document

PDF
Embeddings
Converter

Convert PDF documents into semantic vector embeddings for search, retrieval, and RAG applications. Pages are chunked intelligently by sections and paragraphs, then embedded using text or multimodal models.

Max file size: 200 MB

Estimated: 5-30 sec per document

1 input formats

How It Works

Upload a PDF or provide a URL.

Text is extracted and segmented into semantic chunks.

Diagrams and figures are optionally processed with a vision model.

Each chunk is embedded using your selected model.

Embeddings are returned with chunk text and metadata.

Code Examples

from mixpeek import Mixpeek

client = Mixpeek(api_key="YOUR_API_KEY")

result = client.convert(
    source="https://example.com/whitepaper.pdf",
    from_format="pdf",
    to_format="embeddings",
    options={
        "model": "e5-large-instruct",
        "chunk_size": 512,
        "chunk_overlap": 64,
        "include_figures": True
    }
)

for chunk in result.chunks:
    print(f"Chunk {chunk.index}: {chunk.text[:80]}...")

Use Cases

Build RAG systems over document collections

Enable semantic search across PDF archives

Power question-answering over technical documentation

Create vector indexes for legal document review

Supported Input Formats

PDF

Quick Info

Categorydocument

Max File Size200 MB

Est. Time5-30 sec per document

Extractordocument-descriptor

Try This Conversion

Get started with the Mixpeek API and convert your first file in minutes.

Frequently Asked Questions

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