smart-rag-optimizer⁠ avatar

smart-rag-optimizer⁠

Pricing

from $3.00 / 1,000 scraped and chunked pages

Go to Apify Store
smart-rag-optimizer⁠

smart-rag-optimizer⁠

Lightweight RAG web scraper that extracts web content, removes noise/ads, converts HTML to clean Markdown, and splits text into structured RAG chunks for LLMs and MCP servers.

Pricing

from $3.00 / 1,000 scraped and chunked pages

Rating

0.0

(0)

Developer

LUKA LU

LUKA LU

Maintained by Community

Actor stats

0

Bookmarked

2

Total users

1

Monthly active users

17 hours ago

Last modified

Categories

Security

Limited permissions

Share

🚀 Smart RAG Optimizer

Smart RAG Optimizer is an enterprise-grade, high-performance web crawler and text preprocessor designed specifically to convert any web page into clean, chunked, RAG-ready JSON data for large language Models (LLMs) and Vector Databases.


✨ Key features

  • 🧹 Automated Noise Removal: Automatically strips out non-content elements such as scripts, styles, navigation bars, footer, headers, ads, and popups.
  • 🌐 Automatic Sitemap Detection: Automatically detects and extracts links from sitemap.xml as a primary entry point for full-domain coverage.
  • ⚡️ Hybrid Engine (Cheerio + Playwright): Uses CheerioCrawler for ultra-fast parsing, with seamless optional fallback to PlaywrightCrawler (useBrowser) to bypass firewalls and JavaScript-rendered pages.
  • 📊 Markdown Table Conversion: Automatically transforms complex HTML tables into clean Markdown format to preserve structured context for LLMs.
  • ✂️ Smart Semantic Chunking: Dynamic paragraph-aware splitting with customizable overlap (chunkOverlap) to eliminate context boundary truncation.
  • 🎯 Rich RAG Metadata: Emits Structured JSON complete with chunkId, estimated tokensCount, headingPath breadcrumbs, and raw cleaned text.
  • 🛡️ Built-in Proxy Integration: Direct integration with Apify proxy for dynamic IP rotation and anti-bot mitigation.

💡 Common Use Cases

  • Preparing web data for RAG pipelines (LangChain, LlamaIndex, OpenAI Assistant API, AutoGen).
  • Feeding structured context to Vector Databases (Pinecone, Weaviate, Qdrant, Chroma, Milvus).
  • Building high-accuracy custom Knowledge Bases for Enterprise AI Chatbots.

🛠️ Input Parameters

ParameterTypeDefaultDescription
urlstring(Required)The target webpage URL to extract and process.
chunkSizenumber1000Target character count threshold per chunk.
chunkOverlapnumber200Character overlap between consecutive chunks to retain semantic context.
maxPagesnumber1Maximum number of pages to crawl starting from the URL or Sitemap.
chunkByHeadersbooleantrueEnable smart semantic chunking based on natural paragraphs and sections.
useBrowserbooleanfalseEnable Playwright headful browser rendering to handle heavy JS and bypass firewalls.

📄 Output Sample

[
{
"url": "https://docs.python.org/3.11/",
"title": "Python 3.11.18 documentation",
"totalChunks": 3,
"chunks": [
{
"chunkIndex": 1,
"chunkId": "chunk_1_aHR0cHM6Ly",
"tokensCount": 475,
"headingPath": "Python 3.11.18 documentation > Part 1",
"content": "Welcome! this is the official documentation for python 3.11.18..."
}
],
"rawText": "Welcome! this is the official documentation for python 3.11.18..."
}
]