RAG Document Converter

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📄 Convert documents to clean Markdown optimized for RAG pipelines

👁 Apify Actor

What This Actor Does

• Multi-format support - PDF, DOCX, PPTX, XLSX, HTML, images
• Structure preservation - Keeps headers, tables, lists intact
• RAG-optimized - Clean Markdown for LLM ingestion
• Section chunking - Split by headers for vector stores
• Metadata extraction - Title, author, page count

Use Cases

Input Examples

Basic PDF to Markdown

{
"fileUrls":["https://example.com/document.pdf"],
"outputFormat":"markdown"
}

With Section Chunking

{
"fileUrls":["https://example.com/report.pdf"],
"outputFormat":"markdown",
"chunkBySection":true
}

Multiple Formats

{
"fileUrls":[
"https://example.com/doc.pdf",
"https://example.com/slides.pptx",
"https://example.com/data.xlsx"
],
"outputFormat":"markdown"
}

With OCR

{
"fileUrls":["https://example.com/scanned.pdf"],
"outputFormat":"markdown",
"enableOcr":true
}

Configuration

Supported Formats

Output Formats

Output

{
"source":"https://example.com/document.pdf",
"outputFormat":"markdown",
"outputUrl":"https://api.apify.com/v2/key-value-stores/.../records/converted-12345.md",
"contentPreview":"# Document Title\n\n## Introduction\n\nThis document covers...",
"metadata":{
"title":"Annual Report 2024",
"pageCount":42
},
"pageCount":42,
"success":true
}

With Section Chunking

{
"source":"https://example.com/document.pdf",
"sections":[
{"title":"Introduction","content":"..."},
{"title":"Methodology","content":"..."},
{"title":"Results","content":"..."}
],
"sectionCount":3,
"success":true
}

RAG Integration

LangChain

from langchain.text_splitter import MarkdownTextSplitter
# Get markdown from actor output
markdown = result["contentPreview"]# or fetch from outputUrl
splitter = MarkdownTextSplitter(chunk_size=1000)
chunks = splitter.split_text(markdown)

LlamaIndex

from llama_index import Document
doc = Document(text=markdown, metadata=result["metadata"])

Cost Estimation

Technical Details

• Language: Python 3.12
• Library: IBM Docling
• Memory: 1GB-4GB (depends on document size)
• Features: 10x faster with DoclingParseV2

Limitations

• OCR requires additional processing time
• Very large documents may need more memory
• Some complex layouts may lose formatting

Keywords: docling, rag, pdf, markdown, convert, document, llm, retrieval, langchain, llamaindex