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    embedding

    Text
    Embeddings
    Converter

    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.

    Max file size: 10 MB
    Estimated: < 1 sec per 1000 tokens
    3 input formats

    How It Works

    1

    Provide text content in the request body or upload a text file.

    2

    Text is optionally chunked by token count or semantic boundaries.

    3

    Each chunk is tokenized and processed through the embedding model.

    4

    Dense vectors are returned with chunk text and metadata.

    5

    Optionally, embeddings are stored directly in your namespace.

    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": "text-inputs",
    "bucket_schema": {"properties": {"text": {"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": "text", "type": "text",
               "data": "https://example.com/notes.txt"}],
})

# 3. a collection over that bucket, running the extractor
collection = requests.post(f"{API}/v1/collections", headers=H, json={
    "collection_name": "text-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

    Build semantic search over text documents
    Create embeddings for RAG (Retrieval-Augmented Generation) systems
    Index knowledge base articles for AI-powered support
    Generate query embeddings for similarity matching

    Supported Input Formats

    TXT
    Plain text
    Markdown

    Quick Info

    Categoryembedding
    Max File Size10 MB
    Est. Time< 1 sec per 1000 tokens
    Extractortext-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 text to embeddings?

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