SpringTutorials
    Spring AI Tutorials
    Tutorial 05

    Conversational AI with Documents Using RAG

    Build document chat systems with vector databases, smart chunking strategies, and RetrievalAugmentationAdvisor in Spring AI

    What You'll Learn

    6 sections on building RAG-powered document chat

    Introduction to RAG

    Understanding the RAG Architecture

    Vector Databases – Storing Meaning, Not Just Text

    Building a Document Chat System with RAG

    Document Chunking Strategies

    RAG in Practice with Spring AI

    1
    Introduction to RAG

    1.1

    What Does "Talking to Documents" Mean?

    Understand the concept of querying documents in natural language and getting intelligent, contextual responses.

    1.2

    Why Retrieval-Augmented Generation (RAG) Matters

    Learn why RAG has become essential for grounding LLM responses in factual, domain-specific information.

    Coming Soon

    Detailed content with code examples, diagrams, and best practices is being prepared for this section.

    2
    Understanding the RAG Architecture

    2.1

    End-to-End RAG Workflow Explained

    Walk through the complete RAG pipeline from document ingestion to response generation.

    2.2

    How Retrieval and Generation Work Together

    Explore how semantic search retrieves relevant context that enhances LLM response quality.

    Coming Soon

    Detailed content with code examples, diagrams, and best practices is being prepared for this section.

    3
    Vector Databases – Storing Meaning, Not Just Text

    3.1

    What Is a Vector Database?

    Understand how vector databases differ from traditional databases and why they're crucial for AI applications.

    3.2

    How Semantic Meaning Is Stored and Retrieved

    Learn how embeddings capture meaning and enable similarity-based search.

    3.3

    Popular Vector Database Concepts and Use Cases

    Explore common vector databases like Pinecone, Weaviate, Milvus, and pgvector.

    Coming Soon

    Detailed content with code examples, diagrams, and best practices is being prepared for this section.

    4
    Building a Document Chat System with RAG

    4.1

    Enabling Natural Language Queries on Documents

    Create intuitive interfaces where users can ask questions about their documents.

    4.2

    From User Question to Context-Aware Answer

    Follow the journey from user query through retrieval to AI-generated response.

    Coming Soon

    Detailed content with code examples, diagrams, and best practices is being prepared for this section.

    5
    Document Chunking Strategies

    5.1

    Why Large Documents Must Be Chunked

    Understand the technical constraints that require breaking documents into smaller pieces.

    5.2

    Best Practices for Chunk Size and Overlap

    Learn optimal chunk sizes and overlap strategies for different document types.

    5.3

    Improving Retrieval Accuracy with Smart Chunking

    Advanced techniques like semantic chunking and hierarchical splitting for better results.

    Coming Soon

    Detailed content with code examples, diagrams, and best practices is being prepared for this section.

    6
    RAG in Practice with Spring AI

    6.1

    Simplifying RAG Using RetrievalAugmentationAdvisor

    Leverage Spring AI's built-in advisor to add RAG capabilities with minimal code.

    6.2

    Plug-and-Play Document Retrieval with Spring AI

    Configure vector stores and document loaders for seamless integration.

    6.3

    Putting It All Together: RAG End-to-End Example

    Build a complete document chat application step by step.

    Coming Soon

    Detailed content with code examples, diagrams, and best practices is being prepared for this section.

    What You'll Master

    RAG Fundamentals

    Why RAG matters for document-based AI

    RAG Architecture

    End-to-end retrieval and generation flow

    Vector Databases

    Semantic storage and similarity search

    Document Chat Systems

    Natural language queries on documents

    Chunking Strategies

    Optimal splitting for retrieval accuracy

    Spring AI Integration

    RetrievalAugmentationAdvisor in action

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