:
Enterprise Context Store and RAG on SAP HANA Vector Engine
Published 5/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 24m | Size: 983.63 MB
Vector store design, embeddings, cosine search, lifecycle, orchestration, Claude API, Jupyter notebooks, Test with App
What you'll learn
Build a complete AI context store and RAG pipeline on SAP HANA Cloud Vector Engine from scratch using SQL and Python
Understand vectors, VECTOR_EMBEDDING(), cosine similarity, and HNSW index configuration
Understand how cosine similarity measures semantic distance and ranks retrieved chunks
Understand why SAP HANA Cloud is the foundation context store for enterprise AI — Joule, AI Core, and custom agents all ground here
Design a production context store schema with identity, versioning, section filters, lifecycle, and audit metadata
Analyse the document corpus, decide on a chunking strategy, and insert chunks using VECTOR_EMBEDDING() inline in SQL
Test cosine similarity search on the context store with governance and audit filters
Execute a context lifecycle operation, retire an outdated chunk, insert a corrected version, verify retrieval returns current context
Connect to SAP HANA Cloud from Python using hdbcli, orchestrate the full RAG pipeline in Jupyter notebooks
Retrieve ranked chunks, assemble a structured context package with full source attribution
Send a grounded context package to Claude via the Anthropic API and interpret the full pipeline output
Test the complete pipeline end to end using a chat application with the grounding you just built
Requirements
Access to an SAP HANA Cloud instance with the Vector Engine enabled
Familiarity with any programming language and working with Jupyter notebooks
Basic familiarity with SQL
No prior knowledge of vector databases is required
Familiarity with basic AI and GenAI concepts will be helpful
An Anthropic API key (or other LLMs) with a small amount of credits. Sometimes the signup credits are enough. Course token usage is minimum.
Description
This course uses a financial services use case to build a complete AI context store and RAG pipeline on SAP HANA Cloud Vector Engine, designed and implemented from first principles.
The course opens with a live demo using the chat client. We ask actual analyst questions with and without grounding and set the stage for the complete design and development of the context store.
We spend time understanding how SAP HANA Cloud is the foundation context store for enterprise AI including Joule, AI Core, custom agents, and MCP all ground their answers here. We cover vectors, what they are, how VECTOR_EMBEDDING() computes them inside HANA, the difference between DOCUMENT and QUERY embedding types, and how cosine similarity measures semantic distance.
We then analyze the document corpus — a credit risk policy, an earnings report, and a portfolio risk register. We design the context store schema and build the table with full lifecycle support including embeddings, document identity, versioning, section filters, governance flags, and audit metadata. We configure the HNSW vector index — build and search parameters, what each controls, and the exact syntax HANA requires.
We work through chunking — why paragraph-level chunking was chosen for this corpus and why good chunking decisions matter more than most people expect.
We insert all chunks across three documents using VECTOR_EMBEDDING() inline in SQL and test similarity search using COSINE_SIMILARITY() with governance filters. We then execute a full context lifecycle operation — retire the outdated entry, insert the corrected version, and verify the query returns current context.
We move to the Python notebooks. We connect to HANA using hdbcli, retrieve ranked chunks, assemble the context package with full source attribution, and send it to Claude via the Anthropic API with a grounded system prompt. We examine the full pipeline output — chunk scores, context package, cited answer, and pipeline summary.
The course closes with the chat client demo. The same questions asked with and without grounding drawn from the context store you just built.
Disclaimer
SAP public documentation, community blogs, and other resources were used for research and credits are due to the respective parties.
SAP HANA Cloud and all SAP products mentioned are products of SAP SE. Anthropic Claude is a product of Anthropic. I am not associated with either.
AI was used as a research assistant in producing this course. Vantara Capital Group is a fictional dataset created for educational purposes.
Who this course is for
SAP professionals who work with or near AI projects and want to understand what is actually happening under the hood
SAP architects, developers and consultants who need to build or advise on enterprise AI implementations
Anyone building on SAP HANA Cloud who wants to add AI retrieval capabilities to their applications
Technical leads evaluating SAP's AI stack who want to understand the foundation layer before adopting managed services
SAP Basis and platform teams who need to understand what the Vector Engine is and how it is used in production You said: Most SAP professionals working with AI today are using tools they do not fully understand.
Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me
No Password - Links are Interchangeable