feat(instrumentation-llamaindex): migrate to OTel 1.40 GenAI semantic conventions #925
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,3 @@ | ||
| import type * as llamaindex from "llamaindex"; | ||
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| import { | ||
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@@ -13,15 +12,31 @@ import { | |
| } from "@opentelemetry/api"; | ||
| import { safeExecuteInTheMiddle } from "@opentelemetry/instrumentation"; | ||
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| import { | ||
| SpanAttributes, | ||
| FinishReasons, | ||
| } from "@traceloop/ai-semantic-conventions"; | ||
| import { | ||
| ATTR_GEN_AI_INPUT_MESSAGES, | ||
| ATTR_GEN_AI_OPERATION_NAME, | ||
| ATTR_GEN_AI_OUTPUT_MESSAGES, | ||
| ATTR_GEN_AI_PROVIDER_NAME, | ||
| ATTR_GEN_AI_REQUEST_MODEL, | ||
| ATTR_GEN_AI_REQUEST_TEMPERATURE, | ||
| ATTR_GEN_AI_REQUEST_TOP_P, | ||
| ATTR_GEN_AI_RESPONSE_FINISH_REASONS, | ||
| ATTR_GEN_AI_RESPONSE_ID, | ||
| ATTR_GEN_AI_RESPONSE_MODEL, | ||
| ATTR_GEN_AI_USAGE_INPUT_TOKENS, | ||
| ATTR_GEN_AI_USAGE_OUTPUT_TOKENS, | ||
| GEN_AI_OPERATION_NAME_VALUE_CHAT, | ||
| GEN_AI_PROVIDER_NAME_VALUE_OPENAI, | ||
| } from "@opentelemetry/semantic-conventions/incubating"; | ||
| import { | ||
| formatInputMessages, | ||
| formatOutputMessage, | ||
| mapOpenAIContentBlock, | ||
| } from "@traceloop/instrumentation-utils"; | ||
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| import { LlamaIndexInstrumentationConfig } from "./types"; | ||
| import { shouldSendPrompts, llmGeneratorWrapper } from "./utils"; | ||
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@@ -33,9 +48,23 @@ type AsyncResponseType = | |
| | AsyncIterable<llamaindex.ChatResponseChunk> | ||
| | AsyncIterable<llamaindex.CompletionResponse>; | ||
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| const classNameToProviderName: Record<string, string> = { | ||
| OpenAI: GEN_AI_PROVIDER_NAME_VALUE_OPENAI, | ||
| // Future providers: Anthropic: "anthropic", Gemini: "gcp.gemini", etc. | ||
| // See well-known values: https://opentelemetry.io/docs/specs/semconv/registry/attributes/gen-ai/#gen-ai-provider-name | ||
| }; | ||
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| export const openAIFinishReasonMap: Record<string, string> = { | ||
| stop: FinishReasons.STOP, | ||
| length: FinishReasons.LENGTH, | ||
| tool_calls: FinishReasons.TOOL_CALL, | ||
| content_filter: FinishReasons.CONTENT_FILTER, | ||
| function_call: FinishReasons.TOOL_CALL, | ||
| }; | ||
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| export class CustomLLMInstrumentation { | ||
| constructor( | ||
| private config: () => LlamaIndexInstrumentationConfig, | ||
| private diag: DiagLogger, | ||
| private tracer: () => Tracer, | ||
| ) {} | ||
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@@ -50,44 +79,34 @@ export class CustomLLMInstrumentation { | |
| const messages = params?.messages; | ||
| const streaming = params?.stream; | ||
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| const span = plugin.tracer().startSpan(`chat ${this.metadata.model}`, { | ||
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Contributor
There was a problem hiding this comment. Just verifying this is the new converntion ?
Member
Author
There was a problem hiding this comment. Yes. This is the new OTel 1.40 GenAI convention — span name format is {operation} {model} (e.g. chat gpt-4o), as specified in the https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/. 
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| kind: SpanKind.CLIENT, | ||
| }); | ||
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| try { | ||
| span.setAttribute( | ||
| ATTR_GEN_AI_PROVIDER_NAME, | ||
| classNameToProviderName[className] ?? className.toLowerCase(), | ||
| ); | ||
| span.setAttribute(ATTR_GEN_AI_REQUEST_MODEL, this.metadata.model); | ||
| span.setAttribute( | ||
| ATTR_GEN_AI_OPERATION_NAME, | ||
| GEN_AI_OPERATION_NAME_VALUE_CHAT, | ||
| ); | ||
| span.setAttribute(ATTR_GEN_AI_REQUEST_TOP_P, this.metadata.topP); | ||
| span.setAttribute( | ||
| ATTR_GEN_AI_REQUEST_TEMPERATURE, | ||
| this.metadata.temperature, | ||
| ); | ||
| if (shouldSendPrompts(plugin.config()) && messages) { | ||
| span.setAttribute( | ||
| ATTR_GEN_AI_INPUT_MESSAGES, | ||
| formatInputMessages(messages, mapOpenAIContentBlock), | ||
| ); | ||
| } | ||
| } catch (e) { | ||
| plugin.diag.warn(e); | ||
| plugin.config().exceptionLogger?.(e); | ||
| } | ||
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| const execContext = trace.setSpan(context.active(), span); | ||
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@@ -138,36 +157,62 @@ export class CustomLLMInstrumentation { | |
| ): T { | ||
| span.setAttribute(ATTR_GEN_AI_RESPONSE_MODEL, metadata.model); | ||
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| try { | ||
| const raw = (result as any).raw; | ||
| if (raw?.id) { | ||
| span.setAttribute(ATTR_GEN_AI_RESPONSE_ID, raw.id); | ||
| } | ||
| const finishReason: string | null = | ||
| raw?.choices?.[0]?.finish_reason ?? null; | ||
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| // finish_reasons: metadata, not content — always set outside shouldSendPrompts | ||
| if (finishReason != null) { | ||
| span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [ | ||
| openAIFinishReasonMap[finishReason] ?? finishReason, | ||
| ]); | ||
| } | ||
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| // Token usage: always set when available | ||
| const usage = raw?.usage; | ||
| if (usage) { | ||
| span.setAttribute(ATTR_GEN_AI_USAGE_INPUT_TOKENS, usage.prompt_tokens); | ||
| span.setAttribute( | ||
| ATTR_GEN_AI_USAGE_OUTPUT_TOKENS, | ||
| usage.completion_tokens, | ||
| ); | ||
| span.setAttribute( | ||
| SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS, | ||
| usage.total_tokens, | ||
| ); | ||
| } | ||
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| // output messages: content — always set inside shouldSendPrompts | ||
| if ( | ||
| shouldSendPrompts(this.config()) && | ||
| (result as llamaindex.ChatResponse).message | ||
| ) { | ||
| const content = (result as llamaindex.ChatResponse).message.content; | ||
| // Normalize to array so mapOpenAIContentBlock handles both string and block array | ||
| const contentArray = typeof content === "string" ? [content] : content; | ||
| span.setAttribute( | ||
| ATTR_GEN_AI_OUTPUT_MESSAGES, | ||
| formatOutputMessage( | ||
| contentArray, | ||
| finishReason, | ||
| openAIFinishReasonMap, | ||
| GEN_AI_OPERATION_NAME_VALUE_CHAT, | ||
| mapOpenAIContentBlock, | ||
| ), | ||
| ); | ||
| } | ||
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| span.setStatus({ code: SpanStatusCode.OK }); | ||
| } catch (e) { | ||
| this.diag.warn(e); | ||
| this.config().exceptionLogger?.(e); | ||
| } | ||
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| span.end(); | ||
| return result; | ||
| } | ||
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@@ -178,14 +223,67 @@ export class CustomLLMInstrumentation { | |
| metadata: llamaindex.LLMMetadata, | ||
| ): T { | ||
| span.setAttribute(ATTR_GEN_AI_RESPONSE_MODEL, metadata.model); | ||
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| return llmGeneratorWrapper(result, execContext, (message, lastChunk) => { | ||
| try { | ||
| // Extract finish_reason and usage from the last chunk's raw OpenAI | ||
| // response — available when stream_options: { include_usage: true } | ||
| // is set on the LLM (OpenAI sends usage in the final streaming chunk). | ||
| const lastRaw = lastChunk?.raw as any; | ||
| if (lastRaw?.id) { | ||
| span.setAttribute(ATTR_GEN_AI_RESPONSE_ID, lastRaw.id); | ||
| } | ||
| const finishReason: string | null = | ||
| lastRaw?.choices?.[0]?.finish_reason ?? null; | ||
| const usage = lastRaw?.usage ?? null; | ||
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| if (finishReason != null) { | ||
| span.setAttribute(ATTR_GEN_AI_RESPONSE_FINISH_REASONS, [ | ||
| openAIFinishReasonMap[finishReason] ?? finishReason, | ||
| ]); | ||
| } | ||
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| if (usage) { | ||
| span.setAttribute( | ||
| ATTR_GEN_AI_USAGE_INPUT_TOKENS, | ||
| usage.prompt_tokens, | ||
| ); | ||
| span.setAttribute( | ||
| ATTR_GEN_AI_USAGE_OUTPUT_TOKENS, | ||
| usage.completion_tokens, | ||
| ); | ||
| span.setAttribute( | ||
| SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS, | ||
| usage.total_tokens, | ||
| ); | ||
| } | ||
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| if (!finishReason && !usage) { | ||
| this.diag.debug( | ||
| "LlamaIndex streaming: no finish_reason or usage in last chunk. " + | ||
| "Set stream_options: { include_usage: true } on the LLM to capture token usage.", | ||
| ); | ||
| } | ||
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| // Note: streaming only produces text parts — LlamaIndex's streaming interface | ||
| // yields text deltas only, not full content blocks. Tool calls or multi-modal | ||
| // content are collapsed into a single text string by llmGeneratorWrapper. | ||
| if (shouldSendPrompts(this.config())) { | ||
| span.setAttribute( | ||
| ATTR_GEN_AI_OUTPUT_MESSAGES, | ||
| formatOutputMessage( | ||
| [message], | ||
| finishReason, | ||
| openAIFinishReasonMap, | ||
| GEN_AI_OPERATION_NAME_VALUE_CHAT, | ||
| mapOpenAIContentBlock, | ||
| ), | ||
| ); | ||
| } | ||
| } catch (e) { | ||
| this.diag.warn(e); | ||
| this.config().exceptionLogger?.(e); | ||
| } | ||
| span.setStatus({ code: SpanStatusCode.OK }); | ||
| span.end(); | ||
| }) as any; | ||
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The import from the incubating was when the att were not official yet.
See if some of the att can be imported from the official import now:
} from "@opentelemetry/semantic-conventions";
If not leave it of course
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Checked it — in v1.40.0, the main @opentelemetry/semantic-conventions entry point only exports stable attributes (HTTP, network, URL, etc.).
All GEN_AI attributes are still in Development status and only available via /incubating.
Confirmed in the https://opentelemetry.io/docs/specs/semconv/gen-ai/ and the
https://github.com/open-telemetry/semantic-conventions/releases/tag/v1.40.0.
So keeping the import from /incubating for now.