Building Generative AI Search Applications
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Curriculum
Generative AI
Delivery methods
On-Site, Virtual
Duration
2 days
This course builds upon knowledge gained in the Introduction to Building Generative AI Applications course and prepares you to search solutions that leverage generative AI (GenAI) features to make discovery easier, faster, and more effective. The course consists of presentations, demos, and hands-on labs where you work with GenAI models and services, and build realistic GenAI search applications.
Learning objectives
After successfully completing this course, you will be able to:
- Identify opportunities to leverage GenAI-enabled search solutions for multiple use cases
- Understand key concepts specific to GenAI search, such as embeddings, vector search, and Retrieval-Augmented Generation (RAG)
- Evaluate various technology stacks and select architectures best suited for their use cases
- Design solutions using best practices
- Begin building search applications from scratch, or by using managed platforms
Who should attend
This course is designed for developers, solution architects, and others involved in the design, development, and operation of software systems.
Course outline
- Hands-On Tour of Real-World Search Applications
- What Are the Benefits to GenAI-Enabled Search?
- What Are the Risks of Such Solutions?
- Embeddings and Similarity Searches
- Generative AI for Composing Search Responses
- Citations
- Risks and Mitigations
- A Sample Architecture
- Creating Text and Image Embeddings
- Google Vertex AI
- OpenAI
- Open Source Options
- Databases that Support Vector Search
- Google Matching Engine
- Azure Cognitive Search
- Redis
- Postgres and Pgvector
- LangChain
- LangChain Concepts
- Models
- Prompts
- Memory
- Indexes
- Chains
- Creating Embeddings Using LangChain
- Creating Retrieval Chains with LangChain
- Creating the Retrieval Chain
- Doing the Similarity Search
- Composing the Response
- Google Vertex AI Agent Builder
- Apps, Engines, and Datastores
- Ingesting Data
- Integrating the Backend Into Your Applications
- Controlling Access
- Advanced Features Such as Facets, Autocomplete, Etc.
- Azure OpenAI Services on Your Data
- Ingesting Into Azure Cognitive Search and Document Intelligence
- Semantic Search
- Controlling Access
- Searching via API or the Azure OpenAI Studio
