Add time to first token for opentelemetry-util-genai

instrumentation-loongsuite/loongsuite-instrumentation-agentscope/src/opentelemetry/instrumentation/agentscope/_wrapper.py

@@ -17,6 +17,7 @@
 from __future__ import annotations
 
 import logging
+import timeit
 from functools import wraps
 from typing import Any, AsyncGenerator
 
@@ -57,7 +58,13 @@ async def _wrap_streaming_response(
         """Wrap streaming response to update invocation when done."""
         try:
             last_chunk = None
+            first_token_received = False
             async for chunk in generator:
+                # Record time when first token is received
+                if not first_token_received:
+                    first_token_received = True
+                    invocation.monotonic_first_token_s = timeit.default_timer()
+
                 last_chunk = chunk
                 yield chunk
 

instrumentation-loongsuite/loongsuite-instrumentation-agentscope/tests/test_model.py

@@ -33,6 +33,7 @@ def _assert_chat_span_attributes(
     temperature: float = None,
     max_tokens: int = None,
     top_p: float = None,
+    expect_time_to_first_token: bool = False,
 ):
     """Assert common chat model span attributes"""
     # Span name format: "chat {model}"
@@ -146,6 +147,24 @@ def _assert_chat_span_attributes(
         )
         assert span.attributes["gen_ai.request.top_p"] == top_p
 
+    # Assert time to first token for streaming responses (in nanoseconds)
+    if expect_time_to_first_token:
+        assert "gen_ai.response.time_to_first_token" in span.attributes, (
+            "Missing gen_ai.response.time_to_first_token"
+        )
+        ttft_ns = span.attributes["gen_ai.response.time_to_first_token"]
+        assert isinstance(ttft_ns, int), (
+            f"time_to_first_token should be an integer (nanoseconds), got {type(ttft_ns)}"
+        )
+        assert ttft_ns > 0, (
+            f"time_to_first_token should be positive, got {ttft_ns}"
+        )
+    else:
+        # For non-streaming responses, TTFT should not be present
+        assert "gen_ai.response.time_to_first_token" not in span.attributes, (
+            "gen_ai.response.time_to_first_token should not be present for non-streaming"
+        )
+
 
 @pytest.mark.vcr()
 def test_model_call_basic(instrument_no_content, span_exporter, request):
@@ -192,6 +211,7 @@ async def call_model():
         request_model="qwen-max",
         expect_input_messages=False,  # Do not capture content by default
         expect_output_messages=False,  # Do not capture content by default
+        expect_time_to_first_token=True,
     )
 
     print("✓ Model call (basic) completed successfully")
@@ -237,6 +257,7 @@ async def call_model():
         request_model="qwen-max",
         expect_input_messages=False,
         expect_output_messages=False,
+        expect_time_to_first_token=True,
     )
 
     print("✓ Model call (with messages) completed successfully")
@@ -274,6 +295,7 @@ async def test_model_call_async(instrument_no_content, span_exporter, request):
         request_model="qwen-max",
         expect_input_messages=False,
         expect_output_messages=False,
+        expect_time_to_first_token=True,
     )
 
     print("✓ Model call (async) completed successfully")
@@ -321,6 +343,7 @@ async def call_model():
         request_model="qwen-max",
         expect_input_messages=False,
         expect_output_messages=False,
+        expect_time_to_first_token=True,
     )
 
     print("✓ Model call (streaming) completed successfully")
@@ -369,6 +392,7 @@ async def call_model():
         request_model="qwen-max",
         expect_input_messages=False,
         expect_output_messages=False,
+        expect_time_to_first_token=True,
     )
 
     print("✓ Model call (with parameters) completed successfully")
@@ -417,6 +441,7 @@ async def call_model():
         request_model="qwen-max",
         expect_input_messages=True,  # Content capture enabled
         expect_output_messages=True,  # Content capture enabled
+        expect_time_to_first_token=False,
     )
 
     print("✓ Model call (with content capture) completed successfully")
@@ -459,6 +484,7 @@ async def call_model():
         request_model="qwen-max",
         expect_input_messages=False,  # Content capture disabled
         expect_output_messages=False,  # Content capture disabled
+        expect_time_to_first_token=True,
     )
 
     print("✓ Model call (no content capture) completed successfully")
@@ -505,6 +531,7 @@ async def call_model(content: str):
             request_model="qwen-max",
             expect_input_messages=False,
             expect_output_messages=False,
+            expect_time_to_first_token=True,
         )
 
     print("✓ Model call (multiple sequential) completed successfully")

instrumentation-loongsuite/loongsuite-instrumentation-dashscope/pyproject.toml

@@ -10,7 +10,7 @@ readme = "README.rst"
 license = "Apache-2.0"
 requires-python = ">=3.9"
 authors = [
-  { name = "LoongSuite Python Agent Authors", email = "qp467389@alibaba-inc.com" },
+  { name = "Minghui Zhang", email = "zmh1625lumian@gmail.com" },
 ]
 classifiers = [
   "Development Status :: 4 - Beta",

instrumentation-loongsuite/loongsuite-instrumentation-dashscope/src/opentelemetry/instrumentation/dashscope/patch/generation.py

@@ -17,6 +17,7 @@
 from __future__ import annotations
 
 import logging
+import timeit
 
 from opentelemetry.util.genai.types import Error
 
@@ -169,9 +170,15 @@ def _wrap_sync_generator(
     """
     last_response = None
     accumulated_text = ""
+    first_token_received = False
 
     try:
         for chunk in generator:
+            # Record time when first token is received
+            if not first_token_received:
+                first_token_received = True
+                invocation.monotonic_first_token_s = timeit.default_timer()
+
             last_response = chunk
 
             # If incremental_output is True, accumulate text from each chunk
@@ -224,9 +231,15 @@ async def _wrap_async_generator(
     """
     last_response = None
     accumulated_text = ""
+    first_token_received = False
 
     try:
         async for chunk in generator:
+            # Record time when first token is received
+            if not first_token_received:
+                first_token_received = True
+                invocation.monotonic_first_token_s = timeit.default_timer()
+
             last_response = chunk
 
             # If incremental_output is True, accumulate text from each chunk

instrumentation-loongsuite/loongsuite-instrumentation-dashscope/src/opentelemetry/instrumentation/dashscope/patch/image_synthesis.py

@@ -126,7 +126,6 @@ def wrap_image_synthesis_async_call(
     try:
         # Create invocation object
         invocation = _create_invocation_from_image_synthesis(kwargs, model)
-        invocation.attributes["gen_ai.request.async"] = True
 
         # Start LLM invocation (creates span)
         handler.start_llm(invocation)
@@ -196,7 +195,6 @@ def wrap_image_synthesis_wait(wrapped, instance, args, kwargs, handler=None):
         invocation = _create_invocation_from_image_synthesis({}, "unknown")
         # TODO: Add semantic conventions for wait operations
         invocation.operation_name = "wait generate_content"
-        invocation.attributes["gen_ai.request.async"] = True
         # Note: response_id will be set from response.output.task_id in _update_invocation_from_image_synthesis_response
         # We set task_id here as a fallback
         invocation.response_id = task_id

instrumentation-loongsuite/loongsuite-instrumentation-dashscope/src/opentelemetry/instrumentation/dashscope/patch/multimodal_conversation.py

@@ -17,6 +17,7 @@
 from __future__ import annotations
 
 import logging
+import timeit
 
 from opentelemetry.util.genai.types import Error
 
@@ -111,9 +112,15 @@ def _wrap_multimodal_sync_generator(
     """
     last_response = None
     accumulated_text = ""
+    first_token_received = False
 
     try:
         for chunk in generator:
+            # Record time when first token is received
+            if not first_token_received:
+                first_token_received = True
+                invocation.monotonic_first_token_s = timeit.default_timer()
+
             last_response = chunk
 
             # If incremental_output is True, accumulate text

instrumentation-loongsuite/loongsuite-instrumentation-dashscope/src/opentelemetry/instrumentation/dashscope/patch/video_synthesis.py

@@ -128,7 +128,6 @@ def wrap_video_synthesis_async_call(
     try:
         # Create invocation object
         invocation = _create_invocation_from_video_synthesis(kwargs, model)
-        invocation.attributes["gen_ai.request.async"] = True
 
         # Start LLM invocation (creates span)
         handler.start_llm(invocation)
@@ -191,7 +190,6 @@ def wrap_video_synthesis_wait(wrapped, instance, args, kwargs, handler=None):
         # Create invocation object
         invocation = _create_invocation_from_video_synthesis({}, "unknown")
         invocation.operation_name = "wait generate_content"
-        invocation.attributes["gen_ai.request.async"] = True
         invocation.response_id = task_id
 
         # Start LLM invocation (creates span)

instrumentation-loongsuite/loongsuite-instrumentation-dashscope/src/opentelemetry/instrumentation/dashscope/utils/common.py

@@ -110,7 +110,7 @@ def _extract_task_id(task: Any) -> Optional[str]:
             if task_id:
                 return task_id
     except (KeyError, AttributeError) as e:
-        logger.debug("Failed to extract task_id from task: %s", e)
+        logger.debug("Failed to extract task_id from task parameter: %s", e)
 
     return None
 

instrumentation-loongsuite/loongsuite-instrumentation-dashscope/src/opentelemetry/instrumentation/dashscope/utils/generation.py

@@ -253,10 +253,10 @@ def _extract_tool_definitions(kwargs: dict) -> list[ToolDefinition]:
                 if isinstance(plugins, list):
                     tools = plugins
             except (json.JSONDecodeError, TypeError, AttributeError) as e:
+                # If parsing fails, return empty list
                 logger.debug(
-                    "Failed to extract tool definitions from plugins: %s", e
+                    "Failed to parse tool definitions from response: %s", e
                 )
-                # If parsing fails, return empty list
                 return tool_definitions
 
     # Convert tool definitions to FunctionToolDefinition objects
@@ -421,8 +421,8 @@ def _extract_output_messages(response: Any) -> List[OutputMessage]:
                     )
                 )
     except (KeyError, AttributeError) as e:
-        logger.debug("Failed to extract output messages from response: %s", e)
         # If any attribute access fails, return empty list
+        logger.debug("Failed to extract output messages from response: %s", e)
         return output_messages
 
     return output_messages
@@ -554,7 +554,8 @@ def _update_invocation_from_response(
                 invocation.response_model_name = response_model
         except (KeyError, AttributeError) as e:
             logger.debug(
-                "Failed to extract response model name from response: %s", e
+                "Failed to extract response model from Generation response: %s",
+                e,
             )
 
         # Extract request ID (if available)
@@ -563,7 +564,9 @@ def _update_invocation_from_response(
             if request_id:
                 invocation.response_id = request_id
         except (KeyError, AttributeError) as e:
-            logger.debug("Failed to extract request id from response: %s", e)
+            logger.debug(
+                "Failed to extract request_id from Generation response: %s", e
+            )
     except (KeyError, AttributeError) as e:
         # If any attribute access fails, silently continue with available data
         logger.debug(
@@ -591,7 +594,10 @@ def _create_accumulated_response(original_response, accumulated_text):
                 return original_response
             except (AttributeError, TypeError) as e:
                 # If we can't modify, create a wrapper object
-                logger.debug("Failed to modify output text: %s", e)
+                logger.debug(
+                    "Failed to modify output.text directly, creating wrapper: %s",
+                    e,
+                )
 
         # Create wrapper objects with accumulated text
         class AccumulatedOutput:
@@ -622,5 +628,7 @@ def __init__(self, original_response, accumulated_output):
         return AccumulatedResponse(original_response, accumulated_output)
     except (KeyError, AttributeError) as e:
         # If modification fails, return original response
-        logger.debug("Failed to create accumulated response: %s", e)
+        logger.debug(
+            "Failed to create accumulated response, returning original: %s", e
+        )
         return original_response