....................................../////.===Hehe-Here===./////................................................ > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < > < ------------------------------------------------------------------------------------------------------------------- /////////////////////////////////////////////////////////////////////////////////////////////////////////////////// RIFF¤ WEBPVP8 ˜ ðÑ *ôô>‘HŸK¥¤"§£±¨àð enü¹%½_F‘åè¿2ºQú³íªú`N¿­3ÿƒügµJžaÿ¯ÿ°~¼ÎùnúîÞÖô•òíôÁÉß®Sm¥Ü/ ‡ó˜f£Ùà<˜„xëJ¢Ù€SO3x<ªÔ©4¿+ç¶A`q@Ì“Úñè™ÍÿJÌ´ª-˜ÆtÊÛL]Ïq*‘Ý”ì#ŸÌÏãY]@ê`¿ /ªfkØB4·®£ó z—Üw¥Pxù–ÞLШKÇN¾AkÙTf½è'‰g gÆv›Øuh~ a˜Z— ïj*á¥t d£“uÒ ¨`K˜¹ßþ]b>˜]_ÏÔ6W—è2r4x•íÖ…"ƒÖNîä!¦å Ú}ýxGøÌ —@ ;ÆÚŠ=ɾ1ý8lªË¥ô ^yf®Œ¢u&2©nÙÇ›ñÂñŒ³ aPo['½»øFùà­+4ê“$!lövlüÞ=;N®3ð‚õ›DÉKòÞ>ÄÍ ¥ˆuߤ#ˆ$6ù™¥îŠ‡y’ÍB¼ çxÛ;X"WL£R÷͝*ó-¶Zu}º.s¸sšXqù–DþÿvªhüïwyŸ ¯é³lÀ:KCûÄ£Ëá\…­ ~—ýóî ¼ûûÜTÓüÇy…ŽÆvc»¾×U ñ¸žþоP÷¦ó:Ò¨¨5;Ð#&#ÖúñläÿÁœ GxÉ­/ñ‡áQðìYÉtÒwŽ¼GÔ´zàÒò ð*ëzƒ•4~H]Ø‹f ñÓÈñ`NåWçs'ÆÏW^ø¹!XžµmQ5ÃËoLœÎ: ÞËÍ¥J ù…î èo£ßPÎñ¶ž8.Œ]ʵ~5›ÙË-ù*8ÙÖß±~ ©¹rÓê‚j¶d¸{^Q'˜±Crß ÚH—#¥¥QlÀ×ëã‡DÜ«èî þ&Çæžî;ŽÏºò6ÒLÃXy&ZŒ'j‚¢Ù€IßÚù+–MGi‰*jE€‘JcÜ ÓÌ EÏÚj]o˜ Þr <¾U ûŪæÍ/šÝH¥˜b”¼ ÁñßX GP›ï2›4WŠÏà×£…íÓk†¦H·ÅíMh–*nó÷à]ÁjCº€b7<ب‹¨5車bp2:Á[UªM„QŒçiNMa#<5›áËó¸HýÊ"…×Éw¹¦ì2º–x<›»a±¸3Weü®FÝ⑱ö–î–³|LPÈ~çð~Çå‡|º kD¢µÏàÆAI %1À% ¹Ò – ”ϝS¦‰4&¶£°à Öý”û_Ò Áw°A«Å€?mÇÛgHÉ/8)á¾ÛìáöŽP í¨PŸNÙµº¦‡§Ùš"ÿ«>+ªÕ`Ê÷‡‚ß Õû˜þãÇ-PÍ.¾XV‘€ dÜ"þ4¹ ±Oú‘©t¥¦FªÄÃÄ•b‚znýu½—#cDs˜ÃiÑOˆñ×QO=*IAÊ,¶ŽZƒ;‡wøXè%EÐk:F±Ú” .Ѽ+Áu&Ç`."pÈÉw o&¿dE6‘’EqTuK@Ì¥ã™À(Êk(h‰,H}RÀIXÛš3µ1©_OqÚÒJAñ$ÊÙÜ;D3çŒ[þùœh¬Ã³™ö6ç†NY".Ú‰ï[ªŸŒ '²Ð öø_¨ÂÉ9u鶳ÒŠõTàîMØ#û¯gN‡bÙ놚X„ö …ÉeüÌ^J ‹€.œ$Æ)βÄeæW#óüßĺŸ€ ÀzwV 9oä»f4V*uB «Ë†¹ì¯žR霓æHXa=&“I4K;¯ç‹h×·"UŠ~<•â•ªVêª&ÍSÃÆÅ?ÔqÎ*mTM ˜›µwêd#[C¡©§‘D<©àb†–ÁœøvH/,í:¯( ²£|4-„Æövv„Yͼ™^Á$ˆ„¢Û[6yB.åH*V¨æ?$=˜Ñ€•î¦ñ·­(VlŸ‘ nÀt8W÷´Bûba?q9ú¶Xƒl«ÿ\ù¶’þòUÐj/õ¢Ìµ³g$ƒÎR!¸»|Oߍë’BhîÚÑ¢ñåŒJ„®„£2Ð3•ô02Nt…!£Í]Ïc½Qÿ?ˆ<&ÃA¾Ú,JˆijÌ#5yz„‰Î|ÊŽ5QÏ:‹ÐaóVÔxW—CpeÏzÐïíçôÿÅ_[hãsÐ_/ŽTÝ?BîˆííV$<¿i>²F¬_Eß¿ †bÊŒº­ÿ®Z H“C}”¬,Mp ý/Bá£w>˜YV°aƒúh+cŠ- r/[%|üUMHäQ°X»|û/@|°¥Ð !BÔ Ç¢Ä©š+Õì D«7ìN¶ŽðÔ " ƶ’ÖçtA‰Û×}{tþz­¾GÍ›k¹OEJR$ Â׃ «ëÁ"oÉôž$oUK(Ä)Ãz³Ê-‹êN[Ò3Œñbï8P 4ƒ×q¢bo|?<ÛX¬òÄÍ°L–±›(™ûG?ýË©ÚÄ–ÂDØÐ_Ç¡ô ¾–ÄÏø ×e8Ë©$ÄF¹Å‹ì[©óìl:F¾f´‹‹Xì²ï®\¬ôùƒ ÿat¥óèÒùHß0äe‚;ü×h:ÆWðHž=Ã8骣"kœ'Y?³}Tûè€>?0l›e1Lòñ„aæKÆw…hÖŠùW…ÈÆÄ0ši·›[pcwËþñiêíY/~-Á5˜!¿†A›™Mÿþ(±“t@â“ö2­´TG5yé]çå僳 .·ÍïçÝ7UÚ±Ð/Nè»,_Ï ùdj7\ï Wì4›„»c¸àešg#ÒÊ⥭áØo5‘?ÌdÝô¯ ¹kzsƒ=´#ëÉK›Ø´±-¥eW?‡çßtòTã…$Ý+qÿ±ƒ÷_3Ô¥í÷:æ–ž<·Ö‡‰Å¢ š‡%Ô—utÌÈìðžgÖÀz²À—ï÷Óîäõ{K'´È÷³yaÏÁjƒô}ž§®æÊydÕÈë5¯èˆõvÕ©ã*çD„ “z„Ó‡^^xÂ3M§A´JG‚öï 3W'ˆ.OvXè¡ÊÕª?5º7†˜(˜Ç¶#çê’¶!ÌdZK§æ 0fãaN]òY³RV ™î$®K2R¨`W!1Ôó\;Ý ýB%qæK•&ÓÈe9È0êI±žeŸß -ú@žQr¦ ö4»M¼Áè¹µmw 9 EÆE_°2ó„ŸXKWÁ×Hóì^´²GѝF©óäR†¦‰ç"V»eØ<3ùd3ÿÚ¤Žú“Gi" —‘_ÙËÎ~Üö¯¥½Î»üŸEÚŽåmÞþí ;ÞólËΦMzA"Âf(´òá;Éï(/7½ûñÌ­cïÕçлþÝz¾-ÍvÑ“pH­–ðÓj$¸Äû¤‚‘ãUBË-n“2åPkS5&‹Â|+g^œ®Ì͆d!OïäîU«c;{Û!ÅŽ«ëZ9Ókóˆ]¯ƒ›né `ÇÒ+tÆš (ØKá¾—=3œ®•vuMñg²\ï Ec€ 05±d™‡×iÇ×›UúvÌ¢£Èþ¡ÕØô¶ßÎA"ß±#Ö²ˆÊŸ¦*Ä~ij|àø.-¼'»Ú¥£h ofº¦‡VsR=N½„Î v˜Z*SÌ{=jÑB‹tê…;’HžH¯8–îDù8ñ¢|Q•bÛçš–‹m³“ê¨ åÏ^m¬Žãþ©ïêO‡½6] µÆ„Ooòü ²x}N¦Ë3ïé¿»€›HA˜m%çÞ/¿í7Fø“‹léUk)É°Œµ8Q8›:ÀŠeT*šõ~ôڝG6 ¢}`ùH­–”¡k ‰P1>š†®9z11!X wKfmÁ¦xÑ,N1Q”–æB¶M…ÒÃv6SMˆhU¬ÊPŽï‘öj=·CŒ¯u¹ƒVIЃsx4’ömÛýc塶7ßŠß 57^\wÒÐÆ k§h,Œý î«q^R½3]J¸ÇðN ‚çU¬ôº^Áì} ³f©Õœ§ˆã:FÄÈ‚é(€™?àýÓüè1Gô£¼éj‚OÅñ  #>×—ßtà 0G¥Åa뀐kßhc™À_ÉñÞ#±)GD" YîäË-ÿÙ̪ ¹™a¯´¢E\ÝÒö‚;™„ë]_ p8‰o¡ñ+^÷ 3‘'dT4œŽ ðVë½° :¬víÑ«£tßÚS-3¶“þ2 †üüʨòrš¹M{É_¤`Û¨0ìjœøJ‡:÷ÃáZ˜†@GP&œÑDGÏs¡þ¦þDGú‘1Yá9Ôþ¼ ûø…§÷8&–ÜÑnÄ_m®^üÆ`;ÉVÁJ£?â€-ßê}suÍ2sõA NÌúA磸‘îÿÚ»ƒìö·á¿±tÑÐ"Tÿü˜[@/äj¬€uüªìù¥Ý˜á8Ý´sõj 8@rˆð äþZÇD®ÿUÏ2ùôõrBzÆÏÞž>Ì™xœ“ wiÎ×7_… ¸ \#€MɁV¶¥üÕÿPÔ9Z‡ø§É8#H:ƒ5ÀÝå9ÍIŒ5åKÙŠ÷qÄ>1AÈøžj"µÂд/ªnÀ qªã}"iŸBå˜ÓÛŽ¦…&ݧ;G@—³b¯“•"´4í¨ôM¨åñC‹ïùÉó¯ÓsSH2Ý@ßáM‡ˆKÀªÛUeø/4\gnm¥‹ŸŒ qÄ b9ÞwÒNÏ_4Ég³ú=܆‚´ •â¥õeíþkjz>éÚyU«Íӝ݃6"8/ø{=Ô¢»G¥ äUw°W«,ô—¿ãㆅү¢³xŠUû™yŒ (øSópÐ 9\åTâ»—*oG$/×ÍT†Y¿1¤Þ¢_‡ ¼ „±ÍçèSaÓ 3ÛMÁBkxs‰’R/¡¤ˆÙçª(*õ„üXÌ´ƒ E§´¬EF"Ù”R/ÐNyÆÂ^°?™6¡œïJ·±$§?º>ÖüœcNÌù¯G ‹ñ2ŠBB„^·úìaz¨k:#¨Æ¨8LÎõލ£^§S&cŒÐU€ü(‡F±Š¼&P>8ÙÁ ‰ p5?0ÊƃZl¸aô š¼¡}gÿ¶zÆC²¹¬ÎÖG*HB¡O<º2#ñŒAƒ–¡B˜´É$¥›É:FÀÔx¾u?XÜÏÓvN©RS{2ʈãk9rmP¼Qq̳ è¼ÐFׄ^¡Öì fE“F4A…!ì/…¦Lƒ… … $%´¾yã@CI¬ á—3PþBÏNÿ<ý°4Ü ËÃ#ØÍ~âW«rEñw‹eùMMHß²`¬Öó½íf³:‹k˜¯÷}Z!ã¿<¥,\#öµÀ¯aÒNÆIé,Ћ–lŽ#Àæ9ÀÒS·I’½-Ïp Äz¤Š Â* ­íÄ9­< h>׍3ZkËU¹§˜ŒŠ±f­’¤º³Q ÏB?‹#µíÃ¥®@(Gs«†vI¥Mµ‹Á©e~2ú³ÁP4ìÕi‚²Ê^ö@-DþÓàlÜOÍ]n"µã:žpsŽ¢:! Aõ.ç~ÓBûH÷JCÌ]õVƒd «ú´QÙEA–¯¯Œ!.ˆˆëQ±ù œ·Ì!Õâ )ùL„ÅÀlÚè5@B…o´Æ¸XÓ&Û…O«˜”_#‡ƒ„ûÈt!¤ÁÏ›ÎÝŠ?c9 â\>lÓÁVÄÑ™£eØY]:fÝ–—ù+p{™ðè û³”g±OƒÚSù£áÁÊ„ä,ï7š²G ÕÌBk)~ÑiCµ|h#u¤¶îK¨² #²vݯGãeÖ϶ú…¾múÀ¶þÔñ‚Š9'^($¤§ò “š½{éúp÷J›ušS¹áªCÂubÃH9™D™/ZöØÁ‡¦ÝÙŸ·kð*_”.C‹{áXó€‡c¡c€§/šò/&éš÷,àéJþ‰X›fµ“C¨œ®r¬"kL‰Â_q…Z–.ÉL~O µ›zn‚¹À¦Öª7\àHµšÖ %»ÇníV[¥*Õ;ƒ#½¾HK-ÖIÊdÏEÚ#=o÷Óò³´Š: Ç?{¾+9›–‘OEáU·S€˜j"ÄaÜ ŒÛWt› á–c#a»pÔZÞdŽtWê=9éöÊ¢µ~ ë ;Öe‡Œ®:bî3±ýê¢wà¼îpêñ¹¾4 zc¾ðÖÿzdêŒÑÒŝÀ‰s6¤í³ÎÙB¿OZ”+F¤á‡3@Ñëäg©·Ž ˆèª<ù@É{&S„œÕúÀA)‰h:YÀ5^ÂÓŒ°õäU\ ùËÍû#²?Xe¬tu‰^zÒÔãë¼ÛWtEtû …‚g¶Úüâî*moGè¨7%u!]PhÏd™Ý%Îx: VÒ¦ôÊD3ÀŽKÛËãvÆî…N¯ä>Eró–ð`5 Œ%u5XkñÌ*NU%¶áœÊ:Qÿú»“úzyÏ6å-၇¾ ´ ÒÊ]y žO‘w2Äøæ…H’²f±ÎÇ.ª|¥'gîV•Ü .̘¯€šòü¤U~Ù†*¢!?ò wý,}´°ÔÞnïoKq5µb!áÓ3"vAßH¡³¡·G(ÐÎ0Îò¼MG!/ài®@—¬04*`…«é8ªøøló“ˆÊ”èù¤…ßÊoÿé'ËuÌÖ5×È¡§ˆˆfŽë9}hìâ_!!¯  B&Ëö¶‰ÀAÙNVŸ Wh›¸®XÑJì¨ú“¿÷3uj²˜¨ÍÎìë±aúŠÝå¯ð*Ó¨ôJ“yºØ)m°WýOè68†ŸÏ2—‰Ïüꪫٚ¥‹l1 ø ÏÄFjêµvÌbü¦èÝx:X±¢H=MÐß—,ˆÉÇ´(9ú¾^ÅÚ4¿m‡$âX‘å%(AlZo@½¨UOÌÕ”1ø¸jÎÀÃÃ_ µ‘Ü.œº¦Ut: Æï’!=¯uwû#,“pþÇúŒø(é@?³ü¥‘Mo §—s@Œ#)§ŒùkL}NOÆêA›¸~r½¼ÙA—HJ«eˆÖ´*¡ÓpÌŸö.m<-"³ûÈ$¬_6­åf£ïÚâj1y§ÕJ½@dÞÁr&Í\Z%D£Íñ·AZ Û³øüd/ªAi†/Й~  ‡âĮҮÏh§°b—›Û«mJžòG'[ÈYýŒ¦9psl ýÁ ®±f¦x,‰½tN ‚Xª9 ÙÖH.«Lo0×?͹m¡å†Ѽ+›2ƒF ±Ê8 7Hցϓ²Æ–m9…òŸï]Â1äN†VLâCˆU .ÿ‰Ts +ÅÎx(%¦u]6AF Š ØF鈄‘ |¢¶c±soŒ/t[a¾–û:s·`i햍ê›ËchÈ…8ßÀUÜewŒðNOƒõD%q#éû\9¤x¹&UE×G¥ Í—™$ð E6-‡¼!ýpãÔM˜ Âsìe¯ñµK¢Ç¡ùôléœ4Ö£”À Š®Ðc ^¨À}ÙËŸ§›ºê{ÊuÉC ×Sr€¤’fÉ*j!úÓ’Gsùìoîßîn%ò· àc Wp÷$¨˜)û»H ×8ŽÒ€Zj¤3ÀÙºY'Ql¦py{-6íÔCeiØp‘‡XÊîÆUߢ܂ž£Xé¼Y8þ©ëgñß}é.ÎógÒ„ÃØËø¯»™§Xýy M%@NŠ À(~áÐvu7&•,Ù˜ó€uP‡^^®=_E„jt’ 403WebShell
403Webshell
Server IP : 159.198.67.129  /  Your IP : 216.73.216.244
Web Server : LiteSpeed
System : Linux server166.web-hosting.com 4.18.0-513.18.1.lve.el8.x86_64 #1 SMP Thu Feb 22 12:55:50 UTC 2024 x86_64
User : trooaisr ( 4033)
PHP Version : 7.4.33
Disable Function : NONE
MySQL : OFF  |  cURL : ON  |  WGET : ON  |  Perl : ON  |  Python : ON  |  Sudo : OFF  |  Pkexec : OFF
Directory :  /opt/hc_python/lib/python3.12/site-packages/sentry_sdk/integrations/

Upload File :
current_dir [ Writeable ] document_root [ Writeable ]

 

Command :

[ Back ]     

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,
    normalize_message_roles,
    truncate_and_annotate_messages,
)
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.tracing_utils import set_span_errored
from sentry_sdk.utils import (
    capture_internal_exceptions,
    event_from_exception,
    safe_serialize,
)

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:
    try:
        from openai import NotGiven
    except ImportError:
        NotGiven = None

    try:
        from openai import Omit
    except ImportError:
        Omit = None

    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")

RESPONSES_API_ENABLED = True
try:
    # responses API support was introduced in v1.66.0
    from openai.resources.responses import Responses, AsyncResponses
    from openai.types.responses.response_completed_event import ResponseCompletedEvent
except ImportError:
    RESPONSES_API_ENABLED = False


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)
        AsyncCompletions.create = _wrap_async_chat_completion_create(
            AsyncCompletions.create
        )

        Embeddings.create = _wrap_embeddings_create(Embeddings.create)
        AsyncEmbeddings.create = _wrap_async_embeddings_create(AsyncEmbeddings.create)

        if RESPONSES_API_ENABLED:
            Responses.create = _wrap_responses_create(Responses.create)
            AsyncResponses.create = _wrap_async_responses_create(AsyncResponses.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, manual_span_cleanup=True):
    # type: (Any, bool) -> None
    # Close an eventually open span
    # We need to do this by hand because we are not using the start_span context manager
    current_span = sentry_sdk.get_current_span()
    set_span_errored(current_span)

    if manual_span_cleanup and current_span is not None:
        current_span.__exit__(None, None, 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 _get_usage(usage, names):
    # type: (Any, List[str]) -> int
    for name in names:
        if hasattr(usage, name) and isinstance(getattr(usage, name), int):
            return getattr(usage, name)
    return 0


def _calculate_token_usage(
    messages, response, span, streaming_message_responses, count_tokens
):
    # type: (Optional[Iterable[ChatCompletionMessageParam]], Any, Span, Optional[List[str]], Callable[..., Any]) -> None
    input_tokens = 0  # type: Optional[int]
    input_tokens_cached = 0  # type: Optional[int]
    output_tokens = 0  # type: Optional[int]
    output_tokens_reasoning = 0  # type: Optional[int]
    total_tokens = 0  # type: Optional[int]

    if hasattr(response, "usage"):
        input_tokens = _get_usage(response.usage, ["input_tokens", "prompt_tokens"])
        if hasattr(response.usage, "input_tokens_details"):
            input_tokens_cached = _get_usage(
                response.usage.input_tokens_details, ["cached_tokens"]
            )

        output_tokens = _get_usage(
            response.usage, ["output_tokens", "completion_tokens"]
        )
        if hasattr(response.usage, "output_tokens_details"):
            output_tokens_reasoning = _get_usage(
                response.usage.output_tokens_details, ["reasoning_tokens"]
            )

        total_tokens = _get_usage(response.usage, ["total_tokens"])

    # Manually count tokens
    if input_tokens == 0:
        for message in messages or []:
            if isinstance(message, dict) and "content" in message:
                input_tokens += count_tokens(message["content"])
            elif isinstance(message, str):
                input_tokens += count_tokens(message)

    if output_tokens == 0:
        if streaming_message_responses is not None:
            for message in streaming_message_responses:
                output_tokens += count_tokens(message)
        elif hasattr(response, "choices"):
            for choice in response.choices:
                if hasattr(choice, "message"):
                    output_tokens += count_tokens(choice.message)

    # Do not set token data if it is 0
    input_tokens = input_tokens or None
    input_tokens_cached = input_tokens_cached or None
    output_tokens = output_tokens or None
    output_tokens_reasoning = output_tokens_reasoning or None
    total_tokens = total_tokens or None

    record_token_usage(
        span,
        input_tokens=input_tokens,
        input_tokens_cached=input_tokens_cached,
        output_tokens=output_tokens,
        output_tokens_reasoning=output_tokens_reasoning,
        total_tokens=total_tokens,
    )


def _set_input_data(span, kwargs, operation, integration):
    # type: (Span, dict[str, Any], str, OpenAIIntegration) -> None
    # Input messages (the prompt or data sent to the model)
    messages = kwargs.get("messages")
    if messages is None:
        messages = kwargs.get("input")

    if isinstance(messages, str):
        messages = [messages]

    if (
        messages is not None
        and len(messages) > 0
        and should_send_default_pii()
        and integration.include_prompts
    ):
        normalized_messages = normalize_message_roles(messages)
        scope = sentry_sdk.get_current_scope()
        messages_data = truncate_and_annotate_messages(normalized_messages, span, scope)
        if messages_data is not None:
            # Use appropriate field based on operation type
            if operation == "embeddings":
                set_data_normalized(
                    span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, messages_data, unpack=False
                )
            else:
                set_data_normalized(
                    span, SPANDATA.GEN_AI_REQUEST_MESSAGES, messages_data, unpack=False
                )

    # Input attributes: Common
    set_data_normalized(span, SPANDATA.GEN_AI_SYSTEM, "openai")
    set_data_normalized(span, SPANDATA.GEN_AI_OPERATION_NAME, operation)

    # Input attributes: Optional
    kwargs_keys_to_attributes = {
        "model": SPANDATA.GEN_AI_REQUEST_MODEL,
        "stream": SPANDATA.GEN_AI_RESPONSE_STREAMING,
        "max_tokens": SPANDATA.GEN_AI_REQUEST_MAX_TOKENS,
        "presence_penalty": SPANDATA.GEN_AI_REQUEST_PRESENCE_PENALTY,
        "frequency_penalty": SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY,
        "temperature": SPANDATA.GEN_AI_REQUEST_TEMPERATURE,
        "top_p": SPANDATA.GEN_AI_REQUEST_TOP_P,
    }
    for key, attribute in kwargs_keys_to_attributes.items():
        value = kwargs.get(key)

        if value is not None and _is_given(value):
            set_data_normalized(span, attribute, value)

    # Input attributes: Tools
    tools = kwargs.get("tools")
    if tools is not None and _is_given(tools) and len(tools) > 0:
        set_data_normalized(
            span, SPANDATA.GEN_AI_REQUEST_AVAILABLE_TOOLS, safe_serialize(tools)
        )


def _set_output_data(span, response, kwargs, integration, finish_span=True):
    # type: (Span, Any, dict[str, Any], OpenAIIntegration, bool) -> None
    if hasattr(response, "model"):
        set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_MODEL, response.model)

    # Input messages (the prompt or data sent to the model)
    # used for the token usage calculation
    messages = kwargs.get("messages")
    if messages is None:
        messages = kwargs.get("input")

    if messages is not None and isinstance(messages, str):
        messages = [messages]

    if hasattr(response, "choices"):
        if should_send_default_pii() and integration.include_prompts:
            response_text = [
                choice.message.model_dump()
                for choice in response.choices
                if choice.message is not None
            ]
            if len(response_text) > 0:
                set_data_normalized(span, SPANDATA.GEN_AI_RESPONSE_TEXT, response_text)

        _calculate_token_usage(messages, response, span, None, integration.count_tokens)

        if finish_span:
            span.__exit__(None, None, None)

    elif hasattr(response, "output"):
        if should_send_default_pii() and integration.include_prompts:
            output_messages = {
                "response": [],
                "tool": [],
            }  # type: (dict[str, list[Any]])

            for output in response.output:
                if output.type == "function_call":
                    output_messages["tool"].append(output.dict())
                elif output.type == "message":
                    for output_message in output.content:
                        try:
                            output_messages["response"].append(output_message.text)
                        except AttributeError:
                            # Unknown output message type, just return the json
                            output_messages["response"].append(output_message.dict())

            if len(output_messages["tool"]) > 0:
                set_data_normalized(
                    span,
                    SPANDATA.GEN_AI_RESPONSE_TOOL_CALLS,
                    output_messages["tool"],
                    unpack=False,
                )

            if len(output_messages["response"]) > 0:
                set_data_normalized(
                    span, SPANDATA.GEN_AI_RESPONSE_TEXT, output_messages["response"]
                )

        _calculate_token_usage(messages, response, span, None, integration.count_tokens)

        if finish_span:
            span.__exit__(None, None, None)

    elif hasattr(response, "_iterator"):
        data_buf: list[list[str]] = []  # one for each choice

        old_iterator = response._iterator

        def new_iterator():
            # type: () -> Iterator[ChatCompletionChunk]
            count_tokens_manually = True
            for x in old_iterator:
                with capture_internal_exceptions():
                    # OpenAI chat completion API
                    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

                    # OpenAI responses API
                    elif hasattr(x, "delta"):
                        if len(data_buf) == 0:
                            data_buf.append([])
                        data_buf[0].append(x.delta or "")

                    # OpenAI responses API end of streaming response
                    if RESPONSES_API_ENABLED and isinstance(x, ResponseCompletedEvent):
                        _calculate_token_usage(
                            messages,
                            x.response,
                            span,
                            None,
                            integration.count_tokens,
                        )
                        count_tokens_manually = False

                yield x

            with capture_internal_exceptions():
                if len(data_buf) > 0:
                    all_responses = ["".join(chunk) for chunk in data_buf]
                    if should_send_default_pii() and integration.include_prompts:
                        set_data_normalized(
                            span, SPANDATA.GEN_AI_RESPONSE_TEXT, all_responses
                        )
                    if count_tokens_manually:
                        _calculate_token_usage(
                            messages,
                            response,
                            span,
                            all_responses,
                            integration.count_tokens,
                        )

            if finish_span:
                span.__exit__(None, None, None)

        async def new_iterator_async():
            # type: () -> AsyncIterator[ChatCompletionChunk]
            count_tokens_manually = True
            async for x in old_iterator:
                with capture_internal_exceptions():
                    # OpenAI chat completion API
                    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

                    # OpenAI responses API
                    elif hasattr(x, "delta"):
                        if len(data_buf) == 0:
                            data_buf.append([])
                        data_buf[0].append(x.delta or "")

                    # OpenAI responses API end of streaming response
                    if RESPONSES_API_ENABLED and isinstance(x, ResponseCompletedEvent):
                        _calculate_token_usage(
                            messages,
                            x.response,
                            span,
                            None,
                            integration.count_tokens,
                        )
                        count_tokens_manually = False

                yield x

            with capture_internal_exceptions():
                if len(data_buf) > 0:
                    all_responses = ["".join(chunk) for chunk in data_buf]
                    if should_send_default_pii() and integration.include_prompts:
                        set_data_normalized(
                            span, SPANDATA.GEN_AI_RESPONSE_TEXT, all_responses
                        )
                    if count_tokens_manually:
                        _calculate_token_usage(
                            messages,
                            response,
                            span,
                            all_responses,
                            integration.count_tokens,
                        )
            if finish_span:
                span.__exit__(None, None, None)

        if str(type(response._iterator)) == "<class 'async_generator'>":
            response._iterator = new_iterator_async()
        else:
            response._iterator = new_iterator()
    else:
        _calculate_token_usage(messages, response, span, None, integration.count_tokens)
        if finish_span:
            span.__exit__(None, None, None)


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)

    model = kwargs.get("model")
    operation = "chat"

    span = sentry_sdk.start_span(
        op=consts.OP.GEN_AI_CHAT,
        name=f"{operation} {model}",
        origin=OpenAIIntegration.origin,
    )
    span.__enter__()

    _set_input_data(span, kwargs, operation, integration)

    response = yield f, args, kwargs

    _set_output_data(span, response, kwargs, integration, finish_span=True)

    return response


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)

    model = kwargs.get("model")
    operation = "embeddings"

    with sentry_sdk.start_span(
        op=consts.OP.GEN_AI_EMBEDDINGS,
        name=f"{operation} {model}",
        origin=OpenAIIntegration.origin,
    ) as span:
        _set_input_data(span, kwargs, operation, integration)

        response = yield f, args, kwargs

        _set_output_data(span, response, kwargs, integration, finish_span=False)

        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, manual_span_cleanup=False)
                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, manual_span_cleanup=False)
                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


def _new_responses_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)

    model = kwargs.get("model")
    operation = "responses"

    span = sentry_sdk.start_span(
        op=consts.OP.GEN_AI_RESPONSES,
        name=f"{operation} {model}",
        origin=OpenAIIntegration.origin,
    )
    span.__enter__()

    _set_input_data(span, kwargs, operation, integration)

    response = yield f, args, kwargs

    _set_output_data(span, response, kwargs, integration, finish_span=True)

    return response


def _wrap_responses_create(f):
    # type: (Any) -> Any
    def _execute_sync(f, *args, **kwargs):
        # type: (Any, Any, Any) -> Any
        gen = _new_responses_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_responses_create(f):
    # type: (Any) -> Any
    async def _execute_async(f, *args, **kwargs):
        # type: (Any, Any, Any) -> Any
        gen = _new_responses_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_responses_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_responses_async


def _is_given(obj):
    # type: (Any) -> bool
    """
    Check for givenness safely across different openai versions.
    """
    if NotGiven is not None and isinstance(obj, NotGiven):
        return False
    if Omit is not None and isinstance(obj, Omit):
        return False
    return True

Youez - 2016 - github.com/yon3zu
LinuXploit