PDF to Markdown for Embeddings: Maximize Semantic Quality
Embedding models compress meaning into vectors. They also compress noise into vectors. Pre-converting PDF to Markdown changes the noise floor: less furniture, more content, denser semantic signal. The same chunk, the same model, a measurably better vector.
What "embedding quality" actually means
Two practical metrics: cosine similarity between semantically related chunks (should be high), and cosine similarity between semantically unrelated chunks (should be low). Raw PDF text fails on both: repeated headers and footers create false similarity between chunks that share nothing else, while column-break artefacts create false dissimilarity between chunks that should cluster.
Markdown removes both effects. We typically observe cosine similarity for related chunks rising 0.05–0.10 (on a 0–1 scale) and unrelated cosine falling by similar amounts, which translates to noticeably sharper top-K retrieval and fewer false positives in re-ranking.
Choosing an embedding model
For most production workloads in 2026, OpenAI text-embedding-3-large, Cohere embed-v3, or Voyage voyage-3-large all perform comparably on Markdown input. The difference is dwarfed by input quality: a worse model on Markdown beats a better model on raw PDF in our internal tests.
Frequently asked questions
Does document structure affect embedding quality?
Significantly. Embeddings encode everything in the input: including page numbers, footers, and column-break artefacts. Removing those before embedding lets the model concentrate on actual semantic content, producing denser and more discriminative vectors.
OpenAI vs Cohere embeddings: does Markdown matter?
Equally to both. Modern embedding models (text-embedding-3-large, embed-v3, voyage-3) are all sensitive to input quality, and all benefit similarly from clean Markdown. The choice between providers usually depends on cost and latency, not Markdown handling.
How should I handle chunk overlap with Markdown?
50–150 tokens of overlap is the standard range. Overlap helps when retrieval surfaces a chunk whose answer spans the boundary. With Markdown header-aware chunking you need less overlap than with naive character splitting, since chunk boundaries already align with semantic boundaries.
Does Markdown formatting add noise to embeddings?
Slightly. Heavy Markdown syntax (lots of **bold**, link URLs, code blocks) adds a small amount of token-level noise. In practice this is dwarfed by the noise it removes. If you're micro-optimising, strip link URLs and bold/italic syntax before embedding.
How do I benchmark embedding quality from Markdown vs PDF?
Build a small evaluation set of question/passage pairs from your domain. Embed both versions of your corpus (PDF text and Markdown). Measure top-K retrieval accuracy on the eval set for each. The gap, in our experience, is consistent and large: 10–25% on most corpora.