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LiteLLM - Getting Started

https://github.com/BerriAI/litellm

Call 100+ LLMs using the same Input/Output Format

Basic usage

Open In Colab
pip install litellm
from litellm import completion
import os

## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-api-key"

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

Streaming

Set stream=True in the completion args.

from litellm import completion
import os

## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-api-key"

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

for chunk in response:
print(chunk)

Exception handling

LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM.

from openai.error import OpenAIError
from litellm import completion

os.environ["ANTHROPIC_API_KEY"] = "bad-key"
try:
# some code
completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}])
except OpenAIError as e:
print(e)

Logging Observability - Log LLM Input/Output (Docs)

LiteLLM exposes pre defined callbacks to send data to Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack

from litellm import completion

## set env variables for logging tools
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"

os.environ["OPENAI_API_KEY"]

# set callbacks
litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase

#openai call
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])

Calculate Costs, Usage, Latency

Pass the completion response to litellm.completion_cost(completion_response=response) and get the cost

from litellm import completion, completion_cost
import os
os.environ["OPENAI_API_KEY"] = "your-api-key"

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

cost = completion_cost(completion_response=response)
print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}")

Output

Cost for completion call with gpt-3.5-turbo:  $0.0000775000

Track Costs, Usage, Latency for streaming

We use a custom callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback

  • We define a callback function to calculate cost def track_cost_callback()
  • In def track_cost_callback() we check if the stream is complete - if "complete_streaming_response" in kwargs
  • Use litellm.completion_cost() to calculate cost, once the stream is complete
import litellm

# track_cost_callback
def track_cost_callback(
kwargs, # kwargs to completion
completion_response, # response from completion
start_time, end_time # start/end time
):
try:
# check if it has collected an entire stream response
if "complete_streaming_response" in kwargs:
# for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost
completion_response=kwargs["complete_streaming_response"]
input_text = kwargs["messages"]
output_text = completion_response["choices"][0]["message"]["content"]
response_cost = litellm.completion_cost(
model = kwargs["model"],
messages = input_text,
completion=output_text
)
print("streaming response_cost", response_cost)
except:
pass
# set callback
litellm.success_callback = [track_cost_callback] # set custom callback function

# litellm.completion() call
response = completion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "Hi 👋 - i'm openai"
}
],
stream=True
)

More details