A Smarter Way to Unlock Unstructured Data: SAS RAM in Action
16m + Hands-On Practice
Available in:
A Smarter Way to Unlock Unstructured Data: SAS RAM in Action
AIRAM : AIRAM1
The SAS Retrieval Agent Manager workshop teaches you how to build and automate retrieval-augmented generation (RAG) workflows (pipelines), covering essential steps like data ingestion from various unstructured sources, organizing data into collections, configuring embedding models and extraction tools, evaluating pipeline performance, and selecting optimal configurations.
You will learn to interact with data through a chat interface, create agents for advanced processing and automation, and set up workflow automations.
Overall, you will gain hands-on skills in building, evaluating, and automating enterprise retrieval-augmented generation solutions.
You will learn to interact with data through a chat interface, create agents for advanced processing and automation, and set up workflow automations.
Overall, you will gain hands-on skills in building, evaluating, and automating enterprise retrieval-augmented generation solutions.
Learn How To
- Build and automate end-to-end RAG pipelines, including data ingestion, organization, vectorization, evaluation, and automation.
- Load unstructured data (PDF, JSON, images, text, Word), organizing it into collections, and configuring embedding models and extraction tools.
- Evaluate pipeline configurations using manual and automated tests, interpret results, and select the best-performing setup.
- Interact with collections through a chat interface and create agents to automate querying and post-processing.
- Create agents for advanced data processing, automation, and integration.
- Set up workflow automation so that updates to sources trigger downstream processing and agent reinitialization, reducing manual effort.
Who Should Attend
- Technical Roles:
- AI/ML Engineers: Focused on configuring embedding models, integrating large language models (LLMs), optimizing vector databases, and automating workflows for efficient RAG pipeline operations.
- Application Developers: Those who build, extend, or integrate SAS RAM with other enterprise systems through APIs and automation tools.
- Business Roles:
- Business Analysts & Research Analysts: Although not strictly technical, these users interact with the system to streamline discovery of relevant enterprise information and validate outputs using built-in evaluation frameworks.
Prerequisites
Before attending this course, you should shoud have:
- Familiarity with Large Language Models (LLMs).
- Experience with Python.
- Basic Knowledge of Embedding Models and Vector Databases.
- Basic Data Processing and Workflow Automation skills.
SAS Products Covered
SAS Machine Learning;SAS Viya
Course Outline
Concepts and Introduction
- What Is Retrieval Augmented Generation?
- Introduction to SASRaportowanie w SAS Enterprise Guide i SAS Add-In for Microsoft Office; Retrieval Agent Manager
Configuration
- Configure LLMs and Vector Databases
Retrieve Source Data
- Use a custom script to query and download PDF files from arXiv, an open-access repository widely used for preprints in physics, computer science, and AI.
Configure a collection and vectorize the collection
- Vectorize the source documents.
- Store the embeddings in a vector database for efficient retrieval.
Evaluation Phase
- Create both user-driven and automated evaluations to validate the setup.
- Once the configuration meets quality standards, designate it as the Champion for production use.
Chat
- Start interacting with the Retrieval-Augmented Generation (RAG) system for contextual responses.
Agents
- Build an agent that leverages the curated collection. Chat with the agent.
Automation
- Implement automation for a change in the source.
Optional Practice: Code Generation