Sold by: Cohere
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Rerank improves search systems by sorting documents based on their semantic similarity to a query. Choose fast, when you need a fast Reranking model. Choose pro, when you need the Reranking model with the best performance.
Overview
Cohere's Rerank v4.0 endpoint enables businesses to significantly improve search and retrieval-augmented generation systems. As input, it takes a query and list of potentially relevant documents. Rerank v4.0 then returns the documents as a list sorted by semantic similarity to the provided query. As an intelligent cross-encoding AI model, Rerank v4.0 is able to understand the meaning behind enterprise data and user questions. Rerank v4.0 can be implemented with just a few lines of code, delivers leading performance across over 100 languages, and is uniquely capable of understanding complex information which requires reasoning. These attributes make Rerank v4.0 particularly well suited for global organizations within Finance, Healthcare, Energy, Government, and Manufacturing. Rerank v4.0 can be added to existing systems, whether keyword or semantic, to improve performance.
Highlights
- Cohere's Rerank v4.0 is uniquely capable of understanding complex documents and queries. This leads to more accurate search results when user questions have multiple aspects or require reasoning. Rerank v4.0 also offers strong performance on semi-structured data such as Code, Tables, and JSON Documents. These attributes make the model ideal for global organizations within industries such as Finance, Healthcare, Energy, Government, and Manufacturing.
- Cohere's Rerank v4.0 can be added to existing search and retrieval-augmented generation (RAG) systems with just a few lines of code. This ease of implementation makes is simple to boost semantic understanding and improve search results.
- Cohere's Rerank v4.0 offers leading multilingual performance in over 100 languages, including but not limited to: Arabic, Chinese, English, French, German, Hindi, Japanese, Korean, Portuguese, Russian, and Spanish. This is useful for global organizations who operate across various languages and require a performant AI model to improve their search systems.
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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 | $3.50 |
ml.p5.4xlarge Inference (Real-Time) Recommended | Model inference on the ml.p5.4xlarge instance type, real-time mode | $3.50 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $3.50 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $3.50 |
Dimensions summary
You pay by the hour for the compute instance that runs the rerank-v4.0-fast model. Pricing splits along two lines: instance type and inference mode. Three instance sizes are available: ml.g5.xlarge, ml.g5.2xlarge, and ml.p5.4xlarge. Real-time mode is offered on all three sizes for low-latency requests. The ml.g5.2xlarge also offers a batch mode for processing groups of documents at once. Your hourly cost depends on which instance and mode you choose, so larger or higher-throughput instances bill at their own rate. You run as many hours as you need.
Top-of-mind questions for buyers
Each dimension bills for the compute instance hosting the rerank-v4.0-fast model. The ml.g5.xlarge and ml.g5.2xlarge use GPU instances for lower-latency and throughput needs. The ml.p5.4xlarge uses higher-capacity GPU hardware. You choose based on your latency and throughput requirements; each instance bills at its own hourly rate.
Real-time mode processes requests as they arrive, suited to low-latency lookups. It runs on all three instance sizes. Batch mode processes groups of documents together and is offered only on the ml.g5.2xlarge. Both meter by host-hours. Your bill depends on how long the chosen instance and mode run.
Billing meters host-hours, so charges accrue while the instance runs. A fully stopped instance stops software charges. Underlying AWS infrastructure fees, such as storage, may still apply separately. To avoid software charges, shut down instances you are not using.
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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
A key feature update adjusts the default maximum token limit for per-model reranking to balance performance and resource use, with customization options available via API or configuration. Critical bug fixes resolve the "Empty EncodedTexts" issue in Rerank and Embed endpoints by improving chunking logic for oversized inputs and adding safeguards to ensure valid outputs.
Additional details
Inputs
- Summary
The model accepts JSON requests that specifies the input texts to be reranked. The maximum number number of documents that can be passed into a single rerank call is 1000. Note: The documentation below is for Version 2 of the Rerank API.
Req { “model”: “...”, "query": "...?", "documents": [“”...], "max_tokens_per_doc": 1, "top_n": 100 }
Res
{ "results": [ { "index": 0, "relevance_score": 0.0048297215 } ],
- Input MIME type
- application/json
https://github.com/cohere-ai/cohere-developer-experience/blob/main/notebooks/sagemaker/Rerank%20Models.ipynb
https://github.com/cohere-ai/cohere-developer-experience/blob/main/notebooks/sagemaker/Rerank%20Models.ipynb
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 search query. Queries longer than 2000 tokens get automatically truncated. | Type: FreeText | Yes |
documents | A list of texts that will be compared to the `query`. For optimal performance we recommend against sending more than 1,000 documents in a single request. **Note**: long documents will automatically be truncated to the value of max_tokens_per_doc. **Note**: structured data should be formatted as YAML strings for best performance. | Type: FreeText | No |
top_n | Limits the number of returned rerank results to the specified value. If not passed, all the rerank results will be returned. | Default value: [] Type: Integer Minimum: 1 | No |
max_tokens_per_doc | Defaults to 4096. Long documents will be automatically truncated to the specified number of tokens. Compatibility: 'max_tokens_per_doc' is a parameter introduced in Rerank API Version 2 (`"api_version": 2`). | Default value: 4096 Type: Integer Minimum: 1 Maximum: 40000 | No |
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Contact us at support+aws@cohere.com or at https://cohere.com/contact-sales support+aws@cohere.com
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Rerank will return a sorted list of documents based on the semantic similarity between the query and documents in over 100 languages.
Cohere provides a multilingual representative AI model, that translates texts and images into numerical vectors that models can understand.
Rerank improves search systems by sorting documents based on their semantic similarity to a query.
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