OpenAI

LiteLLM supports OpenAI Chat + Text completion and embedding calls.

Required API Keys

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

Usage

import os 
from litellm import completion

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

# openai call
response = completion(
    model = "gpt-3.5-turbo", 
    messages=[{ "content": "Hello, how are you?","role": "user"}]
)

Optional Keys - OpenAI Organization, OpenAI API Base

import os 
os.environ["OPENAI_ORGANIZATION"] = "your-org-id"       # OPTIONAL
os.environ["OPENAI_API_BASE"] = "openaiai-api-base"     # OPTIONAL

OpenAI Chat Completion Models

Model Name	Function Call
gpt-4-1106-preview	response = completion(model="gpt-4-1106-preview", messages=messages)
gpt-3.5-turbo	response = completion(model="gpt-3.5-turbo", messages=messages)
gpt-3.5-turbo-0301	response = completion(model="gpt-3.5-turbo-0301", messages=messages)
gpt-3.5-turbo-0613	response = completion(model="gpt-3.5-turbo-0613", messages=messages)
gpt-3.5-turbo-16k	response = completion(model="gpt-3.5-turbo-16k", messages=messages)
gpt-3.5-turbo-16k-0613	response = completion(model="gpt-3.5-turbo-16k-0613", messages=messages)
gpt-4	response = completion(model="gpt-4", messages=messages)
gpt-4-0314	response = completion(model="gpt-4-0314", messages=messages)
gpt-4-0613	response = completion(model="gpt-4-0613", messages=messages)
gpt-4-32k	response = completion(model="gpt-4-32k", messages=messages)
gpt-4-32k-0314	response = completion(model="gpt-4-32k-0314", messages=messages)
gpt-4-32k-0613	response = completion(model="gpt-4-32k-0613", messages=messages)

These also support the OPENAI_API_BASE environment variable, which can be used to specify a custom API endpoint.

OpenAI Vision Models

Model Name	Function Call
gpt-4-vision-preview	response = completion(model="gpt-4-vision-preview", messages=messages)

Usage

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"
                                }
                            }
                        ]
        }
    ],
)

OpenAI Text Completion Models / Instruct Models

Model Name	Function Call
gpt-3.5-turbo-instruct	response = completion(model="gpt-3.5-turbo-instruct", messages=messages)
text-davinci-003	response = completion(model="text-davinci-003", messages=messages)
ada-001	response = completion(model="ada-001", messages=messages)
curie-001	response = completion(model="curie-001", messages=messages)
babbage-001	response = completion(model="babbage-001", messages=messages)
babbage-002	response = completion(model="babbage-002", messages=messages)
davinci-002	response = completion(model="davinci-002", messages=messages)

Setting Organization-ID for completion calls

This can be set in one of the following ways:

• Environment Variable OPENAI_ORGANIZATION
• Params to litellm.completion(model=model, organization="your-organization-id")
• Set as litellm.organization="your-organization-id"

import os 
from litellm import completion

os.environ["OPENAI_API_KEY"] = "your-api-key"
os.environ["OPENAI_ORGANIZATION"] = "your-org-id" # OPTIONAL

response = completion(
    model = "gpt-3.5-turbo", 
    messages=[{ "content": "Hello, how are you?","role": "user"}]
)

Using Helicone Proxy with LiteLLM

import os 
import litellm
from litellm import completion

os.environ["OPENAI_API_KEY"] = ""

# os.environ["OPENAI_API_BASE"] = ""
litellm.api_base = "https://oai.hconeai.com/v1"
litellm.headers = {
    "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}",
    "Helicone-Cache-Enabled": "true",
}

messages = [{ "content": "Hello, how are you?","role": "user"}]

# openai call
response = completion("gpt-3.5-turbo", messages)

Using OpenAI Proxy with LiteLLM

import os 
import litellm
from litellm import completion

os.environ["OPENAI_API_KEY"] = ""

# set custom api base to your proxy
# either set .env or litellm.api_base
# os.environ["OPENAI_API_BASE"] = ""
litellm.api_base = "your-openai-proxy-url"


messages = [{ "content": "Hello, how are you?","role": "user"}]

# openai call
response = completion("openai/your-model-name", messages)

If you need to set api_base dynamically, just pass it in completions instead - completions(...,api_base="your-proxy-api-base")

For more check out setting API Base/Keys