fix: flatten React Router structure — no nested Routes, each page is a top-level route
This commit is contained in:
enzo
@@ -0,0 +1,376 @@
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import copy
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from typing import TYPE_CHECKING
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import sentry_sdk
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from sentry_sdk import consts
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from sentry_sdk.ai.monitoring import record_token_usage
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from sentry_sdk.ai.utils import (
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get_start_span_function,
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set_data_normalized,
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transform_openai_content_part,
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truncate_and_annotate_embedding_inputs,
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truncate_and_annotate_messages,
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)
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from sentry_sdk.consts import SPANDATA
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from sentry_sdk.integrations import DidNotEnable, Integration
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from sentry_sdk.scope import should_send_default_pii
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from sentry_sdk.tracing_utils import (
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has_span_streaming_enabled,
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should_truncate_gen_ai_input,
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)
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from sentry_sdk.utils import event_from_exception
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if TYPE_CHECKING:
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from datetime import datetime
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from typing import Any, Dict, List
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try:
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import litellm # type: ignore[import-not-found]
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from litellm import failure_callback, input_callback, success_callback
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except ImportError:
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raise DidNotEnable("LiteLLM not installed")
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# Stash the span on a top-level key of the per-request kwargs dict litellm passes
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# to every callback, so it lives and dies with the request.
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_SPAN_KEY = "_sentry_span"
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def _store_span(kwargs: "Dict[str, Any]", span: "Any") -> None:
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kwargs[_SPAN_KEY] = span
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def _peek_span(kwargs: "Dict[str, Any]") -> "Any":
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return kwargs.get(_SPAN_KEY)
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def _pop_span(kwargs: "Dict[str, Any]") -> "Any":
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return kwargs.pop(_SPAN_KEY, None)
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def _convert_message_parts(messages: "List[Dict[str, Any]]") -> "List[Dict[str, Any]]":
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"""
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Convert the message parts from OpenAI format to the `gen_ai.request.messages` format
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using the OpenAI-specific transformer (LiteLLM uses OpenAI's message format).
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Deep copies messages to avoid mutating original kwargs.
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"""
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# Deep copy to avoid mutating original messages from kwargs
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messages = copy.deepcopy(messages)
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for message in messages:
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if not isinstance(message, dict):
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continue
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content = message.get("content")
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if isinstance(content, (list, tuple)):
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transformed = []
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for item in content:
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if isinstance(item, dict):
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result = transform_openai_content_part(item)
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# If transformation succeeded, use the result; otherwise keep original
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transformed.append(result if result is not None else item)
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else:
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transformed.append(item)
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message["content"] = transformed
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return messages
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def _input_callback(kwargs: "Dict[str, Any]") -> None:
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"""Handle the start of a request."""
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client = sentry_sdk.get_client()
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integration = client.get_integration(LiteLLMIntegration)
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if integration is None:
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return
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# Get key parameters
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full_model = kwargs.get("model", "")
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try:
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model, provider, _, _ = litellm.get_llm_provider(full_model)
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except Exception:
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model = full_model
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provider = "unknown"
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call_type = kwargs.get("call_type", None)
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if call_type == "embedding" or call_type == "aembedding":
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operation = "embeddings"
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else:
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operation = "chat"
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# Start a new span/transaction
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if has_span_streaming_enabled(client.options):
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span = sentry_sdk.traces.start_span(
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name=f"{operation} {model}",
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attributes={
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"sentry.op": (
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consts.OP.GEN_AI_CHAT
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if operation == "chat"
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else consts.OP.GEN_AI_EMBEDDINGS
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),
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"sentry.origin": LiteLLMIntegration.origin,
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},
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)
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else:
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span = get_start_span_function()(
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op=(
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consts.OP.GEN_AI_CHAT
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if operation == "chat"
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else consts.OP.GEN_AI_EMBEDDINGS
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),
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name=f"{operation} {model}",
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origin=LiteLLMIntegration.origin,
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)
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span.__enter__()
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_store_span(kwargs, span)
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# Set basic data
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set_data_normalized(span, SPANDATA.GEN_AI_SYSTEM, provider)
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set_data_normalized(span, SPANDATA.GEN_AI_OPERATION_NAME, operation)
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# Record input/messages if allowed
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if should_send_default_pii() and integration.include_prompts:
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if operation == "embeddings":
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# For embeddings, look for the 'input' parameter
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embedding_input = kwargs.get("input")
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if embedding_input:
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scope = sentry_sdk.get_current_scope()
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# Normalize to list format
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input_list = (
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embedding_input
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if isinstance(embedding_input, list)
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else [embedding_input]
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)
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client = sentry_sdk.get_client()
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messages_data = (
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truncate_and_annotate_embedding_inputs(input_list, span, scope)
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if should_truncate_gen_ai_input(client.options)
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else input_list
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)
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if messages_data is not None:
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set_data_normalized(
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span,
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SPANDATA.GEN_AI_EMBEDDINGS_INPUT,
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messages_data,
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unpack=False,
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)
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else:
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# For chat, look for the 'messages' parameter
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messages = kwargs.get("messages", [])
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if messages:
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client = sentry_sdk.get_client()
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scope = sentry_sdk.get_current_scope()
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messages = _convert_message_parts(messages)
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messages_data = (
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truncate_and_annotate_messages(messages, span, scope)
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if should_truncate_gen_ai_input(client.options)
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else messages
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)
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if messages_data is not None:
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set_data_normalized(
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span,
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SPANDATA.GEN_AI_REQUEST_MESSAGES,
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messages_data,
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unpack=False,
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)
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# Record other parameters
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params = {
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"model": SPANDATA.GEN_AI_REQUEST_MODEL,
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"stream": SPANDATA.GEN_AI_RESPONSE_STREAMING,
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"max_tokens": SPANDATA.GEN_AI_REQUEST_MAX_TOKENS,
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"presence_penalty": SPANDATA.GEN_AI_REQUEST_PRESENCE_PENALTY,
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"frequency_penalty": SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY,
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"temperature": SPANDATA.GEN_AI_REQUEST_TEMPERATURE,
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"top_p": SPANDATA.GEN_AI_REQUEST_TOP_P,
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}
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for key, attribute in params.items():
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value = kwargs.get(key)
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if value is not None:
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set_data_normalized(span, attribute, value)
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async def _async_input_callback(kwargs: "Dict[str, Any]") -> None:
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return _input_callback(kwargs)
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def _success_callback(
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kwargs: "Dict[str, Any]",
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completion_response: "Any",
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start_time: "datetime",
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end_time: "datetime",
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) -> None:
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"""Handle successful completion."""
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span = _peek_span(kwargs)
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if span is None:
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return
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integration = sentry_sdk.get_client().get_integration(LiteLLMIntegration)
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if integration is None:
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return
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try:
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# Record model information
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if hasattr(completion_response, "model"):
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set_data_normalized(
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span, SPANDATA.GEN_AI_RESPONSE_MODEL, completion_response.model
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)
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# Record response content if allowed
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if should_send_default_pii() and integration.include_prompts:
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if hasattr(completion_response, "choices"):
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response_messages = []
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for choice in completion_response.choices:
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if hasattr(choice, "message"):
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if hasattr(choice.message, "model_dump"):
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response_messages.append(choice.message.model_dump())
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elif hasattr(choice.message, "dict"):
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response_messages.append(choice.message.dict())
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else:
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# Fallback for basic message objects
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msg = {}
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if hasattr(choice.message, "role"):
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msg["role"] = choice.message.role
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if hasattr(choice.message, "content"):
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msg["content"] = choice.message.content
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if hasattr(choice.message, "tool_calls"):
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msg["tool_calls"] = choice.message.tool_calls
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response_messages.append(msg)
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if response_messages:
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set_data_normalized(
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span, SPANDATA.GEN_AI_RESPONSE_TEXT, response_messages
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)
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# Record token usage
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if hasattr(completion_response, "usage"):
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usage = completion_response.usage
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record_token_usage(
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span,
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input_tokens=getattr(usage, "prompt_tokens", None),
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output_tokens=getattr(usage, "completion_tokens", None),
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total_tokens=getattr(usage, "total_tokens", None),
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)
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finally:
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is_streaming = kwargs.get("stream")
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# Callback is fired multiple times when streaming a response.
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# Streaming flag checked at https://github.com/BerriAI/litellm/blob/33c3f13443eaf990ac8c6e3da78bddbc2b7d0e7a/litellm/litellm_core_utils/litellm_logging.py#L1603
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if (
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is_streaming is not True
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or "complete_streaming_response" in kwargs
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or "async_complete_streaming_response" in kwargs
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):
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span = _pop_span(kwargs)
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if span is not None:
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span.__exit__(None, None, None)
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async def _async_success_callback(
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kwargs: "Dict[str, Any]",
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completion_response: "Any",
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start_time: "datetime",
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end_time: "datetime",
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) -> None:
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return _success_callback(
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kwargs,
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completion_response,
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start_time,
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end_time,
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)
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def _failure_callback(
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kwargs: "Dict[str, Any]",
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exception: Exception,
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start_time: "datetime",
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end_time: "datetime",
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) -> None:
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"""Handle request failure."""
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span = _pop_span(kwargs)
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if span is None:
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return
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try:
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# Capture the exception
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event, hint = event_from_exception(
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exception,
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client_options=sentry_sdk.get_client().options,
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mechanism={"type": "litellm", "handled": False},
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)
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sentry_sdk.capture_event(event, hint=hint)
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finally:
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# Always finish the span and clean up
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span.__exit__(type(exception), exception, None)
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class LiteLLMIntegration(Integration):
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"""
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LiteLLM integration for Sentry.
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This integration automatically captures LiteLLM API calls and sends them to Sentry
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for monitoring and error tracking. It supports all 100+ LLM providers that LiteLLM
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supports, including OpenAI, Anthropic, Google, Cohere, and many others.
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Features:
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- Automatic exception capture for all LiteLLM calls
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- Token usage tracking across all providers
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- Provider detection and attribution
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- Input/output message capture (configurable)
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- Streaming response support
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- Cost tracking integration
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Usage:
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```python
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import litellm
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import sentry_sdk
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# Initialize Sentry with the LiteLLM integration
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sentry_sdk.init(
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dsn="your-dsn",
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send_default_pii=True
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integrations=[
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sentry_sdk.integrations.LiteLLMIntegration(
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include_prompts=True # Set to False to exclude message content
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)
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]
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)
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# All LiteLLM calls will now be monitored
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response = litellm.completion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hello!"}]
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)
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```
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Configuration:
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- include_prompts (bool): Whether to include prompts and responses in spans.
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Defaults to True. Set to False to exclude potentially sensitive data.
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"""
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identifier = "litellm"
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origin = f"auto.ai.{identifier}"
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def __init__(self: "LiteLLMIntegration", include_prompts: bool = True) -> None:
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self.include_prompts = include_prompts
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@staticmethod
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def setup_once() -> None:
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"""Set up LiteLLM callbacks for monitoring."""
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litellm.input_callback = input_callback or []
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if _input_callback not in litellm.input_callback:
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litellm.input_callback.append(_input_callback)
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if _async_input_callback not in litellm.input_callback:
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litellm.input_callback.append(_async_input_callback)
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litellm.success_callback = success_callback or []
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if _success_callback not in litellm.success_callback:
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litellm.success_callback.append(_success_callback)
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if _async_success_callback not in litellm.success_callback:
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litellm.success_callback.append(_async_success_callback)
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litellm.failure_callback = failure_callback or []
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if _failure_callback not in litellm.failure_callback:
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litellm.failure_callback.append(_failure_callback)
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