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9b7ebb6a7d
* build(pyproject.toml): add new dev dependencies - for type checking * build: reformat files to fit black * ci: reformat to fit black * ci(test-litellm.yml): make tests run clear * build(pyproject.toml): add ruff * fix: fix ruff checks * build(mypy/): fix mypy linting errors * fix(hashicorp_secret_manager.py): fix passing cert for tls auth * build(mypy/): resolve all mypy errors * test: update test * fix: fix black formatting * build(pre-commit-config.yaml): use poetry run black * fix(proxy_server.py): fix linting error * fix: fix ruff safe representation error
955 lines
37 KiB
Python
955 lines
37 KiB
Python
#### What this does ####
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# On success, logs events to Langfuse
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import copy
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import os
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import traceback
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from datetime import datetime
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
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from packaging.version import Version
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import litellm
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from litellm._logging import verbose_logger
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from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info
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from litellm.llms.custom_httpx.http_handler import _get_httpx_client
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from litellm.secret_managers.main import str_to_bool
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from litellm.types.integrations.langfuse import *
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from litellm.types.llms.openai import HttpxBinaryResponseContent
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from litellm.types.utils import (
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EmbeddingResponse,
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ImageResponse,
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ModelResponse,
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RerankResponse,
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StandardLoggingPayload,
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StandardLoggingPromptManagementMetadata,
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TextCompletionResponse,
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TranscriptionResponse,
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)
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import DynamicLoggingCache
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else:
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DynamicLoggingCache = Any
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class LangFuseLogger:
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# Class variables or attributes
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def __init__(
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self,
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langfuse_public_key=None,
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langfuse_secret=None,
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langfuse_host=None,
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flush_interval=1,
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):
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try:
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import langfuse
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from langfuse import Langfuse
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except Exception as e:
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raise Exception(
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f"\033[91mLangfuse not installed, try running 'pip install langfuse' to fix this error: {e}\n{traceback.format_exc()}\033[0m"
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)
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# Instance variables
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self.secret_key = langfuse_secret or os.getenv("LANGFUSE_SECRET_KEY")
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self.public_key = langfuse_public_key or os.getenv("LANGFUSE_PUBLIC_KEY")
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self.langfuse_host = langfuse_host or os.getenv(
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"LANGFUSE_HOST", "https://cloud.langfuse.com"
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)
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if not (
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self.langfuse_host.startswith("http://")
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or self.langfuse_host.startswith("https://")
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):
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# add http:// if unset, assume communicating over private network - e.g. render
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self.langfuse_host = "http://" + self.langfuse_host
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self.langfuse_release = os.getenv("LANGFUSE_RELEASE")
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self.langfuse_debug = os.getenv("LANGFUSE_DEBUG")
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self.langfuse_flush_interval = LangFuseLogger._get_langfuse_flush_interval(
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flush_interval
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)
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http_client = _get_httpx_client()
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self.langfuse_client = http_client.client
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parameters = {
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"public_key": self.public_key,
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"secret_key": self.secret_key,
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"host": self.langfuse_host,
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"release": self.langfuse_release,
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"debug": self.langfuse_debug,
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"flush_interval": self.langfuse_flush_interval, # flush interval in seconds
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"httpx_client": self.langfuse_client,
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}
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self.langfuse_sdk_version: str = langfuse.version.__version__
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if Version(self.langfuse_sdk_version) >= Version("2.6.0"):
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parameters["sdk_integration"] = "litellm"
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self.Langfuse = Langfuse(**parameters)
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# set the current langfuse project id in the environ
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# this is used by Alerting to link to the correct project
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try:
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project_id = self.Langfuse.client.projects.get().data[0].id
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os.environ["LANGFUSE_PROJECT_ID"] = project_id
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except Exception:
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project_id = None
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if os.getenv("UPSTREAM_LANGFUSE_SECRET_KEY") is not None:
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upstream_langfuse_debug = (
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str_to_bool(self.upstream_langfuse_debug)
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if self.upstream_langfuse_debug is not None
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else None
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)
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self.upstream_langfuse_secret_key = os.getenv(
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"UPSTREAM_LANGFUSE_SECRET_KEY"
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)
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self.upstream_langfuse_public_key = os.getenv(
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"UPSTREAM_LANGFUSE_PUBLIC_KEY"
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)
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self.upstream_langfuse_host = os.getenv("UPSTREAM_LANGFUSE_HOST")
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self.upstream_langfuse_release = os.getenv("UPSTREAM_LANGFUSE_RELEASE")
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self.upstream_langfuse_debug = os.getenv("UPSTREAM_LANGFUSE_DEBUG")
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self.upstream_langfuse = Langfuse(
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public_key=self.upstream_langfuse_public_key,
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secret_key=self.upstream_langfuse_secret_key,
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host=self.upstream_langfuse_host,
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release=self.upstream_langfuse_release,
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debug=(
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upstream_langfuse_debug
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if upstream_langfuse_debug is not None
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else False
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),
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)
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else:
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self.upstream_langfuse = None
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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("langfuse_"):
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trace_param_key = metadata_param_key.replace("langfuse_", "", 1)
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if trace_param_key in metadata:
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verbose_logger.warning(
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f"Overwriting Langfuse `{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 Langfuse `{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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def log_event_on_langfuse(
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self,
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kwargs: dict,
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response_obj: Union[
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None,
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dict,
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EmbeddingResponse,
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ModelResponse,
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TextCompletionResponse,
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ImageResponse,
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TranscriptionResponse,
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RerankResponse,
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HttpxBinaryResponseContent,
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],
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start_time: Optional[datetime] = None,
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end_time: Optional[datetime] = None,
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user_id: Optional[str] = None,
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level: str = "DEFAULT",
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status_message: Optional[str] = None,
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) -> dict:
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"""
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Logs a success or error event on Langfuse
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"""
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try:
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verbose_logger.debug(
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f"Langfuse Logging - Enters logging function for model {kwargs}"
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)
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# set default values for input/output for langfuse logging
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input = None
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output = None
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litellm_params = kwargs.get("litellm_params", {})
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litellm_call_id = kwargs.get("litellm_call_id", None)
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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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optional_params = copy.deepcopy(kwargs.get("optional_params", {}))
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prompt = {"messages": kwargs.get("messages")}
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functions = optional_params.pop("functions", None)
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tools = optional_params.pop("tools", None)
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if functions is not None:
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prompt["functions"] = functions
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if tools is not None:
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prompt["tools"] = tools
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# langfuse only accepts str, int, bool, float for logging
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for param, value in optional_params.items():
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if not isinstance(value, (str, int, bool, float)):
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try:
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optional_params[param] = str(value)
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except Exception:
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# if casting value to str fails don't block logging
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pass
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input, output = self._get_langfuse_input_output_content(
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kwargs=kwargs,
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response_obj=response_obj,
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prompt=prompt,
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level=level,
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status_message=status_message,
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)
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verbose_logger.debug(
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f"OUTPUT IN LANGFUSE: {output}; original: {response_obj}"
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)
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trace_id = None
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generation_id = None
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if self._is_langfuse_v2():
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trace_id, generation_id = self._log_langfuse_v2(
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user_id=user_id,
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metadata=metadata,
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litellm_params=litellm_params,
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output=output,
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start_time=start_time,
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end_time=end_time,
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kwargs=kwargs,
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optional_params=optional_params,
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input=input,
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response_obj=response_obj,
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level=level,
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litellm_call_id=litellm_call_id,
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)
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elif response_obj is not None:
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self._log_langfuse_v1(
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user_id=user_id,
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metadata=metadata,
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output=output,
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start_time=start_time,
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end_time=end_time,
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kwargs=kwargs,
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optional_params=optional_params,
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input=input,
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response_obj=response_obj,
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)
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verbose_logger.debug(
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f"Langfuse Layer Logging - final response object: {response_obj}"
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)
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verbose_logger.info("Langfuse Layer Logging - logging success")
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return {"trace_id": trace_id, "generation_id": generation_id}
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except Exception as e:
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verbose_logger.exception(
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"Langfuse Layer Error(): Exception occured - {}".format(str(e))
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)
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return {"trace_id": None, "generation_id": None}
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def _get_langfuse_input_output_content(
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self,
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kwargs: dict,
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response_obj: Union[
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None,
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dict,
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EmbeddingResponse,
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ModelResponse,
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TextCompletionResponse,
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ImageResponse,
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TranscriptionResponse,
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RerankResponse,
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HttpxBinaryResponseContent,
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],
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prompt: dict,
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level: str,
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status_message: Optional[str],
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) -> Tuple[Optional[dict], Optional[Union[str, dict, list]]]:
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"""
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Get the input and output content for Langfuse logging
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Args:
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kwargs: The keyword arguments passed to the function
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response_obj: The response object returned by the function
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prompt: The prompt used to generate the response
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level: The level of the log message
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status_message: The status message of the log message
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Returns:
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input: The input content for Langfuse logging
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output: The output content for Langfuse logging
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"""
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input = None
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output: Optional[Union[str, dict, List[Any]]] = None
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if (
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level == "ERROR"
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and status_message is not None
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and isinstance(status_message, str)
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):
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input = prompt
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output = status_message
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elif 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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input = prompt
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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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input = prompt
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output = self._get_chat_content_for_langfuse(response_obj)
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elif response_obj is not None and isinstance(
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response_obj, litellm.HttpxBinaryResponseContent
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):
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input = prompt
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output = "speech-output"
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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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input = prompt
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output = self._get_text_completion_content_for_langfuse(response_obj)
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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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input = prompt
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output = response_obj.get("data", None)
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elif response_obj is not None and isinstance(
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response_obj, litellm.TranscriptionResponse
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):
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input = prompt
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output = response_obj.get("text", None)
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elif response_obj is not None and isinstance(
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response_obj, litellm.RerankResponse
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):
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input = prompt
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output = response_obj.results
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elif (
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kwargs.get("call_type") is not None
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and kwargs.get("call_type") == "_arealtime"
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and response_obj is not None
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and isinstance(response_obj, list)
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):
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input = kwargs.get("input")
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output = response_obj
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elif (
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kwargs.get("call_type") is not None
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and kwargs.get("call_type") == "pass_through_endpoint"
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and response_obj is not None
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and isinstance(response_obj, dict)
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):
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input = prompt
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output = response_obj.get("response", "")
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return input, output
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async def _async_log_event(
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self, kwargs, response_obj, start_time, end_time, user_id
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):
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"""
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Langfuse SDK uses a background thread to log events
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This approach does not impact latency and runs in the background
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"""
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def _is_langfuse_v2(self):
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import langfuse
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return Version(langfuse.version.__version__) >= Version("2.0.0")
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def _log_langfuse_v1(
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self,
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user_id,
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metadata,
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output,
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start_time,
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end_time,
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kwargs,
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optional_params,
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input,
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response_obj,
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):
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from langfuse.model import CreateGeneration, CreateTrace # type: ignore
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verbose_logger.warning(
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"Please upgrade langfuse to v2.0.0 or higher: https://github.com/langfuse/langfuse-python/releases/tag/v2.0.1"
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)
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trace = self.Langfuse.trace( # type: ignore
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CreateTrace( # type: ignore
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name=metadata.get("generation_name", "litellm-completion"),
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input=input,
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output=output,
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userId=user_id,
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)
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)
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trace.generation(
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CreateGeneration(
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name=metadata.get("generation_name", "litellm-completion"),
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startTime=start_time,
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endTime=end_time,
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model=kwargs["model"],
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modelParameters=optional_params,
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prompt=input,
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completion=output,
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usage={
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"prompt_tokens": response_obj.usage.prompt_tokens,
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"completion_tokens": response_obj.usage.completion_tokens,
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},
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metadata=metadata,
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)
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)
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def _log_langfuse_v2( # noqa: PLR0915
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self,
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user_id: Optional[str],
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metadata: dict,
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litellm_params: dict,
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output: Optional[Union[str, dict, list]],
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start_time: Optional[datetime],
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end_time: Optional[datetime],
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kwargs: dict,
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optional_params: dict,
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input: Optional[dict],
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response_obj,
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level: str,
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litellm_call_id: Optional[str],
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) -> tuple:
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verbose_logger.debug("Langfuse Layer Logging - logging to langfuse v2")
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try:
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metadata = metadata or {}
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standard_logging_object: Optional[StandardLoggingPayload] = cast(
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Optional[StandardLoggingPayload],
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kwargs.get("standard_logging_object", None),
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)
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tags = (
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self._get_langfuse_tags(standard_logging_object=standard_logging_object)
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if self._supports_tags()
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else []
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)
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if standard_logging_object is None:
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end_user_id = None
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prompt_management_metadata: Optional[
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StandardLoggingPromptManagementMetadata
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] = None
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else:
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end_user_id = standard_logging_object["metadata"].get(
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"user_api_key_end_user_id", None
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)
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prompt_management_metadata = cast(
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Optional[StandardLoggingPromptManagementMetadata],
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standard_logging_object["metadata"].get(
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"prompt_management_metadata", None
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),
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)
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# Clean Metadata before logging - never log raw metadata
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# the raw metadata can contain circular references which leads to infinite recursion
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# we clean out all extra litellm metadata params before logging
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clean_metadata: Dict[str, Any] = {}
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if prompt_management_metadata is not None:
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clean_metadata[
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"prompt_management_metadata"
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] = prompt_management_metadata
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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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# Add default langfuse tags
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tags = self.add_default_langfuse_tags(
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tags=tags, kwargs=kwargs, metadata=metadata
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)
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session_id = clean_metadata.pop("session_id", None)
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trace_name = cast(Optional[str], clean_metadata.pop("trace_name", None))
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trace_id = clean_metadata.pop("trace_id", litellm_call_id)
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existing_trace_id = clean_metadata.pop("existing_trace_id", None)
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update_trace_keys = cast(list, clean_metadata.pop("update_trace_keys", []))
|
|
debug = clean_metadata.pop("debug_langfuse", None)
|
|
mask_input = clean_metadata.pop("mask_input", False)
|
|
mask_output = clean_metadata.pop("mask_output", False)
|
|
|
|
clean_metadata = redact_user_api_key_info(metadata=clean_metadata)
|
|
|
|
if trace_name is None and existing_trace_id is None:
|
|
# just log `litellm-{call_type}` as the trace name
|
|
## DO NOT SET TRACE_NAME if trace-id set. this can lead to overwriting of past traces.
|
|
trace_name = f"litellm-{kwargs.get('call_type', 'completion')}"
|
|
|
|
if existing_trace_id is not None:
|
|
trace_params: Dict[str, Any] = {"id": existing_trace_id}
|
|
|
|
# Update the following keys for this trace
|
|
for metadata_param_key in update_trace_keys:
|
|
trace_param_key = metadata_param_key.replace("trace_", "")
|
|
if trace_param_key not in trace_params:
|
|
updated_trace_value = clean_metadata.pop(
|
|
metadata_param_key, None
|
|
)
|
|
if updated_trace_value is not None:
|
|
trace_params[trace_param_key] = updated_trace_value
|
|
|
|
# Pop the trace specific keys that would have been popped if there were a new trace
|
|
for key in list(
|
|
filter(lambda key: key.startswith("trace_"), clean_metadata.keys())
|
|
):
|
|
clean_metadata.pop(key, None)
|
|
|
|
# Special keys that are found in the function arguments and not the metadata
|
|
if "input" in update_trace_keys:
|
|
trace_params["input"] = (
|
|
input if not mask_input else "redacted-by-litellm"
|
|
)
|
|
if "output" in update_trace_keys:
|
|
trace_params["output"] = (
|
|
output if not mask_output else "redacted-by-litellm"
|
|
)
|
|
else: # don't overwrite an existing trace
|
|
trace_params = {
|
|
"id": trace_id,
|
|
"name": trace_name,
|
|
"session_id": session_id,
|
|
"input": input if not mask_input else "redacted-by-litellm",
|
|
"version": clean_metadata.pop(
|
|
"trace_version", clean_metadata.get("version", None)
|
|
), # If provided just version, it will applied to the trace as well, if applied a trace version it will take precedence
|
|
"user_id": end_user_id,
|
|
}
|
|
for key in list(
|
|
filter(lambda key: key.startswith("trace_"), clean_metadata.keys())
|
|
):
|
|
trace_params[key.replace("trace_", "")] = clean_metadata.pop(
|
|
key, None
|
|
)
|
|
|
|
if level == "ERROR":
|
|
trace_params["status_message"] = output
|
|
else:
|
|
trace_params["output"] = (
|
|
output if not mask_output else "redacted-by-litellm"
|
|
)
|
|
|
|
if debug is True or (isinstance(debug, str) and debug.lower() == "true"):
|
|
if "metadata" in trace_params:
|
|
# log the raw_metadata in the trace
|
|
trace_params["metadata"]["metadata_passed_to_litellm"] = metadata
|
|
else:
|
|
trace_params["metadata"] = {"metadata_passed_to_litellm": metadata}
|
|
|
|
cost = kwargs.get("response_cost", None)
|
|
verbose_logger.debug(f"trace: {cost}")
|
|
|
|
clean_metadata["litellm_response_cost"] = cost
|
|
if standard_logging_object is not None:
|
|
clean_metadata["hidden_params"] = standard_logging_object[
|
|
"hidden_params"
|
|
]
|
|
|
|
if (
|
|
litellm.langfuse_default_tags is not None
|
|
and isinstance(litellm.langfuse_default_tags, list)
|
|
and "proxy_base_url" in litellm.langfuse_default_tags
|
|
):
|
|
proxy_base_url = os.environ.get("PROXY_BASE_URL", None)
|
|
if proxy_base_url is not None:
|
|
tags.append(f"proxy_base_url:{proxy_base_url}")
|
|
|
|
api_base = litellm_params.get("api_base", None)
|
|
if api_base:
|
|
clean_metadata["api_base"] = api_base
|
|
|
|
vertex_location = kwargs.get("vertex_location", None)
|
|
if vertex_location:
|
|
clean_metadata["vertex_location"] = vertex_location
|
|
|
|
aws_region_name = kwargs.get("aws_region_name", None)
|
|
if aws_region_name:
|
|
clean_metadata["aws_region_name"] = aws_region_name
|
|
|
|
if self._supports_tags():
|
|
if "cache_hit" in kwargs:
|
|
if kwargs["cache_hit"] is None:
|
|
kwargs["cache_hit"] = False
|
|
clean_metadata["cache_hit"] = kwargs["cache_hit"]
|
|
if existing_trace_id is None:
|
|
trace_params.update({"tags": tags})
|
|
|
|
proxy_server_request = litellm_params.get("proxy_server_request", None)
|
|
if proxy_server_request:
|
|
proxy_server_request.get("method", None)
|
|
proxy_server_request.get("url", None)
|
|
headers = proxy_server_request.get("headers", None)
|
|
clean_headers = {}
|
|
if headers:
|
|
for key, value in headers.items():
|
|
# these headers can leak our API keys and/or JWT tokens
|
|
if key.lower() not in ["authorization", "cookie", "referer"]:
|
|
clean_headers[key] = value
|
|
|
|
# clean_metadata["request"] = {
|
|
# "method": method,
|
|
# "url": url,
|
|
# "headers": clean_headers,
|
|
# }
|
|
trace = self.Langfuse.trace(**trace_params)
|
|
|
|
# Log provider specific information as a span
|
|
log_provider_specific_information_as_span(trace, clean_metadata)
|
|
|
|
generation_id = None
|
|
usage = None
|
|
if response_obj is not None:
|
|
if (
|
|
hasattr(response_obj, "id")
|
|
and response_obj.get("id", None) is not None
|
|
):
|
|
generation_id = litellm.utils.get_logging_id(
|
|
start_time, response_obj
|
|
)
|
|
_usage_obj = getattr(response_obj, "usage", None)
|
|
|
|
if _usage_obj:
|
|
usage = {
|
|
"prompt_tokens": _usage_obj.prompt_tokens,
|
|
"completion_tokens": _usage_obj.completion_tokens,
|
|
"total_cost": cost if self._supports_costs() else None,
|
|
}
|
|
generation_name = clean_metadata.pop("generation_name", None)
|
|
if generation_name is None:
|
|
# if `generation_name` is None, use sensible default values
|
|
# If using litellm proxy user `key_alias` if not None
|
|
# If `key_alias` is None, just log `litellm-{call_type}` as the generation name
|
|
_user_api_key_alias = cast(
|
|
Optional[str], clean_metadata.get("user_api_key_alias", None)
|
|
)
|
|
generation_name = (
|
|
f"litellm-{cast(str, kwargs.get('call_type', 'completion'))}"
|
|
)
|
|
if _user_api_key_alias is not None:
|
|
generation_name = f"litellm:{_user_api_key_alias}"
|
|
|
|
if response_obj is not None:
|
|
system_fingerprint = getattr(response_obj, "system_fingerprint", None)
|
|
else:
|
|
system_fingerprint = None
|
|
|
|
if system_fingerprint is not None:
|
|
optional_params["system_fingerprint"] = system_fingerprint
|
|
|
|
generation_params = {
|
|
"name": generation_name,
|
|
"id": clean_metadata.pop("generation_id", generation_id),
|
|
"start_time": start_time,
|
|
"end_time": end_time,
|
|
"model": kwargs["model"],
|
|
"model_parameters": optional_params,
|
|
"input": input if not mask_input else "redacted-by-litellm",
|
|
"output": output if not mask_output else "redacted-by-litellm",
|
|
"usage": usage,
|
|
"metadata": log_requester_metadata(clean_metadata),
|
|
"level": level,
|
|
"version": clean_metadata.pop("version", None),
|
|
}
|
|
|
|
parent_observation_id = metadata.get("parent_observation_id", None)
|
|
if parent_observation_id is not None:
|
|
generation_params["parent_observation_id"] = parent_observation_id
|
|
|
|
if self._supports_prompt():
|
|
generation_params = _add_prompt_to_generation_params(
|
|
generation_params=generation_params,
|
|
clean_metadata=clean_metadata,
|
|
prompt_management_metadata=prompt_management_metadata,
|
|
langfuse_client=self.Langfuse,
|
|
)
|
|
if output is not None and isinstance(output, str) and level == "ERROR":
|
|
generation_params["status_message"] = output
|
|
|
|
if self._supports_completion_start_time():
|
|
generation_params["completion_start_time"] = kwargs.get(
|
|
"completion_start_time", None
|
|
)
|
|
|
|
generation_client = trace.generation(**generation_params)
|
|
|
|
return generation_client.trace_id, generation_id
|
|
except Exception:
|
|
verbose_logger.error(f"Langfuse Layer Error - {traceback.format_exc()}")
|
|
return None, None
|
|
|
|
@staticmethod
|
|
def _get_chat_content_for_langfuse(
|
|
response_obj: ModelResponse,
|
|
):
|
|
"""
|
|
Get the chat content for Langfuse logging
|
|
"""
|
|
if response_obj.choices and len(response_obj.choices) > 0:
|
|
output = response_obj["choices"][0]["message"].json()
|
|
return output
|
|
else:
|
|
return None
|
|
|
|
@staticmethod
|
|
def _get_text_completion_content_for_langfuse(
|
|
response_obj: TextCompletionResponse,
|
|
):
|
|
"""
|
|
Get the text completion content for Langfuse logging
|
|
"""
|
|
if response_obj.choices and len(response_obj.choices) > 0:
|
|
return response_obj.choices[0].text
|
|
else:
|
|
return None
|
|
|
|
@staticmethod
|
|
def _get_langfuse_tags(
|
|
standard_logging_object: Optional[StandardLoggingPayload],
|
|
) -> List[str]:
|
|
if standard_logging_object is None:
|
|
return []
|
|
return standard_logging_object.get("request_tags", []) or []
|
|
|
|
def add_default_langfuse_tags(self, tags, kwargs, metadata):
|
|
"""
|
|
Helper function to add litellm default langfuse tags
|
|
|
|
- Special LiteLLM tags:
|
|
- cache_hit
|
|
- cache_key
|
|
|
|
"""
|
|
if litellm.langfuse_default_tags is not None and isinstance(
|
|
litellm.langfuse_default_tags, list
|
|
):
|
|
if "cache_hit" in litellm.langfuse_default_tags:
|
|
_cache_hit_value = kwargs.get("cache_hit", False)
|
|
tags.append(f"cache_hit:{_cache_hit_value}")
|
|
if "cache_key" in litellm.langfuse_default_tags:
|
|
_hidden_params = metadata.get("hidden_params", {}) or {}
|
|
_cache_key = _hidden_params.get("cache_key", None)
|
|
if _cache_key is None and litellm.cache is not None:
|
|
# fallback to using "preset_cache_key"
|
|
_preset_cache_key = litellm.cache._get_preset_cache_key_from_kwargs(
|
|
**kwargs
|
|
)
|
|
_cache_key = _preset_cache_key
|
|
tags.append(f"cache_key:{_cache_key}")
|
|
return tags
|
|
|
|
def _supports_tags(self):
|
|
"""Check if current langfuse version supports tags"""
|
|
return Version(self.langfuse_sdk_version) >= Version("2.6.3")
|
|
|
|
def _supports_prompt(self):
|
|
"""Check if current langfuse version supports prompt"""
|
|
return Version(self.langfuse_sdk_version) >= Version("2.7.3")
|
|
|
|
def _supports_costs(self):
|
|
"""Check if current langfuse version supports costs"""
|
|
return Version(self.langfuse_sdk_version) >= Version("2.7.3")
|
|
|
|
def _supports_completion_start_time(self):
|
|
"""Check if current langfuse version supports completion start time"""
|
|
return Version(self.langfuse_sdk_version) >= Version("2.7.3")
|
|
|
|
@staticmethod
|
|
def _get_langfuse_flush_interval(flush_interval: int) -> int:
|
|
"""
|
|
Get the langfuse flush interval to initialize the Langfuse client
|
|
|
|
Reads `LANGFUSE_FLUSH_INTERVAL` from the environment variable.
|
|
If not set, uses the flush interval passed in as an argument.
|
|
|
|
Args:
|
|
flush_interval: The flush interval to use if LANGFUSE_FLUSH_INTERVAL is not set
|
|
|
|
Returns:
|
|
[int] The flush interval to use to initialize the Langfuse client
|
|
"""
|
|
return int(os.getenv("LANGFUSE_FLUSH_INTERVAL") or flush_interval)
|
|
|
|
|
|
def _add_prompt_to_generation_params(
|
|
generation_params: dict,
|
|
clean_metadata: dict,
|
|
prompt_management_metadata: Optional[StandardLoggingPromptManagementMetadata],
|
|
langfuse_client: Any,
|
|
) -> dict:
|
|
from langfuse import Langfuse
|
|
from langfuse.model import (
|
|
ChatPromptClient,
|
|
Prompt_Chat,
|
|
Prompt_Text,
|
|
TextPromptClient,
|
|
)
|
|
|
|
langfuse_client = cast(Langfuse, langfuse_client)
|
|
|
|
user_prompt = clean_metadata.pop("prompt", None)
|
|
if user_prompt is None and prompt_management_metadata is None:
|
|
pass
|
|
elif isinstance(user_prompt, dict):
|
|
if user_prompt.get("type", "") == "chat":
|
|
_prompt_chat = Prompt_Chat(**user_prompt)
|
|
generation_params["prompt"] = ChatPromptClient(prompt=_prompt_chat)
|
|
elif user_prompt.get("type", "") == "text":
|
|
_prompt_text = Prompt_Text(**user_prompt)
|
|
generation_params["prompt"] = TextPromptClient(prompt=_prompt_text)
|
|
elif "version" in user_prompt and "prompt" in user_prompt:
|
|
# prompts
|
|
if isinstance(user_prompt["prompt"], str):
|
|
prompt_text_params = getattr(
|
|
Prompt_Text, "model_fields", Prompt_Text.__fields__
|
|
)
|
|
_data = {
|
|
"name": user_prompt["name"],
|
|
"prompt": user_prompt["prompt"],
|
|
"version": user_prompt["version"],
|
|
"config": user_prompt.get("config", None),
|
|
}
|
|
if "labels" in prompt_text_params and "tags" in prompt_text_params:
|
|
_data["labels"] = user_prompt.get("labels", []) or []
|
|
_data["tags"] = user_prompt.get("tags", []) or []
|
|
_prompt_obj = Prompt_Text(**_data) # type: ignore
|
|
generation_params["prompt"] = TextPromptClient(prompt=_prompt_obj)
|
|
|
|
elif isinstance(user_prompt["prompt"], list):
|
|
prompt_chat_params = getattr(
|
|
Prompt_Chat, "model_fields", Prompt_Chat.__fields__
|
|
)
|
|
_data = {
|
|
"name": user_prompt["name"],
|
|
"prompt": user_prompt["prompt"],
|
|
"version": user_prompt["version"],
|
|
"config": user_prompt.get("config", None),
|
|
}
|
|
if "labels" in prompt_chat_params and "tags" in prompt_chat_params:
|
|
_data["labels"] = user_prompt.get("labels", []) or []
|
|
_data["tags"] = user_prompt.get("tags", []) or []
|
|
|
|
_prompt_obj = Prompt_Chat(**_data) # type: ignore
|
|
|
|
generation_params["prompt"] = ChatPromptClient(prompt=_prompt_obj)
|
|
else:
|
|
verbose_logger.error(
|
|
"[Non-blocking] Langfuse Logger: Invalid prompt format"
|
|
)
|
|
else:
|
|
verbose_logger.error(
|
|
"[Non-blocking] Langfuse Logger: Invalid prompt format. No prompt logged to Langfuse"
|
|
)
|
|
elif (
|
|
prompt_management_metadata is not None
|
|
and prompt_management_metadata["prompt_integration"] == "langfuse"
|
|
):
|
|
try:
|
|
generation_params["prompt"] = langfuse_client.get_prompt(
|
|
prompt_management_metadata["prompt_id"]
|
|
)
|
|
except Exception as e:
|
|
verbose_logger.debug(
|
|
f"[Non-blocking] Langfuse Logger: Error getting prompt client for logging: {e}"
|
|
)
|
|
pass
|
|
|
|
else:
|
|
generation_params["prompt"] = user_prompt
|
|
|
|
return generation_params
|
|
|
|
|
|
def log_provider_specific_information_as_span(
|
|
trace,
|
|
clean_metadata,
|
|
):
|
|
"""
|
|
Logs provider-specific information as spans.
|
|
|
|
Parameters:
|
|
trace: The tracing object used to log spans.
|
|
clean_metadata: A dictionary containing metadata to be logged.
|
|
|
|
Returns:
|
|
None
|
|
"""
|
|
|
|
_hidden_params = clean_metadata.get("hidden_params", None)
|
|
if _hidden_params is None:
|
|
return
|
|
|
|
vertex_ai_grounding_metadata = _hidden_params.get(
|
|
"vertex_ai_grounding_metadata", None
|
|
)
|
|
|
|
if vertex_ai_grounding_metadata is not None:
|
|
if isinstance(vertex_ai_grounding_metadata, list):
|
|
for elem in vertex_ai_grounding_metadata:
|
|
if isinstance(elem, dict):
|
|
for key, value in elem.items():
|
|
trace.span(
|
|
name=key,
|
|
input=value,
|
|
)
|
|
else:
|
|
trace.span(
|
|
name="vertex_ai_grounding_metadata",
|
|
input=elem,
|
|
)
|
|
else:
|
|
trace.span(
|
|
name="vertex_ai_grounding_metadata",
|
|
input=vertex_ai_grounding_metadata,
|
|
)
|
|
|
|
|
|
def log_requester_metadata(clean_metadata: dict):
|
|
returned_metadata = {}
|
|
requester_metadata = clean_metadata.get("requester_metadata") or {}
|
|
for k, v in clean_metadata.items():
|
|
if k not in requester_metadata:
|
|
returned_metadata[k] = v
|
|
|
|
returned_metadata.update({"requester_metadata": requester_metadata})
|
|
|
|
return returned_metadata
|