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litellm / litellm /integrations /langsmith.py

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	#### What this does ####
	# On success, logs events to Langsmith
	import dotenv, os
	import requests
	import requests
	from datetime import datetime

	dotenv.load_dotenv() # Loading env variables using dotenv
	import traceback


	class LangsmithLogger:
	# Class variables or attributes
	def __init__(self):
	self.langsmith_api_key = os.getenv("LANGSMITH_API_KEY")

	def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
	# Method definition
	# inspired by Langsmith http api here: https://github.com/langchain-ai/langsmith-cookbook/blob/main/tracing-examples/rest/rest.ipynb
	metadata = {}
	if "litellm_params" in kwargs:
	metadata = kwargs["litellm_params"].get("metadata", {})
	# set project name and run_name for langsmith logging
	# users can pass project_name and run name to litellm.completion()
	# Example: litellm.completion(model, messages, metadata={"project_name": "my-litellm-project", "run_name": "my-langsmith-run"})
	# if not set litellm will use default project_name = litellm-completion, run_name = LLMRun
	project_name = metadata.get("project_name", "litellm-completion")
	run_name = metadata.get("run_name", "LLMRun")
	print_verbose(
	f"Langsmith Logging - project_name: {project_name}, run_name {run_name}"
	)
	try:
	print_verbose(
	f"Langsmith Logging - Enters logging function for model {kwargs}"
	)
	import requests
	import datetime
	from datetime import timezone

	try:
	start_time = kwargs["start_time"].astimezone(timezone.utc).isoformat()
	end_time = kwargs["end_time"].astimezone(timezone.utc).isoformat()
	except:
	start_time = datetime.datetime.utcnow().isoformat()
	end_time = datetime.datetime.utcnow().isoformat()

	# filter out kwargs to not include any dicts, langsmith throws an erros when trying to log kwargs
	new_kwargs = {}
	for key in kwargs:
	value = kwargs[key]
	if key == "start_time" or key == "end_time":
	pass
	elif type(value) != dict:
	new_kwargs[key] = value

	requests.post(
	"https://api.smith.langchain.com/runs",
	json={
	"name": run_name,
	"run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain"
	"inputs": {**new_kwargs},
	"outputs": response_obj.json(),
	"session_name": project_name,
	"start_time": start_time,
	"end_time": end_time,
	},
	headers={"x-api-key": self.langsmith_api_key},
	)
	print_verbose(
	f"Langsmith Layer Logging - final response object: {response_obj}"
	)
	except:
	# traceback.print_exc()
	print_verbose(f"Langsmith Layer Error - {traceback.format_exc()}")
	pass