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How Spring AI RAG works
Retrieval-augmented generation (RAG) has two distinct stages. During ingestion, source material is prepared as Spring AI Document objects and written to a vector store. At question time, the application searches that store for related documents and adds the retrieved text to the context sent to the chat model. RAG gives the model relevant material to use; it does not guarantee that the material is complete or that the answer is correct. Spring AI describes this flow in its retrieval-augmented generation reference.
Spring AI’s VectorStore interface lets application code work with a common abstraction, but the application still needs a configured implementation and its corresponding embedding integration. The vector database reference describes preparing documents and adding them to a store.
Choose and pin the integrations
The code below shows the core Spring AI API, not a complete provider-specific project: no model provider, embedding model, vector-store implementation, or Spring Boot patch version is prescribed here. Select integrations supported by your Spring AI release, then use the matching starters, configuration properties, and credentials from their documentation. Do not copy dependency coordinates from a different Spring AI release: the 2.0 upgrade notes document API and module changes, including the vector-store advisor module rename.
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For the direct advisor example, include the current module named spring-ai-vector-store-advisor, along with the selected chat-model, embedding-model, and vector-store dependencies. The modular example uses spring-ai-rag. Consult the release-specific API overview and upgrade notes for coordinates and compatibility details.
Ingest documents into a vector store
Ingestion is separate from answering questions. Read source material with a reader appropriate to its format, prepare one or more Document records, and add them to the configured store. Readers and splitters handle particular source formats or divide content into smaller pieces; the vector-store abstraction does not mean every file is automatically understood.
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import java.util.List;
import org.springframework.ai.document.Document;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.stereotype.Component;
@Component
class KnowledgeIngestor {
private final VectorStore vectorStore;
KnowledgeIngestor(VectorStore vectorStore) {
this.vectorStore = vectorStore;
}
void ingest() {
List<Document> documents = List.of(
new Document(
"Employees may request annual leave through the staff portal.",
Map.of("source", "staff-handbook", "section", "leave")
),
new Document(
"Expense claims must include an itemized receipt.",
Map.of("source", "staff-handbook", "section", "expenses")
)
);
vectorStore.add(documents);
}
}Add import java.util.Map; to this example. The metadata illustrates how source labels or sections can be attached to documents for later filtering. In a real application, replace the inline sample with your own reader and any chunking strategy appropriate to the content. Run ingestion as a controlled startup job, administrative operation, or separate pipeline rather than blindly adding the same records on every application request.
Answer with QuestionAnswerAdvisor
For a straightforward question-answer flow, build a ChatClient with a QuestionAnswerAdvisor backed by the same vector store used for ingestion. The advisor performs a similarity search and augments the user’s text with retrieved context before the chat model generates a response.
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import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore_search.advisor.QuestionAnswerAdvisor;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
class RagConfiguration {
@Bean
ChatClient ragChatClient(ChatClient.Builder builder, VectorStore vectorStore) {
return builder
.defaultAdvisors(new QuestionAnswerAdvisor(vectorStore))
.build();
}
}Use the client to submit a question:
String answer = ragChatClient.prompt()
.user("How do employees request annual leave?")
.call()
.content();The advisor’s retrieval behavior and available options are release-specific; check the RAG reference for the version you use. In particular, verify imports and constructors against your resolved dependencies when integrating the snippet into an application.
Use RetrievalAugmentationAdvisor for a modular flow
Choose RetrievalAugmentationAdvisor when retrieval needs to be composed with distinct query transformation or document-processing steps. Spring AI documents this advisor as a configurable RAG flow, including a VectorStoreDocumentRetriever for vector-store retrieval. Its module is spring-ai-rag.
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A modular flow is useful when a direct vector-store question-answer advisor does not provide enough control over how queries are formed or how retrieved content is prepared. Query transformers can revise or expand a query; document post-processors can rerank results, remove irrelevant or redundant items, or otherwise refine context before generation. Consult the RAG API reference for the release-specific builder API and supported components rather than combining example code from different versions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Tune what reaches the model
Retrieval settings determine which evidence the model can use. The right values depend on your corpus and retrieval implementation; treat configuration examples as starting points to evaluate, not universal settings.
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- Top-k results: Controls how many matching documents are returned. A larger set may provide broader coverage, but can also introduce less relevant material and consume more prompt context.
- Similarity threshold: Excludes results below a relevance cutoff. A strict cutoff may omit useful content; a permissive one may let weak matches through. The suitable threshold depends on the data and the store’s retrieval behavior.
- Metadata filters: Limit eligible documents by attributes such as source or section. The reference also describes runtime filters, which can apply constraints based on the current request.
- Query transformation: Rewriting or expanding an ambiguous or conversational question may help retrieval find relevant content, but adds processing to the request.
- Post-processing: Reranking, removing redundancy, or compressing retrieved material can improve the context presented to the model, but should be assessed against the application’s needs.
Spring AI documents these controls, but the cited reference does not establish universal benchmark settings or a numeric promise of improved performance. Evaluate retrieval with representative questions and inspect both the documents returned and the resulting answers.
Handle empty or weak retrieval
A system should not present an unsupported answer as though retrieval found evidence. Spring AI’s modular RAG advisor does not allow empty retrieved context by default and instructs the model not to answer when that happens. The reference also documents an option to allow empty context. Decide deliberately which behavior fits the application, and test what users see when no relevant documents are available.
For either advisor, assess weak matches as well as no matches: a non-empty result can still be irrelevant. Decide how the application should communicate uncertainty or direct the user to another source, and do not treat the presence of RAG as a factuality guarantee.
Select a vector store for the application
Spring AI provides a common interface over multiple vector-store implementations, not a ranking of providers. Compare candidate integrations against the requirements that matter for your application:
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- How it is deployed and operated in your environment.
- Whether its metadata-filtering features meet your retrieval needs.
- Whether its persistence and data-management behavior suits the corpus.
- Whether it fits project constraints, including infrastructure and operational requirements.
The cited Spring AI references do not establish a best provider, comparative performance, or pricing. Confirm those details with the store’s own documentation and evaluate a candidate using your workload.
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