smart-rag-optimizer
Pricing
from $3.00 / 1,000 scraped and chunked pages
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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
Maintained by CommunityActor stats
0
Bookmarked
2
Total users
1
Monthly active users
17 hours ago
Last modified
Categories
Security
Limited permissionsShare
🚀 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.xmlas a primary entry point for full-domain coverage. - ⚡️ Hybrid Engine (Cheerio + Playwright): Uses
CheerioCrawlerfor ultra-fast parsing, with seamless optional fallback toPlaywrightCrawler(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, estimatedtokensCount,headingPathbreadcrumbs, 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
| Parameter | Type | Default | Description |
|---|---|---|---|
url | string | (Required) | The target webpage URL to extract and process. |
chunkSize | number | 1000 | Target character count threshold per chunk. |
chunkOverlap | number | 200 | Character overlap between consecutive chunks to retain semantic context. |
maxPages | number | 1 | Maximum number of pages to crawl starting from the URL or Sitemap. |
chunkByHeaders | boolean | true | Enable smart semantic chunking based on natural paragraphs and sections. |
useBrowser | boolean | false | Enable 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..."}]