Why a PDF to Markdown API is essential for RAG pipelines
Clean Markdown output from PDFs is the difference between a working RAG pipeline and silent retrieval failure. Why a dedicated API beats open-source tools.
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AnyMD Blog
Engineering, guides, and benchmarks on document-to-markdown conversion, RAG pipelines, and building fast APIs in Rust.
The fastest PDF to Markdown API — benchmarks vs PyMuPDF4LLM, Marker, and docling
AnyMD converts PDFs to Markdown in 0.3–0.8 seconds for typical documents — 4–10× faster than Python alternatives. Here are the numbers.
Read more →Building a RAG ingestion pipeline that actually works
Why Markdown is the best intermediate format for RAG pipelines, and why raw PDF parsing silently destroys retrieval quality.
Read more →Convert documents to Markdown from the CLI or Python
One API, one authentication key, 15+ input formats. How to normalise any document library into clean Markdown for AI training data, RAG, or archiving.
Read more →Zero data retention — how AnyMD handles your documents
AnyMD does not store, read, or log your document content. Files are converted in memory and discarded instantly. Only page count is tracked for billing.
Read more →Convert DOCX to Markdown with Python
Use AnyMD's REST API from Python to batch-convert DOCX files to Markdown — with async, error handling, pagination, and streaming examples.
Read more →Node.js document conversion to Markdown
Convert PDFs, DOCX, and PPTX to Markdown from Node.js using fetch or axios. With TypeScript types, streaming, and Express middleware patterns.
Read more →Rust document conversion with reqwest
Convert documents to Markdown from Rust — using reqwest, tokio, and serde. Benchmark showing how Rust clients pair with Rust server for lowest latency.
Read more →Go document to Markdown conversion
A clean Go package for converting office documents to Markdown. Context-aware, retry-able, with shared HTTP client patterns.
Read more →Batch document conversion with Bash and jq
Process thousands of files from the command line using curl, jq, and GNU parallel. The fastest way to convert a whole directory.
Read more →Convert documents to Markdown from any language
AnyMD's REST API works from any language — Python, Node.js, Rust, Go, Java, Ruby, PHP, C#, curl. One API, 15+ formats, in-memory, under a second.
Read more →Semantic chunking from Markdown for RAG
Markdown preserves document structure — headings, lists, tables — which makes it the ideal input for semantic chunking strategies. Compare heading-based, token-based, and recursive chunking on AnyMD output.
Read more →Recursive character chunking on Markdown output
LangChain-style recursive chunking works better on clean Markdown than on raw PDF or DOCX text. Benchmarks showing chunk quality and retrieval precision.
Read more →Token-aware chunking for LLM context windows
Split Markdown documents into chunks that fit exactly inside your model's context window — with overlap, heading boundaries, and metadata injection. Works with OpenAI, Claude, and Llama tokenizers.
Read more →Heading-based hierarchical chunking
Use Markdown heading structure (#, ##, ###) to create a hierarchy of chunks — parent sections contain their children. The best chunking strategy for document Q&A and summarization.
Read more →Chunking Markdown tables and lists for structured retrieval
Tables and lists contain dense relational data that naive chunking destroys. How to preserve table headers and list structure when splitting Markdown for vector search.
Read more →Fixed-size vs variable-size chunking on real documents
Benchmark comparing 10 chunking strategies across 500 real documents (PDFs, DOCX, PPTX, EPUB). Which strategy maximizes retrieval precision for RAG?
Read more →Hybrid chunking: semantic + token-aware for production RAG
Combine semantic boundaries (headings, paragraphs) with token limits and overlap. Production-tested pipeline with AnyMD + LangChain/LlamaIndex.
Read more →Chunking code and technical documentation for RAG
How to chunk technical docs, API references, and code examples after converting to Markdown. Code block preservation, function-level splits, and cross-reference tracking.
Read more →AnyMD vs unstructured.io — benchmark comparison
AnyMD is 4–10× faster than unstructured.io across 10 formats and 500 documents. Full latency breakdown, format support comparison, and pricing analysis.
Read more →Java document conversion to Markdown
Convert documents to clean Markdown from Java using AnyMD's REST API, Apache HttpClient, and concurrency best practices — with async batch processing, retry with backoff, and connection pooling.
Read more →PHP document conversion to Markdown
Integrate AnyMD document conversion into PHP projects with cURL, error handling, and batch processing — with WordPress and Laravel integration patterns.
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