mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 03:04:13 +00:00
Merge branch 'main' into litellm_openmeter_integration
This commit is contained in:
Krish Dholakia
GitHub
commit
fffbb73465
31 changed files with 227 additions and 98 deletions
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@ -40,7 +40,7 @@ jobs:
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pip install "aioboto3==12.3.0"
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pip install langchain
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pip install lunary==0.2.5
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pip install "langfuse==2.7.3"
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pip install "langfuse==2.27.1"
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pip install numpydoc
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pip install traceloop-sdk==0.0.69
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pip install openai
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@ -7,7 +7,7 @@ repos:
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rev: 7.0.0 # The version of flake8 to use
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hooks:
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- id: flake8
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exclude: ^litellm/tests/|^litellm/proxy/proxy_cli.py|^litellm/integrations/|^litellm/proxy/tests/
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exclude: ^litellm/tests/|^litellm/proxy/proxy_cli.py|^litellm/proxy/tests/
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additional_dependencies: [flake8-print]
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files: litellm/.*\.py
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- repo: local
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@ -17,6 +17,7 @@ class LangFuseLogger:
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):
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try:
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from langfuse import Langfuse
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import langfuse
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except Exception as e:
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raise Exception(
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f"\033[91mLangfuse not installed, try running 'pip install langfuse' to fix this error: {e}\n{traceback.format_exc()}\033[0m"
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@ -27,14 +28,20 @@ class LangFuseLogger:
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self.langfuse_host = os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com")
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self.langfuse_release = os.getenv("LANGFUSE_RELEASE")
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self.langfuse_debug = os.getenv("LANGFUSE_DEBUG")
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self.Langfuse = Langfuse(
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public_key=self.public_key,
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secret_key=self.secret_key,
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host=self.langfuse_host,
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release=self.langfuse_release,
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debug=self.langfuse_debug,
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flush_interval=flush_interval, # flush interval in seconds
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)
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parameters = {
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"public_key": self.public_key,
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"secret_key": self.secret_key,
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"host": self.langfuse_host,
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"release": self.langfuse_release,
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"debug": self.langfuse_debug,
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"flush_interval": flush_interval, # flush interval in seconds
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}
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if Version(langfuse.version.__version__) >= Version("2.6.0"):
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parameters["sdk_integration"] = "litellm"
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self.Langfuse = Langfuse(**parameters)
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# set the current langfuse project id in the environ
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# this is used by Alerting to link to the correct project
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@ -369,8 +376,6 @@ class LangFuseLogger:
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"headers": clean_headers,
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}
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print_verbose(f"trace_params: {trace_params}")
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trace = self.Langfuse.trace(**trace_params)
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generation_id = None
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@ -420,8 +425,6 @@ class LangFuseLogger:
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"completion_start_time", None
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)
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print_verbose(f"generation_params: {generation_params}")
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generation_client = trace.generation(**generation_params)
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return generation_client.trace_id, generation_id
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|
||||
6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/ui/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","5",{"name":"next-size-adjust"}]]
|
||||
1:null
|
||||
|
|
|
|||
|
|
@ -7550,7 +7550,7 @@ async def model_metrics(
|
|||
"LiteLLM_SpendLogs"
|
||||
WHERE
|
||||
"startTime" >= NOW() - INTERVAL '30 days'
|
||||
AND "model" = $1
|
||||
AND "model" = $1 AND "cache_hit" != 'True'
|
||||
GROUP BY
|
||||
api_base,
|
||||
model,
|
||||
|
|
@ -7599,7 +7599,7 @@ async def model_metrics(
|
|||
for day in _daily_entries:
|
||||
entry = {"date": str(day)}
|
||||
for model_key, latency in _daily_entries[day].items():
|
||||
entry[model_key] = round(latency, 8)
|
||||
entry[model_key] = latency
|
||||
response.append(entry)
|
||||
|
||||
return {
|
||||
|
|
@ -7608,6 +7608,67 @@ async def model_metrics(
|
|||
}
|
||||
|
||||
|
||||
@router.get(
|
||||
"/model/metrics/slow_responses",
|
||||
description="View number of hanging requests per model_group",
|
||||
tags=["model management"],
|
||||
include_in_schema=False,
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
)
|
||||
async def model_metrics_slow_responses(
|
||||
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
_selected_model_group: Optional[str] = "gpt-4-32k",
|
||||
startTime: Optional[datetime] = None,
|
||||
endTime: Optional[datetime] = None,
|
||||
):
|
||||
global prisma_client, llm_router, proxy_logging_obj
|
||||
if prisma_client is None:
|
||||
raise ProxyException(
|
||||
message="Prisma Client is not initialized",
|
||||
type="internal_error",
|
||||
param="None",
|
||||
code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
)
|
||||
startTime = startTime or datetime.now() - timedelta(days=30)
|
||||
endTime = endTime or datetime.now()
|
||||
|
||||
alerting_threshold = (
|
||||
proxy_logging_obj.slack_alerting_instance.alerting_threshold or 300
|
||||
)
|
||||
alerting_threshold = int(alerting_threshold)
|
||||
|
||||
sql_query = """
|
||||
SELECT
|
||||
api_base,
|
||||
COUNT(*) AS total_count,
|
||||
SUM(CASE
|
||||
WHEN ("endTime" - "startTime") >= (INTERVAL '1 SECOND' * CAST($1 AS INTEGER)) THEN 1
|
||||
ELSE 0
|
||||
END) AS slow_count
|
||||
FROM
|
||||
"LiteLLM_SpendLogs"
|
||||
WHERE
|
||||
"model" = $2
|
||||
AND "cache_hit" != 'True'
|
||||
GROUP BY
|
||||
api_base
|
||||
ORDER BY
|
||||
slow_count DESC;
|
||||
"""
|
||||
|
||||
db_response = await prisma_client.db.query_raw(
|
||||
sql_query, alerting_threshold, _selected_model_group
|
||||
)
|
||||
|
||||
if db_response is not None:
|
||||
for row in db_response:
|
||||
_api_base = row.get("api_base") or ""
|
||||
if "/openai/" in _api_base:
|
||||
_api_base = _api_base.split("/openai/")[0]
|
||||
row["api_base"] = _api_base
|
||||
return db_response
|
||||
|
||||
|
||||
@router.get(
|
||||
"/model/metrics/exceptions",
|
||||
description="View number of failed requests per model on config.yaml",
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "litellm"
|
||||
version = "1.35.34"
|
||||
version = "1.35.35"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
authors = ["BerriAI"]
|
||||
license = "MIT"
|
||||
|
|
@ -80,7 +80,7 @@ requires = ["poetry-core", "wheel"]
|
|||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.commitizen]
|
||||
version = "1.35.34"
|
||||
version = "1.35.35"
|
||||
version_files = [
|
||||
"pyproject.toml:^version"
|
||||
]
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ google-cloud-aiplatform==1.47.0 # for vertex ai calls
|
|||
anthropic[vertex]==0.21.3
|
||||
google-generativeai==0.5.0 # for vertex ai calls
|
||||
async_generator==1.10.0 # for async ollama calls
|
||||
langfuse==2.7.3 # for langfuse self-hosted logging
|
||||
langfuse==2.27.1 # for langfuse self-hosted logging
|
||||
datadog-api-client==2.23.0 # for datadog logging
|
||||
prometheus_client==0.20.0 # for /metrics endpoint on proxy
|
||||
orjson==3.9.15 # fast /embedding responses
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -1 +1 @@
|
|||
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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@ -1 +1 @@
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|||
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|
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|
@ -1,7 +1,7 @@
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4:I[5613,[],""]
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5:I[31778,[],""]
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|
||||
6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/ui/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","5",{"name":"next-size-adjust"}]]
|
||||
1:null
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import {
|
|||
} from "@tremor/react";
|
||||
import { TabPanel, TabPanels, TabGroup, TabList, Tab, TextInput, Icon } from "@tremor/react";
|
||||
import { Select, SelectItem, MultiSelect, MultiSelectItem } from "@tremor/react";
|
||||
import { modelInfoCall, userGetRequesedtModelsCall, modelCreateCall, Model, modelCostMap, modelDeleteCall, healthCheckCall, modelUpdateCall, modelMetricsCall, modelExceptionsCall } from "./networking";
|
||||
import { modelInfoCall, userGetRequesedtModelsCall, modelCreateCall, Model, modelCostMap, modelDeleteCall, healthCheckCall, modelUpdateCall, modelMetricsCall, modelExceptionsCall, modelMetricsSlowResponsesCall } from "./networking";
|
||||
import { BarChart, AreaChart } from "@tremor/react";
|
||||
import {
|
||||
Button as Button2,
|
||||
|
|
@ -205,6 +205,7 @@ const ModelDashboard: React.FC<ModelDashboardProps> = ({
|
|||
const [modelExceptions, setModelExceptions] = useState<any[]>([]);
|
||||
const [allExceptions, setAllExceptions] = useState<any[]>([]);
|
||||
const [failureTableData, setFailureTableData] = useState<any[]>([]);
|
||||
const [slowResponsesData, setSlowResponsesData] = useState<any[]>([]);
|
||||
|
||||
const EditModelModal: React.FC<EditModelModalProps> = ({ visible, onCancel, model, onSubmit }) => {
|
||||
const [form] = Form.useForm();
|
||||
|
|
@ -479,39 +480,51 @@ const handleEditSubmit = async (formValues: Record<string, any>) => {
|
|||
setAllExceptions(modelExceptionsResponse.exception_types);
|
||||
|
||||
|
||||
let modelMetricsData = modelMetricsResponse.data;
|
||||
let successdeploymentToSuccess: Record<string, number> = {};
|
||||
for (let i = 0; i < modelMetricsData.length; i++) {
|
||||
let element = modelMetricsData[i];
|
||||
let _model_name = element.model;
|
||||
let _num_requests = element.num_requests;
|
||||
successdeploymentToSuccess[_model_name] = _num_requests
|
||||
}
|
||||
console.log("successdeploymentToSuccess:", successdeploymentToSuccess)
|
||||
const slowResponses = await modelMetricsSlowResponsesCall(
|
||||
accessToken,
|
||||
userID,
|
||||
userRole,
|
||||
null
|
||||
)
|
||||
|
||||
let failureTableData = [];
|
||||
let _failureData = modelExceptionsResponse.data;
|
||||
for (let i = 0; i < _failureData.length; i++) {
|
||||
const model = _failureData[i];
|
||||
let _model_name = model.model;
|
||||
let total_exceptions = model.total_exceptions;
|
||||
let total_Requests = successdeploymentToSuccess[_model_name];
|
||||
if (total_Requests == null) {
|
||||
total_Requests = 0
|
||||
}
|
||||
let _data = {
|
||||
model: _model_name,
|
||||
total_exceptions: total_exceptions,
|
||||
total_Requests: total_Requests,
|
||||
failure_rate: total_Requests / total_exceptions
|
||||
}
|
||||
failureTableData.push(_data);
|
||||
// sort failureTableData by failure_rate
|
||||
failureTableData.sort((a, b) => b.failure_rate - a.failure_rate);
|
||||
console.log("slowResponses:", slowResponses)
|
||||
|
||||
setFailureTableData(failureTableData);
|
||||
console.log("failureTableData:", failureTableData);
|
||||
}
|
||||
setSlowResponsesData(slowResponses);
|
||||
|
||||
|
||||
// let modelMetricsData = modelMetricsResponse.data;
|
||||
// let successdeploymentToSuccess: Record<string, number> = {};
|
||||
// for (let i = 0; i < modelMetricsData.length; i++) {
|
||||
// let element = modelMetricsData[i];
|
||||
// let _model_name = element.model;
|
||||
// let _num_requests = element.num_requests;
|
||||
// successdeploymentToSuccess[_model_name] = _num_requests
|
||||
// }
|
||||
// console.log("successdeploymentToSuccess:", successdeploymentToSuccess)
|
||||
|
||||
// let failureTableData = [];
|
||||
// let _failureData = modelExceptionsResponse.data;
|
||||
// for (let i = 0; i < _failureData.length; i++) {
|
||||
// const model = _failureData[i];
|
||||
// let _model_name = model.model;
|
||||
// let total_exceptions = model.total_exceptions;
|
||||
// let total_Requests = successdeploymentToSuccess[_model_name];
|
||||
// if (total_Requests == null) {
|
||||
// total_Requests = 0
|
||||
// }
|
||||
// let _data = {
|
||||
// model: _model_name,
|
||||
// total_exceptions: total_exceptions,
|
||||
// total_Requests: total_Requests,
|
||||
// failure_rate: total_Requests / total_exceptions
|
||||
// }
|
||||
// failureTableData.push(_data);
|
||||
// // sort failureTableData by failure_rate
|
||||
// failureTableData.sort((a, b) => b.failure_rate - a.failure_rate);
|
||||
|
||||
// setFailureTableData(failureTableData);
|
||||
// console.log("failureTableData:", failureTableData);
|
||||
// }
|
||||
|
||||
} catch (error) {
|
||||
console.error("There was an error fetching the model data", error);
|
||||
|
|
@ -691,6 +704,18 @@ const handleEditSubmit = async (formValues: Record<string, any>) => {
|
|||
setModelExceptions(modelExceptionsResponse.data);
|
||||
setAllExceptions(modelExceptionsResponse.exception_types);
|
||||
|
||||
|
||||
const slowResponses = await modelMetricsSlowResponsesCall(
|
||||
accessToken,
|
||||
userID,
|
||||
userRole,
|
||||
modelGroup
|
||||
)
|
||||
|
||||
console.log("slowResponses:", slowResponses)
|
||||
|
||||
setSlowResponsesData(slowResponses);
|
||||
|
||||
} catch (error) {
|
||||
console.error("Failed to fetch model metrics", error);
|
||||
}
|
||||
|
|
@ -720,17 +745,20 @@ const handleEditSubmit = async (formValues: Record<string, any>) => {
|
|||
return (
|
||||
<div className="w-150 rounded-tremor-default border border-tremor-border bg-tremor-background p-2 text-tremor-default shadow-tremor-dropdown">
|
||||
{date && <p className="text-tremor-content-emphasis mb-2">Date: {date}</p>}
|
||||
{sortedPayload.map((category: any, idx: number) => (
|
||||
{sortedPayload.map((category: any, idx: number) => {
|
||||
const roundedValue = parseFloat(category.value.toFixed(5));
|
||||
const displayValue =
|
||||
roundedValue === 0 && category.value > 0 ? "<0.00001" : roundedValue.toFixed(5);
|
||||
return (
|
||||
<div key={idx} className="flex justify-between">
|
||||
<div className="flex items-center space-x-2">
|
||||
<div className={`w-2 h-2 mt-1 rounded-full bg-${category.color}-500`} />
|
||||
<p className="text-tremor-content">{category.dataKey}</p>
|
||||
</div>
|
||||
<p className="font-medium text-tremor-content-emphasis text-righ ml-2">
|
||||
{category.value.toFixed(5)}
|
||||
</p>
|
||||
<p className="font-medium text-tremor-content-emphasis text-righ ml-2">{displayValue}</p>
|
||||
</div>
|
||||
))}
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
|
@ -1107,7 +1135,7 @@ const handleEditSubmit = async (formValues: Record<string, any>) => {
|
|||
|
||||
<Grid numItems={2}>
|
||||
<Col>
|
||||
<Card className="mr-2">
|
||||
<Card className="mr-2 max-h-[400px] min-h-[400px]">
|
||||
<Title>Avg Latency per Token</Title><p className="text-gray-500 italic"> (seconds/token)</p>
|
||||
<Text className="text-gray-500 italic mt-1 mb-1">average Latency for successfull requests divided by the total tokens</Text>
|
||||
{ modelMetrics && modelMetricsCategories && (
|
||||
|
|
@ -1123,29 +1151,26 @@ const handleEditSubmit = async (formValues: Record<string, any>) => {
|
|||
/>
|
||||
)}
|
||||
|
||||
|
||||
|
||||
</Card>
|
||||
</Col>
|
||||
<Col>
|
||||
<Card className="ml-2">
|
||||
<Card className="ml-2 max-h-[400px] min-h-[400px] overflow-y-auto">
|
||||
<Table>
|
||||
<TableHead>
|
||||
<TableRow>
|
||||
<TableHeaderCell>Model</TableHeaderCell>
|
||||
<TableHeaderCell>Success Requests</TableHeaderCell>
|
||||
<TableHeaderCell>Error Requests</TableHeaderCell>
|
||||
<TableHeaderCell>Failure %</TableHeaderCell>
|
||||
<TableHeaderCell>Deployment</TableHeaderCell>
|
||||
<TableHeaderCell>Success Responses</TableHeaderCell>
|
||||
<TableHeaderCell>Slow Responses <p>Success Responses taking 600+s</p></TableHeaderCell>
|
||||
|
||||
</TableRow>
|
||||
</TableHead>
|
||||
<TableBody>
|
||||
{failureTableData.map((metric, idx) => (
|
||||
{slowResponsesData.map((metric, idx) => (
|
||||
<TableRow key={idx}>
|
||||
<TableCell>{metric.model}</TableCell>
|
||||
<TableCell>{metric.total_Requests}</TableCell>
|
||||
<TableCell>{metric.total_exceptions}</TableCell>
|
||||
<TableCell>{metric.failure_rate}%</TableCell>
|
||||
<TableCell>{metric.api_base}</TableCell>
|
||||
<TableCell>{metric.total_count}</TableCell>
|
||||
<TableCell>{metric.slow_count}</TableCell>
|
||||
|
||||
</TableRow>
|
||||
|
||||
))}
|
||||
|
|
|
|||
|
|
@ -475,6 +475,46 @@ export const modelMetricsCall = async (
|
|||
};
|
||||
|
||||
|
||||
|
||||
export const modelMetricsSlowResponsesCall = async (
|
||||
accessToken: String,
|
||||
userID: String,
|
||||
userRole: String,
|
||||
modelGroup: String | null,
|
||||
) => {
|
||||
/**
|
||||
* Get all models on proxy
|
||||
*/
|
||||
try {
|
||||
let url = proxyBaseUrl ? `${proxyBaseUrl}/model/metrics/slow_responses` : `/model/metrics/slow_responses`;
|
||||
if (modelGroup) {
|
||||
url = `${url}?_selected_model_group=${modelGroup}`
|
||||
}
|
||||
// message.info("Requesting model data");
|
||||
const response = await fetch(url, {
|
||||
method: "GET",
|
||||
headers: {
|
||||
Authorization: `Bearer ${accessToken}`,
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const errorData = await response.text();
|
||||
message.error(errorData, 20);
|
||||
throw new Error("Network response was not ok");
|
||||
}
|
||||
const data = await response.json();
|
||||
// message.info("Received model data");
|
||||
return data;
|
||||
// Handle success - you might want to update some state or UI based on the created key
|
||||
} catch (error) {
|
||||
console.error("Failed to create key:", error);
|
||||
throw error;
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
export const modelExceptionsCall = async (
|
||||
accessToken: String,
|
||||
userID: String,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue