Sold by: Voyage AIÂ
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Rich multimodal embedding model that can vectorize interleaved text and content-rich images. 32K context length.
Overview
Multimodal embedding models are neural networks that transform multiple modalities, such as text and images, 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-multimodal-3 is a state-of-the-art multimodal embedding model that uniquely vectorizes interleaved texts + images while capturing visual features from PDFs, slides, tables, figures, and more, eliminating complex document parsing. It improves retrieval accuracy by an average of 19.63% over the next best-performing multimodal embedding model when evaluated across 3 multimodal retrieval tasks (20 total datasets). Latency is 75 ms for a single query with at most 200 tokens, and throughput is 57M tokens per hour at $0.06 per 1M tokens on an ml.g6.xlarge. Learn more about voyage-multimodal-3 here: https://blog.voyageai.com/2024/11/12/voyage-multimodal-3/Â
Highlights
- Unlike existing multimodal embedding models, `voyage-multimodal-3` is capable of vectorizing interleaved texts + images and capturing key visual features from screenshots of PDFs, slides, tables, figures, and more, thereby eliminating the need for complex document parsing.
- Improves retrieval accuracy by an average of 19.63% over the next best-performing multimodal embedding model when evaluated across 3 multimodal retrieval tasks (20 total datasets).
- 32K token context length. Latency is 75 ms for a single query with at most 200 tokens, and throughput is 57M tokens per hour at $0.06 per 1M tokens on an ml.g6.xlarge.
Details
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Deployed on AWS
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Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $3.03 |
ml.g6.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6.xlarge instance type, real-time mode | $2.2534 |
ml.g6.2xlarge Inference (Real-Time) | Model inference on the ml.g6.2xlarge instance type, real-time mode | $2.444 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.816 |
ml.g5.8xlarge Inference (Real-Time) | Model inference on the ml.g5.8xlarge instance type, real-time mode | $6.12 |
ml.g6.4xlarge Inference (Real-Time) | Model inference on the ml.g6.4xlarge instance type, real-time mode | $3.308 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $3.03 |
ml.g5.4xlarge Inference (Real-Time) | Model inference on the ml.g5.4xlarge instance type, real-time mode | $4.06 |
ml.g6.8xlarge Inference (Real-Time) | Model inference on the ml.g6.8xlarge instance type, real-time mode | $5.036 |
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Refunds to be processed under the conditions specified in EULA. Please contact contact@voyageai.com for further assistance.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
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Version release notes
We are excited to announce the initial release of voyage-multimodal-3.
Additional details
Inputs
- Summary
- inputs: List[dict] - A list of multimodal inputs to be vectorized. (see input description for details).
- input_type: str, optional (default=null) - "query" or "document".
- truncation: bool, optional (default=True) - Truncate input.
- output_encoding: str, optional (default=null) - Encoding (e.g., Base64)
- id: str, optional (default=null) - 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 128K. Every 560 pixels of an image counts as a token.
- Input MIME type
- text/csv, application/json, application/jsonlines
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_multimodal_embedding_input.json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_batch_input_multimodal_embedding.jsonl
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
inputs | List of dictionaries, each containing a single key "content," whose value is a list of dictionaries, each representing a single piece of text or image - specified by the following keys:
1. "type": Specifies the type of the piece of the content - "text" or "image_base64".
2. "text": Only present when "type" is "text". Text string.
3. "image_base64": Only present when "type" is "image_base64". Base64-encoded image in the data URL format data:[<mediatype>];base64,<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 128K. Every 560 pixels of an image counts as a token. Supported mediatypes: "image/png", "image/jpeg", "image/webp", and "image/gif". | Yes |
input_type | Type of the input. Defaults to null. Other options: "query", "document". | Default value: null
Type: FreeText | No |
truncation | Whether to truncate the inputs to fit within the context length. - If True, an over-length input will be truncated to fit within the context length. - If False, an error will be raised if any input exceeds the context length. | Default value: True
Type: Categorical
Allowed values: True, False | No |
output_encoding | Format in which the embeddings are encoded. We currently support two options: - null (default): the embeddings are represented as lists of floating-point numbers; - "base64": the embeddings are represented as a Base64-encoded NumPy array of single-precision floats. | Default value: null
Type: Categorical
Allowed values: base64, null | No |
id | Batch transform request ID. If specified, this will be returned in the output. | Default value: null
Type: FreeText | No |
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Please email us at contact@voyageai.com for inquiries and customer support. Join our Discord channel: https://discord.gg/zAU7GQEmvTÂ
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