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Enterprise Context Store and RAG on SAP HANA Vector Engine
New
2 students
Created byJunaid Ahmed
Last updated 5/2026
English

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  • Execute a context lifecycle operation, retire an outdated chunk, insert a corrected version, verify retrieval returns current context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

Course content

1 section • 14 lectures • 1h 24m total length

  • What are we going to do in this course2:11

    A walkthrough of the full pipeline, context store, vector engine, RAG, notebooks, chat app. What we build, in what order, and why each layer exists.

  • Test the RAG Demo on a Chat Client with and without Grounding12:54

    The course opens with the chat client running against the Vantara Capital corpus. We ask four analyst questions  with grounding off and on. The contrast sets the stage for everything that follows.

  • Resources1:54

    Find all the necessary code files in the resources.

  • Why SAP HANA in the foundation context store for SAP Enterprise4:25

    AP HANA Cloud as the enterprise grounding layer. How Joule, AI Core, custom agents, and MCP all reach into HANA for context.

  • First principle view of Vectors, Embeddings and Cosine Similarity7:05

    What a vector is. How VECTOR_EMBEDDING() computes one inside HANA. How Cosine Similarity works and scores with a visual representations.

  • Vectors, Embeddings and Cosine Similarity
  • What is Content Chunking and our approach to the corpus4:13

    We use a visualizer to understand chunking, its purpose, some techniques and parameters.

  • Designing the Context Store: Table Structure and Schema8:14

    The context schema. We go through every column its purpose and what it stores

  • Create the FIN_CONTEXT_VANTARA context store table
  • Schema and Table Structure
  • Configuring the HNSW Vector Index3:55

    BUILD CONFIGURATION and SEARCH CONFIGURATION parameters.

  • Index Configuration
  • Inserting Document Chunks into the Context Store6:24

    Inserting all chunks into context table using VECTOR_EMBEDDING() inline in the INSERT statement.

  • Insert document chunks into the context store using VECTOR_EMBEDDING()
  • Testing Cosine Search on Vector Embeddings4:37

    COSINE_SIMILARITY() against the loaded context store.

  • Run cosine similarity search with governance filters
  • Context Lifecycle operations: Retiring and Replacing Context3:44

    The CRE concentration entry in v1.0 is outdated and incorrect. We retire it, insert v2.0 MEDIUM risk, 23.4 percent, within policy. Rerun the same query.

  • Context Lifecycle
  • Updated Search / Context Lifecycle
  • Assembling the Context Package for the LLM(Jupyter Notebooks)11:10

    Install hdbcli and anthropic, configure all connection parameters and pipeline settings in one place. HANA credentials, API key, table name, embedding model, top K, Claude model, and temperature. Connect to HANA and verify the context store.

    retrieve_context() sends the question to HANA, VECTOR_EMBEDDING() embeds it inline as QUERY type, COSINE_SIMILARITY() returns the top K ranked chunks with scores.

    assemble_context() builds the structured context package with proper attributions, source and scores

  • Package Assembly
  • Connecting to Claude: The API Call and RAG Pipeline Output(Jupyter Notebooks)8:28

    ask_claude() sends the package and question to Claude, constrained by a system prompt to answer only from provided context and cite every source. Inspect the full pipeline output including retrieved chunks with scores, the context package, the grounded cited answer, and the pipeline summary.

  • Send the context package to Claude and interpret the pipeline output
  • RAG Pipeline End to End
  • The Complete RAG Pipeline Tested from the Chat Client5:10

    The chat client demo repeated with the context store now built and understood.

  • Running the Chat APP

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

Build a Production-Grade AI Context Store on SAP HANA Cloud From First Principles

SAP HANA Cloud now ships with a native vector engine. Enterprises running SAP landscapes will build their AI grounding infrastructure on it for Joule, for custom agents, for any RAG-based application that needs to retrieve enterprise context. Understanding how to design and implement that layer from first principles such as schema, embeddings, indexing, retrieval, and pipeline assembly is a critical technical skill. This course introduces you to those fundamental skills in full, using a real corpus, real SQL, and a real API integration. You will leave with working code, a mental model of how context engineering operates at the data layer, and a RAG pipeline.

You will design and implement a complete AI context store and RAG pipeline on SAP HANA Cloud Vector Engine from schema design to live query, using a financial services use case and a native implementation that connects directly to SAP HANA Cloud, without using the AI Launchpad and GenAI Hub.

The course opens with a live chat client demo. You will see actual analyst questions answered with and without grounding, so you understand exactly what you are building toward and why it matters.

This course is deliberately compact, designed for professionals with limited time who need the highest possible takeaway per hour invested.

What You Will Build and Learn:

  • Understand why SAP HANA Cloud is the enterprise foundation context store, grounding Joule, AI Core, custom agents, and MCP  and how it sits at the center of enterprise AI architectures

  • Understand vectors from the ground up: how VECTOR_EMBEDDING() computes them inside HANA, the difference between DOCUMENT and QUERY embedding types, and how cosine similarity measures semantic distance

  • Analyze a real document corpus — a credit risk policy, an earnings report, and a portfolio risk register, and make deliberate design decisions before writing a single query

  • Design and build the full context store schema with embeddings, document identity, versioning, section filters, governance flags, and audit metadata  with complete lifecycle support from day one

  • Configure the HNSW vector index with precision build and search parameters, what each controls, and the exact syntax HANA requires

  • Understand why chunking decisions matter more than most practitioners expect, and why paragraph-level chunking was the right choice for this corpus

  • Insert all chunks across three documents using VECTOR_EMBEDDING() inline in SQL, run similarity search with COSINE_SIMILARITY() and governance filters, and execute a full context lifecycle operation — retire, replace, and verify

  • Move to Python: connect to HANA using hdbcli, retrieve ranked chunks, assemble a context package with full source attribution, and send it to Claude via the Anthropic API with a grounded system prompt

  • Examine the full pipeline output end to end chunk scores, context package, cited answer, and pipeline summary

  • Close with the same live chat client demo from the opening same questions, now grounded against the context store you built

Course Salient Features:

  • Professionally authored and edited video content built for practitioners and new learners

  • Every concept is implemented and demonstrated in the course.

  • Financial services dataset (Vantara Capital Group) created specifically for this course

  • Full SQL and Python coverage from HANA schema to Anthropic API call

  • Native HANA implementation no middleware, no abstraction layers

Disclaimer: SAP public documentation, community blogs, and other resources were used for research. 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.

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