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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

    from mixpeek import Mixpeek
    

    client = Mixpeek(api_key="YOUR_API_KEY")
    

    result = client.convert(
        source="The quick brown fox jumps over the lazy dog.",
        from_format="text",
        to_format="embeddings",
        options={
            "model": "e5-large-instruct",
            "input_type": "passage"
        }
    )
    

    print(f"Dimensions: {len(result.embedding)}")
    print(f"First 5 values: {result.embedding[:5]}")

    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

    Try This Conversion

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

    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.