RAG Implementation

Build Retrieval-Augmented Generation systems that extend AI capabilities with external knowledge sources.

Overview

This skill covers: document processing, embedding generation, vector storage, retrieval configuration, and RAG pipeline implementation.

When to Use

• Building Q&A systems over proprietary documents
• Creating chatbots with factual information from knowledge bases
• Implementing semantic search with natural language queries
• Reducing hallucinations with grounded, sourced responses
• Building documentation assistants and research tools
• Enabling AI systems to access domain-specific knowledge

Instructions

Step 1: Choose Vector Database

Select based on your requirements:

Step 2: Select Embedding Model

Step 3: Implement Document Processing Pipeline

1. Load documents from source (file system, database, API)
2. Clean and preprocess (remove formatting, normalize text)
3. Split documents into chunks with appropriate strategy
4. Generate embeddings for each chunk
5. Store embeddings in vector database with metadata

Validation: Verify embeddings were generated successfully:

List<Embedding> embeddings = embeddingModel.embedAll(segments);
if (embeddings.isEmpty() || embeddings.get(0).dimension() != expectedDim) {
    throw new IllegalStateException("Embedding generation failed");
}

Step 4: Configure Retrieval Strategy

Choose the appropriate strategy:

• Dense Retrieval: Semantic similarity via embeddings (default for most cases)
• Hybrid Search: Dense + sparse retrieval for better coverage
• Metadata Filtering: Filter by document attributes
• Reranking: Cross-encoder reranking for high-precision requirements

Step 5: Build RAG Pipeline

1. Create content retriever with your embedding store
2. Configure AI service with retriever and chat memory
3. Implement prompt template with context injection
4. Add response validation and grounding checks

Validation: Test with known queries to verify context injection works correctly.

Error Handling: For batch ingestion, wrap in retry logic:

for (Document doc : documents) {
    int attempts = 0;
    while (attempts < 3) {
        try {
            store.add(embeddingModel.embed(doc).content(), doc.toTextSegment());
            break;
        } catch (EmbeddingException e) {
            attempts++;
            if (attempts == 3) throw new RuntimeException("Failed after 3 retries", e);
        }
    }
}

Step 6: Evaluate and Optimize

1. Measure retrieval metrics: precision@k, recall@k, MRR
2. Evaluate answer quality: faithfulness, relevance
3. Monitor performance and user feedback
4. Iterate on chunking, retrieval, and prompt parameters

Examples

Example 1: Basic Document Q&A

List<Document> documents = FileSystemDocumentLoader.loadDocuments("/docs");

InMemoryEmbeddingStore<TextSegment> store = new InMemoryEmbeddingStore<>();
EmbeddingStoreIngestor.ingest(documents, store);

DocumentAssistant assistant = AiServices.builder(DocumentAssistant.class)
    .chatModel(chatModel)
    .contentRetriever(EmbeddingStoreContentRetriever.from(store))
    .build();

String answer = assistant.answer("What is the company policy on remote work?");

Example 2: Metadata-Filtered Retrieval

EmbeddingStoreContentRetriever retriever = EmbeddingStoreContentRetriever.builder()
    .embeddingStore(store)
    .embeddingModel(embeddingModel)
    .maxResults(5)
    .minScore(0.7)
    .filter(metadataKey("category").isEqualTo("technical"))
    .build();

Example 3: Multi-Source RAG Pipeline

ContentRetriever webRetriever = EmbeddingStoreContentRetriever.from(webStore);
ContentRetriever docRetriever = EmbeddingStoreContentRetriever.from(docStore);

List<Content> results = new ArrayList<>();
results.addAll(webRetriever.retrieve(query));
results.addAll(docRetriever.retrieve(query));

List<Content> topResults = reranker.reorder(query, results).subList(0, 5);

Example 4: RAG with Chat Memory

Assistant assistant = AiServices.builder(Assistant.class)
    .chatModel(chatModel)
    .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
    .contentRetriever(retriever)
    .build();

assistant.chat("Tell me about the product features");
assistant.chat("What about pricing for those features?");  // Maintains context

Best Practices

Document Preparation

• Clean documents before ingestion; remove irrelevant content and formatting
• Add relevant metadata for filtering and context

Chunking Strategy

• Use 500-1000 tokens per chunk for optimal balance
• Include 10-20% overlap to preserve context at boundaries
• Test different sizes for your specific use case

Retrieval Optimization

• Start with high k values (10-20), then filter/rerank
• Use metadata filtering to improve relevance
• Monitor retrieval quality and iterate based on user feedback

Performance

• Cache embeddings for frequently accessed content
• Use batch processing for document ingestion
• Optimize vector store indexing for your scale

Constraints and Warnings

System Constraints

• Embedding models have maximum token limits per document
• Vector databases require proper indexing for performance
• Chunk boundaries may lose context for complex documents
• Hybrid search requires additional infrastructure

Quality Warnings

• Retrieval quality depends heavily on chunking strategy
• Embedding models may not capture domain-specific semantics
• Metadata filtering requires proper document annotation
• Reranking adds latency to query responses

Security Warnings

• Never hardcode credentials: Use environment variables for API keys and passwords
• Validate external content: Documents from file systems, APIs, or web sources may contain malicious content (prompt injection)
• Apply content filtering on retrieved documents before passing to LLM
• Restrict allowed data source URLs and file paths using allowlists

Resources

Reference Documentation

• Vector Database Comparison
• Embedding Models Guide
• Retrieval Strategies
• Document Chunking
• LangChain4j RAG Guide