Transform product discovery with AI recommendations

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Integrate AI-driven product recommendations, automated review summarization, and enhanced search capabilities into an e-commerce storefront.

Detailed description

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This quickstart shows how an e-commerce storefront can seamlessly integrate AI-driven product recommendations, automated review summarization, and enhanced search capabilities to improve customer engagement and conversion rates.

• Product recommendations deliver personalized suggestions based on browsing history and product similarity, helping customers discover what they love.
• Review summaries distill countless reviews into actionable information, accelerating buying decisions.
• Intelligent search uses a hybrid approach with semantic and symbolic search understanding customer intent, making it easier to find the perfect item.

See how customers can get a better experience while business owners unlock higher click-through rates, better conversations and strong customer loyalty.

This quickstart is a complete, cloud-native product recommender system showcasing search, recommendations, reviews, and a Kubeflow training pipeline on OpenShift AI. Technical components include:

• Backend (FastAPI) with PostgreSQL + pgvector + Feast
• Frontend (React) with semantic text/image search
• Training pipeline (Kubeflow Pipelines) to build and push embeddings
• Helm charts for one-command install/uninstall on OpenShift

Architecture diagrams

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• Feature Store: Feast (offline Parquet, online Postgres + pgvector)
• Embeddings: Two-tower training + BGE text encoding for search
• Search: Approximate Nearest Neighbor search over semantic vector embeddings
• Images: Synthetic catalog images; text-to-image generated assets

Requirements

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Prerequisites

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• Access to an OpenShift cluster (with OpenShift AI installed)
• CLI tools: oc and helm
• Container registry access to push images (e.g., quay.io)

Recommended OpenShift AI components enabled: DataSciencePipelines, Feast Operator, Model Registry, KServe/ModelMesh (Managed in your DataScienceCluster).

Minimum hardware requirements

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• CPU: 6-8 cores
• Memory: 16-20Gi
• Storage: 150-200Gi

Minimum software requirements

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• OpenShift 4.17.0+ cluster with OpenShift AI
• oc CLI 4.17.0+ and Helm 3.x
• Access to quay.io to be able to pull down container images

Required user permissions

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• Namespace admin permissions in the target OpenShift project
• Container registry access to pull images from quay.io and registry.redhat.io
• OpenShift AI access to create DataSciencePipelines and Feast components
• Storage provisioning rights to create persistent volumes (PVCs)

Deploy

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1. Clone and enter the repo

git clone https://github.com/<your-username>/product-recommender-system.git
cd product-recommender-system/helm

git clone https://github.com/<your-username>/product-recommender-system.git
cd product-recommender-system/helm

2. Install

make install NAMESPACE=<namespace> minio.userId=<minio user Id> minio.password=<minio password> OLLAMA_MODEL=<ollama model name> MODEL_ENDPOINT=<http://model-url.com/v1>

make install NAMESPACE=<namespace> minio.userId=<minio user Id> minio.password=<minio password> OLLAMA_MODEL=<ollama model name> MODEL_ENDPOINT=<http://model-url.com/v1>

This deploys: Postgres+pgvector, Feast registry/secret, backend, frontend, and the training pipeline server.

3. Access routes (after pods Ready)

# Frontend URL
FRONTEND=$(oc -n <ns> get route product-recommender-system-frontend -o jsonpath='{.spec.host}')
echo "https://$FRONTEND"

# Pipeline UI (DSP) URL
DSP=$(oc -n <ns> get route ds-pipeline-dspa -o jsonpath='{.spec.host}')
echo "https://$DSP"

# Frontend URL
FRONTEND=$(oc -n <ns> get route product-recommender-system-frontend -o jsonpath='{.spec.host}')
echo "https://$FRONTEND"

# Pipeline UI (DSP) URL
DSP=$(oc -n <ns> get route ds-pipeline-dspa -o jsonpath='{.spec.host}')
echo "https://$DSP"

Delete

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make uninstall NAMESPACE=<ns>

make uninstall NAMESPACE=<ns>

Additional details

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Configuration you’ll change most often

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• Images
  • Backend+Frontend: frontendBackendImage in helm/product-recommender-system/values.yaml
  • Training: pipelineJobImage (training container image)
  • Core library (as a base in backend image): applicationImage (if used)
• LLM for review generation (optional)
  • Set llm.secret.data.LLM_API_KEY (or bring your own secret)
  • Backend env: USE_LLM_FOR_REVIEWS, LLM_API_BASE, LLM_MODEL, LLM_TIMEOUT
• Database/Feast integration
  • DB connection comes from the pgvector secret (created by the chart)
  • Feast TLS secret name: feast-feast-recommendation-registry-tls (mounted in backend & training)

How search works

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• Semantic Approximate Nearest Neighbor search over item text embeddings (BGE)

If you add more modalities (e.g., category vectors), stack only equal-dimension tensors or compute per-field similarities and fuse (max/weighted) without stacking.

AI Review Summarization

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• What it does: Uses an LLM to condense recent product reviews into a short, helpful summary covering sentiment, pros, cons, and an overall recommendation.
• Endpoint:
  • GET /products/{product_id}/reviews/summarize — returns AI-generated summary text.
• Notes:
  • Requires at least 4 reviews to produce a summary; otherwise returns a friendly message.
  • Review summary generated real time upon clicking the 'AI Summarize' button on the product page.

Detailed docs live in component READMEs:

• recommendation-core/README.md
• recommendation-training/README.md
• backend/README.md
• frontend/README.md
• helm/README.md

Contributions

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• Contributions welcome via PRs; please update component READMEs when changing behavior