RAG Post Processor - Text Cleaner & Chunker for LLM Pipelines
Pricing
from $0.25 / 1,000 chunk processeds
RAG Post Processor - Text Cleaner & Chunker for LLM Pipelines
Clean and chunk scraped text for RAG and LLM pipelines. Strips HTML, collapses whitespace, splits into overlapping chunks ready for embedding. Works standalone or chained after any scraper. Per-row billing.
Pricing
from $0.25 / 1,000 chunk processeds
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Developer
Jordan Wagner
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1
Monthly active users
20 days ago
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Clean and chunk raw scraped text for RAG and LLM pipelines. Drop it after any scraper actor and get embedding-ready chunks in seconds.
What it does
- Strips HTML tags and boilerplate
- Collapses whitespace and normalizes line breaks
- Splits text into overlapping chunks (default: 1000 chars, 100 overlap)
- Returns structured output with chunk index, length, and timestamp
- Works standalone or chained after Website Content Crawler and similar actors
Input
| Field | Type | Default | Description |
|---|---|---|---|
data | array | required | Array of objects from a previous scraper. Each object needs a text, content, body, or html field. |
chunk_size | integer | 1000 | Max characters per chunk |
overlap | integer | 100 | Character overlap between chunks |
Example input
{"data": [{ "text": "Your raw scraped content goes here. It can be long, messy HTML or plain text." }],"chunk_size": 1000,"overlap": 100}
Output
Each chunk is returned as a dataset item:
{"original_id": "item_0","chunk_index": 0,"total_chunks": 3,"chunk_text": "Cleaned and chunked text ready for embedding...","chunk_length_chars": 487,"cleaned_at": "2026-06-20 04:46:29.330000+00:00"}
Pricing
$0.0003 per output row. No subscription required — pay only for what you use.
Use with PowerShell
Install the companion PowerShell module to call this actor from your automation scripts:
Import-Module RAGPostProcessorInvoke-RAGPostProcessor -InputText "Your scraped text here" -VerboseOutput
Chaining with other actors
Works directly after Website Content Crawler, Cheerio Scraper, or any actor that outputs a text or content field. Use the Apify actor-to-actor API to pipe output from a scraper straight into this processor.
Common use cases
- Preparing scraped web content for vector databases (Pinecone, Weaviate, Chroma)
- Cleaning LangChain / LlamaIndex document ingestion pipelines
- Pre-processing data for OpenAI embeddings or similar APIs
- Automating RAG pipeline data prep without custom code