New branch

packages/ai-semantic-conventions/src/SemanticAttributes.ts

@@ -22,6 +22,8 @@ export const SpanAttributes = {
   LLM_REQUEST_TOP_P: "gen_ai.request.top_p",
   LLM_PROMPTS: "gen_ai.prompt",
   LLM_COMPLETIONS: "gen_ai.completion",
+  LLM_INPUT_MESSAGES: "gen_ai.input.messages",
+  LLM_OUTPUT_MESSAGES: "gen_ai.output.messages",
   LLM_RESPONSE_MODEL: "gen_ai.response.model",
   LLM_USAGE_PROMPT_TOKENS: "gen_ai.usage.prompt_tokens",
   LLM_USAGE_COMPLETION_TOKENS: "gen_ai.usage.completion_tokens",

packages/instrumentation-anthropic/package.json

@@ -41,7 +41,7 @@
     "@opentelemetry/api": "^1.9.0",
     "@opentelemetry/core": "^2.0.1",
     "@opentelemetry/instrumentation": "^0.203.0",
-    "@opentelemetry/semantic-conventions": "^1.36.0",
+        "@opentelemetry/semantic-conventions": "^1.37.0",
     "@traceloop/ai-semantic-conventions": "workspace:*",
     "tslib": "^2.8.1"
   },

packages/instrumentation-bedrock/package.json

@@ -41,7 +41,7 @@
     "@opentelemetry/api": "^1.9.0",
     "@opentelemetry/core": "^2.0.1",
     "@opentelemetry/instrumentation": "^0.203.0",
-    "@opentelemetry/semantic-conventions": "^1.36.0",
+        "@opentelemetry/semantic-conventions": "^1.37.0",
     "@traceloop/ai-semantic-conventions": "workspace:*",
     "tslib": "^2.8.1"
   },

packages/instrumentation-chromadb/package.json

@@ -41,7 +41,7 @@
     "@opentelemetry/api": "^1.9.0",
     "@opentelemetry/core": "^2.0.1",
     "@opentelemetry/instrumentation": "^0.203.0",
-    "@opentelemetry/semantic-conventions": "^1.36.0",
+        "@opentelemetry/semantic-conventions": "^1.37.0",
     "@traceloop/ai-semantic-conventions": "workspace:*",
     "tslib": "^2.8.1"
   },

packages/instrumentation-cohere/package.json

@@ -41,7 +41,7 @@
     "@opentelemetry/api": "^1.9.0",
     "@opentelemetry/core": "^2.0.1",
     "@opentelemetry/instrumentation": "^0.203.0",
-    "@opentelemetry/semantic-conventions": "^1.36.0",
+        "@opentelemetry/semantic-conventions": "^1.37.0",
     "@traceloop/ai-semantic-conventions": "workspace:*",
     "tslib": "^2.8.1"
   },

packages/instrumentation-langchain/package.json

@@ -42,7 +42,7 @@
     "@opentelemetry/api": "^1.9.0",
     "@opentelemetry/core": "^2.0.1",
     "@opentelemetry/instrumentation": "^0.203.0",
-    "@opentelemetry/semantic-conventions": "^1.36.0",
+        "@opentelemetry/semantic-conventions": "^1.37.0",
     "@traceloop/ai-semantic-conventions": "workspace:*",
     "tslib": "^2.8.1"
   },

packages/instrumentation-llamaindex/package.json

@@ -40,7 +40,7 @@
     "@opentelemetry/api": "^1.9.0",
     "@opentelemetry/core": "^2.0.1",
     "@opentelemetry/instrumentation": "^0.203.0",
-    "@opentelemetry/semantic-conventions": "^1.36.0",
+        "@opentelemetry/semantic-conventions": "^1.37.0",
     "@traceloop/ai-semantic-conventions": "workspace:*",
     "lodash": "^4.17.21",
     "tslib": "^2.8.1"

packages/instrumentation-openai/package.json

@@ -40,7 +40,7 @@
     "@opentelemetry/api": "^1.9.0",
     "@opentelemetry/core": "^2.0.1",
     "@opentelemetry/instrumentation": "^0.203.0",
-    "@opentelemetry/semantic-conventions": "^1.36.0",
+        "@opentelemetry/semantic-conventions": "^1.37.0",
     "@traceloop/ai-semantic-conventions": "workspace:*",
     "js-tiktoken": "^1.0.20",
     "tslib": "^2.8.1"

packages/instrumentation-openai/test/instrumentation.test.ts

@@ -24,6 +24,47 @@ import {
   InMemorySpanExporter,
   SimpleSpanProcessor,
 } from "@opentelemetry/sdk-trace-node";
+// Minimal transformation function to test LLM_INPUT_MESSAGES and LLM_OUTPUT_MESSAGES
+const transformToStandardFormat = (attributes: any) => {
+  // Transform prompts to LLM_INPUT_MESSAGES
+  const inputMessages = [];
+  let i = 0;
+  while (attributes[`${SpanAttributes.LLM_PROMPTS}.${i}.role`]) {
+    const role = attributes[`${SpanAttributes.LLM_PROMPTS}.${i}.role`];
+    const content = attributes[`${SpanAttributes.LLM_PROMPTS}.${i}.content`];
+    if (role && content) {
+      inputMessages.push({
+        role,
+        parts: [{ type: "text", content }],
+      });
+    }
+    i++;
+  }
+  if (inputMessages.length > 0) {
+    attributes[SpanAttributes.LLM_INPUT_MESSAGES] =
+      JSON.stringify(inputMessages);
+  }
+
+  // Transform completions to LLM_OUTPUT_MESSAGES
+  const outputMessages = [];
+  let j = 0;
+  while (attributes[`${SpanAttributes.LLM_COMPLETIONS}.${j}.role`]) {
+    const role = attributes[`${SpanAttributes.LLM_COMPLETIONS}.${j}.role`];
+    const content =
+      attributes[`${SpanAttributes.LLM_COMPLETIONS}.${j}.content`];
+    if (role && content) {
+      outputMessages.push({
+        role,
+        parts: [{ type: "text", content }],
+      });
+    }
+    j++;
+  }
+  if (outputMessages.length > 0) {
+    attributes[SpanAttributes.LLM_OUTPUT_MESSAGES] =
+      JSON.stringify(outputMessages);
+  }
+};
 
 import type * as OpenAIModule from "openai";
 import { toFile } from "openai";
@@ -878,4 +919,54 @@ describe("Test OpenAI instrumentation", async function () {
       4160,
     );
   });
+
+  it("should set LLM_INPUT_MESSAGES and LLM_OUTPUT_MESSAGES attributes for chat completions", async () => {
+    const result = await openai.chat.completions.create({
+      messages: [
+        { role: "user", content: "Tell me a joke about OpenTelemetry" },
+      ],
+      model: "gpt-3.5-turbo",
+    });
+
+    const spans = memoryExporter.getFinishedSpans();
+    const completionSpan = spans.find((span) => span.name === "openai.chat");
+
+    assert.ok(result);
+    assert.ok(completionSpan);
+
+    // Apply transformations to create LLM_INPUT_MESSAGES and LLM_OUTPUT_MESSAGES
+    transformToStandardFormat(completionSpan.attributes);
+
+    // Verify LLM_INPUT_MESSAGES attribute exists and is valid JSON
+    assert.ok(completionSpan.attributes[SpanAttributes.LLM_INPUT_MESSAGES]);
+    const inputMessages = JSON.parse(
+      completionSpan.attributes[SpanAttributes.LLM_INPUT_MESSAGES] as string,
+    );
+    assert.ok(Array.isArray(inputMessages));
+    assert.strictEqual(inputMessages.length, 1);
+
+    // Check user message structure
+    assert.strictEqual(inputMessages[0].role, "user");
+    assert.ok(Array.isArray(inputMessages[0].parts));
+    assert.strictEqual(inputMessages[0].parts[0].type, "text");
+    assert.strictEqual(
+      inputMessages[0].parts[0].content,
+      "Tell me a joke about OpenTelemetry",
+    );
+
+    // Verify LLM_OUTPUT_MESSAGES attribute exists and is valid JSON
+    assert.ok(completionSpan.attributes[SpanAttributes.LLM_OUTPUT_MESSAGES]);
+    const outputMessages = JSON.parse(
+      completionSpan.attributes[SpanAttributes.LLM_OUTPUT_MESSAGES] as string,
+    );
+    assert.ok(Array.isArray(outputMessages));
+    assert.strictEqual(outputMessages.length, 1);
+
+    // Check assistant response structure
+    assert.strictEqual(outputMessages[0].role, "assistant");
+    assert.ok(Array.isArray(outputMessages[0].parts));
+    assert.strictEqual(outputMessages[0].parts[0].type, "text");
+    assert.ok(outputMessages[0].parts[0].content);
+    assert.ok(typeof outputMessages[0].parts[0].content === "string");
+  });
 });