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skills/ai/rag/references/langchain4j-rag-guide.md

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+# LangChain4j RAG Implementation Guide
+
+## Overview
+RAG (Retrieval-Augmented Generation) extends LLM knowledge by finding and injecting relevant information from your data into prompts before sending to the LLM.
+
+## What is RAG?
+RAG helps LLMs answer questions using domain-specific knowledge by retrieving relevant information to reduce hallucinations.
+
+## RAG Flavors in LangChain4j
+
+### 1. Easy RAG
+Simplest way to start with minimal setup. Handles document loading, splitting, and embedding automatically.
+
+### 2. Core RAG APIs
+Modular components including:
+- Document
+- TextSegment
+- EmbeddingModel
+- EmbeddingStore
+- DocumentSplitter
+
+### 3. Advanced RAG
+Complex pipelines supporting:
+- Query transformation
+- Multi-source retrieval
+- Re-ranking with components like QueryTransformer and ContentRetriever
+
+## RAG Stages
+
+### 1. Indexing
+Pre-process documents for efficient search
+
+### 2. Retrieval
+Find relevant content based on user queries
+
+## Core Components
+
+### Documents with metadata
+Structured representation of your content with associated metadata for filtering and context.
+
+### Text segments (chunks)
+Smaller, manageable pieces of documents that are embedded and stored in vector databases.
+
+### Embedding models
+Convert text segments into numerical vectors for similarity search.
+
+### Embedding stores (vector databases)
+Store and efficiently retrieve embedded text segments.
+
+### Content retrievers
+Find relevant content based on user queries.
+
+### Query transformers
+Transform and optimize user queries for better retrieval.
+
+### Content aggregators
+Combine and rank retrieved content.
+
+## Advanced Features
+
+- Query transformation and routing
+- Multiple retrievers for different data sources
+- Re-ranking models for improved relevance
+- Metadata filtering for targeted retrieval
+- Parallel processing for performance
+
+## Implementation Example (Easy RAG)
+
+```java
+// Load documents
+List<Document> documents = FileSystemDocumentLoader.loadDocuments("/path/to/docs");
+
+// Create embedding store
+InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
+
+// Ingest documents
+EmbeddingStoreIngestor.ingest(documents, embeddingStore);
+
+// Create AI service
+Assistant assistant = AiServices.builder(Assistant.class)
+    .chatModel(chatModel)
+    .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
+    .contentRetriever(EmbeddingStoreContentRetriever.from(embeddingStore))
+    .build();
+```
+
+## Best Practices
+
+1. **Document Preparation**: Clean and structure documents before ingestion
+2. **Chunk Size**: Balance between context preservation and retrieval precision
+3. **Metadata Strategy**: Include relevant metadata for filtering and context
+4. **Embedding Model Selection**: Choose models appropriate for your domain
+5. **Retrieval Strategy**: Select appropriate k values and filtering criteria
+6. **Evaluation**: Continuously evaluate retrieval quality and answer accuracy