跳至主要內容

/embeddings

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"])

Async Usage - aembedding()​

LiteLLM provides an asynchronous version of the embedding function called aembedding:

from litellm import aembedding
import asyncio

async def get_embedding():
    response = await aembedding(
        model='text-embedding-ada-002',
        input=["good morning from litellm"]
    )
    return response

response = asyncio.run(get_embedding())
print(response)

Proxy Usage​

NOTE For vertex_ai,

export GOOGLE_APPLICATION_CREDENTIALS="absolute/path/to/service_account.json"

Add model to config​

model_list:
- model_name: textembedding-gecko
  litellm_params:
    model: vertex_ai/textembedding-gecko

general_settings:
  master_key: sk-1234

Start proxy​

litellm --config /path/to/config.yaml 

# RUNNING on http://0.0.0.0:4000

Test​

curl --location 'http://0.0.0.0:4000/embeddings' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{"input": ["Academia.edu uses"], "model": "textembedding-gecko", "encoding_format": "base64"}'

Image Embeddings​

For models that support image embeddings, you can pass in a base64 encoded image string to the input param.

from litellm import embedding
import os

# set your api key
os.environ["COHERE_API_KEY"] = ""

response = embedding(model="cohere/embed-english-v3.0", input=["<base64 encoded image>"])

Input Params for litellm.embedding()​

資訊

Any non-openai params, will be treated as provider-specific params, and sent in the request body as kwargs to the provider.

See Reserved Params

See Example

Required Fields​

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

  • input: string or 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,

  • dimensions: integer (Optional) The number of dimensions the resulting output embeddings should have. Only supported in OpenAI/Azure text-embedding-3 and later models.

  • encoding_format: string (Optional) The format to return the embeddings in. Can be either "float" or "base64". Defaults to encoding_format="float"

  • timeout: integer (Optional) - 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(
    model="text-embedding-3-small",
    input=["good morning from litellm", "this is another item"],
    metadata={"anything": "good day"},
    dimensions=5 # Only supported in text-embedding-3 and later models.
)
Model NameFunction CallRequired OS Variables
text-embedding-3-smallembedding('text-embedding-3-small', input)os.environ['OPENAI_API_KEY']
text-embedding-3-largeembedding('text-embedding-3-large', input)os.environ['OPENAI_API_KEY']
text-embedding-ada-002embedding('text-embedding-ada-002', input)os.environ['OPENAI_API_KEY']

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://0.0.0.0:4000/"       # 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 NameFunction Call
Amazon Nova Multimodal Embeddingsembedding(model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", input=input)
Amazon Nova (Async)embedding(model="bedrock/async_invoke/amazon.nova-2-multimodal-embeddings-v1:0", input=input, input_type="text", output_s3_uri="s3://bucket/")
Titan Embeddings - G1embedding(model="amazon.titan-embed-text-v1", input=input)
Cohere Embeddings - Englishembedding(model="cohere.embed-english-v3", input=input)
Cohere Embeddings - Multilingualembedding(model="cohere.embed-multilingual-v3", input=input)
TwelveLabs Marengo (Async)embedding(model="bedrock/async_invoke/us.twelvelabs.marengo-embed-2-7-v1:0", input=input, input_type="text")

TwelveLabs Bedrock Embedding Models​

TwelveLabs Marengo models support multimodal embeddings (text, image, video, audio) and require the input_type parameter to specify the input format.

Usage​

from litellm import embedding
import os

# Set AWS credentials
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = "us-east-1"

# Text embedding
response = embedding(
    model="bedrock/us.twelvelabs.marengo-embed-2-7-v1:0",
    input=["Hello world from LiteLLM!"],
    input_type="text"  # Required parameter
)

# Image embedding (base64)
response = embedding(
    model="bedrock/async_invoke/us.twelvelabs.marengo-embed-2-7-v1:0",
    input=["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQ..."],
    input_type="image",  # Required parameter
    output_s3_uri="s3://your-bucket/async-invoke-output/"
)

# Video embedding (S3 URL)
response = embedding(
    model="bedrock/async_invoke/us.twelvelabs.marengo-embed-2-7-v1:0",
    input=["s3://your-bucket/video.mp4"],
    input_type="video",  # Required parameter
    output_s3_uri="s3://your-bucket/async-invoke-output/"
)

Required Parameters​

ParameterDescriptionValues
input_typeType of input content"text", "image", "video", "audio"

Supported Models​

Model NameFunction CallNotes
TwelveLabs Marengo 2.7 (Sync)embedding(model="bedrock/us.twelvelabs.marengo-embed-2-7-v1:0", input=input, input_type="text")Text embeddings only
TwelveLabs Marengo 2.7 (Async)embedding(model="bedrock/async_invoke/us.twelvelabs.marengo-embed-2-7-v1:0", input=input, input_type="text/image/video/audio")All input types, requires output_s3_uri

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

NVIDIA NIM Embedding Models​

API keys​

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

import os
os.environ["NVIDIA_NIM_API_KEY"] = ""  # api key
os.environ["NVIDIA_NIM_API_BASE"] = "" # nim endpoint url

Usage​

from litellm import embedding
import os
os.environ['NVIDIA_NIM_API_KEY'] = ""
response = embedding(
    model='nvidia_nim/<model_name>', 
    input=["good morning from litellm"],
    input_type="query"
)

input_type Parameter for Embedding Models​

Certain embedding models, such as nvidia/embed-qa-4 and the E5 family, operate in dual modes—one for indexing documents (passages) and another for querying. To maintain high retrieval accuracy, it's essential to specify how the input text is being used by setting the input_type parameter correctly.

Usage​

Set the input_type parameter to one of the following values:

  • "passage" – for embedding content during indexing (e.g., documents).
  • "query" – for embedding content during retrieval (e.g., user queries).

Warning: Incorrect usage of input_type can lead to a significant drop in retrieval performance.

All models listed here are supported:

Model NameFunction Call
NV-Embed-QAembedding(model="nvidia_nim/NV-Embed-QA", input)
nvidia/nv-embed-v1embedding(model="nvidia_nim/nvidia/nv-embed-v1", input)
nvidia/nv-embedqa-mistral-7b-v2embedding(model="nvidia_nim/nvidia/nv-embedqa-mistral-7b-v2", input)
nvidia/nv-embedqa-e5-v5embedding(model="nvidia_nim/nvidia/nv-embedqa-e5-v5", input)
nvidia/embed-qa-4embedding(model="nvidia_nim/nvidia/embed-qa-4", input)
nvidia/llama-3.2-nv-embedqa-1b-v1embedding(model="nvidia_nim/nvidia/llama-3.2-nv-embedqa-1b-v1", input)
nvidia/llama-3.2-nv-embedqa-1b-v2embedding(model="nvidia_nim/nvidia/llama-3.2-nv-embedqa-1b-v2", input)
snowflake/arctic-embed-lembedding(model="nvidia_nim/snowflake/arctic-embed-l", input)
baai/bge-m3embedding(model="nvidia_nim/baai/bge-m3", input)

HuggingFace Embedding Models​

LiteLLM supports all Feature-Extraction + Sentence Similarity 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"]
)

Usage - Set input_type​

LiteLLM infers input type (feature-extraction or sentence-similarity) by making a GET request to the api base.

Override this, by setting the input_type yourself.

from litellm import embedding
import os
os.environ['HUGGINGFACE_API_KEY'] = ""
response = embedding(
    model='huggingface/microsoft/codebert-base', 
    input=["good morning from litellm", "you are a good bot"],
    api_base = "https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud", 
    input_type="sentence-similarity"
)

Usage - Custom API Base​

from litellm import embedding
import os
os.environ['HUGGINGFACE_API_KEY'] = ""
response = embedding(
    model='huggingface/microsoft/codebert-base', 
    input=["good morning from litellm"],
    api_base = "https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud"
)
Model NameFunction CallRequired OS Variables
microsoft/codebert-baseembedding('huggingface/microsoft/codebert-base', input=input)os.environ['HUGGINGFACE_API_KEY']
BAAI/bge-large-zhembedding('huggingface/BAAI/bge-large-zh', input=input)os.environ['HUGGINGFACE_API_KEY']
any-hf-embedding-modelembedding('huggingface/hf-embedding-model', input=input)os.environ['HUGGINGFACE_API_KEY']

Mistral AI Embedding Models​

All models listed here https://docs.mistral.ai/platform/endpoints are supported

Usage​

from litellm import embedding
import os

os.environ['MISTRAL_API_KEY'] = ""
response = embedding(
    model="mistral/mistral-embed",
    input=["good morning from litellm"],
)
print(response)
Model NameFunction Call
mistral-embedembedding(model="mistral/mistral-embed", input)

Gemini AI Embedding Models​

API keys​

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

import os
os.environ["GEMINI_API_KEY"] = ""

Usage - Embedding​

from litellm import embedding
response = embedding(
  model="gemini/text-embedding-004",
  input=["good morning from litellm"],
)
print(response)

All models listed here are supported:

Model NameFunction Call
text-embedding-004embedding(model="gemini/text-embedding-004", input)
gemini-embedding-2-previewembedding(model="gemini/gemini-embedding-2-preview", input)
gemini-embedding-2 (GA)embedding(model="gemini/gemini-embedding-2", input)

Gemini Embedding 2 Preview (Multimodal)​

gemini-embedding-2-preview supports multimodal embeddings—text, images, audio, video, and PDF in a single request. See blog post for details. The GA model id gemini-embedding-2 exposes the same behavior—swap the model name in any example below. See GA blog for cost-map coverage and pricing notes.

Response shape

For the Gemini API path (gemini/gemini-embedding-2-preview), each input element returns its own embedding (indexed 0..N-1)—same semantics as OpenAI's /embeddings. LiteLLM routes to Gemini's batchEmbedContents endpoint with one EmbedContentRequest per input. This differs from the Vertex AI path, which combines all parts into a single unified vector—see Vertex AI embeddings docs.

Input formats:

  • Data URIs: data:image/png;base64,<encoded_data>
  • Gemini file references: files/abc123 (pre-uploaded via Gemini Files API)

Supported MIME types: image/png, image/jpeg, audio/mpeg, audio/wav, video/mp4, video/quicktime, application/pdf

from litellm import embedding
import os
os.environ["GEMINI_API_KEY"] = ""

# Text + Image (base64)
response = embedding(
    model="gemini/gemini-embedding-2-preview",
    input=[
        "The food was delicious and the waiter...",
        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAIAQMAAAD+wSzIAAAABlBMVEX///+/v7+jQ3Y5AAAADklEQVQI12P4AIX8EAgALgAD/aNpbtEAAAAASUVORK5CYII"
    ],
)
print(response)

Optional: dimensions maps to Gemini's outputDimensionality.

Combined Multimodal Embeddings​

By default, each element in the input list produces a separate embedding (OpenAI-compatible). To combine multiple inputs into a single embedding (e.g., text + image representing one entity), wrap them in a nested list:

from litellm import embedding

# Separate: 2 inputs → 2 embeddings
response = embedding(
    model="gemini/gemini-embedding-2-preview",
    input=["a red shoe", "data:image/png;base64,..."],
)
# response.data has 2 embeddings

# Combined: text + image → 1 embedding
response = embedding(
    model="gemini/gemini-embedding-2-preview",
    input=[["a red shoe", "data:image/png;base64,..."]],
)
# response.data has 1 embedding representing both together

# Mixed: 1 combined + 1 separate → 2 embeddings
response = embedding(
    model="gemini/gemini-embedding-2-preview",
    input=[["a red shoe", "data:image/png;base64,..."], "just text"],
)
# response.data has 2 embeddings

This is useful for representing multi-modal entities (e.g., a product with a name + photo) as a single vector for search and retrieval. Gemini API only — Vertex AI always returns a single combined vector regardless of input shape (see Vertex AI embeddings docs).

Vertex AI Embedding Models​

Usage - Embedding​

import litellm
from litellm import embedding
litellm.vertex_project = "hardy-device-38811" # Your Project ID
litellm.vertex_location = "us-central1"  # proj location

response = embedding(
    model="vertex_ai/textembedding-gecko",
    input=["good morning from litellm"],
)
print(response)

Supported Models​

All models listed here are supported

Model NameFunction Call
textembedding-geckoembedding(model="vertex_ai/textembedding-gecko", input)
textembedding-gecko-multilingualembedding(model="vertex_ai/textembedding-gecko-multilingual", input)
textembedding-gecko-multilingual@001embedding(model="vertex_ai/textembedding-gecko-multilingual@001", input)
textembedding-gecko@001embedding(model="vertex_ai/textembedding-gecko@001", input)
textembedding-gecko@003embedding(model="vertex_ai/textembedding-gecko@003", input)
text-embedding-preview-0409embedding(model="vertex_ai/text-embedding-preview-0409", input)
text-multilingual-embedding-preview-0409embedding(model="vertex_ai/text-multilingual-embedding-preview-0409", input)

Voyage AI Embedding Models​

Usage - Embedding​

from litellm import embedding
import os

os.environ['VOYAGE_API_KEY'] = ""
response = embedding(
    model="voyage/voyage-01",
    input=["good morning from litellm"],
)
print(response)

Supported Models​

All models listed here https://docs.voyageai.com/embeddings/#models-and-specifics are supported

Model NameFunction Call
voyage-01embedding(model="voyage/voyage-01", input)
voyage-lite-01embedding(model="voyage/voyage-lite-01", input)
voyage-lite-01-instructembedding(model="voyage/voyage-lite-01-instruct", input)

Provider-specific Params​

資訊

Any non-openai params, will be treated as provider-specific params, and sent in the request body as kwargs to the provider.

See Reserved Params

Example​

Cohere v3 Models have a required parameter: input_type, it can be one of the following four values:

  • input_type="search_document": (default) Use this for texts (documents) you want to store in your vector database
  • input_type="search_query": Use this for search queries to find the most relevant documents in your vector database
  • input_type="classification": Use this if you use the embeddings as an input for a classification system
  • input_type="clustering": Use this if you use the embeddings for text clustering

https://txt.cohere.com/introducing-embed-v3/

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" # 👈 PROVIDER-SPECIFIC PARAM
)

Nebius AI Studio Embedding Models​

Usage - Embedding​

from litellm import embedding
import os

os.environ['NEBIUS_API_KEY'] = ""
response = embedding(
    model="nebius/BAAI/bge-en-icl",
    input=["Good morning from litellm!"],
)
print(response)

Supported Models​

All supported models can be found here: https://studio.nebius.ai/models/embedding

Model NameFunction Call
BAAI/bge-en-iclembedding(model="nebius/BAAI/bge-en-icl", input)
BAAI/bge-multilingual-gemma2embedding(model="nebius/BAAI/bge-multilingual-gemma2", input)
intfloat/e5-mistral-7b-instructembedding(model="nebius/intfloat/e5-mistral-7b-instruct", input)
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