Tag: spring-ai
All the articles with the tag "spring-ai".
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Building a document Q&A chatbot with Spring AI and RAG
This post assembles all of Module 3 and 4 into one working application — ingestion pipeline, pgvector storage, QuestionAnswerAdvisor, streaming responses, and a simple chat interface. By the end, you have a chatbot that answers from your own documents.
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Chunking strategy in RAG — the decision that silently kills answer quality
How you split documents before indexing determines whether your RAG pipeline retrieves useful context or useless fragments. Chunk too large and embeddings average out. Chunk too small and context is missing. This post covers the tradeoffs.
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Building your first RAG pipeline with Spring AI
Spring AI's QuestionAnswerAdvisor wires retrieval directly into ChatClient. Attach it to your VectorStore and every call automatically retrieves relevant context before the LLM sees the question. This post builds the complete pipeline.
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What is RAG and why your AI app almost certainly needs it
LLMs know a lot, but they don't know about your business. RAG — Retrieval-Augmented Generation — fixes this by retrieving relevant documents at query time and injecting them into the prompt. Here is why it exists and when to use it.
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Semantic search in Spring AI — find by meaning, not by keyword
With documents indexed in pgvector, VectorStore.similaritySearch() finds the most relevant chunks for any query. This post covers SearchRequest, similarity thresholds, metadata filters, and how to expose semantic search as an API endpoint.