Dome fixes

Author: lenatraceloop

packages/instrumentation-llamaindex/src/custom-llm-instrumentation.ts

@@ -24,6 +24,7 @@ import {
   ATTR_GEN_AI_REQUEST_MODEL,
   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,
@@ -48,6 +49,8 @@ type AsyncResponseType =
 
 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
 };
 
 export const openAIFinishReasonMap: Record<string, string> = {
@@ -151,6 +154,9 @@ export class CustomLLMInstrumentation {
 
     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;
 
@@ -219,6 +225,9 @@ export class CustomLLMInstrumentation {
         // 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;
@@ -244,6 +253,16 @@ export class CustomLLMInstrumentation {
           );
         }
 
+        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.",
+          );
+        }
+
+        // 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,

packages/instrumentation-llamaindex/test/instrumentation.test.ts

@@ -31,6 +31,7 @@ import {
   ATTR_GEN_AI_PROVIDER_NAME,
   ATTR_GEN_AI_REQUEST_MODEL,
   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,
@@ -235,6 +236,7 @@ function makeMockChat(options: {
     return {
       message: { role: "assistant", content: responseContent },
       raw: {
+        id: "chatcmpl-test123",
         choices: [{ finish_reason: finishReason }],
         usage: {
           prompt_tokens: promptTokens,
@@ -264,6 +266,7 @@ function makeMockChatWithStreamUsage(options: {
         yield {
           delta: responseContent,
           raw: {
+            id: "chatcmpl-test123",
             choices: [{ finish_reason: finishReason }],
             usage: {
               prompt_tokens: promptTokens,
@@ -436,6 +439,38 @@ describe("CustomLLMInstrumentation — OTel 1.40 attributes", () => {
       );
     });
 
+    it("sets gen_ai.response.id", async () => {
+      const instr = makeInstrumentation();
+      const chat = makeMockChat({});
+      const wrapped = instr.chatWrapper({ className: "OpenAI" })(chat as any);
+      await wrapped.call(
+        { metadata: mockLLMMeta },
+        { messages: [{ role: "user", content: "hi" }] },
+      );
+
+      const span = otelExporter.getFinishedSpans()[0];
+      assert.strictEqual(
+        span.attributes[ATTR_GEN_AI_RESPONSE_ID],
+        "chatcmpl-test123",
+      );
+    });
+
+    it("unknown finish_reason passes through as-is to span attribute", async () => {
+      const instr = makeInstrumentation();
+      const chat = makeMockChat({ finishReason: "some_future_reason" });
+      const wrapped = instr.chatWrapper({ className: "OpenAI" })(chat as any);
+      await wrapped.call(
+        { metadata: mockLLMMeta },
+        { messages: [{ role: "user", content: "hi" }] },
+      );
+
+      const span = otelExporter.getFinishedSpans()[0];
+      assert.deepStrictEqual(
+        span.attributes[ATTR_GEN_AI_RESPONSE_FINISH_REASONS],
+        ["some_future_reason"],
+      );
+    });
+
     it("sets token usage attributes", async () => {
       const instr = makeInstrumentation();
       const chat = makeMockChat({ promptTokens: 10, completionTokens: 5 });