mirror of
https://github.com/BerriAI/litellm.git
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d57be47b0f
* ci(config.yml): add a 'check_code_quality' step Addresses https://github.com/BerriAI/litellm/issues/5991 * ci(config.yml): check why circle ci doesn't pick up this test * ci(config.yml): fix to run 'check_code_quality' tests * fix(__init__.py): fix unprotected import * fix(__init__.py): don't remove unused imports * build(ruff.toml): update ruff.toml to ignore unused imports * fix: fix: ruff + pyright - fix linting + type-checking errors * fix: fix linting errors * fix(lago.py): fix module init error * fix: fix linting errors * ci(config.yml): cd into correct dir for checks * fix(proxy_server.py): fix linting error * fix(utils.py): fix bare except causes ruff linting errors * fix: ruff - fix remaining linting errors * fix(clickhouse.py): use standard logging object * fix(__init__.py): fix unprotected import * fix: ruff - fix linting errors * fix: fix linting errors * ci(config.yml): cleanup code qa step (formatting handled in local_testing) * fix(_health_endpoints.py): fix ruff linting errors * ci(config.yml): just use ruff in check_code_quality pipeline for now * build(custom_guardrail.py): include missing file * style(embedding_handler.py): fix ruff check
356 lines
13 KiB
Python
356 lines
13 KiB
Python
# What is this?
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## Log success + failure events to Braintrust
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import copy
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import json
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import os
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import threading
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import traceback
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import uuid
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from datetime import datetime
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from typing import Literal, Optional
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import dotenv
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import httpx
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from pydantic import BaseModel
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import litellm
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from litellm 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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HTTPHandler,
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get_async_httpx_client,
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httpxSpecialProvider,
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)
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from litellm.utils import get_formatted_prompt
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global_braintrust_http_handler = get_async_httpx_client(
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llm_provider=httpxSpecialProvider.LoggingCallback
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)
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global_braintrust_sync_http_handler = HTTPHandler()
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API_BASE = "https://api.braintrustdata.com/v1"
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def get_utc_datetime():
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import datetime as dt
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from datetime import datetime
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if hasattr(dt, "UTC"):
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return datetime.now(dt.UTC) # type: ignore
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else:
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return datetime.utcnow() # type: ignore
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class BraintrustLogger(CustomLogger):
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def __init__(
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self, api_key: Optional[str] = None, api_base: Optional[str] = None
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) -> None:
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super().__init__()
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self.validate_environment(api_key=api_key)
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self.api_base = api_base or API_BASE
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self.default_project_id = None
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self.api_key: str = api_key or os.getenv("BRAINTRUST_API_KEY") # type: ignore
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self.headers = {
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"Authorization": "Bearer " + self.api_key,
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"Content-Type": "application/json",
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}
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def validate_environment(self, api_key: Optional[str]):
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"""
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Expects
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BRAINTRUST_API_KEY
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in the environment
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"""
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missing_keys = []
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if api_key is None and os.getenv("BRAINTRUST_API_KEY", None) is None:
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missing_keys.append("BRAINTRUST_API_KEY")
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if len(missing_keys) > 0:
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raise Exception("Missing keys={} in environment.".format(missing_keys))
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@staticmethod
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def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict:
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"""
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Adds metadata from proxy request headers to Langfuse logging if keys start with "langfuse_"
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and overwrites litellm_params.metadata if already included.
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For example if you want to append your trace to an existing `trace_id` via header, send
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`headers: { ..., langfuse_existing_trace_id: your-existing-trace-id }` via proxy request.
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"""
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if litellm_params is None:
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return metadata
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if litellm_params.get("proxy_server_request") is None:
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return metadata
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if metadata is None:
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metadata = {}
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proxy_headers = (
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litellm_params.get("proxy_server_request", {}).get("headers", {}) or {}
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)
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for metadata_param_key in proxy_headers:
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if metadata_param_key.startswith("braintrust"):
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trace_param_key = metadata_param_key.replace("braintrust", "", 1)
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if trace_param_key in metadata:
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verbose_logger.warning(
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f"Overwriting Braintrust `{trace_param_key}` from request header"
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)
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else:
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verbose_logger.debug(
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f"Found Braintrust `{trace_param_key}` in request header"
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)
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metadata[trace_param_key] = proxy_headers.get(metadata_param_key)
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return metadata
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async def create_default_project_and_experiment(self):
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project = await global_braintrust_http_handler.post(
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f"{self.api_base}/project", headers=self.headers, json={"name": "litellm"}
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)
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project_dict = project.json()
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self.default_project_id = project_dict["id"]
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def create_sync_default_project_and_experiment(self):
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project = global_braintrust_sync_http_handler.post(
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f"{self.api_base}/project", headers=self.headers, json={"name": "litellm"}
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)
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project_dict = project.json()
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self.default_project_id = project_dict["id"]
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def log_success_event(self, kwargs, response_obj, start_time, end_time):
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verbose_logger.debug("REACHES BRAINTRUST SUCCESS")
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try:
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litellm_call_id = kwargs.get("litellm_call_id")
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project_id = kwargs.get("project_id", None)
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if project_id is None:
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if self.default_project_id is None:
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self.create_sync_default_project_and_experiment()
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project_id = self.default_project_id
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prompt = {"messages": kwargs.get("messages")}
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output = None
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if response_obj is not None and (
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kwargs.get("call_type", None) == "embedding"
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or isinstance(response_obj, litellm.EmbeddingResponse)
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):
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output = None
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elif response_obj is not None and isinstance(
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response_obj, litellm.ModelResponse
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):
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output = response_obj["choices"][0]["message"].json()
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elif response_obj is not None and isinstance(
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response_obj, litellm.TextCompletionResponse
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):
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output = response_obj.choices[0].text
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elif response_obj is not None and isinstance(
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response_obj, litellm.ImageResponse
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):
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output = response_obj["data"]
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litellm_params = kwargs.get("litellm_params", {})
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metadata = (
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litellm_params.get("metadata", {}) or {}
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) # if litellm_params['metadata'] == None
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metadata = self.add_metadata_from_header(litellm_params, metadata)
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clean_metadata = {}
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try:
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metadata = copy.deepcopy(
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metadata
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) # Avoid modifying the original metadata
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except Exception:
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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, dict)
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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] = copy.deepcopy(value)
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metadata = new_metadata
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tags = []
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if isinstance(metadata, dict):
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for key, value in metadata.items():
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# generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy
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if (
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litellm.langfuse_default_tags is not None
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and isinstance(litellm.langfuse_default_tags, list)
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and key in litellm.langfuse_default_tags
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):
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tags.append(f"{key}:{value}")
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# clean litellm metadata before logging
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if key in [
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"headers",
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"endpoint",
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"caching_groups",
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"previous_models",
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]:
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continue
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else:
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clean_metadata[key] = value
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cost = kwargs.get("response_cost", None)
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if cost is not None:
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clean_metadata["litellm_response_cost"] = cost
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metrics: Optional[dict] = None
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usage_obj = getattr(response_obj, "usage", None)
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if usage_obj and isinstance(usage_obj, litellm.Usage):
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litellm.utils.get_logging_id(start_time, response_obj)
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metrics = {
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"prompt_tokens": usage_obj.prompt_tokens,
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"completion_tokens": usage_obj.completion_tokens,
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"total_tokens": usage_obj.total_tokens,
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"total_cost": cost,
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}
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request_data = {
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"id": litellm_call_id,
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"input": prompt,
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"output": output,
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"metadata": clean_metadata,
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"tags": tags,
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}
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if metrics is not None:
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request_data["metrics"] = metrics
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try:
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global_braintrust_sync_http_handler.post(
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url=f"{self.api_base}/project_logs/{project_id}/insert",
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json={"events": [request_data]},
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headers=self.headers,
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)
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except httpx.HTTPStatusError as e:
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raise Exception(e.response.text)
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except Exception as e:
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raise e # don't use verbose_logger.exception, if exception is raised
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async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
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verbose_logger.debug("REACHES BRAINTRUST SUCCESS")
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try:
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litellm_call_id = kwargs.get("litellm_call_id")
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project_id = kwargs.get("project_id", None)
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if project_id is None:
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if self.default_project_id is None:
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await self.create_default_project_and_experiment()
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project_id = self.default_project_id
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prompt = {"messages": kwargs.get("messages")}
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output = None
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if response_obj is not None and (
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kwargs.get("call_type", None) == "embedding"
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or isinstance(response_obj, litellm.EmbeddingResponse)
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):
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output = None
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elif response_obj is not None and isinstance(
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response_obj, litellm.ModelResponse
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):
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output = response_obj["choices"][0]["message"].json()
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elif response_obj is not None and isinstance(
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response_obj, litellm.TextCompletionResponse
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):
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output = response_obj.choices[0].text
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elif response_obj is not None and isinstance(
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response_obj, litellm.ImageResponse
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):
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output = response_obj["data"]
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litellm_params = kwargs.get("litellm_params", {})
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metadata = (
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litellm_params.get("metadata", {}) or {}
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) # if litellm_params['metadata'] == None
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metadata = self.add_metadata_from_header(litellm_params, metadata)
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clean_metadata = {}
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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, datetime):
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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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tags = []
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if isinstance(metadata, dict):
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for key, value in metadata.items():
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# generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy
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if (
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litellm.langfuse_default_tags is not None
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and isinstance(litellm.langfuse_default_tags, list)
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and key in litellm.langfuse_default_tags
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):
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tags.append(f"{key}:{value}")
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# clean litellm metadata before logging
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if key in [
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"headers",
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"endpoint",
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"caching_groups",
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"previous_models",
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]:
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continue
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else:
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clean_metadata[key] = value
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cost = kwargs.get("response_cost", None)
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if cost is not None:
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clean_metadata["litellm_response_cost"] = cost
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metrics: Optional[dict] = None
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usage_obj = getattr(response_obj, "usage", None)
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if usage_obj and isinstance(usage_obj, litellm.Usage):
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litellm.utils.get_logging_id(start_time, response_obj)
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metrics = {
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"prompt_tokens": usage_obj.prompt_tokens,
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"completion_tokens": usage_obj.completion_tokens,
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"total_tokens": usage_obj.total_tokens,
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"total_cost": cost,
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}
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request_data = {
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"id": litellm_call_id,
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"input": prompt,
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"output": output,
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"metadata": clean_metadata,
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"tags": tags,
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}
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if metrics is not None:
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request_data["metrics"] = metrics
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try:
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await global_braintrust_http_handler.post(
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url=f"{self.api_base}/project_logs/{project_id}/insert",
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json={"events": [request_data]},
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headers=self.headers,
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)
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except httpx.HTTPStatusError as e:
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raise Exception(e.response.text)
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except Exception as e:
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raise e # don't use verbose_logger.exception, if exception is raised
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def log_failure_event(self, kwargs, response_obj, start_time, end_time):
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return super().log_failure_event(kwargs, response_obj, start_time, end_time)
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