📝 Markdown Harvest

A Rust crate designed to extract, clean, and convert web content from URLs found in text messages into clean Markdown format. Originally created as an auxiliary component for Retrieval-Augmented Generation (RAG) solutions to process URLs submitted by users.

📋 Table of Contents

Overview
Features
Quick Start
Installation
Usage Examples
API Documentation
Content Processing Pipeline
Supported Platforms
Contributing
License
Changelog

Overview

Markdown Harvest was initially developed as part of a Retrieval-Augmented Generation (RAG) system where users submit text containing URLs, and the system needs to extract meaningful content from those URLs for further analysis or processing. This crate handles the extraction, cleaning, and structuring of web content automatically.

🎯 Why Markdown Harvest?

🚀 Built for AI/RAG Systems: Specifically designed for content preprocessing in AI workflows
🧹 Smart Content Extraction: Removes ads, navigation, and irrelevant elements automatically
📝 Markdown Output: Clean, structured Markdown perfect for LLM processing
🔄 Batch Processing: Handle multiple URLs efficiently in a single operation
🛡️ Robust Error Handling: Gracefully handles network issues and invalid URLs

Use Case Process Flow

📄 Standard Content Processing

graph LR
    A[User Input] --> B{Identifies URLs}
    B -->|Yes| C[Retrieves HTTP Content]
    C --> D[Processes & Extracts Data]
    D --> E[Augments Context]
    E --> F[Generates Response with Model]
    B -->|No| F
    F -->|Contextualized response| A

📦 Chunks Feature Process Flow (RAG Systems)

graph TD
    A[User Input with URLs] --> B[Extract URLs]
    B --> C[HTTP Content Retrieval]
    C --> D[HTML to Markdown Conversion]
    D --> E[Semantic Chunking]
    E --> F{Overlap Configuration}
    F -->|With Overlap| G[Generate Overlapping Chunks]
    F -->|No Overlap| H[Generate Standard Chunks]
    G --> I[Chunk Processing Pipeline]
    H --> I
    I --> J[Generate Embeddings]
    J --> K[Store in Vector Database]
    K --> L[Index for Semantic Search]
    L --> M[RAG Context Enhancement]
    M --> N[Enhanced AI Response]
    
    style E fill:#e1f5fe
    style G fill:#f3e5f5
    style H fill:#f3e5f5
    style I fill:#e8f5e8
    style M fill:#fff3e0

✨ Features

🔍 URL Detection: Automatically identifies HTTP/HTTPS URLs in text using regex patterns
🎯 Smart Content Extraction: Extracts only relevant content from HTML <body> elements
📄 HTML to Markdown Conversion: Converts HTML content to clean, readable Markdown while preserving structure and removing unwanted elements
🧹 Content Cleaning: Removes JavaScript, CSS, advertisements, and navigation elements
📦 Semantic Chunking: Optional chunks feature for RAG systems using MarkdownSplitter with semantic boundaries and configurable overlap
🤖 Multi-Platform User Agents: Rotates between different browser user agents to avoid detection
⚡ Configurable HTTP Options: Customizable timeout, redirect limits, and cookie management
🏗️ Builder Pattern API: Fluent and intuitive configuration with HttpConfig::builder()
🛡️ Error Handling: Graceful handling of network errors and invalid URLs
📝 Clean Text Output: Normalizes whitespace and removes common non-content patterns
⚡ Asynchronous Processing: High-performance async/await support for concurrent URL processing
🔄 Callback Architecture: Flexible callback system for real-time result streaming
🧪 Comprehensive Testing: 55+ unit tests with 100% API coverage including async functionality, chunks, and overlap

🚀 Quick Start

use markdown_harvest::{MarkdownHarvester, HttpConfig};

fn main() {
    let text = "Check this out: https://example.com/article";
    let config = HttpConfig::default(); // Use default HTTP configuration
    let results = MarkdownHarvester::get_hyperlinks_content(text.to_string(), config);
    
    for (url, content) in results {
        println!("URL: {}\nContent: {}", url, content);
    }
}

📦 Installation

Add this to your Cargo.toml:

[dependencies]
markdown-harvest = "0.1.5"

# For RAG systems with semantic chunking and overlap support
markdown-harvest = { version = "0.1.5", features = ["chunks"] }

📚 Usage Examples

📝 Synchronous Processing (Traditional)

use markdown_harvest::{MarkdownHarvester, HttpConfig};

fn main() {
    let text = "Check out this article: https://example.com/article.html and this one too: https://news.site.com/story";
    
    // Use default configuration
    let config = HttpConfig::default();
    let results = MarkdownHarvester::get_hyperlinks_content(text.to_string(), config);
    
    for (url, content) in results {
        println!("URL: {}", url);
        println!("Markdown Content:\n{}", content);
        println!("---");
    }
}

⚡ Asynchronous Processing (High Performance)

use markdown_harvest::{MarkdownHarvester, HttpConfig};
use std::sync::{Arc, Mutex};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let text = "Check out: https://example.com and https://httpbin.org/json";
    let config = HttpConfig::builder().timeout(30000).build();
    
    // Collect results in a thread-safe vector
    let results = Arc::new(Mutex::new(Vec::new()));
    let results_clone = results.clone();
    
    let callback = move |url: Option<String>, content: Option<String>| {
        let results = results_clone.clone();
        async move {
            if let (Some(url), Some(content)) = (url, content) {
                let mut results = results.lock().unwrap();
                results.push((url, content));
                println!("✅ Processed URL with {} characters", content.len());
            }
        }
    };
    
    MarkdownHarvester::get_hyperlinks_content_async(text.to_string(), config, callback).await?;
    
    let final_results = results.lock().unwrap();
    println!("📊 Total URLs processed: {}", final_results.len());
    
    Ok(())
}

🔄 Real-time Processing with Immediate Output

use markdown_harvest::{MarkdownHarvester, HttpConfig};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let text = "Visit https://example.com for more info";
    let config = HttpConfig::default();
    
    // Process and display results immediately as they arrive
    let callback = |url: Option<String>, content: Option<String>| async move {
        match (url, content) {
            (Some(url), Some(content)) => {
                println!("🚀 Processed: {}", url);
                println!("📄 Content length: {} characters", content.len());
                // Save to database, send to API, etc.
            }
            (None, None) => {
                println!("ℹ️ No URLs found in the provided text");
            }
            _ => unreachable!(),
        }
    };
    
    MarkdownHarvester::get_hyperlinks_content_async(text.to_string(), config, callback).await?;
    
    Ok(())
}

💻 Interactive CLI Mode

The crate provides an interactive CLI mode for testing:

cargo run

Then enter text containing URLs when prompted.

🔧 Advanced HTTP Configuration

use markdown_harvest::{MarkdownHarvester, HttpConfig};

fn main() {
    let text = "Articles: https://site1.com and https://site2.com";
    
    // Custom HTTP configuration with Builder pattern
    let config = HttpConfig::builder()
        .timeout(10000)        // 10 second timeout
        .max_redirect(5)       // Allow up to 5 redirects
        .cookie_store(true)    // Enable cookie storage for sessions
        .build();
    
    let results = MarkdownHarvester::get_hyperlinks_content(text.to_string(), config);
    
    for (url, content) in results {
        println!("Processed: {}", url);
        println!("Content length: {} chars", content.len());
    }
}

🎯 Different Configuration Examples

use markdown_harvest::{MarkdownHarvester, HttpConfig};

// Quick timeout for fast responses only
let fast_config = HttpConfig::builder()
    .timeout(3000)  // 3 seconds
    .build();

// Conservative configuration for slow sites
let patient_config = HttpConfig::builder()
    .timeout(30000)     // 30 seconds
    .max_redirect(10)   // More redirects allowed
    .cookie_store(true) // Handle authentication
    .build();

// Use different configs for different scenarios
let urgent_text = "Breaking news: https://news-site.com/urgent";
let deep_text = "Research: https://academic-site.edu/paper";

let urgent_results = MarkdownHarvester::get_hyperlinks_content(urgent_text.to_string(), fast_config);
let research_results = MarkdownHarvester::get_hyperlinks_content(deep_text.to_string(), patient_config);

📦 Semantic Chunking for RAG Systems (chunks feature)

Feature gate: chunks - Enable with markdown-harvest = { version = "0.1.5", features = ["chunks"] }

The chunks feature provides semantic text splitting optimized for RAG (Retrieval-Augmented Generation) systems using MarkdownSplitter with intelligent boundary detection.

🔄 Synchronous Chunking

use markdown_harvest::{MarkdownHarvester, HttpConfig};

#[cfg(feature = "chunks")]
fn main() {
    let text = "Research these articles: https://example.com/article1 and https://example.com/article2";
    let config = HttpConfig::default();
    let chunk_size = 1000; // 1000 characters per chunk
    
    let results = MarkdownHarvester::get_hyperlinks_content_as_chunks(
        text.to_string(), 
        config, 
        chunk_size,
        Some(100) // 100 characters overlap for better context preservation
    );
    
    for (url, chunks) in results {
        println!("📄 URL: {}", url);
        println!("📦 Generated {} semantic chunks:", chunks.len());
        
        for (i, chunk) in chunks.iter().enumerate() {
            println!("  Chunk {}: {} chars", i + 1, chunk.len());
            println!("  Content: {}\n---", chunk.chars().take(100).collect::<String>());
        }
    }
}

⚡ Asynchronous Chunking

use markdown_harvest::{MarkdownHarvester, HttpConfig};
use std::sync::{Arc, Mutex};

#[cfg(feature = "chunks")]
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let text = "Process these for RAG: https://docs.example.com https://blog.example.com";
    let config = HttpConfig::builder()
        .timeout(15000)
        .build();
    let chunk_size = 800; // Optimal for embedding models
    
    // Real-time chunk processing for RAG pipeline
    let callback = |url: Option<String>, chunks: Option<Vec<String>>| async move {
        match (url, chunks) {
            (Some(url), Some(chunks)) => {
                println!("🔗 Processing {} chunks from: {}", chunks.len(), url);
                
                for (i, chunk) in chunks.iter().enumerate() {
                    println!("  📦 Chunk {}: {} chars", i + 1, chunk.len());
                    
                    // RAG Pipeline Integration:
                    // 1. Generate embeddings for this semantic chunk
                    // 2. Store in vector database with metadata
                    // 3. Index for semantic search
                    // 4. Preserve document context and structure
                }
            }
            (None, None) => {
                println!("ℹ️ No URLs found in text");
            }
            _ => unreachable!(),
        }
    };
    
    MarkdownHarvester::get_hyperlinks_content_as_chunks_async(
        text.to_string(),
        config,
        chunk_size,
        Some(80), // 80 characters overlap - optimal for embedding models
        callback
    ).await?;
    
    Ok(())
}

🧠 Semantic Chunking Benefits

The MarkdownSplitter uses intelligent semantic levels for optimal RAG performance:

📊 Heading Preservation: Keeps headers with their content sections
📝 Paragraph Integrity: Maintains paragraph boundaries and flow
📋 List Coherence: Preserves list items and hierarchical structure
💻 Code Block Unity: Keeps code blocks intact as single units
🔗 Link Context: Maintains inline formatting and link relationships
⚖️ Semantic Balance: Optimizes chunk size vs. content coherence

Chunk Size Recommendations for RAG:

Small Models: 400-800 characters
Medium Models: 800-1500 characters
Large Models: 1500-2500 characters

🔄 Chunk Overlap Examples

The chunk_overlap parameter enables context preservation between adjacent chunks:

use markdown_harvest::{MarkdownHarvester, HttpConfig};

#[cfg(feature = "chunks")]
fn main() {
    let text = "Process: https://example.com/documentation";
    let config = HttpConfig::default();
    
    // Without overlap - standard chunking
    let standard_chunks = MarkdownHarvester::get_hyperlinks_content_as_chunks(
        text.clone(), 
        config.clone(), 
        1000, 
        None  // No overlap
    );
    // Result: [Chunk1][Chunk2][Chunk3]
    
    // With overlap - better context preservation
    let overlap_chunks = MarkdownHarvester::get_hyperlinks_content_as_chunks(
        text, 
        config, 
        1000, 
        Some(200)  // 200 characters overlap
    );
    // Result: [Chunk1][Chunk1+2][Chunk2+3][Chunk3] (200 char overlap)
    
    println!("Standard chunks: {}", standard_chunks.len());
    println!("Overlap chunks: {}", overlap_chunks.len());
}

Overlap Size Recommendations:

Use Case	Chunk Size	Recommended Overlap	Overlap %
Small Embeddings	400-800	100-200 chars	25-50%
Medium Embeddings	800-1500	150-300 chars	15-20%
Large Embeddings	1500-2500	200-400 chars	10-15%
Code Documentation	1000-2000	200-500 chars	20-25%
Academic Papers	1500-3000	300-600 chars	20-25%

Benefits of Overlap:

🔗 Context Continuity: Important information doesn't get "cut" between chunks
📈 Improved Retrieval: Higher probability of finding relevant information
🧠 Better Embeddings: More coherent semantic representations
⚡ Flexible Tuning: Adjust overlap based on content type and model requirements

📖 API Documentation

Core Functions

Synchronous Processing

// Main function to extract content from URLs in text (blocking)
MarkdownHarvester::get_hyperlinks_content(text: String, http_config: HttpConfig) -> Vec<(String, String)>

Asynchronous Processing

// Async function for high-performance concurrent processing
MarkdownHarvester::get_hyperlinks_content_async<F, Fut>(
    text: String, 
    http_config: HttpConfig, 
    callback: F
) -> Result<(), Box<dyn std::error::Error>>
where 
    F: Fn(Option<String>, Option<String>) -> Fut + Clone,
    Fut: Future<Output = ()>

Semantic Chunking Functions (chunks feature)

// Synchronous chunking for RAG systems with optional overlap
MarkdownHarvester::get_hyperlinks_content_as_chunks(
    text: String, 
    http_config: HttpConfig,
    chunk_size: usize,
    chunk_overlap: Option<usize>  // ← NEW: Overlap between chunks (must be < chunk_size)
) -> Vec<(String, Vec<String>)>

// Asynchronous chunking with real-time callback processing and optional overlap
MarkdownHarvester::get_hyperlinks_content_as_chunks_async<F, Fut>(
    text: String,
    http_config: HttpConfig,
    chunk_size: usize,
    chunk_overlap: Option<usize>,  // ← NEW: Overlap between chunks (must be < chunk_size)
    callback: F
) -> Result<(), Box<dyn std::error::Error>>
where 
    F: Fn(Option<String>, Option<Vec<String>>) -> Fut + Clone,
    Fut: Future<Output = ()>

Overlap Parameter Details:

chunk_overlap: Option<usize> - Optional overlap between adjacent chunks
None - No overlap (standard chunking behavior)
Some(n) - n characters overlap between chunks
Constraint: overlap must be less than chunk_size
Validation: Invalid values return empty results with stderr warning

HTTP Configuration

// HTTP configuration with Builder pattern
HttpConfig::default() -> HttpConfig
HttpConfig::builder() -> HttpConfigBuilder

HttpConfigBuilder::new() -> HttpConfigBuilder
HttpConfigBuilder::timeout(ms: u64) -> HttpConfigBuilder
HttpConfigBuilder::max_redirect(count: usize) -> HttpConfigBuilder
HttpConfigBuilder::cookie_store(enabled: bool) -> HttpConfigBuilder
HttpConfigBuilder::build() -> HttpConfig

Utility Functions

// User agent utilities
UserAgent::random() -> UserAgent
UserAgent::to_string(&self) -> String

When to Use Async vs Sync

Feature	Synchronous	Asynchronous
Processing	Sequential - one URL at a time	Parallel - all URLs concurrently
Results	Returns after ALL URLs complete	Streams results as EACH URL completes
Use Case	Need all results before proceeding	Real-time processing as URLs finish
Performance	Slower for multiple URLs	Faster for multiple URLs
Complexity	Simple function call	Requires tokio runtime + callbacks
Memory Usage	Collects all results in Vec	Streams results via callbacks
Error Handling	Direct Result handling	Callback-based error handling
Integration	Easy to integrate	Better for async/await codebases

🔧 HTTP Configuration Options

Option	Type	Default	Description
`timeout`	`Option<u64>`	`None`	Request timeout in milliseconds
`max_redirect`	`Option<usize>`	`None`	Maximum number of redirects to follow
`cookie_store`	`bool`	`false`	Enable cookie storage for session management

Supported Platforms & User Agents

The crate includes user agents for:

Windows: Chrome, Firefox, Edge
macOS: Chrome, Safari, Firefox
Linux: Chrome, Firefox
Android: Chrome, Firefox
iOS: Safari, Chrome

🏗️ Dependencies

reqwest - HTTP client with both blocking and async support
scraper - HTML parsing and CSS selector engine
html2md - Intelligent HTML to Markdown conversion
regex - URL detection and content filtering
rand - Random user agent selection
tokio - Async runtime for high-performance concurrent processing
futures - Async utilities and combinators
text-splitter - Semantic Markdown chunking for RAG systems (optional, chunks feature)

🤖 AI Integration Context

This crate was specifically designed to serve as a content extraction component in Retrieval-Augmented Generation (RAG) workflows where:

👥 Users submit messages containing URLs alongside other text
🧠 AI systems need structured content from those URLs for analysis
📝 Clean, readable Markdown is required preserving essential content and structure while removing HTML markup, scripts, ads, and links
🔄 Multiple URLs need processing in batch operations
🛡️ Reliability is crucial with proper error handling and fallbacks

The extracted content can then be fed into language models, search systems, or other AI components for further processing.

🎯 Perfect for RAG Systems

Vector Database Integration: Clean Markdown is ideal for embedding generation
Token Optimization: Removes unnecessary content to reduce token usage
Batch Processing: Handle multiple URLs from user queries efficiently
Content Quality: Preserves semantic structure while removing noise

⚙️ Markdown Transformation Details

The crate performs intelligent HTML to Markdown conversion that preserves essential formatting while removing clutter:

✅ Preserved Elements

Headers: <h1> → # Header, <h2> → ## Header
Emphasis: <strong> → **bold**, <em> → *italic*
Lists: <ul><li> → - item, <ol><li> → 1. item
Blockquotes: <blockquote> → > quote text
Scientific names: <i>Bertholletia excelsa</i> → *Bertholletia excelsa*

❌ Removed Elements

Links: [text](url) → text (keeps text, removes URL)
Images: <img> tags completely removed
Media: <iframe>, <video>, <audio> elements stripped
Navigation: <nav>, <header>, <footer>, <aside> sections
Metadata: Author bylines, publication dates, tag lists
Advertisements: Elements with ad-related classes or IDs

🧹 Text Cleanup

Normalizes excessive whitespace and line breaks
Removes photo captions and image attribution text
Filters out navigation phrases ("click here", "read more")
Eliminates code blocks and technical markup
Preserves paragraph structure and readability

🔄 Content Processing Pipeline

graph TD
    A[🔍 Input Text] --> B{URL Detection}
    B -->|URLs Found| C[🌐 HTTP Request]
    B -->|No URLs| D[⚡ Return Empty]
    C --> E[📄 HTML Parsing]
    E --> F[✂️ Content Extraction]
    F --> G[🧹 Clean & Filter]
    G --> H[📝 Markdown Conversion]
    H --> I[🔧 Final Cleanup]
    I --> J[✅ Output]

🔍 Input: Raw text from user containing URLs
🎯 Detection: Regex-based URL extraction with punctuation cleanup
🌐 Fetching: HTTP requests with randomized user agents
📄 HTML Parsing: Document parsing with scraper crate
✂️ Body Extraction: Extracts only content from HTML <body> element
🚫 Media Removal: Strips images, iframes, videos, and other non-textual elements
🧹 Structure Cleaning: Removes scripts, styles, navigation, headers, footers, and ads
🎯 Content Selection: Focuses on relevant elements (articles, main content, headings, paragraphs)
📝 Markdown Conversion: Transforms cleaned HTML to structured Markdown using html2md
🔗 Link Processing: Converts [text](url) links to plain text, removes standalone URLs
✨ Format Preservation: Maintains headers, bold, italic, lists, and blockquotes
🔧 Final Cleanup: Removes metadata, navigation text, and excessive whitespace
✅ Output: Clean, readable Markdown content paired with source URLs

⚠️ Error Handling

The crate handles various error conditions gracefully:

🌐 Network timeouts and connection errors
🔗 Invalid or malformed URLs
📄 Empty or missing content
🚫 Server errors (404, 500, etc.)
🛡️ Blocked requests or rate limiting

🔄 Migration from v0.1.2

⚠️ Breaking Change: v0.1.3 introduces a breaking change in the API.

Before (v0.1.2)

use markdown_harvest::MarkdownHarvester;

let text = "Check https://example.com";
let results = MarkdownHarvester::get_hyperlinks_content(text.to_string());

After (v0.1.3)

use markdown_harvest::{MarkdownHarvester, HttpConfig};

let text = "Check https://example.com";
let config = HttpConfig::default(); // Add this line
let results = MarkdownHarvester::get_hyperlinks_content(text.to_string(), config); // Add config parameter

Quick Migration Tips

Import HttpConfig: Add HttpConfig to your use statement
Create config: Use HttpConfig::default() for same behavior as before
Pass config: Add the config as the second parameter to get_hyperlinks_content()

The change enables powerful new features like custom timeouts, redirect control, and cookie management while maintaining the same core functionality.

🤝 Contributing

Contributions are welcome! Here's how to get started:

🍴 Fork the repository
🔧 Create a feature branch (git checkout -b feature/amazing-feature)
💾 Commit your changes (git commit -m 'Add amazing feature')
📤 Push to the branch (git push origin feature/amazing-feature)
🔀 Open a Pull Request

Development Setup

# Clone the repository
git clone https://github.com/franciscotbjr/markdown-harvest
cd markdown-harvest

# Run tests
cargo test

# Run the interactive CLI
cargo run

# Format code
cargo fmt

# Check for issues
cargo clippy

📄 License

Licensed under the MIT License. See LICENSE for details.

📋 Changelog

v0.1.5 🔄 NEW: Chunk Overlap Support

🔄 Chunk Overlap Parameter: Added optional chunk_overlap parameter to both sync and async chunking functions
🧠 Context Preservation: Configurable overlap between adjacent chunks for better context continuity in RAG systems
⚖️ Smart Validation: Automatic validation ensuring overlap < chunk_size with graceful error handling
📊 Flexible Configuration: Support for overlap sizes from 0% to 99% of chunk size
🧪 Enhanced Testing: 6 new unit tests for overlap functionality (49→55 total tests)
📚 Comprehensive Documentation: Complete examples with overlap recommendations for different embedding models
🔧 ChunkConfig Integration: Native use of text-splitter's ChunkConfig.with_overlap() functionality
✅ Backward Compatible: No breaking changes - overlap parameter is optional (None = no overlap)

v0.1.5 📦 NEW: Semantic Chunking for RAG Systems

📦 Semantic Chunking Feature: New optional chunks feature for RAG systems using MarkdownSplitter
🔧 Smart Boundary Detection: Intelligent semantic splitting preserving document structure
⚡ Dual Processing Modes: Both sync (get_hyperlinks_content_as_chunks) and async (get_hyperlinks_content_as_chunks_async) implementations
🧠 RAG Optimized: Semantic levels preserve headings, paragraphs, code blocks, and lists as coherent units
📊 Flexible Chunk Sizes: Configurable chunk sizes with recommendations for different embedding models
🧪 Enhanced Testing: 8 new chunk-specific unit tests (41→49 total tests)
📚 Comprehensive Documentation: Complete examples and integration guides for RAG workflows
🏗️ Optional Dependency: text-splitter v0.28 with Markdown support as optional feature
✅ Backward Compatible: No breaking changes - chunks feature is completely optional

v0.1.4 🚀 NEW: Async Processing

⚡ Asynchronous Processing Support: Complete async/await implementation for high-performance concurrent URL processing
🚀 Performance Improvements: Faster processing when handling multiple URLs simultaneously through parallel processing
📚 Enhanced Examples: Updated main.rs with interactive examples showing both sync and async processing modes
🧪 Async Test Suite: 8 new async unit tests covering all async methods (27→36 total tests)
🔄 Callback Architecture: Flexible callback system supporting custom processing pipelines
📖 Comprehensive Documentation: Complete documentation with 3 detailed async examples
✅ Backward Compatible: No breaking changes - all existing sync code continues to work

v0.1.3 ⚠️ BREAKING CHANGES

🏗️ HTTP Configuration with Builder Pattern: Complete HTTP configuration system
💥 API Change: get_hyperlinks_content() now requires HttpConfig parameter
⚡ New Features: Configurable timeout, redirects, and cookie management
🧪 Testing: 17 new unit tests (10→27 total) with 100% API coverage
📚 Enhanced Documentation: Updated examples and migration guide

v0.1.2

🔧 Component Architecture: Separated responsibilities with HttpClient and ContentProcessor
🎯 Facade Pattern: MarkdownHarvester as clean interface
🧪 Unit Tests: Comprehensive testing for all components

v0.1.0

✨ Initial release
🔍 URL detection and content extraction
🤖 Multi-platform user agent support
🧹 Content cleaning and normalization
💻 Interactive CLI mode

Built with ❤️ for RAG systems and AI workflows

⭐ Star this repo if it helps your project!

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.claude		.claude
.github/workflows		.github/workflows
assets		assets
examples		examples
src		src
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
DEV_NOTES.md		DEV_NOTES.md
LICENSE		LICENSE
README.md		README.md
powershell_cache_script_permanent.ps1		powershell_cache_script_permanent.ps1
sample-result.md		sample-result.md
samples.md		samples.md

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nilayparikh/markdown-harvest

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