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Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

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voyage-code-3 Embedding Model Free trial

By:
Voyage AI
Latest Version:
v1.0.0
Text embedding model for code retrieval and AI applications. 32K context length. Multiple output dimensions and embedding quantization.
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    Product Overview

    Text embedding models are neural networks that transform texts into numerical vectors. They are a crucial building block for semantic search/retrieval systems and retrieval-augmented generation (RAG) and are responsible for the retrieval quality. voyage-code-3 is optimized for code retrieval, outperforming OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite of 238 code retrieval datasets, respectively. By supporting smaller dimensions with Matryoshka learning and quantized formats like int8 and binary, voyage-code-3 can also dramatically reduce storage and search costs with minimal impact on retrieval quality. Latency is 90 ms for a single query with at most 100 tokens, and throughput is 12.6M tokens per hour at $0.22 per 1M tokens on an ml.g6.xlarge. Learn more about voyage-code-3 here: https://blog.voyageai.com/2024/12/04/voyage-code-3/

    Key Data

    Version
    v1.0.0
    Usage Instructions
    Release Notes
    By
    Voyage AI
    Categories
    Embeddings
    Natural Language Processing
    Classification-Text
    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Optimized for code retrieval. Outperforms OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite of 238 code retrieval datasets, respectively.

    • Supports embeddings of 2048, 1024, 512, and 256 dimensions and offers multiple embedding quantization, including float (32-bit floating point), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8).

    • 32K token context length. Latency is 90 ms for a single query with at most 100 tokens. 12.6M tokens per hour at $0.22 per 1M tokens on an ml.g6.xlarge.

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.

    Contact us to request contract pricing for this product.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Model Realtime Inference$1.6901/hr

    running on ml.g6.xlarge

    Model Batch Transform$2.2725/hr

    running on ml.g5.2xlarge

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Realtime Inference$1.1267/host/hr

    running on ml.g6.xlarge

    SageMaker Batch Transform$1.515/host/hr

    running on ml.g5.2xlarge

    About Free trial

    Try this product for 7 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.

    Model Realtime Inference

    For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.
    InstanceType
    Realtime Inference/hr
    ml.g6.2xlarge
    $1.833
    ml.g5.xlarge
    $2.112
    ml.g5.8xlarge
    $4.59
    ml.g6.4xlarge
    $2.481
    ml.g5.2xlarge
    $2.2725
    ml.g5.4xlarge
    $3.045
    ml.g6.8xlarge
    $3.777
    ml.g6.xlarge
    Vendor Recommended
    $1.6901

    Usage Information

    Model input and output details

    Input

    Summary
    1. input: str or List[str] - Text(s)
    2. input_type: str, optional (default=None) - "query" or "document".
    3. truncation: bool, optional (default=True) - Truncate input.
    4. output_dimension: int, optional (default=None) - Dimensions for embeddings.
    5. output_dtype: str, optional (default="float") - Embedding data type
    6. encoding_format: str, optional (default=None) - Encoding (e.g., Base64)
    7. id: str, optional (default=None) - Batch request ID.
    Limitations for input type
    The maximum tokens for each text is 32K, the maximum length of the list is 128, and the total number of tokens in the list is at most 32K.
    Input MIME type
    text/csv, application/json, application/jsonlines
    Sample input data
    Expand all input descriptions
    Field name
    input
    Description
    A single text string, or a list of texts as a list of strings.
    Required
    Yes
    Data type
    FreeText
    Limitations
    The maximum tokens for each text is 32K, the maximum length of the list is 128, and the total number of tokens in the list is at most 32K.
    Field name
    input_type
    Description
    Type of the input text. Default to None. Other options: "query", "document".
    Required
    No
    Data type
    FreeText
    Default value
    None
    Field name
    truncation
    Description
    Whether to truncate the input texts to fit within the context length. - If True, over-length input texts will be truncated to fit within the context length. - If False, an error will be raised if any given text exceeds the context length.
    Required
    No
    Data type
    Categorical
    Default value
    True
    Allowed values
    True, False
    Field name
    output_dimension
    Description
    The number of dimensions for resulting output embeddings. Options: 2048, 1024, 512, 256, `None`. If `None`, then output dimension is 1024.
    Required
    No
    Data type
    Integer
    Range
    Default value
    None
    Field name
    output_dtype
    Description
    The data type for the resulting output embeddings. * "float": 32-bit single-precision floating-point numbers. This is provides the highest precision / retrieval accuracy. * "int8" and "uint8": 8-bit integers ranging from -128 to 127 and 0 to 255, respectively. * "binary" and "ubinary": 8-bit integers that represent bit-packed, quantized single-bit embedding values: int8 for binary and uint8 for ubinary. The length of the returned list of integers is 1/8 of output_dimension.
    Required
    No
    Data type
    Categorical
    Default value
    float
    Allowed values
    float, int8, uint8, binary, ubinary
    Field name
    encoding_format
    Description
    Format in which the embeddings are encoded. * None (default): embeddings are represented as a list of numbers in the data type specified by the output_dtype parameter (default is float); * "base64": embeddings are Base64-encoded NumPy array of (1) numpy.float32 for output_dtype=float, (2) numpy.int8 for output_dtype=int8, binary, and (3) numpy.uint8 for output_dtype=uint8, ubinary
    Required
    No
    Data type
    Categorical
    Default value
    None
    Allowed values
    base64, None
    Field name
    id
    Description
    Batch transform request ID. If specified, this will be returned in the output.
    Required
    No
    Data type
    FreeText
    Default value
    None

    Output

    Summary

    The API will respond with a JSON object containing a list of embedding objects, along with other information such as model name, token usage, and batch transform ID if applicable.

    Output MIME type
    application/json, text/csv, application/jsonlines
    Sample output data
    Expand all output descriptions
    Field name
    embedding
    Description
    A list of embeddings for the corresponding list of input texts. The data type of each embedding is determined by output_dtype: float for lists of floats, and int8, uint8, binary, or ubinary for lists of integers.
    Always returned
    Yes
    Data type
    Continuous (Float)
    Range

    Sample notebook

    Additional Resources

    End User License Agreement

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    Support Information

    voyage-code-3 Embedding Model

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