Sold by: Voyage AIÂ
Deployed on AWS
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Text embedding model optimized for legal retrieval and AI applications. Tops the MTEB leaderboard for legal retrieval. 16K 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-law-2 is an embedding model optimized for retrieving legal texts. It excels in AI applications such as semantic case retrieval, legal question answering, and legal AI assistants. On 8 legal retrieval tasks, voyage-law-2 shows a 5.62% improvement over alternatives, including OpenAI v3 large, Cohere English v3, and E5 Mistral. It also enhances general-purpose corpora and long-context retrieval tasks, exceeding OpenAI v3 large by over 15% on average. 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-law-2 here: https://blog.voyageai.com/2024/04/15/domain-specific-embeddings-and-retrieval-legal-edition-voyage-law-2/Â
Highlights
- Optimized for law retrieval. On 8 legal retrieval tasks, voyage-law-2 has a significant 5.62% improvement over any alternatives, including OpenAI v3 large, Cohere English v3 and E5 Mistral. Tops the [MTEB leaderboard for legal retrieval](https://huggingface.co/spaces/mteb/leaderboard?task=retrieval&language=law).
- Consistent enhancements across general-purpose corpora and long-context retrieval tasks, exceeding OpenAI v3 large on average by over 15%.
- 16K token context length, well-suited for applications on long documents. 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.
Details
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.112 |
ml.g5.8xlarge Inference (Real-Time) | Model inference on the ml.g5.8xlarge instance type, real-time mode | $4.59 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $2.2725 |
ml.g5.4xlarge Inference (Real-Time) | Model inference on the ml.g5.4xlarge instance type, real-time mode | $3.045 |
ml.g5.16xlarge Inference (Real-Time) | Model inference on the ml.g5.16xlarge instance type, real-time mode | $7.68 |
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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-law-2.
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.
- Limitations for input type
- The maximum number of tokens for each text is 16K, 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
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 number of tokens for each text is 16K, the maximum length of the list is 128, and the total number of tokens in the list is at most 32K. | 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 |
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