القائمة
Fast document parsing for RAG pipelines via LiteParse (Rust, 100x faster than Python parsers)
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| name | liteparse |
| description | Fast document parsing for RAG pipelines via LiteParse (Rust, 100x faster than Python parsers) |
| tags | ["rag","document-parsing","rust","pdf","llm"] |
| related_skills | ["crawl4ai","huggingface-hub"] |
Parse PDFs, DOCX, HTML, and other documents 100x faster than Python parsers. From the LlamaIndex team.
cargo install liteparse or pip install liteparseliteparse document.pdf --output ./parsed/
liteparse ./documents/ --recursive --output ./parsed/
from liteparse import parse
result = parse("document.pdf")
print(result.markdown) # Clean markdown output
print(result.tables) # Extracted tables as dataframes
print(result.images) # Extracted images as paths
print(result.metadata) # Title, author, dates
liteparse serve --port 8091
# Add to agent config:
# "mcpServers": {"liteparse": {"command": "liteparse", "args": ["serve", "--port", "8091"]}}
| Format | Support | Notes |
|---|---|---|
| ✅ Full | Text, tables, images, metadata | |
| DOCX | ✅ Full | Headings, lists, tables |
| HTML | ✅ Full | DOM-aware extraction |
| Markdown | ✅ Passthrough | Clean formatting |
| EPUB | ✅ Full | Chapter structure preserved |
# Parse a test document and verify output
liteparse test.pdf --output ./test-output/
cat ./test-output/test.md # Should contain extracted text
ls ./test-output/ # Should contain images if present
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