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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 NameFunction Call
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 Text Completion Models / Instruct Models​

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