Sold by: Voyage AI
Deployed on AWS
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General-purpose reranker optimized for both latency and quality. Context length: 4K for queries & documents with up to 1K 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-lite-1 is a reranker optimized for both latency and quality. Over a comprehensive evaluation encompassing 27 datasets across diverse topics—ranging from technical docs and code to law, finance, web reviews, long documents, medicine, and conversations—rerank-lite-1 consistently outperformed alternatives, such as bge-reranker-large and Cohere’s rerank-english-v2.0 on average by 14.43% and 9%, respectively. Moreover, rerank-lite-1 improves recall over only a first-stage search in almost all cases. Latency is 445 ms for 25K tokens, and throughput is 202M tokens per hour at $0.01 per 1M tokens on an ml.g6.xlarge. Learn more about rerank-lite-1 here: https://blog.voyageai.com/2024/03/15/boosting-your-search-and-rag-with-voyages-rerankers/
Highlights
- Optimized for both latency and quality.
- `rerank-lite-1` emerges as the consistently superior reranker across all domains and first-stage search methods (e.g., BM25, OpenAI v3 large, voyage-large-2), outperforming alternatives, such as bge-reranker-large and Cohere’s rerank-english-v2.0 on average by 14.43% and 9%, respectively.
- 4K token context length for queries and documents, with up to 1K token context length for queries; well-suited for applications on long documents. Latencies are 445 ms (1 GPU), 135 ms (4 GPUs), and 90 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. 202M tokens per hour at $0.01 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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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-lite-1.
Additional details
Inputs
- Summary
- query: str - The query as a string. Maximum of 1K 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 sum of the number of tokens in the query and any single document cannot exceed 4K. The total number of tokens ("num of query tokens × num of documents + sum of the number of tokens in all documents") cannot exceed 300K.
- 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 1000 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 4000.
- 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 200K. | 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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Support
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General-purpose reranker optimized for latency & quality with multilingual support. Context length: 8K for queries & docs (2K for queries).
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