AlkantarClanX12
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from functools import wraps
from sentry_sdk import consts
from sentry_sdk.ai.monitoring import record_token_usage
from sentry_sdk.consts import SPANDATA
from sentry_sdk.ai.utils import set_data_normalized
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from typing import Any, Callable, Iterator
from sentry_sdk.tracing import Span
import sentry_sdk
from sentry_sdk.scope import should_send_default_pii
from sentry_sdk.integrations import DidNotEnable, Integration
from sentry_sdk.utils import capture_internal_exceptions, event_from_exception
try:
from cohere.client import Client
from cohere.base_client import BaseCohere
from cohere import (
ChatStreamEndEvent,
NonStreamedChatResponse,
)
if TYPE_CHECKING:
from cohere import StreamedChatResponse
except ImportError:
raise DidNotEnable("Cohere not installed")
try:
# cohere 5.9.3+
from cohere import StreamEndStreamedChatResponse
except ImportError:
from cohere import StreamedChatResponse_StreamEnd as StreamEndStreamedChatResponse
COLLECTED_CHAT_PARAMS = {
"model": SPANDATA.AI_MODEL_ID,
"k": SPANDATA.AI_TOP_K,
"p": SPANDATA.AI_TOP_P,
"seed": SPANDATA.AI_SEED,
"frequency_penalty": SPANDATA.AI_FREQUENCY_PENALTY,
"presence_penalty": SPANDATA.AI_PRESENCE_PENALTY,
"raw_prompting": SPANDATA.AI_RAW_PROMPTING,
}
COLLECTED_PII_CHAT_PARAMS = {
"tools": SPANDATA.AI_TOOLS,
"preamble": SPANDATA.AI_PREAMBLE,
}
COLLECTED_CHAT_RESP_ATTRS = {
"generation_id": "ai.generation_id",
"is_search_required": "ai.is_search_required",
"finish_reason": "ai.finish_reason",
}
COLLECTED_PII_CHAT_RESP_ATTRS = {
"citations": "ai.citations",
"documents": "ai.documents",
"search_queries": "ai.search_queries",
"search_results": "ai.search_results",
"tool_calls": "ai.tool_calls",
}
class CohereIntegration(Integration):
identifier = "cohere"
origin = f"auto.ai.{identifier}"
def __init__(self, include_prompts=True):
# type: (CohereIntegration, bool) -> None
self.include_prompts = include_prompts
@staticmethod
def setup_once():
# type: () -> None
BaseCohere.chat = _wrap_chat(BaseCohere.chat, streaming=False)
Client.embed = _wrap_embed(Client.embed)
BaseCohere.chat_stream = _wrap_chat(BaseCohere.chat_stream, streaming=True)
def _capture_exception(exc):
# type: (Any) -> None
event, hint = event_from_exception(
exc,
client_options=sentry_sdk.get_client().options,
mechanism={"type": "cohere", "handled": False},
)
sentry_sdk.capture_event(event, hint=hint)
def _wrap_chat(f, streaming):
# type: (Callable[..., Any], bool) -> Callable[..., Any]
def collect_chat_response_fields(span, res, include_pii):
# type: (Span, NonStreamedChatResponse, bool) -> None
if include_pii:
if hasattr(res, "text"):
set_data_normalized(
span,
SPANDATA.AI_RESPONSES,
[res.text],
)
for pii_attr in COLLECTED_PII_CHAT_RESP_ATTRS:
if hasattr(res, pii_attr):
set_data_normalized(span, "ai." + pii_attr, getattr(res, pii_attr))
for attr in COLLECTED_CHAT_RESP_ATTRS:
if hasattr(res, attr):
set_data_normalized(span, "ai." + attr, getattr(res, attr))
if hasattr(res, "meta"):
if hasattr(res.meta, "billed_units"):
record_token_usage(
span,
prompt_tokens=res.meta.billed_units.input_tokens,
completion_tokens=res.meta.billed_units.output_tokens,
)
elif hasattr(res.meta, "tokens"):
record_token_usage(
span,
prompt_tokens=res.meta.tokens.input_tokens,
completion_tokens=res.meta.tokens.output_tokens,
)
if hasattr(res.meta, "warnings"):
set_data_normalized(span, "ai.warnings", res.meta.warnings)
@wraps(f)
def new_chat(*args, **kwargs):
# type: (*Any, **Any) -> Any
integration = sentry_sdk.get_client().get_integration(CohereIntegration)
if (
integration is None
or "message" not in kwargs
or not isinstance(kwargs.get("message"), str)
):
return f(*args, **kwargs)
message = kwargs.get("message")
span = sentry_sdk.start_span(
op=consts.OP.COHERE_CHAT_COMPLETIONS_CREATE,
name="cohere.client.Chat",
origin=CohereIntegration.origin,
)
span.__enter__()
try:
res = f(*args, **kwargs)
except Exception as e:
_capture_exception(e)
span.__exit__(None, None, None)
raise e from None
with capture_internal_exceptions():
if should_send_default_pii() and integration.include_prompts:
set_data_normalized(
span,
SPANDATA.AI_INPUT_MESSAGES,
list(
map(
lambda x: {
"role": getattr(x, "role", "").lower(),
"content": getattr(x, "message", ""),
},
kwargs.get("chat_history", []),
)
)
+ [{"role": "user", "content": message}],
)
for k, v in COLLECTED_PII_CHAT_PARAMS.items():
if k in kwargs:
set_data_normalized(span, v, kwargs[k])
for k, v in COLLECTED_CHAT_PARAMS.items():
if k in kwargs:
set_data_normalized(span, v, kwargs[k])
set_data_normalized(span, SPANDATA.AI_STREAMING, False)
if streaming:
old_iterator = res
def new_iterator():
# type: () -> Iterator[StreamedChatResponse]
with capture_internal_exceptions():
for x in old_iterator:
if isinstance(x, ChatStreamEndEvent) or isinstance(
x, StreamEndStreamedChatResponse
):
collect_chat_response_fields(
span,
x.response,
include_pii=should_send_default_pii()
and integration.include_prompts,
)
yield x
span.__exit__(None, None, None)
return new_iterator()
elif isinstance(res, NonStreamedChatResponse):
collect_chat_response_fields(
span,
res,
include_pii=should_send_default_pii()
and integration.include_prompts,
)
span.__exit__(None, None, None)
else:
set_data_normalized(span, "unknown_response", True)
span.__exit__(None, None, None)
return res
return new_chat
def _wrap_embed(f):
# type: (Callable[..., Any]) -> Callable[..., Any]
@wraps(f)
def new_embed(*args, **kwargs):
# type: (*Any, **Any) -> Any
integration = sentry_sdk.get_client().get_integration(CohereIntegration)
if integration is None:
return f(*args, **kwargs)
with sentry_sdk.start_span(
op=consts.OP.COHERE_EMBEDDINGS_CREATE,
name="Cohere Embedding Creation",
origin=CohereIntegration.origin,
) as span:
if "texts" in kwargs and (
should_send_default_pii() and integration.include_prompts
):
if isinstance(kwargs["texts"], str):
set_data_normalized(span, "ai.texts", [kwargs["texts"]])
elif (
isinstance(kwargs["texts"], list)
and len(kwargs["texts"]) > 0
and isinstance(kwargs["texts"][0], str)
):
set_data_normalized(
span, SPANDATA.AI_INPUT_MESSAGES, kwargs["texts"]
)
if "model" in kwargs:
set_data_normalized(span, SPANDATA.AI_MODEL_ID, kwargs["model"])
try:
res = f(*args, **kwargs)
except Exception as e:
_capture_exception(e)
raise e from None
if (
hasattr(res, "meta")
and hasattr(res.meta, "billed_units")
and hasattr(res.meta.billed_units, "input_tokens")
):
record_token_usage(
span,
prompt_tokens=res.meta.billed_units.input_tokens,
total_tokens=res.meta.billed_units.input_tokens,
)
return res
return new_embed