mirror of
https://github.com/BerriAI/litellm.git
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304 lines
10 KiB
Python
304 lines
10 KiB
Python
#### What this does ####
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# On success, logs events to Langsmith
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import asyncio
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import os
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import random
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import traceback
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import types
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from datetime import datetime
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from typing import Any, List, Optional, Union
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import dotenv # type: ignore
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import httpx
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import requests # type: ignore
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from pydantic import BaseModel # type: ignore
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import litellm
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from litellm._logging import verbose_logger
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.llms.custom_httpx.http_handler import (
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AsyncHTTPHandler,
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get_async_httpx_client,
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httpxSpecialProvider,
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)
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class LangsmithInputs(BaseModel):
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model: Optional[str] = None
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messages: Optional[List[Any]] = None
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stream: Optional[bool] = None
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call_type: Optional[str] = None
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litellm_call_id: Optional[str] = None
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completion_start_time: Optional[datetime] = None
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temperature: Optional[float] = None
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max_tokens: Optional[int] = None
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custom_llm_provider: Optional[str] = None
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input: Optional[List[Any]] = None
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log_event_type: Optional[str] = None
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original_response: Optional[Any] = None
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response_cost: Optional[float] = None
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# LiteLLM Virtual Key specific fields
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user_api_key: Optional[str] = None
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user_api_key_user_id: Optional[str] = None
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user_api_key_team_alias: Optional[str] = None
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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(CustomLogger):
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def __init__(self, batch_size=1):
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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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self.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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self.async_httpx_client = get_async_httpx_client(
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llm_provider=httpxSpecialProvider.LoggingCallback
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)
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self.batch_size = batch_size
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self.log_queue = []
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def _prepare_log_data(self, kwargs, response_obj, start_time, end_time):
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import datetime
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from datetime import datetime as dt
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from datetime import timezone
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metadata = kwargs.get("litellm_params", {}).get("metadata", {}) or {}
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new_metadata = {}
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for key, value in metadata.items():
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if (
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isinstance(value, list)
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or isinstance(value, str)
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or isinstance(value, int)
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or isinstance(value, float)
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):
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new_metadata[key] = value
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elif isinstance(value, BaseModel):
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new_metadata[key] = value.model_dump_json()
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elif isinstance(value, dict):
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for k, v in value.items():
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if isinstance(v, dt):
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value[k] = v.isoformat()
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new_metadata[key] = value
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metadata = new_metadata
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kwargs["user_api_key"] = metadata.get("user_api_key", None)
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kwargs["user_api_key_user_id"] = metadata.get("user_api_key_user_id", None)
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kwargs["user_api_key_team_alias"] = metadata.get(
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"user_api_key_team_alias", None
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)
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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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run_id = metadata.get("id", None) or str(random.randint(1000, 9999))
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parent_run_id = metadata.get("parent_run_id", None)
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trace_id = metadata.get("trace_id", None)
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session_id = metadata.get("session_id", None)
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dotted_order = metadata.get("dotted_order", None)
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tags = metadata.get("tags", []) or []
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verbose_logger.debug(
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f"Langsmith Logging - project_name: {project_name}, run_name {run_name}"
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)
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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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logged_kwargs = LangsmithInputs(**kwargs)
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kwargs = logged_kwargs.model_dump()
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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 key == "original_response" and not isinstance(value, str):
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new_kwargs[key] = str(value)
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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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elif not is_serializable(value=value):
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continue
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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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"tags": tags,
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"extra": metadata,
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}
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if run_id:
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data["id"] = run_id
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if parent_run_id:
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data["parent_run_id"] = parent_run_id
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if trace_id:
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data["trace_id"] = trace_id
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if session_id:
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data["session_id"] = session_id
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if dotted_order:
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data["dotted_order"] = dotted_order
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verbose_logger.debug("Langsmith Logging data on langsmith: %s", data)
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return data
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def _send_batch(self):
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if not self.log_queue:
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return
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url = f"{self.langsmith_base_url}/runs/batch"
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headers = {"x-api-key": self.langsmith_api_key}
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try:
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response = requests.post(
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url=url,
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json=self.log_queue,
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headers=headers,
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)
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if response.status_code >= 300:
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verbose_logger.error(
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f"Langsmith Error: {response.status_code} - {response.text}"
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)
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else:
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verbose_logger.debug(
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f"Batch of {len(self.log_queue)} runs successfully created"
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)
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self.log_queue.clear()
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except Exception as e:
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verbose_logger.error(f"Langsmith Layer Error - {traceback.format_exc()}")
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def log_success_event(self, kwargs, response_obj, start_time, end_time):
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try:
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sampling_rate = (
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float(os.getenv("LANGSMITH_SAMPLING_RATE"))
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if os.getenv("LANGSMITH_SAMPLING_RATE") is not None
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and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit()
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else 1.0
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)
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random_sample = random.random()
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if random_sample > sampling_rate:
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verbose_logger.info(
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"Skipping Langsmith logging. Sampling rate={}, random_sample={}".format(
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sampling_rate, random_sample
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)
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)
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return # Skip logging
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verbose_logger.debug(
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"Langsmith Sync Layer Logging - kwargs: %s, response_obj: %s",
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kwargs,
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response_obj,
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)
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data = self._prepare_log_data(kwargs, response_obj, start_time, end_time)
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self.log_queue.append(data)
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if len(self.log_queue) >= self.batch_size:
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self._send_batch()
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except:
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verbose_logger.error(f"Langsmith Layer Error - {traceback.format_exc()}")
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async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
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try:
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sampling_rate = (
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float(os.getenv("LANGSMITH_SAMPLING_RATE"))
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if os.getenv("LANGSMITH_SAMPLING_RATE") is not None
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and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit()
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else 1.0
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)
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random_sample = random.random()
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if random_sample > sampling_rate:
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verbose_logger.info(
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"Skipping Langsmith logging. Sampling rate={}, random_sample={}".format(
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sampling_rate, random_sample
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)
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)
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return # Skip logging
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verbose_logger.debug(
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"Langsmith Async Layer Logging - kwargs: %s, response_obj: %s",
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kwargs,
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response_obj,
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)
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data = self._prepare_log_data(kwargs, response_obj, start_time, end_time)
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self.log_queue.append(data)
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if len(self.log_queue) >= self.batch_size:
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await self._async_send_batch()
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except:
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verbose_logger.error(f"Langsmith Layer Error - {traceback.format_exc()}")
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async def _async_send_batch(self):
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import json
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if not self.log_queue:
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return
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url = f"{self.langsmith_base_url}/runs/batch"
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headers = {"x-api-key": self.langsmith_api_key}
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try:
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response = await self.async_httpx_client.post(
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url=url,
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json={
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"post": self.log_queue,
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},
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headers=headers,
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)
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if response.status_code >= 300:
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verbose_logger.error(
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f"Langsmith Error: {response.status_code} - {response.text}"
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)
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else:
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verbose_logger.debug(
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f"Batch of {len(self.log_queue)} runs successfully created"
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)
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self.log_queue.clear()
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except Exception as e:
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verbose_logger.error(f"Langsmith Layer Error - {traceback.format_exc()}")
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def get_run_by_id(self, run_id):
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url = f"{self.langsmith_base_url}/runs/{run_id}"
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response = requests.get(
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url=url,
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headers={"x-api-key": self.langsmith_api_key},
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)
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return response.json()
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