This will automatically use the environment variable VOYAGE_API_KEY. changedbuild-with-claude/embeddings
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99When selecting an embeddings provider, there are several factors you can consider depending on your needs and preferences:
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11* Dataset size and domain specificity: size of the model training dataset and its relevance to the domain you want to embed. Larger or more domain-specific data generally produces better in-domain embeddings
12* Inference performance: embedding lookup speed and end-to-end latency. This is a particularly important consideration for large-scale production deployments
13* Customization: options for continued training on private data, or specialization of models for very specific domains. This can improve performance on unique vocabularies
11* Dataset size and domain specificity: size of the model training dataset and its relevance to the domain you want to embed. Larger or more domain-specific data generally produces better in-domain embeddings.
12* Inference performance: embedding lookup speed and end-to-end latency. This is a particularly important consideration for large-scale production deployments.
13* Customization: options for continued training on private data, or specialization of models for very specific domains. This can improve performance on unique vocabularies.
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1515## How to get embeddings with Anthropic
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8989For more detail, see [Voyage AI quick start](https://www.mongodb.com/docs/voyageai/quickstart/) in the MongoDB documentation.
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91You can obtain the embeddings by either using the official [`voyageai` Python package](https://github.com/voyage-ai/voyageai-python) or HTTP requests, as described in the following sections. Voyage AI also has an official TypeScript client. To use it with a model API key from Atlas, set its `environment` option to `https://ai.mongodb.com/v1`, as described in [TypeScript client](https://www.mongodb.com/docs/voyageai/api-and-clients/#typescript-client) in the MongoDB documentation.
91You can obtain the embeddings by using either the official [`voyageai` Python package](https://github.com/voyage-ai/voyageai-python) or HTTP requests, as described in the following sections. Voyage AI also has an official TypeScript client. To use it with a model API key from Atlas, set its `environment` option to `https://ai.mongodb.com/v1`, as described in [TypeScript client](https://www.mongodb.com/docs/voyageai/api-and-clients/#typescript-client) in the MongoDB documentation.
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9393### Voyage AI Python library
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143143The response is a JSON object containing the embeddings and the token usage:
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145```json
145```text wrap
146146{
147147 "object": "list",
148148 "data": [
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