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Text embedding model optimized for general-purpose (including multilingual) retrieval/search and AI applications. 32K context length.
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-3 is a general-purpose embedding that: [1] outperforms OpenAI v3 large across all eight evaluated domains (tech, code, web, law, finance, multilingual, conservation, and long-context) by 7.55% on average, [2] has a 3-4x smaller embedding dimension (1024) compared to OpenAI (3072) and E5 Mistral (4096), resulting in 3-4x lower vectorDB costs, and [3] supports a 32K-token context length, compared to OpenAI (8K) and Cohere (512). Latency is 62.5 ms for a single query with at most 200 tokens, and throughput is 40M tokens per hour at $0.08 per 1M tokens on an ml.g6.xlarge. Learn more about voyage-3 here: https://blog.voyageai.com/2024/09/18/voyage-3/Â
Highlights
- Optimized for general-purpose and multilingual retrieval quality and outperforms OpenAI v3 large across all eight evaluated domains (tech, code, web, law, finance, multilingual, conservation, and long-context) by 7.55% on average.
- 3-4x smaller embedding dimension (1024) compared to OpenAI (3072) and E5 Mistral (4096), resulting in 3-4x lower vectorDB costs.
- 32K token context length, well-suited for applications on long documents. Latency is 62.5 ms for a single query with at most 200 tokens. 40M tokens per hour at $0.08 per 1M tokens on an ml.g6.xlarge.
Details
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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.g6.16xlarge Inference (Real-Time) | Model inference on the ml.g6.16xlarge instance type, real-time mode | $8.492 |
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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
We are excited to announce the initial release of voyage-3.
Additional details
Inputs
- Summary
- input: str or List[str] - Single text or list of texts.
- input_type: str, optional (default=None) - May also be "query" or "document".
- truncation: bool, optional (default=True) - True: Truncates. False: raises error if any given text exceeds the context length.
- encoding_format: str, optional (default=None) - Embedding format. None: float list; "base64": compressed encoding.
- id: str, optional (default=None) - Batch transform 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 160K.
- Input MIME type
- text/csv, application/json, application/jsonlines
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_embedding_input.json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_batch_input_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 |
|---|---|---|---|
input | A single text string, or a list of texts as a list of strings. | 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 160K. | Yes |
input_type | Type of the input text. Default to None. Other options: "query", "document". | Default value: None
Type: FreeText | No |
truncation | 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. | Default value: True
Type: Categorical
Allowed values: True, False | No |
encoding_format | Format in which the embeddings are encoded. We currently support two options: - None (default): the embeddings are represented as lists of floating-point numbers; - "base64": the embeddings are compressed to Base64 encodings. | Default value: None
Type: Categorical
Allowed values: base64, None | No |
id | Batch transform request ID. If specified, this will be returned in the output. | Default value: None
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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