mirror of https://github.com/BerriAI/litellm.git synced 2025-04-26 03:04:13 +00:00

* docs: stable release notes

* docs: additional doc improvements

* docs(anthropic_unified.md): add doc on unified anthropic endpoint

* docs: update docs

2025-03-08 16:19:07 -08:00

8.1 KiB

Raw Permalink Blame History

import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

Using Vision Models

Quick Start

Example passing images to a model

import os 
from litellm import completion

os.environ["OPENAI_API_KEY"] = "your-api-key"

# openai call
response = completion(
    model = "gpt-4-vision-preview", 
    messages=[
        {
            "role": "user",
            "content": [
                            {
                                "type": "text",
                                "text": "What’s in this image?"
                            },
                            {
                                "type": "image_url",
                                "image_url": {
                                "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                                }
                            }
                        ]
        }
    ],
)

Define vision models on config.yaml

model_list:
  - model_name: gpt-4-vision-preview # OpenAI gpt-4-vision-preview
    litellm_params:
      model: openai/gpt-4-vision-preview
      api_key: os.environ/OPENAI_API_KEY
  - model_name: llava-hf          # Custom OpenAI compatible model
    litellm_params:
      model: openai/llava-hf/llava-v1.6-vicuna-7b-hf
      api_base: http://localhost:8000
      api_key: fake-key
    model_info:
      supports_vision: True        # set supports_vision to True so /model/info returns this attribute as True

Run proxy server

litellm --config config.yaml

Test it using the OpenAI Python SDK

import os 
from openai import OpenAI

client = OpenAI(
    api_key="sk-1234", # your litellm proxy api key
)

response = client.chat.completions.create(
    model = "gpt-4-vision-preview",  # use model="llava-hf" to test your custom OpenAI endpoint
    messages=[
        {
            "role": "user",
            "content": [
                            {
                                "type": "text",
                                "text": "What’s in this image?"
                            },
                            {
                                "type": "image_url",
                                "image_url": {
                                "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                                }
                            }
                        ]
        }
    ],
)

Checking if a model supports `vision`

Use litellm.supports_vision(model="") -> returns True if model supports vision and False if not

assert litellm.supports_vision(model="openai/gpt-4-vision-preview") == True
assert litellm.supports_vision(model="vertex_ai/gemini-1.0-pro-vision") == True
assert litellm.supports_vision(model="openai/gpt-3.5-turbo") == False
assert litellm.supports_vision(model="xai/grok-2-vision-latest") == True
assert litellm.supports_vision(model="xai/grok-2-latest") == False

Define vision models on config.yaml

model_list:
  - model_name: gpt-4-vision-preview # OpenAI gpt-4-vision-preview
    litellm_params:
      model: openai/gpt-4-vision-preview
      api_key: os.environ/OPENAI_API_KEY
  - model_name: llava-hf          # Custom OpenAI compatible model
    litellm_params:
      model: openai/llava-hf/llava-v1.6-vicuna-7b-hf
      api_base: http://localhost:8000
      api_key: fake-key
    model_info:
      supports_vision: True        # set supports_vision to True so /model/info returns this attribute as True

Run proxy server

litellm --config config.yaml

Call /model_group/info to check if your model supports vision

curl -X 'GET' \
  'http://localhost:4000/model_group/info' \
  -H 'accept: application/json' \
  -H 'x-api-key: sk-1234'

Expected Response

{
  "data": [
    {
      "model_group": "gpt-4-vision-preview",
      "providers": ["openai"],
      "max_input_tokens": 128000,
      "max_output_tokens": 4096,
      "mode": "chat",
      "supports_vision": true, # 👈 supports_vision is true
      "supports_function_calling": false
    },
    {
      "model_group": "llava-hf",
      "providers": ["openai"],
      "max_input_tokens": null,
      "max_output_tokens": null,
      "mode": null,
      "supports_vision": true, # 👈 supports_vision is true
      "supports_function_calling": false
    }
  ]
}

Explicitly specify image type

If you have images without a mime-type, or if litellm is incorrectly inferring the mime type of your image (e.g. calling gs:// url's with vertex ai), you can set this explicity via the format param.

"image_url": {
  "url": "gs://my-gs-image",
  "format": "image/jpeg"
}

LiteLLM will use this for any API endpoint, which supports specifying mime-type (e.g. anthropic/bedrock/vertex ai).

For others (e.g. openai), it will be ignored.

import os 
from litellm import completion

os.environ["ANTHROPIC_API_KEY"] = "your-api-key"

# openai call
response = completion(
    model = "claude-3-7-sonnet-latest", 
    messages=[
        {
            "role": "user",
            "content": [
                            {
                                "type": "text",
                                "text": "What’s in this image?"
                            },
                            {
                                "type": "image_url",
                                "image_url": {
                                  "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
                                  "format": "image/jpeg"
                                }
                            }
                        ]
        }
    ],
)

Define vision models on config.yaml

model_list:
  - model_name: gpt-4-vision-preview # OpenAI gpt-4-vision-preview
    litellm_params:
      model: openai/gpt-4-vision-preview
      api_key: os.environ/OPENAI_API_KEY
  - model_name: llava-hf          # Custom OpenAI compatible model
    litellm_params:
      model: openai/llava-hf/llava-v1.6-vicuna-7b-hf
      api_base: http://localhost:8000
      api_key: fake-key
    model_info:
      supports_vision: True        # set supports_vision to True so /model/info returns this attribute as True

Run proxy server

litellm --config config.yaml

Test it using the OpenAI Python SDK

import os 
from openai import OpenAI

client = OpenAI(
    api_key="sk-1234", # your litellm proxy api key
)

response = client.chat.completions.create(
    model = "gpt-4-vision-preview",  # use model="llava-hf" to test your custom OpenAI endpoint
    messages=[
        {
            "role": "user",
            "content": [
                            {
                                "type": "text",
                                "text": "What’s in this image?"
                            },
                            {
                                "type": "image_url",
                                "image_url": {
                                "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
                                "format": "image/jpeg"
                                }
                            }
                        ]
        }
    ],
)

Spec

"image_url": str

OR 

"image_url": {
  "url": "url OR base64 encoded str",
  "detail": "openai-only param", 
  "format": "specify mime-type of image"
}

8.1 KiB Raw Permalink Blame History Unescape Escape