openlit
Version:
OpenTelemetry-native Auto instrumentation library for monitoring LLM Applications, facilitating the integration of observability into your GenAI-driven projects
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TypeScript
import { Span, Tracer } from '@opentelemetry/api';
import BaseWrapper, { BaseSpanAttributes } from '../base-wrapper';
declare class AzureAIInferenceWrapper extends BaseWrapper {
static aiSystem: string;
static defaultServerAddress: string;
static defaultServerPort: number;
/**
* Extracts server address and port from an endpoint URL string.
*/
static parseEndpoint(endpoint: string): {
serverAddress: string;
serverPort: number;
};
static _patchChatComplete(tracer: Tracer, serverAddress: string, serverPort: number): any;
static _chatCompletion({ body, genAIEndpoint, httpResponse, span, serverAddress, serverPort, }: {
body: any;
genAIEndpoint: string;
httpResponse: any;
span: Span;
serverAddress: string;
serverPort: number;
}): Promise<any>;
/**
* Wraps an SSE body stream (Node.js IncomingMessage / ReadableStream) to
* aggregate telemetry while passing through chunks to the caller.
* Returns an async-iterable that yields the raw SSE buffers/strings so
* downstream consumers (e.g. createSseStream) keep working.
*/
static _wrapSseStream(body: any, requestBody: any, genAIEndpoint: string, span: Span, serverAddress: string, serverPort: number): any;
static _chatCompletionCommonSetter({ body, genAIEndpoint, result, span, serverAddress, serverPort, ttft, tbt, }: {
body: any;
genAIEndpoint: string;
result: any;
span: Span;
serverAddress: string;
serverPort: number;
ttft?: number;
tbt?: number;
}): BaseSpanAttributes;
static _patchEmbeddings(tracer: Tracer, serverAddress: string, serverPort: number): any;
}
export default AzureAIInferenceWrapper;