Sold by: Voyage AI
Deployed on AWS
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General-purpose reranker optimized for quality with multilingual support. Context length: 16K for queries & documents (4K for queries).
Overview
Rerankers are neural networks that predict the relevancy scores between a query and documents and rank them based on the scores. They are used to refine search results in semantic search/retrieval systems and retrieval-augmented generation (RAG). rerank-2 is a cutting-edge reranker optimized for quality, improving accuracy atop OpenAI v3 large by an average of 13.89%—2.3x the improvement attained by the latest Cohere reranker (English v3). rerank-2 is also natively multilingual, beating Cohere multilingual v3 by 8.83% on 51 datasets across 31 languages. It supports a 16K-token combined context length for a query-document pair, with up to 4K tokens for the query. Latency is 1.5 s for 25K tokens, and throughput is 60M tokens per hour at $0.05 per 1M tokens on an ml.g6.xlarge. Learn more about rerank-2 here: https://blog.voyageai.com/2024/09/30/rerank-2/
Highlights
- Optimized for quality, improving accuracy atop OpenAI v3 large by an average of 13.89% —2.3x the improvement attained by the latest Cohere reranker (English v3).
- Natively multilingual, beating Cohere multilingual v3 by 8.83% on 51 datasets across 31 languages.
- 16K token context length for queries and documents, with up to 4K token context length for queries; well-suited for applications on long documents. Latencies are 1.5 s (1 GPU), 415 ms (4 GPUs), and 245 ms (8 GPUs) for 25K tokens. We recommend using multiple GPUs to reduce latency. The supported 12xlarge and 24xlarge instances come with 4 GPUs each, while the 48xlarge instances are equipped with 8 GPUs. 60M tokens per hour at $0.05 per 1M tokens on an ml.g6.xlarge
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.12xlarge Inference (Batch) Recommended | Model inference on the ml.g5.12xlarge instance type, batch mode | $0.00 |
ml.g6.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6.xlarge instance type, real-time mode | $1.69005 |
ml.g6.12xlarge Inference (Real-Time) | Model inference on the ml.g6.12xlarge instance type, real-time mode | $5.752 |
ml.g6.24xlarge Inference (Real-Time) | Model inference on the ml.g6.24xlarge instance type, real-time mode | $8.344 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.112 |
ml.g6.48xlarge Inference (Real-Time) | Model inference on the ml.g6.48xlarge instance type, real-time mode | $16.688 |
ml.g5.12xlarge Inference (Real-Time) | Model inference on the ml.g5.12xlarge instance type, real-time mode | $7.09 |
ml.g5.48xlarge Inference (Real-Time) | Model inference on the ml.g5.48xlarge instance type, real-time mode | $20.36 |
ml.g5.24xlarge Inference (Real-Time) | Model inference on the ml.g5.24xlarge instance type, real-time mode | $10.18 |
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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 rerank-2.
Additional details
Inputs
- Summary
- query: str - The query as a string. Maximum of 4K tokens.
- documents: List[str] - The documents to be reranked as a list of strings. Maximum of 1K documents.
- top_k: int, optional (default=None) - The number of most relevant documents to return. If not specified, the reranking results of all documents will be returned.
- truncation: bool, optional (default=True) - True: Truncates. False: raises error if any given text exceeds the context length.
- Limitations for input type
- The query and any document must not exceed 16K tokens. Total tokens ("query tokens × documents + sum of all document tokens") are capped at 160K × # GPUs. Supported instances—xlarge, 12xlarge/24xlarge, and 48xlarge—provide 1, 4, and 8 GPUs, respectively.
- Input MIME type
- text/csv, application/json, application/jsonlines
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_reranker_input.json
https://github.com/voyage-ai/voyageai-aws/blob/main/sample_reranker_input.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
query | The query as a string. The query can contain a maximum of 4000 tokens. | Type: FreeText | Yes |
documents | The documents to be reranked as a list of strings.
- The number of documents cannot exceed 1000.
- The sum of the number of tokens in the query and the number of tokens in any single document cannot exceed 16000.
- The total number of tokens, defined as "the number of query tokens × the number of documents + sum of the number of tokens in all documents", cannot exceed 160K x # GPUs. The supported xlarge, 12xlarge/24xlarge, and 48xlarge instances come with 1, 4, and 8 GPUs each, respectively. | Type: FreeText | Yes |
top_k | The number of most relevant documents to return. If not specified, the reranking results of all documents will be returned. | Default value: None
Type: Integer | No |
truncation | Whether to truncate the input to satisfy the "context length limit" on the query and the documents. | Default value: True
Type: Categorical
Allowed values: True, False | No |
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