A Practical Introduction to Spring AI

This course helps students to understand different aspects of Spring AI. Ollama is used for hosting the LLM models locally which will allow students to work with language models without requiring any subscription.

ChatModel

In this section student will get an idea on the purpose of ChatModel and how it can be used in different ways to interact with language models.

ChatClient

In this section, students will get an idea about ChatClient interface which internally use ChatModel but provide convenient developer friendly methods to interact with language models. Also, student will get to know how to have a memory in their flow so that language model can able to recall previous discussion to answer query.

Embed Model

In this section, students will get an understanding of embedding models and their purpose in AI engineering. They will able to interact with embedding models and will also interact with vector store for storage and retrieval of the data.

Retrieval Augmented Generation (RAG)

In this section, students will get to know about RAG flow and the role of chat and embedding models in this setup.

Model Context Protocol (MCP)

In this section, students will get to know about tool calling feature and how it is closely related to MCP. Also how Spring AI can be used to setup MCP client and server interaction.

Model Evaluation

In this section, students get to know how models can be evaluated using Spring AI evaluators.