&`i8$ddlmZmZmZmZmZddlZddlmZddl m Z ddl m Z dZ dZdZd Zdd eeed eefd Zd eeeffdZd edeeeffdZe dGdde jZe dGdde jZe dGdde jZdS))DictListOptionalTupleUnionN)context)metrics) PublicAPI deploymentreplica applicationroutetag_keysreturnc|t}tj|St|vrt dtdt |vrt dt dt |vrt dt dtt f}tjjr |t fz }|r||z}n|}|S)z&Add serve context tags to the tag_keysN''' tag is reserved for Ray Serve metrics)tupler_get_internal_replica_contextDEPLOYMENT_TAG ValueError REPLICA_TAGAPPLICATION_TAGapp_name)rray_serve_tagss e/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/serve/metrics.py_add_serve_metric_tagsrs77,..6!!T^TTTUUUhQ[QQQRRR(""U_UUUVVV%k2N,..7-?,,"!H,! O default_tagsctj|St|vrtdtdt|vrtdtdt |vrtdt dtj}|j|t<|j|t<|jr|j|t <|S)z5Add serve context tags and values to the default_tagsNrr) rrrrrrr replica_tagr)rreplica_contexts r_add_serve_metric_default_tagsr#)s,..6%%T^TTTUUUl""Q[QQQRRR,&&U_UUUVVV;==O#2#=L / ;LA(7(@ _% rtagsctjj}t|vrt|vr|j|t<dSdSdS)z/Add serve context tag values to the metric tagsN)rayserver_get_serve_request_context ROUTE_TAGr)rr$_request_contexts r_add_serve_context_tag_valuesr+<sNy(CCEEH$!6!6*0Y!6!6rbeta) stabilityc eZdZdZ ddededeeeffd Zdeeefffd Z dd e e e fd eeefffd Z xZS)CounteraA serve cumulative metric that is monotonically increasing. This corresponds to Prometheus' counter metric: https://prometheus.io/docs/concepts/metric_types/#counter Serve-related tags ("deployment", "replica", "application", "route") are added automatically if not provided. .. code-block:: python @serve.deployment class MyDeployment: def __init__(self): self.num_requests = 0 self.my_counter = metrics.Counter( "my_counter", description=("The number of odd-numbered requests " "to this deployment."), tag_keys=("model",), ) self.my_counter.set_default_tags({"model": "123"}) def __call__(self): self.num_requests += 1 if self.num_requests % 2 == 1: self.my_counter.inc() .. note:: Before Ray 2.10, this exports a Prometheus gauge metric instead of a counter metric. Starting in Ray 2.10, this exports both the proper counter metric (with a suffix "_total") and gauge metric (for compatibility). The gauge metric will be removed in a future Ray release and you can set `RAY_EXPORT_COUNTER_AS_GAUGE=0` to disable exporting it in the meantime. Args: name: Name of the metric. description: Description of the metric. tag_keys: Tag keys of the metric. Nname descriptionrc|r4t|tstdt|t |}t ||||idSNz&tag_keys should be a tuple type, got:  isinstancer TypeErrortypersuper__init__set_default_tagsselfr1r2r __class__s rr:zCounter.__init__p  Jx77 LDNNLL *(33 {H555 b!!!!!rrcdtt|dSNr9r;r#r=rr>s rr;zCounter.set_default_tags{*   !? !M!MNNNNNr?valuer$cvt|j|t||dS)zcIncrement the counter by the given value, add serve context tag values to the tags N)r+ _tag_keysr9incr=rFr$r>s rrIz Counter.inc~5 &dnd;;;  E4     rr0N)rEN)__name__ __module__ __qualname____doc__strrrr:rr;rintfloatrI __classcell__r>s@rr/r/Ds((VRV " " "&) ":B5::N " " " " " "OT#s(^OOOOOO!!sEz*!S#X!!!!!!!!!!rr/c eZdZdZ d dededeeeffd Zdeeefffd Z dd e e e fd eeefffd Z xZS)GaugeaGauges keep the last recorded value and drop everything before. This corresponds to Prometheus' gauge metric: https://prometheus.io/docs/concepts/metric_types/#gauge Serve-related tags ("deployment", "replica", "application", "route") are added automatically if not provided. .. code-block:: python @serve.deployment class MyDeployment: def __init__(self): self.num_requests = 0 self.my_gauge = metrics.Gauge( "my_gauge", description=("The current memory usage."), tag_keys=("model",), ) self.my_counter.set_default_tags({"model": "123"}) def __call__(self): process = psutil.Process() self.gauge.set(process.memory_info().rss) Args: name: Name of the metric. description: Description of the metric. tag_keys: Tag keys of the metric. r0Nr1r2rc|r4t|tstdt|t |}t ||||idSr4r5r<s rr:zGauge.__init__r?rrcdtt|dSrArBrCs rr;zGauge.set_default_tagsrDrrFr$cvt|j|t||dS)z[Set the gauge to the given value, add serve context tag values to the tags N)r+rHr9setrJs rr[z Gauge.setrKrrLrA)rMrNrOrPrQrrr:rr;rrRrSr[rTrUs@rrWrWs@RV " " "&) ":B5::N " " " " " "OT#s(^OOOOOO!!sEz*!$sCx.!!!!!!!!!!rrWc eZdZdZ ddededeedeeeffd Z d e eefffd Z dd e e efd e eefffd ZxZS) HistogramaTracks the size and number of events in buckets. Histograms allow you to calculate aggregate quantiles such as 25, 50, 95, 99 percentile latency for an RPC. This corresponds to Prometheus' histogram metric: https://prometheus.io/docs/concepts/metric_types/#histogram Serve-related tags ("deployment", "replica", "application", "route") are added automatically if not provided. .. code-block:: python @serve.deployment class MyDeployment: def __init__(self): self.my_histogram = Histogram( "my_histogram", description=("Histogram of the __call__ method running time."), boundaries=[1,2,4,8,16,32,64], tag_keys=("model",), ) self.my_histogram.set_default_tags({"model": "123"}) def __call__(self): start = time.time() self.my_histogram.observe(time.time() - start) Args: name: Name of the metric. description: Description of the metric. boundaries: Boundaries of histogram buckets. tag_keys: Tag keys of the metric. r0Nr1r2 boundariesrc|r4t|tstdt|t |}t |||||idSr4r5)r=r1r2r^rr>s rr:zHistogram.__init__s  Jx77 LDNNLL *(33 {JAAA b!!!!!rrcdtt|dSrArBrCs rr;zHistogram.set_default_tagsrDrrFr$cvt|j|t||dS)zRObserve the given value, add serve context tag values to the tags N)r+rHr9observerJs rrbzHistogram.observes5 &dnd;;; t$$$$$r)r0NNrA)rMrNrOrPrQrrSrrr:rr;rrRrbrTrUs@rr]r]s!!L"&)- " " " "K " 5:& " " " " " "OT#s(^OOOOOO%%U3:.%d38n%%%%%%%%%%rr]rA)typingrrrrrr& ray.serverray.utilr ray.util.annotationsr rrrr)rQrr#r+r/rWr]rrrhs55555555555555 ******   XeCj%9U3Z6c3h&1E1c3h1111 V>!>!>!>!>!go>!>!>!B V3!3!3!3!3!GM3!3!3!l V;%;%;%;%;%!;%;%;%;%;%r