Embedding Models

Quick Start

from litellm import embedding
import os
os.environ['OPENAI_API_KEY'] = ""
response = embedding(model='text-embedding-ada-002', input=["good morning from litellm"])

Input Params for litellm.embedding()

Required Fields

• model: string - ID of the model to use. model='text-embedding-ada-002'

• input: array - Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for text-embedding-ada-002), cannot be an empty string, and any array must be 2048 dimensions or less.

input=["good morning from litellm"]

Optional LiteLLM Fields

• user: string (optional) A unique identifier representing your end-user,

• timeout: integer - The maximum time, in seconds, to wait for the API to respond. Defaults to 600 seconds (10 minutes).

• api_base: string (optional) - The api endpoint you want to call the model with

• api_version: string (optional) - (Azure-specific) the api version for the call

• api_key: string (optional) - The API key to authenticate and authorize requests. If not provided, the default API key is used.

• api_type: string (optional) - The type of API to use.

Output from litellm.embedding()

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        -0.0022326677571982145,
        0.010749882087111473,
        ...
        ...
        ...
   
      ]
    }
  ],
  "model": "text-embedding-ada-002-v2",
  "usage": {
    "prompt_tokens": 10,
    "total_tokens": 10
  }
}

OpenAI Embedding Models

Usage

from litellm import embedding
import os
os.environ['OPENAI_API_KEY'] = ""
response = embedding('text-embedding-ada-002', input=["good morning from litellm"])

Model Name	Function Call	Required OS Variables
text-embedding-ada-002	embedding('text-embedding-ada-002', input)	os.environ['OPENAI_API_KEY']

Azure OpenAI Embedding Models

API keys

This can be set as env variables or passed as params to litellm.embedding()

import os
os.environ['AZURE_API_KEY'] = 
os.environ['AZURE_API_BASE'] = 
os.environ['AZURE_API_VERSION'] = 

Usage

from litellm import embedding
response = embedding(
    model="azure/<your deployment name>",
    input=["good morning from litellm"],
    api_key=api_key,
    api_base=api_base,
    api_version=api_version,
)
print(response)

Model Name	Function Call
text-embedding-ada-002	embedding(model="azure/<your deployment name>", input=input)

h/t to Mikko for this integration

OpenAI Compatible Embedding Models

Use this for calling /embedding endpoints on OpenAI Compatible Servers, example https://github.com/xorbitsai/inference

Note add openai/ prefix to model so litellm knows to route to OpenAI

Usage

from litellm import embedding
response = embedding(
  model = "openai/<your-llm-name>",     # add `openai/` prefix to model so litellm knows to route to OpenAI
  api_base="http://0.0.0.0:8000/"       # set API Base of your Custom OpenAI Endpoint
  input=["good morning from litellm"]
)

Bedrock Embedding

API keys

This can be set as env variables or passed as params to litellm.embedding()

import os
os.environ["AWS_ACCESS_KEY_ID"] = ""  # Access key
os.environ["AWS_SECRET_ACCESS_KEY"] = "" # Secret access key
os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2

Usage

from litellm import embedding
response = embedding(
    model="amazon.titan-embed-text-v1",
    input=["good morning from litellm"],
)
print(response)

Model Name	Function Call
Titan Embeddings - G1	embedding(model="amazon.titan-embed-text-v1", input=input)

Cohere Embedding Models

https://docs.cohere.com/reference/embed

Usage

from litellm import embedding
os.environ["COHERE_API_KEY"] = "cohere key"

# cohere call
response = embedding(
    model="embed-english-v3.0", 
    input=["good morning from litellm", "this is another item"], 
    input_type="search_document" # optional param for v3 llms
)

Model Name	Function Call
embed-english-v3.0	embedding(model="embed-english-v3.0", input=["good morning from litellm", "this is another item"])
embed-english-light-v3.0	embedding(model="embed-english-light-v3.0", input=["good morning from litellm", "this is another item"])
embed-multilingual-v3.0	embedding(model="embed-multilingual-v3.0", input=["good morning from litellm", "this is another item"])
embed-multilingual-light-v3.0	embedding(model="embed-multilingual-light-v3.0", input=["good morning from litellm", "this is another item"])
embed-english-v2.0	embedding(model="embed-english-v2.0", input=["good morning from litellm", "this is another item"])
embed-english-light-v2.0	embedding(model="embed-english-light-v2.0", input=["good morning from litellm", "this is another item"])
embed-multilingual-v2.0	embedding(model="embed-multilingual-v2.0", input=["good morning from litellm", "this is another item"])

HuggingFace Embedding Models

LiteLLM supports all Feature-Extraction Embedding models: https://huggingface.co/models?pipeline_tag=feature-extraction

Usage

from litellm import embedding
import os
os.environ['HUGGINGFACE_API_KEY'] = ""
response = embedding(
    model='huggingface/microsoft/codebert-base', 
    input=["good morning from litellm"]
)

Model Name	Function Call	Required OS Variables
microsoft/codebert-base	embedding('huggingface/microsoft/codebert-base', input=input)	os.environ['HUGGINGFACE_API_KEY']
BAAI/bge-large-zh	embedding('huggingface/BAAI/bge-large-zh', input=input)	os.environ['HUGGINGFACE_API_KEY']
any-hf-embedding-model	embedding('huggingface/hf-embedding-model', input=input)	os.environ['HUGGINGFACE_API_KEY']