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OpenAI

LiteLLM supports OpenAI Chat + 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-4o", 
    messages=[{ "content": "Hello, how are you?","role": "user"}]
)

Usage - LiteLLM Proxy Server​

Here's how to call OpenAI models with the LiteLLM Proxy Server

1. Save key in your environment​

export OPENAI_API_KEY=""

2. Start the proxy​

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: openai/gpt-3.5-turbo                          # The `openai/` prefix will call openai.chat.completions.create
      api_key: os.environ/OPENAI_API_KEY
  - model_name: gpt-3.5-turbo-instruct
    litellm_params:
      model: text-completion-openai/gpt-3.5-turbo-instruct # The `text-completion-openai/` prefix will call openai.completions.create
      api_key: os.environ/OPENAI_API_KEY

3. Test it​

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "gpt-3.5-turbo",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'

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 NameFunction Call
gpt-4o-miniresponse = completion(model="gpt-4o-mini", messages=messages)
gpt-4o-mini-2024-07-18response = completion(model="gpt-4o-mini-2024-07-18", messages=messages)
gpt-4oresponse = completion(model="gpt-4o", messages=messages)
gpt-4o-2024-08-06response = completion(model="gpt-4o-2024-08-06", messages=messages)
gpt-4o-2024-05-13response = completion(model="gpt-4o-2024-05-13", messages=messages)
gpt-4-turboresponse = completion(model="gpt-4-turbo", messages=messages)
gpt-4-turbo-previewresponse = completion(model="gpt-4-0125-preview", messages=messages)
gpt-4-0125-previewresponse = completion(model="gpt-4-0125-preview", messages=messages)
gpt-4-1106-previewresponse = completion(model="gpt-4-1106-preview", messages=messages)
gpt-3.5-turbo-1106response = completion(model="gpt-3.5-turbo-1106", messages=messages)
gpt-3.5-turboresponse = completion(model="gpt-3.5-turbo", messages=messages)
gpt-3.5-turbo-0301response = completion(model="gpt-3.5-turbo-0301", messages=messages)
gpt-3.5-turbo-0613response = completion(model="gpt-3.5-turbo-0613", messages=messages)
gpt-3.5-turbo-16kresponse = completion(model="gpt-3.5-turbo-16k", messages=messages)
gpt-3.5-turbo-16k-0613response = completion(model="gpt-3.5-turbo-16k-0613", messages=messages)
gpt-4response = completion(model="gpt-4", messages=messages)
gpt-4-0314response = completion(model="gpt-4-0314", messages=messages)
gpt-4-0613response = completion(model="gpt-4-0613", messages=messages)
gpt-4-32kresponse = completion(model="gpt-4-32k", messages=messages)
gpt-4-32k-0314response = completion(model="gpt-4-32k-0314", messages=messages)
gpt-4-32k-0613response = 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 NameFunction Call
gpt-4oresponse = completion(model="gpt-4o", messages=messages)
gpt-4-turboresponse = completion(model="gpt-4-turbo", messages=messages)
gpt-4-vision-previewresponse = 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 Fine Tuned Models​

Model NameFunction Call
fine tuned gpt-4-0613response = completion(model="ft:gpt-4-0613", messages=messages)
fine tuned gpt-4o-2024-05-13response = completion(model="ft:gpt-4o-2024-05-13", messages=messages)
fine tuned gpt-3.5-turbo-0125response = completion(model="ft:gpt-3.5-turbo-0125", messages=messages)
fine tuned gpt-3.5-turbo-1106response = completion(model="ft:gpt-3.5-turbo-1106", messages=messages)
fine tuned gpt-3.5-turbo-0613response = completion(model="ft:gpt-3.5-turbo-0613", messages=messages)

Advanced​

Getting OpenAI API Response Headers​

Set litellm.return_response_headers = True to get raw response headers from OpenAI

You can expect to always get the _response_headers field from litellm.completion(), litellm.embedding() functions

litellm.return_response_headers = True

# /chat/completion
response = completion(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "user",
            "content": "hi",
        }
    ],
)
print(f"response: {response}")
print("_response_headers=", response._response_headers)
Expected Response Headers from OpenAI
{
  "date": "Sat, 20 Jul 2024 22:05:23 GMT",
  "content-type": "application/json",
  "transfer-encoding": "chunked",
  "connection": "keep-alive",
  "access-control-allow-origin": "*",
  "openai-model": "text-embedding-ada-002",
  "openai-organization": "*****",
  "openai-processing-ms": "20",
  "openai-version": "2020-10-01",
  "strict-transport-security": "max-age=15552000; includeSubDomains; preload",
  "x-ratelimit-limit-requests": "5000",
  "x-ratelimit-limit-tokens": "5000000",
  "x-ratelimit-remaining-requests": "4999",
  "x-ratelimit-remaining-tokens": "4999999",
  "x-ratelimit-reset-requests": "12ms",
  "x-ratelimit-reset-tokens": "0s",
  "x-request-id": "req_cc37487bfd336358231a17034bcfb4d9",
  "cf-cache-status": "DYNAMIC",
  "set-cookie": "__cf_bm=E_FJY8fdAIMBzBE2RZI2.OkMIO3lf8Hz.ydBQJ9m3q8-1721513123-1.0.1.1-6OK0zXvtd5s9Jgqfz66cU9gzQYpcuh_RLaUZ9dOgxR9Qeq4oJlu.04C09hOTCFn7Hg.k.2tiKLOX24szUE2shw; path=/; expires=Sat, 20-Jul-24 22:35:23 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None, *cfuvid=SDndIImxiO3U0aBcVtoy1TBQqYeQtVDo1L6*Nlpp7EU-1721513123215-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None",
  "x-content-type-options": "nosniff",
  "server": "cloudflare",
  "cf-ray": "8a66409b4f8acee9-SJC",
  "content-encoding": "br",
  "alt-svc": "h3=\":443\"; ma=86400"
}

Parallel Function calling​

See a detailed walthrough of parallel function calling with litellm here

import litellm
import json
# set openai api key
import os
os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request
# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_weather(location, unit="fahrenheit"):
    """Get the current weather in a given location"""
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
    elif "san francisco" in location.lower():
        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})

messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]

response = litellm.completion(
    model="gpt-3.5-turbo-1106",
    messages=messages,
    tools=tools,
    tool_choice="auto",  # auto is default, but we'll be explicit
)
print("\nLLM Response1:\n", response)
response_message = response.choices[0].message
tool_calls = response.choices[0].message.tool_calls

Setting extra_headers for completion calls​

import os 
from litellm import completion

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

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

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"}]
)

Set ssl_verify=False​

This is done by setting your own httpx.Client

  • For litellm.completion set litellm.client_session=httpx.Client(verify=False)
  • For litellm.acompletion set litellm.aclient_session=AsyncClient.Client(verify=False)
import litellm, httpx

# for completion
litellm.client_session = httpx.Client(verify=False)
response = litellm.completion(
    model="gpt-3.5-turbo",
    messages=messages,
)

# for acompletion
litellm.aclient_session = httpx.AsyncClient(verify=False)
response = litellm.acompletion(
    model="gpt-3.5-turbo",
    messages=messages,
)

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