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:

Requirement Recommended

Production scalability Pinecone, Milvus

Open-source Weaviate, Qdrant

Local development Chroma, FAISS

Hybrid search Weaviate with BM25

Step 2: Select Embedding Model

Use Case Model

General purpose text-embedding-ada-002

Fast and lightweight all-MiniLM-L6-v2

Multilingual e5-large-v2

Best performance bge-large-en-v1.5

Step 3: Implement Document Processing Pipeline

• Load documents from source (file system, database, API)

• Clean and preprocess (remove formatting, normalize text)

• Split documents into chunks with appropriate strategy

• Generate embeddings for each chunk

• 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

• Create content retriever with your embedding store

• Configure AI service with retriever and chat memory

• Implement prompt template with context injection

• 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

• Measure retrieval metrics: precision@k, recall@k, MRR

• Evaluate answer quality: faithfulness, relevance

• Monitor performance and user feedback

• 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