What is the ContentRetriever and RetrievalAugmentor in LangChain4j advanced RAG?

LangChain4j's advanced RAG API introduces a cleaner abstraction hierarchy above the basic EmbeddingStoreContentRetriever. The two key interfaces are ContentRetriever and RetrievalAugmentor.

ContentRetriever is the interface responsible for fetching relevant content given a query. Multiple implementations are available:

• EmbeddingStoreContentRetriever — retrieves via vector similarity from an EmbeddingStore
• WebSearchContentRetriever — fetches live web results (e.g., via Tavily, Google) for up-to-date information
• SqlDatabaseContentRetriever — generates and executes SQL to retrieve structured data (text-to-SQL RAG)

RetrievalAugmentor is the higher-level orchestrator that sits between the user query and the LLM call. The default implementation, DefaultRetrievalAugmentor, exposes a full pipeline with configurable stages:

• Query transformer — Rewrites or decomposes the original query (e.g., query compression using conversation history, or HyDE — Hypothetical Document Embeddings)
• Query router — Routes queries to one or more ContentRetrievers based on the query type
• Content aggregator — Merges results from multiple retrievers
• Content injector — Formats retrieved content for injection into the prompt

RetrievalAugmentor augmentor = DefaultRetrievalAugmentor.builder()
    .queryTransformer(new CompressingQueryTransformer(chatModel))
    .contentRetriever(EmbeddingStoreContentRetriever.from(store))
    .contentInjector(DefaultContentInjector.builder()
        .promptTemplate(PromptTemplate.from("Context:\n{{contents}}\n\nQuestion: {{userMessage}}"))
        .build())
    .build();

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