Mixpeek Logo
    Login / SignupDemo
    data

    JSON
    Embeddings
    Converter

    Convert JSON objects and arrays into semantic vector embeddings. Supports nested structures, field selection, and configurable serialization strategies for optimal embedding quality.

    Max file size: 500 MB
    Estimated: 1-5 sec per 1000 records
    3 input formats

    How It Works

    1

    Upload a JSON file or provide raw JSON in the request body.

    2

    Fields are selected and serialized into text representations.

    3

    Text representations are chunked if they exceed model context length.

    4

    Each record is embedded using the selected text embedding model.

    5

    Embeddings are returned alongside source record identifiers.

    Code Examples

    from mixpeek import Mixpeek
    

    client = Mixpeek(api_key="YOUR_API_KEY")
    

    result = client.convert(
        source="https://example.com/products.json",
        from_format="json",
        to_format="embeddings",
        options={
            "model": "e5-large-instruct",
            "fields": ["title", "description", "category"],
            "id_field": "product_id"
        }
    )
    

    for record in result.embeddings:
        print(f"{record.id}: dim={len(record.vector)}")

    Use Cases

    Embed product catalogs for semantic search
    Create vector indexes from API response data
    Build recommendation systems from structured metadata
    Enable natural-language queries over JSON datasets

    Supported Input Formats

    JSON
    JSONL
    NDJSON

    Quick Info

    Categorydata
    Max File Size500 MB
    Est. Time1-5 sec per 1000 records
    Extractorstructured-data-descriptor

    Try This Conversion

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

    Frequently Asked Questions

    Related Converters

    CSV
    Embeddings

    CSV to Embeddings

    Convert CSV files into vector embeddings by selecting and combining columns into text representations. Supports header mapping, custom delimiters, and batch processing for large datasets.

    Text
    Embeddings

    Text to Embeddings

    Convert text strings, paragraphs, or documents into dense vector embeddings using state-of-the-art language models. Supports batching, chunking, and multiple model options for optimal retrieval performance.

    Mixed
    Embeddings

    Multimodal to Embeddings

    Generate unified vector embeddings from mixed-modality inputs -- text, images, audio, and video combined. Enables cross-modal search where any modality can query any other modality in a single vector space.

    Ready to convert json to embeddings?

    Start using the Mixpeek JSON to Embeddings in minutes. Sign up for a free API key and follow the documentation to get started.