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Embeddings - `/embeddings`

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Claude Code Knowledge Pack7/10/2026

Overview

Embeddings - /embeddings

See supported Embedding Providers & Models here

Supported Input Formats

The /v1/embeddings endpoint follows the OpenAI embeddings API specification. The following input formats are supported:

FormatExample
String"input": "Hello"
Array of strings"input": ["Hello", "World"]
Array of tokens (integers)"input": [1234, 5678, 9012]
Array of token arrays"input": [[1234, 5678], [9012, 3456]]

Quick start

Here's how to route between GPT-J embedding (sagemaker endpoint), Amazon Titan embedding (Bedrock) and Azure OpenAI embedding on the proxy server:

  1. Set models in your config.yaml
model_list:
  - model_name: sagemaker-embeddings
    litellm_params: 
      model: "sagemaker/berri-benchmarking-gpt-j-6b-fp16"
  - model_name: amazon-embeddings
    litellm_params:
      model: "bedrock/amazon.titan-embed-text-v1"
  - model_name: azure-embeddings
    litellm_params: 
      model: "azure/azure-embedding-model"
      api_base: "os.environ/AZURE_API_BASE" # os.getenv("AZURE_API_BASE")
      api_key: "os.environ/AZURE_API_KEY" # os.getenv("AZURE_API_KEY")
      api_version: "2023-07-01-preview"

general_settings:
  master_key: sk-1234 # [OPTIONAL] if set all calls to proxy will require either this key or a valid generated token
  1. Start the proxy
$ litellm --config /path/to/config.yaml
  1. Test the embedding call
curl --location 'http://0.0.0.0:4000/v1/embeddings' \\
--header 'Authorization: Bearer sk-1234' \\
--header 'Content-Type: application/json' \\
--data '{
    "input": "The food was delicious and the waiter..",
    "model": "sagemaker-embeddings",
}'