Cohere Embed Multilingual 3
The cohere.embed-multilingual-v3.0 model transforms each phrase, sentence, or paragraph that you input, into an array.
You can use the embedding models for finding similarity in phrases that are similar in context or category. Embeddings are typically stored in a vector database. Embeddings are mostly used for semantic searches where the search function focuses on the meaning of the text that it's searching through rather than finding results based on keywords.
Available in These Regions
- Brazil East (Sao Paulo)
- Germany Central (Frankfurt)
- Japan Central (Osaka)
- Saudi Arabia Central (Riyadh) (dedicated AI cluster only)
- UAE East (Dubai)
- UK South (London)
- US Midwest (Chicago)
Key Features
- Works for both English and multilingual.
- Model creates a 1,024-dimensional vector for each embedding.
- Maximum 96 sentences per run.
- Maximum 512 tokens for each input.
- Best for use cases when:
- Instead of English, the documents are written in one of the supported languages.
- The documents are written in more than one language and those languages are one of the supported languages.
On-Demand Mode
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You pay as you go for each inference call when you use the models in the playground or when you call the models through the API.
- Low barrier to start using Generative AI.
- Great for experimentation, proof of concept, and model evaluation.
- Available for the pretrained models in regions not listed as (dedicated AI cluster only).
Dynamic Throttling Limit Adjustment for On-Demand Mode
OCI Generative AI dynamically adjusts the request throttling limit for each active tenancy based on model demand and system capacity to optimize resource allocation and ensure fair access.
This adjustment depends on the following factors:
- The current maximum throughput supported by the target model.
- Any unused system capacity at the time of adjustment.
- Each tenancy’s historical throughput usage and any specified override limits set for that tenancy.
Note: Because of dynamic throttling, rate limits are undocumented and can change to meet system-wide demand.
Because of the dynamic throttling limit adjustment, we recommend implementing a back-off strategy, which involves delaying requests after a rejection. Without one, repeated rapid requests can lead to further rejections over time, increased latency, and potential temporary blocking of client by the Generative AI service. By using a back-off strategy, such as an exponential back-off strategy, you can distribute requests more evenly, reduce load, and improve retry success, following industry best practices and enhancing the overall stability and performance of your integration to the service.
Dedicated AI Cluster for the Model
To reach a model through a dedicated AI cluster in any listed region, you must create an endpoint for that model on a dedicated AI cluster. For the cluster unit size that matches this model, see the following table.
| Base Model | Fine-Tuning Cluster | Hosting Cluster | Pricing Page Information | Request Cluster Limit Increase |
|---|---|---|---|---|
|
Not available for fine-tuning |
|
|
|
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If you don't have enough cluster limits in your tenancy for hosting an Embed model on a dedicated AI cluster, request the
dedicated-unit-embed-cohere-countlimit to increase by 1.
Release and Retirement Dates
| Model | Release Date | On-Demand Retirement Date | Dedicated Mode Retirement Date |
|---|---|---|---|
cohere.embed-multilingual-v3.0
|
2024-02-07 | 2026-01-22 | cohere.embed-v4.0
|
Embedding Model Parameter
When using the embedding models, you can get a different output by changing the following parameter.
- Truncate
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Whether to truncate the start or end tokens in a sentence, when that sentence exceeds the maximum number of allowed tokens. For example, a sentence has 516 tokens, but the maximum token size is 512. If you select to truncate the end, the last 4 tokens of that sentence are cut off.