OpenAI
LiteLLM supports OpenAI Chat + Text completion and embedding calls.
API Keys​
import os
os.environ["OPENAI_API_KEY"] = "your-api-key"
Need a dedicated key? Email us @ krrish@berri.ai
See all supported models by the litellm api key
Usage​
import os
from litellm import completion
os.environ["OPENAI_API_KEY"] = "your-api-key"
messages = [{ "content": "Hello, how are you?","role": "user"}]
# openai call
response = completion("gpt-3.5-turbo", messages)
OpenAI Chat Completion Models​
| Model Name | Function Call | Required OS Variables |
|---|---|---|
| gpt-3.5-turbo | completion('gpt-3.5-turbo', messages) | os.environ['OPENAI_API_KEY'] |
| gpt-3.5-turbo-0301 | completion('gpt-3.5-turbo-0301', messages) | os.environ['OPENAI_API_KEY'] |
| gpt-3.5-turbo-0613 | completion('gpt-3.5-turbo-0613', messages) | os.environ['OPENAI_API_KEY'] |
| gpt-3.5-turbo-16k | completion('gpt-3.5-turbo-16k', messages) | os.environ['OPENAI_API_KEY'] |
| gpt-3.5-turbo-16k-0613 | completion('gpt-3.5-turbo-16k-0613', messages) | os.environ['OPENAI_API_KEY'] |
| gpt-4 | completion('gpt-4', messages) | os.environ['OPENAI_API_KEY'] |
| gpt-4-0314 | completion('gpt-4-0314', messages) | os.environ['OPENAI_API_KEY'] |
| gpt-4-0613 | completion('gpt-4-0613', messages) | os.environ['OPENAI_API_KEY'] |
| gpt-4-32k | completion('gpt-4-32k', messages) | os.environ['OPENAI_API_KEY'] |
| gpt-4-32k-0314 | completion('gpt-4-32k-0314', messages) | os.environ['OPENAI_API_KEY'] |
| gpt-4-32k-0613 | completion('gpt-4-32k-0613', messages) | os.environ['OPENAI_API_KEY'] |
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 Name | Function Call | Required OS Variables |
|---|---|---|
| gpt-3.5-turbo-instruct | completion('gpt-3.5-turbo-instruct', messages) | os.environ['OPENAI_API_KEY' |
| text-davinci-003 | completion('text-davinci-003', messages) | os.environ['OPENAI_API_KEY'] |
| ada-001 | completion('ada-001', messages) | os.environ['OPENAI_API_KEY'] |
| curie-001 | completion('curie-001', messages) | os.environ['OPENAI_API_KEY'] |
| babbage-001 | completion('babbage-001', messages) | os.environ['OPENAI_API_KEY'] |
| babbage-002 | completion('ada-001', messages) | os.environ['OPENAI_API_KEY'] |
| davinci-002 | completion('davinci-002', messages) | os.environ['OPENAI_API_KEY'] |
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