterns involved. Use case: Creating AI shopping assistants. This is a common scenario where the E-commerce Catalog RAG Template saves significant development time by providing pre-built handling for the specific data patterns involved.

This template comes in multiple variations to match your specific needs: Variation 1: Catalog-only pipeline — suited for different complexity levels and data characteristics. Variation 2: Catalog + reviews combined pipeline — suited for different complexity levels and data characteristics. Variation 3: Multi-marketplace aggregation pipeline — suited for different complexity levels and data characteristics. Choose the variation that best matches your data complexity and processing requirements. You can always upgrade to a more advanced variation as your needs evolve.

Getting started with this template takes minutes, not days. Here is the complete setup process: Step 1: Connect your product catalog data source (API, CSV, database) Step 2: Add customer review data source if available Step 3: Configure product-aware chunking and metadata extraction Step 4: Set up hybrid search with category and price filtering Step 5: Test with natural language product queries Each step includes validation checks to ensure your pipeline is configured correctly before processing begins.

The E-commerce Catalog RAG Template supports extensive customization. Key configuration options include chunking strategy (fixed-size, semantic, or document-structure-aware), embedding model selection (OpenAI, Cohere, or open-source alternatives), target vector database (Pinecone, Qdrant, Milvus, Weaviate, PgVector, or MongoDB Atlas), and metadata extraction rules. All settings can be adjusted through the IngestIQ dashboard or API.

When using this template, start with the default settings and iterate based on retrieval quality. Monitor chunk sizes to ensure they are neither too small (losing context) nor too large (diluting relevance). Use the built-in evaluation tools to measure retrieval accuracy before deploying to production. Set up incremental sync rather than full re-processing to keep your pipeline efficient as data volumes grow.