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Merge pull request #3359 from BerriAI/litellm_docs_trackin_cost

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docs - update track cost with custom callbacks
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Ishaan Jaff 2024-04-29 13:19:25 -07:00 • committed by GitHub
commit 96608b1f93
1 changed files with 6 additions and 30 deletions
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36
docs/my-website/docs/observability/custom_callback.md
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@ -331,49 +331,25 @@ response = litellm.completion(model="gpt-3.5-turbo", messages=messages, metadata
## Examples
### Custom Callback to track costs for Streaming + Non-Streaming
By default, the response cost is accessible in the logging object via `kwargs["response_cost"]` on success (sync + async)
```python
# Step 1. Write your custom callback function
def track_cost_callback(
kwargs, # kwargs to completion
completion_response, # response from completion
start_time, end_time # start/end time
):
try:
# init logging config
logging.basicConfig(
filename='cost.log',
level=logging.INFO,
format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S'
)
# 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)
logging.info(f"Model {kwargs['model']} Cost: ${response_cost:.8f}")
# for non streaming responses
else:
# we pass the completion_response obj
if kwargs["stream"] != True:
response_cost = litellm.completion_cost(completion_response=completion_response)
print("regular response_cost", response_cost)
logging.info(f"Model {completion_response.model} Cost: ${response_cost:.8f}")
response_cost = kwargs["response_cost"] # litellm calculates response cost for you
print("regular response_cost", response_cost)
except:
pass
# Assign the custom callback function
# Step 2. Assign the custom callback function
litellm.success_callback = [track_cost_callback]
# Step 3. Make litellm.completion call
response = completion(
model="gpt-3.5-turbo",
messages=[