Spring AI Examples

Examples using Spring AI with Mistral AI and Ollama LLM providers.

🛠️ Pre-Requisites

• Java 25
• Maven 4
• Mistral AI API key
• Ollama
• Docker

📚 Use Cases

1️⃣ Part 1

• Ollama API: Simple local inference using Ollama's low-level API.
• Mistral AI API: Simple remote inference using Mistral AI's low-level API.
• Chat Model: Example demonstrating the abstraction provided by the chat model.
• Chat Client: Example covering the following features:
  • Thinking model,
  • JSON structured output,
  • Chat memory,
  • Request and response message logging.

2️⃣ Part 2 🚧

• RAG: Example covering the following features:
  • Embedding model,
  • JSON data with metadata reading to add documents to the vector store,
  • Question and answer with a filtered search limiting data retrieval from the vector store,
  • JSON structured output,
  • Chat memory,
  • Request and response message logging,
  • Docker compose containing PostgreSQL for JDBC chat memory and Qdrant vector store.

🧠 Models

Chat Models

• Ollama:
  • Gemma 3 12B,
  • Mistral Small 3.2 Latest,
  • Qwen 3 14B.
• Mistral AI:
  • Mistral Small Latest,
  • Mistral Medium Latest,
  • Magistral Medium Latest.

Embedding Models

• Ollama:
  • Embedding Gemma Latest.
• Mistral AI:
  • Mistral Embed.

🗃️ Vector Stores

• Qdrant