Vaadin Documentation RAG Service

A sophisticated, hierarchically-aware Retrieval-Augmented Generation (RAG) system for Vaadin documentation that understands document structure, provides framework-specific filtering, and enables intelligent parent-child navigation through documentation sections.

🎯 Project Overview

This project provides an advanced RAG system with enhanced hybrid search that:

• Understands Hierarchical Structure: Navigates parent-child relationships within and across documentation files
• Enhanced Hybrid Search: Combines semantic and intelligent keyword search with native Pinecone reranking for superior relevance
• Framework Filtering: Intelligently filters content for Vaadin Flow (Java) vs Hilla (React) frameworks
• Agent-Friendly: Provides MCP (Model Context Protocol) server for seamless IDE assistant integration
• Production Ready: Clean architecture with dependency injection, comprehensive testing, and error handling

🏗️ Architecture

vaadin-documentation-services/
├── packages/
│   ├── core-types/              # Shared TypeScript interfaces
│   ├── 1-asciidoc-converter/    # AsciiDoc → Markdown + metadata extraction
│   ├── 2-embedding-generator/   # Markdown → Vector database with hierarchical chunking
│   └── mcp-server/              # MCP server with hierarchical navigation
├── package.json                 # Bun workspace configuration
└── PROJECT_PLAN.md             # Complete project documentation

Data Flow

flowchart TD
    subgraph "Step 1: Documentation Processing"
        VaadinDocs["📚 Vaadin Docs<br/>(AsciiDoc)"] 
        Converter["🔄 AsciiDoc Converter<br/>• Framework detection<br/>• URL generation<br/>• Markdown output"]
        Processor["⚡ Embedding Generator<br/>• Hierarchical chunking<br/>• Parent-child relationships<br/>• OpenAI embeddings"]
    end

    subgraph "Step 2: Agent Integration"
        Pinecone["🗄️ Pinecone Vector DB<br/>• Rich metadata<br/>• Hierarchical relationships<br/>• Framework tags"]
        MCP["🤖 MCP Server<br/>• search_vaadin_docs<br/>• get_full_document<br/>• Full document retrieval"]
        IDEs["💻 IDE Assistants<br/>• Context-aware search<br/>• Hierarchical exploration<br/>• Framework-specific help"]
    end

    VaadinDocs --> Converter
    Converter --> Processor
    Processor --> Pinecone
    Pinecone <--> MCP
    MCP <--> IDEs

    classDef processing fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    classDef storage fill:#f3e5f5,stroke:#4a148c,stroke-width:2px
    classDef api fill:#e8f5e8,stroke:#2e7d32,stroke-width:2px
    classDef agent fill:#fff3e0,stroke:#e65100,stroke-width:2px

    class VaadinDocs,Converter,Processor processing
    class Pinecone storage
    class MCP api
    class IDEs agent

✨ Key Features

🔍 Intelligent Search

• Enhanced Hybrid Search: Combines semantic similarity with intelligent keyword extraction and scoring
• Native Pinecone Reranking: Uses Pinecone's bge-reranker-v2-m3 for optimal result ranking
• Framework Awareness: Filters Flow vs Hilla content with common content inclusion
• Query Preprocessing: Smart keyword extraction with stopword filtering for better search quality

🌳 Hierarchical Navigation

• Parent-Child Relationships: Navigate from specific details to broader context
• Cross-File Links: Understand relationships between different documentation files
• Context Breadcrumbs: Maintain navigation context for better user experience

🎛️ Developer Experience

• MCP Integration: Standardized protocol for IDE assistant integration
• TypeScript: Full type safety across all packages
• Comprehensive Testing: Unit tests, integration tests, and hierarchical workflow validation
• Clean Architecture: Dependency injection and interface-based design

🚀 Quick Start

Prerequisites

• Bun runtime v1.3.6
• OpenAI API key (for embeddings)
• Pinecone API key and index

Installation

# Clone and install dependencies
git clone https://github.com/vaadin/vaadin-documentation-services
cd vaadin-documentation-services
bun install

Environment Setup

# Create .env file with your API keys
echo "OPENAI_API_KEY=your_openai_api_key" > .env
echo "PINECONE_API_KEY=your_pinecone_api_key" >> .env
echo "PINECONE_INDEX=your_pinecone_index" >> .env

Running the System

1. Process Documentation (One-time setup)

# Convert AsciiDoc to Markdown with metadata
cd packages/1-asciidoc-converter
bun run convert

# Generate embeddings and populate vector database
cd ../2-embedding-generator
bun run generate

2. Use MCP Server with IDE Assistant

The MCP server is deployed and available remotely via HTTP transport at: https://mcp.vaadin.com/

Configure your IDE assistant to use the Streamable HTTP transport:

import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
const transport = new StreamableHTTPClientTransport(
  new URL("https://mcp.vaadin.com/")
);

📦 Package Details

Core Types (packages/core-types/)

Shared TypeScript interfaces used across all packages:

• DocumentChunk: Core documentation chunk structure
• RetrievalResult: Search result with relevance scoring
• Framework: Type-safe framework definitions

AsciiDoc Converter (packages/1-asciidoc-converter/)

Converts Vaadin AsciiDoc documentation to Markdown with metadata:

• Framework Detection: Automatically detects Flow/Hilla/common content
• URL Generation: Creates proper Vaadin.com documentation links
• Include Processing: Handles AsciiDoc include directives
• Metadata Extraction: Preserves semantic information in frontmatter

cd packages/1-asciidoc-converter
bun run convert          # Convert all documentation
bun run test            # Run framework detection tests

Embedding Generator (packages/2-embedding-generator/)

Creates vector embeddings with hierarchical relationships:

• Hierarchical Chunking: Preserves document structure and relationships
• Parent-Child Links: Creates cross-file and intra-file relationship mapping
• LangChain Integration: Uses MarkdownHeaderTextSplitter for intelligent chunking
• Batch Processing: Efficient embedding generation and Pinecone upsertion

cd packages/2-embedding-generator
bun run generate        # Generate embeddings from Markdown
bun run test           # Run chunking and relationship tests

MCP Server (packages/mcp-server/)

Model Context Protocol server for IDE assistant integration:

• Document Tools: search_vaadin_docs and get_full_document
• Full Document Retrieval: Complete documentation pages with context
• Framework Awareness: Intelligent framework detection and filtering
• Error Handling: Graceful degradation for missing content

cd packages/mcp-server
bun run build          # Build for distribution
bun run test           # Run document-based tests

Available Tools:

• search_vaadin_docs: Search with semantic and keyword matching
• get_full_document: Retrieve complete documentation pages
• get_vaadin_version: Get latest Vaadin version and release timestamp

🧪 Testing

Each package includes comprehensive test suites:

# Test individual packages
cd packages/1-asciidoc-converter && bun run test
cd packages/2-embedding-generator && bun run test  
cd packages/mcp-server && bun run test

📈 Performance & Metrics

Search Quality

• 100% Framework Detection Accuracy: Flow, Hilla, and common content correctly identified
• Enhanced Hybrid Search: Semantic + keyword search with native Pinecone reranking dramatically improves relevance
• Contextual Navigation: Parent-child relationships enable better result exploration
• 4,982 Document Chunks: Complete coverage of 378 Vaadin documentation files with 5-level hierarchy

System Performance

• Parallel Processing: Semantic and keyword search executed in parallel with intelligent merging
• Native Reranking: Pinecone's bge-reranker-v2-m3 provides superior result ranking
• Query Preprocessing: Smart keyword extraction with stopword filtering improves search quality
• Efficient Chunking: Optimized token limits with intelligent content splitting
• Clean Architecture: Dependency injection enables easy performance optimization

Production Readiness

• 100% API Backward Compatibility: All existing integrations continue to work
• Robust Error Handling: Graceful fallbacks ensure system reliability
• Fresh Data: Recently updated with complete Vaadin documentation coverage

🌐 Deployment

MCP Server

The MCP server is available at:

• Production: https://mcp.vaadin.com/
• Health Check: https://mcp.vaadin.com/health

Documentation Processing

Automated via GitHub Actions:

• Daily Updates: Documentation re-processed automatically
• Manual Triggers: Can be triggered via GitHub Actions UI
• Error Notifications: Automated alerts for processing failures

🔧 Development

Workspace Structure

This project uses Bun workspaces for package management:

bun install           # Install all dependencies
bun run build         # Build all packages
bun run test          # Test all packages

Adding New Features

1. Core Types: Add interfaces to packages/core-types/
2. Processing: Extend converters in packages/1-asciidoc-converter/ or packages/2-embedding-generator/
3. Integration: Update MCP tools in packages/mcp-server/

Architecture Principles

• Single Responsibility: Each package has a clear, focused purpose
• Interface-Based Design: Clean contracts between components
• Dependency Injection: Testable and swappable implementations
• Type Safety: Full TypeScript coverage with strict configuration

📚 Documentation

• Project Plan: Complete project breakdown and progress tracking
• Project Brief: Original requirements and problem definition
• Package READMEs: Detailed documentation for each package

🏆 Project Success

This project successfully delivered:

✅ Sophisticated RAG System: Replaced naive implementation with hierarchically-aware search
✅ Enhanced User Experience: Agents can now navigate from specific details to broader context
✅ Production Quality: Clean architecture, comprehensive testing, and error handling
✅ Framework Intelligence: Accurate Flow/Hilla content separation with common content inclusion
✅ Developer Integration: Seamless IDE assistant integration via MCP protocol

The system now provides intelligent, context-aware documentation search that understands the hierarchical structure of Vaadin documentation and enables sophisticated agent interactions.

📄 License

MIT - See license file for details.

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