End of Workshop
Congratulations!
You have successfully completed Building Intelligent Apps with RAG on Kubernetes: From Raw Data to Real-Time Insights!
Starting from the business problem, thousands of resolved incident tickets locked away in ServiceNow with no way to search or leverage them, you built a complete, end-to-end Retrieval-Augmented Generation system on {ocp-ai}.
What You Built
Throughout this workshop, you:
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Deployed a mock ServiceNow API via ArgoCD to serve as the incident data source
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Stood up a Milvus vector database to store and search vector embeddings at scale
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Imported and ran a Data Science Pipeline in {ocp-ai} that fetched incident records, generated embeddings, and ingested them into Milvus
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Queried the RAG system from a Jupyter Notebook, retrieving context-aware answers grounded in your private data
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Explored event-driven ingestion using Knative and Kafka to automatically process PDF documents in real-time
Each of these components — the data source, the vector store, the orchestration pipeline, and the serving layer — mirrors what a production RAG deployment looks like inside an enterprise.
