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    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

    import os, requests

API = "https://api.mixpeek.com"
H = {"Authorization": f"Bearer {os.environ['MIXPEEK_API_KEY']}",
     "X-Namespace": os.environ["NAMESPACE_ID"]}

# 1. a bucket, with a schema that declares the field you will send
bucket = requests.post(f"{API}/v1/buckets", headers=H, json={
    "bucket_name": "json-inputs",
    "bucket_schema": {"properties": {"json": {"type": "text"}}},
}).json()

# 2. land the file as an object. the URL goes in data, on the blob
requests.post(f"{API}/v1/buckets/{bucket['bucket_id']}/objects", headers=H, json={
    "key_prefix": "run-1",
    "blobs": [{"property": "json", "type": "text",
               "data": "https://example.com/records.json"}],
})

# 3. a collection over that bucket, running the extractor
collection = requests.post(f"{API}/v1/collections", headers=H, json={
    "collection_name": "json-to-embeddings",
    "source": {"type": "bucket", "bucket_ids": [bucket["bucket_id"]]},
    "feature_extractor": {"feature_extractor_name": "text_extractor", "version": "v1"},
}).json()

# 4. run extraction over the bucket
requests.post(f"{API}/v1/buckets/{bucket['bucket_id']}/batches", headers=H, json={
    "collection_ids": [collection["collection_id"]],
    "auto_submit": True,
})

# 5. read the output
docs = requests.get(
    f"{API}/v1/collections/{collection['collection_id']}/documents", headers=H
).json()
print(docs)

    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

    Processing millions of files?

    Run this as a managed pipeline over your whole library, no infrastructure to build or maintain. Talk to us about processing at scale.

    Run it over a library

    Mixpeek runs this conversion as a pipeline over a whole library in your object storage, with the output landing as queryable documents. It is not a single-file converter.

    Frequently Asked Questions

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    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.