Tag: spring-ai
All the articles with the tag "spring-ai".
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Embedding and storing documents with Spring AI — a step-by-step guide
Before you can search your knowledge base semantically, you need to read documents, split them into chunks, generate embeddings, and store them in the vector database. Spring AI's ETL pipeline handles all of it.
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Setting up pgvector with Spring AI — store and search embeddings in PostgreSQL
pgvector adds native vector search to PostgreSQL. Spring AI auto-configures the schema and wires an EmbeddingModel to it automatically. This post sets up the complete stack with Docker Compose and verifies it works.
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Vector databases explained — why regular databases are not enough for AI
Semantic search requires finding the nearest neighbours among millions of high-dimensional vectors. PostgreSQL with B-tree indexes was not built for this. Here is what vector databases do differently and which options work with Spring AI.
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What are embeddings? A practical explanation for Java developers
Embeddings are the foundation of semantic search, RAG, and most production AI features. This post explains what they are, what they look like, and why they matter — without the maths.
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Streaming LLM responses in Spring AI for a better user experience
LLMs generate text token by token. Streaming lets your users see that text as it arrives instead of staring at a loading spinner. This post shows how to wire Spring AI's stream() to a Server-Sent Events endpoint.