Current File : /opt/hc_python/lib/python3.12/site-packages/sentry_sdk/integrations//openai.py
from functools import wraps
import sentry_sdk
from sentry_sdk import consts
from sentry_sdk.ai.monitoring import record_token_usage
from sentry_sdk.ai.utils import set_data_normalized
from sentry_sdk.consts import SPANDATA
from sentry_sdk.integrations import DidNotEnable, Integration
from sentry_sdk.scope import should_send_default_pii
from sentry_sdk.utils import (
capture_internal_exceptions,
event_from_exception,
)
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from typing import Any, Iterable, List, Optional, Callable, AsyncIterator, Iterator
from sentry_sdk.tracing import Span
try:
from openai.resources.chat.completions import Completions, AsyncCompletions
from openai.resources import Embeddings, AsyncEmbeddings
if TYPE_CHECKING:
from openai.types.chat import ChatCompletionMessageParam, ChatCompletionChunk
except ImportError:
raise DidNotEnable("OpenAI not installed")
class OpenAIIntegration(Integration):
identifier = "openai"
origin = f"auto.ai.{identifier}"
def __init__(self, include_prompts=True, tiktoken_encoding_name=None):
# type: (OpenAIIntegration, bool, Optional[str]) -> None
self.include_prompts = include_prompts
self.tiktoken_encoding = None
if tiktoken_encoding_name is not None:
import tiktoken # type: ignore
self.tiktoken_encoding = tiktoken.get_encoding(tiktoken_encoding_name)
@staticmethod
def setup_once():
# type: () -> None
Completions.create = _wrap_chat_completion_create(Completions.create)
Embeddings.create = _wrap_embeddings_create(Embeddings.create)
AsyncCompletions.create = _wrap_async_chat_completion_create(
AsyncCompletions.create
)
AsyncEmbeddings.create = _wrap_async_embeddings_create(AsyncEmbeddings.create)
def count_tokens(self, s):
# type: (OpenAIIntegration, str) -> int
if self.tiktoken_encoding is not None:
return len(self.tiktoken_encoding.encode_ordinary(s))
return 0
def _capture_exception(exc):
# type: (Any) -> None
event, hint = event_from_exception(
exc,
client_options=sentry_sdk.get_client().options,
mechanism={"type": "openai", "handled": False},
)
sentry_sdk.capture_event(event, hint=hint)
def _calculate_chat_completion_usage(
messages, response, span, streaming_message_responses, count_tokens
):
# type: (Iterable[ChatCompletionMessageParam], Any, Span, Optional[List[str]], Callable[..., Any]) -> None
completion_tokens = 0 # type: Optional[int]
prompt_tokens = 0 # type: Optional[int]
total_tokens = 0 # type: Optional[int]
if hasattr(response, "usage"):
if hasattr(response.usage, "completion_tokens") and isinstance(
response.usage.completion_tokens, int
):
completion_tokens = response.usage.completion_tokens
if hasattr(response.usage, "prompt_tokens") and isinstance(
response.usage.prompt_tokens, int
):
prompt_tokens = response.usage.prompt_tokens
if hasattr(response.usage, "total_tokens") and isinstance(
response.usage.total_tokens, int
):
total_tokens = response.usage.total_tokens
if prompt_tokens == 0:
for message in messages:
if "content" in message:
prompt_tokens += count_tokens(message["content"])
if completion_tokens == 0:
if streaming_message_responses is not None:
for message in streaming_message_responses:
completion_tokens += count_tokens(message)
elif hasattr(response, "choices"):
for choice in response.choices:
if hasattr(choice, "message"):
completion_tokens += count_tokens(choice.message)
if prompt_tokens == 0:
prompt_tokens = None
if completion_tokens == 0:
completion_tokens = None
if total_tokens == 0:
total_tokens = None
record_token_usage(span, prompt_tokens, completion_tokens, total_tokens)
def _new_chat_completion_common(f, *args, **kwargs):
# type: (Any, *Any, **Any) -> Any
integration = sentry_sdk.get_client().get_integration(OpenAIIntegration)
if integration is None:
return f(*args, **kwargs)
if "messages" not in kwargs:
# invalid call (in all versions of openai), let it return error
return f(*args, **kwargs)
try:
iter(kwargs["messages"])
except TypeError:
# invalid call (in all versions), messages must be iterable
return f(*args, **kwargs)
kwargs["messages"] = list(kwargs["messages"])
messages = kwargs["messages"]
model = kwargs.get("model")
streaming = kwargs.get("stream")
span = sentry_sdk.start_span(
op=consts.OP.OPENAI_CHAT_COMPLETIONS_CREATE,
name="Chat Completion",
origin=OpenAIIntegration.origin,
)
span.__enter__()
res = yield f, args, kwargs
with capture_internal_exceptions():
if should_send_default_pii() and integration.include_prompts:
set_data_normalized(span, SPANDATA.AI_INPUT_MESSAGES, messages)
set_data_normalized(span, SPANDATA.AI_MODEL_ID, model)
set_data_normalized(span, SPANDATA.AI_STREAMING, streaming)
if hasattr(res, "choices"):
if should_send_default_pii() and integration.include_prompts:
set_data_normalized(
span,
SPANDATA.AI_RESPONSES,
list(map(lambda x: x.message, res.choices)),
)
_calculate_chat_completion_usage(
messages, res, span, None, integration.count_tokens
)
span.__exit__(None, None, None)
elif hasattr(res, "_iterator"):
data_buf: list[list[str]] = [] # one for each choice
old_iterator = res._iterator
def new_iterator():
# type: () -> Iterator[ChatCompletionChunk]
with capture_internal_exceptions():
for x in old_iterator:
if hasattr(x, "choices"):
choice_index = 0
for choice in x.choices:
if hasattr(choice, "delta") and hasattr(
choice.delta, "content"
):
content = choice.delta.content
if len(data_buf) <= choice_index:
data_buf.append([])
data_buf[choice_index].append(content or "")
choice_index += 1
yield x
if len(data_buf) > 0:
all_responses = list(
map(lambda chunk: "".join(chunk), data_buf)
)
if should_send_default_pii() and integration.include_prompts:
set_data_normalized(
span, SPANDATA.AI_RESPONSES, all_responses
)
_calculate_chat_completion_usage(
messages,
res,
span,
all_responses,
integration.count_tokens,
)
span.__exit__(None, None, None)
async def new_iterator_async():
# type: () -> AsyncIterator[ChatCompletionChunk]
with capture_internal_exceptions():
async for x in old_iterator:
if hasattr(x, "choices"):
choice_index = 0
for choice in x.choices:
if hasattr(choice, "delta") and hasattr(
choice.delta, "content"
):
content = choice.delta.content
if len(data_buf) <= choice_index:
data_buf.append([])
data_buf[choice_index].append(content or "")
choice_index += 1
yield x
if len(data_buf) > 0:
all_responses = list(
map(lambda chunk: "".join(chunk), data_buf)
)
if should_send_default_pii() and integration.include_prompts:
set_data_normalized(
span, SPANDATA.AI_RESPONSES, all_responses
)
_calculate_chat_completion_usage(
messages,
res,
span,
all_responses,
integration.count_tokens,
)
span.__exit__(None, None, None)
if str(type(res._iterator)) == "<class 'async_generator'>":
res._iterator = new_iterator_async()
else:
res._iterator = new_iterator()
else:
set_data_normalized(span, "unknown_response", True)
span.__exit__(None, None, None)
return res
def _wrap_chat_completion_create(f):
# type: (Callable[..., Any]) -> Callable[..., Any]
def _execute_sync(f, *args, **kwargs):
# type: (Any, *Any, **Any) -> Any
gen = _new_chat_completion_common(f, *args, **kwargs)
try:
f, args, kwargs = next(gen)
except StopIteration as e:
return e.value
try:
try:
result = f(*args, **kwargs)
except Exception as e:
_capture_exception(e)
raise e from None
return gen.send(result)
except StopIteration as e:
return e.value
@wraps(f)
def _sentry_patched_create_sync(*args, **kwargs):
# type: (*Any, **Any) -> Any
integration = sentry_sdk.get_client().get_integration(OpenAIIntegration)
if integration is None or "messages" not in kwargs:
# no "messages" means invalid call (in all versions of openai), let it return error
return f(*args, **kwargs)
return _execute_sync(f, *args, **kwargs)
return _sentry_patched_create_sync
def _wrap_async_chat_completion_create(f):
# type: (Callable[..., Any]) -> Callable[..., Any]
async def _execute_async(f, *args, **kwargs):
# type: (Any, *Any, **Any) -> Any
gen = _new_chat_completion_common(f, *args, **kwargs)
try:
f, args, kwargs = next(gen)
except StopIteration as e:
return await e.value
try:
try:
result = await f(*args, **kwargs)
except Exception as e:
_capture_exception(e)
raise e from None
return gen.send(result)
except StopIteration as e:
return e.value
@wraps(f)
async def _sentry_patched_create_async(*args, **kwargs):
# type: (*Any, **Any) -> Any
integration = sentry_sdk.get_client().get_integration(OpenAIIntegration)
if integration is None or "messages" not in kwargs:
# no "messages" means invalid call (in all versions of openai), let it return error
return await f(*args, **kwargs)
return await _execute_async(f, *args, **kwargs)
return _sentry_patched_create_async
def _new_embeddings_create_common(f, *args, **kwargs):
# type: (Any, *Any, **Any) -> Any
integration = sentry_sdk.get_client().get_integration(OpenAIIntegration)
if integration is None:
return f(*args, **kwargs)
with sentry_sdk.start_span(
op=consts.OP.OPENAI_EMBEDDINGS_CREATE,
description="OpenAI Embedding Creation",
origin=OpenAIIntegration.origin,
) as span:
if "input" in kwargs and (
should_send_default_pii() and integration.include_prompts
):
if isinstance(kwargs["input"], str):
set_data_normalized(span, SPANDATA.AI_INPUT_MESSAGES, [kwargs["input"]])
elif (
isinstance(kwargs["input"], list)
and len(kwargs["input"]) > 0
and isinstance(kwargs["input"][0], str)
):
set_data_normalized(span, SPANDATA.AI_INPUT_MESSAGES, kwargs["input"])
if "model" in kwargs:
set_data_normalized(span, SPANDATA.AI_MODEL_ID, kwargs["model"])
response = yield f, args, kwargs
prompt_tokens = 0
total_tokens = 0
if hasattr(response, "usage"):
if hasattr(response.usage, "prompt_tokens") and isinstance(
response.usage.prompt_tokens, int
):
prompt_tokens = response.usage.prompt_tokens
if hasattr(response.usage, "total_tokens") and isinstance(
response.usage.total_tokens, int
):
total_tokens = response.usage.total_tokens
if prompt_tokens == 0:
prompt_tokens = integration.count_tokens(kwargs["input"] or "")
record_token_usage(span, prompt_tokens, None, total_tokens or prompt_tokens)
return response
def _wrap_embeddings_create(f):
# type: (Any) -> Any
def _execute_sync(f, *args, **kwargs):
# type: (Any, *Any, **Any) -> Any
gen = _new_embeddings_create_common(f, *args, **kwargs)
try:
f, args, kwargs = next(gen)
except StopIteration as e:
return e.value
try:
try:
result = f(*args, **kwargs)
except Exception as e:
_capture_exception(e)
raise e from None
return gen.send(result)
except StopIteration as e:
return e.value
@wraps(f)
def _sentry_patched_create_sync(*args, **kwargs):
# type: (*Any, **Any) -> Any
integration = sentry_sdk.get_client().get_integration(OpenAIIntegration)
if integration is None:
return f(*args, **kwargs)
return _execute_sync(f, *args, **kwargs)
return _sentry_patched_create_sync
def _wrap_async_embeddings_create(f):
# type: (Any) -> Any
async def _execute_async(f, *args, **kwargs):
# type: (Any, *Any, **Any) -> Any
gen = _new_embeddings_create_common(f, *args, **kwargs)
try:
f, args, kwargs = next(gen)
except StopIteration as e:
return await e.value
try:
try:
result = await f(*args, **kwargs)
except Exception as e:
_capture_exception(e)
raise e from None
return gen.send(result)
except StopIteration as e:
return e.value
@wraps(f)
async def _sentry_patched_create_async(*args, **kwargs):
# type: (*Any, **Any) -> Any
integration = sentry_sdk.get_client().get_integration(OpenAIIntegration)
if integration is None:
return await f(*args, **kwargs)
return await _execute_async(f, *args, **kwargs)
return _sentry_patched_create_async
