mirror of
https://github.com/BerriAI/litellm.git
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110 lines
4.4 KiB
Python
110 lines
4.4 KiB
Python
#### What this does ####
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# On success, logs events to Langsmith
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import dotenv, os # type: ignore
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import requests # type: ignore
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from datetime import datetime
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import traceback
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import asyncio
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import types
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from pydantic import BaseModel # type: ignore
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def is_serializable(value):
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non_serializable_types = (
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types.CoroutineType,
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types.FunctionType,
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types.GeneratorType,
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BaseModel,
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)
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return not isinstance(value, non_serializable_types)
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class LangsmithLogger:
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# Class variables or attributes
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def __init__(self):
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self.langsmith_api_key = os.getenv("LANGSMITH_API_KEY")
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self.langsmith_project = os.getenv("LANGSMITH_PROJECT", "litellm-completion")
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self.langsmith_default_run_name = os.getenv(
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"LANGSMITH_DEFAULT_RUN_NAME", "LLMRun"
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)
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def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
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# Method definition
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# inspired by Langsmith http api here: https://github.com/langchain-ai/langsmith-cookbook/blob/main/tracing-examples/rest/rest.ipynb
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metadata = (
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kwargs.get("litellm_params", {}).get("metadata", {}) or {}
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) # if metadata is None
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# set project name and run_name for langsmith logging
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# users can pass project_name and run name to litellm.completion()
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# Example: litellm.completion(model, messages, metadata={"project_name": "my-litellm-project", "run_name": "my-langsmith-run"})
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# if not set litellm will fallback to the environment variable LANGSMITH_PROJECT, then to the default project_name = litellm-completion, run_name = LLMRun
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project_name = metadata.get("project_name", self.langsmith_project)
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run_name = metadata.get("run_name", self.langsmith_default_run_name)
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print_verbose(
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f"Langsmith Logging - project_name: {project_name}, run_name {run_name}"
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)
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langsmith_base_url = os.getenv(
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"LANGSMITH_BASE_URL", "https://api.smith.langchain.com"
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)
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try:
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print_verbose(
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f"Langsmith Logging - Enters logging function for model {kwargs}"
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)
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import requests
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import datetime
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from datetime import timezone
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try:
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start_time = kwargs["start_time"].astimezone(timezone.utc).isoformat()
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end_time = kwargs["end_time"].astimezone(timezone.utc).isoformat()
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except:
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start_time = datetime.datetime.utcnow().isoformat()
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end_time = datetime.datetime.utcnow().isoformat()
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# filter out kwargs to not include any dicts, langsmith throws an erros when trying to log kwargs
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new_kwargs = {}
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for key in kwargs:
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value = kwargs[key]
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if key == "start_time" or key == "end_time" or value is None:
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pass
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elif type(value) == datetime.datetime:
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new_kwargs[key] = value.isoformat()
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elif type(value) != dict and is_serializable(value=value):
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new_kwargs[key] = value
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if isinstance(response_obj, BaseModel):
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try:
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response_obj = response_obj.model_dump()
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except:
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response_obj = response_obj.dict() # type: ignore
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data = {
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"name": run_name,
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"run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain"
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"inputs": new_kwargs,
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"outputs": response_obj,
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"session_name": project_name,
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"start_time": start_time,
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"end_time": end_time,
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}
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url = f"{langsmith_base_url}/runs"
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print_verbose(f"Langsmith Logging - About to send data to {url} ...")
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response = requests.post(
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url=url,
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json=data,
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headers={"x-api-key": self.langsmith_api_key},
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)
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if response.status_code >= 300:
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print_verbose(f"Error: {response.status_code}")
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else:
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print_verbose("Run successfully created")
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print_verbose(
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f"Langsmith Layer Logging - final response object: {response_obj}"
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)
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except:
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print_verbose(f"Langsmith Layer Error - {traceback.format_exc()}")
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pass
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