RagChunk: Semantic RAG Splitter & Markdown Cleaner

⚡ RAG Semantic Document Chunker & HTML Cleaner

The only RAG preprocessing tool that thinks like a developer and charges like a cloud function.

Transform any public documentation URL into clean, token-precise, context-rich Markdown chunks, ready to be dropped directly into OpenAI, Pinecone, Weaviate, Chroma, LangChain, or any LLM pipeline. No headless browsers. No bloated DOM trees. No wasted tokens.

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🤔 What Problem Does This Solve?

If you have ever tried to build a RAG (Retrieval-Augmented Generation) pipeline on real-world documentation, you already know the pain:

• You scrape a documentation page and your markdown output is 60% navigation links, sidebar menus, cookie banners, and footer noise, none of which helps your LLM.
• You try to chunk the raw text but end up slicing code blocks in half, breaking tables mid-row, or losing which section a chunk belongs to.
• You burn $15 to $40 in OpenAI embedding tokens per document ingestion run because you are vectorizing sidebar garbage instead of actual content.

This Actor solves all three problems in a single API call.

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🚀 The Differentiator: Up to 40% Token Savings

Standard scrapers ingest entire DOM trees, wasting thousands of dollars on navigation menus, footers, and tracking scripts. This engine runs an advanced Cheerio-based selector priority extraction cascade and link-density evaluation engine to strip layout noise before any chunking occurs.

Live Validation Benchmark (Node.js API Reference Page):

Metric	Value
Raw Markdown Volume	395,669 characters
Post-Processed Clean Markdown	333,964 characters
Net Token Savings	61,705 characters (approximately 15,000 to 20,000 wasted tokens eliminated)

That is real money saved on every single document you ingest.

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✅ Who Is This For?

This tool is built for:

• AI Engineers building RAG pipelines on top of GPT-4, Claude, Gemini, or open-source LLMs.
• Developer Tool Teams ingesting API reference documentation into vector databases.
• Startups & SaaS companies building documentation search, AI assistants, or knowledge base copilots.
• Data engineers automating content ingestion workflows into Pinecone, Weaviate, Chroma, or pgvector.

If you are feeding documentation into a vector database, this is the highest-quality, lowest-cost preprocessing layer available on the market.

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✨ Core Engine Features

🧹 Aggressive Boilerplate Sanitization

Removes navigation menus, sidebars, footers, tracking pixels, cookie banners, and advertisement containers using a multi-layer Cheerio-based DOM cascade before any conversion to Markdown occurs. Unlike raw scrapers that hand you the entire DOM, we hand you only the signal.

🛡️ Code Block Integrity Protection

The chunker tracks open and closed Markdown code fences (```) in real-time. If a split would slice a code block in half, it automatically postpones the split until the fence closes. Your LLM will always receive complete, syntactically valid code samples.

📊 Markdown Table Integrity Protection

Tables carry structured data (such as parameter schemas, API response mappings, and comparison grids). Our engine detects table boundaries and applies a configurable table_hard_multiplier budget to keep entire tables within a single chunk, preventing broken header rows from stranding orphaned data rows in the next chunk.

🏷️ Multi-Header Context Tracking

Every emitted chunk includes a header_contexts array listing all active parent headings within the sliding window boundary. When your RAG retriever returns a chunk about reduce(), it will know whether that chunk came from Parameters, Description, or Edge cases. This eliminates the context-drift problem permanently.

🪙 Exact BPE Token Tracking

Uses the cl100k_base Byte Pair Encoding tokenizer, which is the exact same tokenizer used internally by GPT-4 and GPT-4o, to count tokens with 100% accuracy. No approximations. No character-based guesses. Every chunk respects the token ceiling you configure.

⛓️ Cascading Line Segmentation

Handles dense layouts (such as blog indexes, link lists, or documentation menus) that render thousands of characters onto a single line. The parser automatically cascades down through sentence boundaries, list boundaries, semicolon/comma clauses, and word limits to segment long lines below max_tokens * 0.8. This prevents runaway chunk overlaps and ensures strict token bounds.

🔁 Configurable Sliding Overlap Window

Carries a configurable number of trailing tokens from the previous chunk into the next, ensuring that semantic ideas which straddle chunk boundaries are never completely lost during vector retrieval.

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📥 Input Configuration

Parameter	Type	Default	Description
url	string	required	The exact public URL to scrape, extract, and chunk.
max_tokens	integer	500	Max BPE tokens per chunk (50–2000).
overlap	integer	50	Trailing overlap tokens between consecutive chunks (0–500).
contentSelector	string	none	Optional CSS selector to target specific content containers (e.g. main, article, #content).
min_token_floor	integer	30	Minimum token count to emit a chunk. Discards tiny non-semantic fragments (5–500).
prose_hard_multiplier	number	1.2	Hard token budget multiplier for prose segments before splitting (1.0–2.0).
table_hard_multiplier	number	3.0	Hard token budget multiplier inside tables to prevent row fragmentation (1.5–5.0).

Example Input

{
  "url": "https://docs.stripe.com/api/payment_intents",
  "max_tokens": 400,
  "overlap": 40,
  "contentSelector": "main"
}

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📤 Output Payload

Each Actor run returns a structured JSON dataset where every item is one semantically-clean chunk:

[
  {
    "chunk_index": 0,
    "token_count": 387,
    "header_contexts": [
      "Payment Intents API",
      "Create a PaymentIntent"
    ],
    "content": "## Create a PaymentIntent\n\nCreates a PaymentIntent object.\n\n### Parameters\n\n| Parameter | Type | Description |\n|---|---|---|\n| `amount` | integer | Amount intended to be collected..."
  },
  {
    "chunk_index": 1,
    "token_count": 412,
    "header_contexts": [
      "Create a PaymentIntent",
      "Returns"
    ],
    "content": "Returns a PaymentIntent object if the call succeeded. Returns an error if something goes wrong..."
  }
]

Output Fields Explained:

• chunk_index: Sequential position of the chunk within the document.
• token_count: Exact BPE token count of this chunk's content.
• header_contexts: All active section headings that were open when this chunk was emitted.
• content: The clean, pristine GFM Markdown content ready for embedding.

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🧪 Validated Against 50+ Real-World Documentation Sites

We stress-tested this engine against a comprehensive 50-URL live matrix covering Wikipedia, MDN, technical docs (React, AWS, Stripe), personal blogs (Paul Graham), forums (Stack Overflow), and company landing pages (Hugging Face, Google DeepMind, Microsoft Research). It achieved:

• 100% execution success across all accessible layouts.
• 0 broken code blocks or table rows (100% formatting preservation).
• Runaway overlap prevention on complex dense lists.

Live Metrics Highlight:

Target URL / Layout Type	Clean Markdown	Chunks Generated	Min Tokens	Max Tokens	Avg Tokens	Code Block Integrity
Wikipedia: Retrieval-augmented generation	25,214 chars	33	101	480	353	✅ PASS
Wikipedia: Transformer Architecture	184,897 chars	281	117	679	402	✅ PASS
MDN: Array.prototype.reduce()	17,763 chars	14	328	510	399	✅ PASS
Technical Doc: React useEffect Reference	44,352 chars	36	217	501	367	✅ PASS
Personal Blog: How to Do Great Work (Paul Graham)	69,016 chars	45	65	472	408	✅ PASS
Stripe API: PaymentIntents Reference	21,182 chars	27	70	689	401	✅ PASS
Forum: Stack Overflow Sorted Array Q&A	140,564 chars	141	55	584	346	✅ PASS
Company Blog: Astro Blog (Astro 4 Release)	11,871 chars	8	312	437	380	✅ PASS
Aggregator: Hugging Face Blog Homepage	11,698 chars	16	183	579	428	✅ PASS

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⚙️ Performance Benchmarks

Metric	Value
Median Latency (p50)	~497ms end-to-end
Scraping Engine	Lightweight DOM parsing (no headless browser)
Token Tracking	cl100k_base BPE (GPT-4/GPT-4o compatible)
Memory Footprint	Resource-isolated, no persistent storage
Pricing	$2.00 / 1,000 chunks (pay-per-result, not per-run)

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❓ Frequently Asked Questions

Q: What types of pages does this work best on? A: It excels on developer documentation, API reference pages, technical guides, blog articles, and knowledge base content. Any structured, text-rich public web page.

Q: Does it work on sites protected by Cloudflare or other bot protections? A: The engine uses a multi-tier fetch pipeline. If direct access is blocked, it automatically falls back to a secondary content extraction layer to maximize success rates.

Q: How is this priced? A: You pay $2.00 per 1,000 chunks generated. A typical documentation page generates 8–15 chunks, meaning a full page costs less than $0.03. There are no monthly minimums, no seat fees, and no API key commitments.

Q: Can I use this to ingest private/internal documentation? A: This Actor only fetches publicly accessible URLs. It cannot bypass authentication or access pages behind a login wall.

Q: Which vector databases does the output work with? A: The output is plain JSON with string content fields. It is compatible with every major vector database including Pinecone, Weaviate, Chroma, Qdrant, pgvector, Milvus, and any LangChain / LlamaIndex embedding pipeline.

Q: Does it preserve code examples from documentation? A: Yes. This is one of its core differentiators. The engine specifically detects and protects Markdown code fences, refusing to split a chunk mid-code-block regardless of token budget.

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Legal & Licensing

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