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+import os
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Field
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.llms.custom_httpx.http_handler import (
+ get_async_httpx_client,
+ httpxSpecialProvider,
+)
+
+
+# from here: https://docs.rungalileo.io/galileo/gen-ai-studio-products/galileo-observe/how-to/logging-data-via-restful-apis#structuring-your-records
+class LLMResponse(BaseModel):
+ latency_ms: int
+ status_code: int
+ input_text: str
+ output_text: str
+ node_type: str
+ model: str
+ num_input_tokens: int
+ num_output_tokens: int
+ output_logprobs: Optional[Dict[str, Any]] = Field(
+ default=None,
+ description="Optional. When available, logprobs are used to compute Uncertainty.",
+ )
+ created_at: str = Field(
+ ..., description='timestamp constructed in "%Y-%m-%dT%H:%M:%S" format'
+ )
+ tags: Optional[List[str]] = None
+ user_metadata: Optional[Dict[str, Any]] = None
+
+
+class GalileoObserve(CustomLogger):
+ def __init__(self) -> None:
+ self.in_memory_records: List[dict] = []
+ self.batch_size = 1
+ self.base_url = os.getenv("GALILEO_BASE_URL", None)
+ self.project_id = os.getenv("GALILEO_PROJECT_ID", None)
+ self.headers: Optional[Dict[str, str]] = None
+ self.async_httpx_handler = get_async_httpx_client(
+ llm_provider=httpxSpecialProvider.LoggingCallback
+ )
+ pass
+
+ def set_galileo_headers(self):
+ # following https://docs.rungalileo.io/galileo/gen-ai-studio-products/galileo-observe/how-to/logging-data-via-restful-apis#logging-your-records
+
+ headers = {
+ "accept": "application/json",
+ "Content-Type": "application/x-www-form-urlencoded",
+ }
+ galileo_login_response = litellm.module_level_client.post(
+ url=f"{self.base_url}/login",
+ headers=headers,
+ data={
+ "username": os.getenv("GALILEO_USERNAME"),
+ "password": os.getenv("GALILEO_PASSWORD"),
+ },
+ )
+
+ access_token = galileo_login_response.json()["access_token"]
+
+ self.headers = {
+ "accept": "application/json",
+ "Content-Type": "application/json",
+ "Authorization": f"Bearer {access_token}",
+ }
+
+ def get_output_str_from_response(self, response_obj, kwargs):
+ output = None
+ if response_obj is not None and (
+ kwargs.get("call_type", None) == "embedding"
+ or isinstance(response_obj, litellm.EmbeddingResponse)
+ ):
+ output = None
+ elif response_obj is not None and isinstance(
+ response_obj, litellm.ModelResponse
+ ):
+ output = response_obj["choices"][0]["message"].json()
+ elif response_obj is not None and isinstance(
+ response_obj, litellm.TextCompletionResponse
+ ):
+ output = response_obj.choices[0].text
+ elif response_obj is not None and isinstance(
+ response_obj, litellm.ImageResponse
+ ):
+ output = response_obj["data"]
+
+ return output
+
+ async def async_log_success_event(
+ self, kwargs: Any, response_obj: Any, start_time: Any, end_time: Any
+ ):
+ verbose_logger.debug("On Async Success")
+
+ _latency_ms = int((end_time - start_time).total_seconds() * 1000)
+ _call_type = kwargs.get("call_type", "litellm")
+ input_text = litellm.utils.get_formatted_prompt(
+ data=kwargs, call_type=_call_type
+ )
+
+ _usage = response_obj.get("usage", {}) or {}
+ num_input_tokens = _usage.get("prompt_tokens", 0)
+ num_output_tokens = _usage.get("completion_tokens", 0)
+
+ output_text = self.get_output_str_from_response(
+ response_obj=response_obj, kwargs=kwargs
+ )
+
+ if output_text is not None:
+ request_record = LLMResponse(
+ latency_ms=_latency_ms,
+ status_code=200,
+ input_text=input_text,
+ output_text=output_text,
+ node_type=_call_type,
+ model=kwargs.get("model", "-"),
+ num_input_tokens=num_input_tokens,
+ num_output_tokens=num_output_tokens,
+ created_at=start_time.strftime(
+ "%Y-%m-%dT%H:%M:%S"
+ ), # timestamp str constructed in "%Y-%m-%dT%H:%M:%S" format
+ )
+
+ # dump to dict
+ request_dict = request_record.model_dump()
+ self.in_memory_records.append(request_dict)
+
+ if len(self.in_memory_records) >= self.batch_size:
+ await self.flush_in_memory_records()
+
+ async def flush_in_memory_records(self):
+ verbose_logger.debug("flushing in memory records")
+ response = await self.async_httpx_handler.post(
+ url=f"{self.base_url}/projects/{self.project_id}/observe/ingest",
+ headers=self.headers,
+ json={"records": self.in_memory_records},
+ )
+
+ if response.status_code == 200:
+ verbose_logger.debug(
+ "Galileo Logger:successfully flushed in memory records"
+ )
+ self.in_memory_records = []
+ else:
+ verbose_logger.debug("Galileo Logger: failed to flush in memory records")
+ verbose_logger.debug(
+ "Galileo Logger error=%s, status code=%s",
+ response.text,
+ response.status_code,
+ )
+
+ async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
+ verbose_logger.debug("On Async Failure")