QA nits from CI

Author: Andrei Bratu

pyproject.toml

@@ -1,3 +1,6 @@
+[project]
+name = "humanloop"
+
 [tool.poetry]
 name = "humanloop"
 version = "0.8.22"

src/humanloop/otel/exporter.py

@@ -206,7 +206,13 @@ def _complete_flow_log(self, span_id: int) -> None:
                 if len(flow_children_span_ids) == 0:
                     # All logs in the Trace have been uploaded, mark the Flow Log as complete
                     flow_log_id = self._span_id_to_uploaded_log_id[flow_log_span_id]
-                    self._client.flows.update_log(log_id=flow_log_id, trace_status="complete")
+                    if flow_log_id is None:
+                        logger.error(
+                            "[HumanloopSpanExporter] Cannot complete Flow log %s, log ID is None",
+                            flow_log_span_id,
+                        )
+                    else:
+                        self._client.flows.update_log(log_id=flow_log_id, trace_status="complete")
                 break
 
     def _export_span_dispatch(self, span: ReadableSpan) -> None:
@@ -324,11 +330,14 @@ def _export_flow(self, span: ReadableSpan) -> None:
             key=HUMANLOOP_LOG_KEY,
         )
         # Spans that must be uploaded before the Flow Span is completed
-        prerequisites = read_from_opentelemetry_span(
-            span=span,
-            key=HUMANLOOP_FLOW_PREREQUISITES_KEY,
-        )
-        self._flow_log_prerequisites[span.context.span_id] = set(prerequisites)
+        try:
+            prerequisites: list[int] = read_from_opentelemetry_span(  # type: ignore
+                span=span,
+                key=HUMANLOOP_FLOW_PREREQUISITES_KEY,
+            )
+            self._flow_log_prerequisites[span.context.span_id] = set(prerequisites)
+        except KeyError:
+            self._flow_log_prerequisites[span.context.span_id] = set()
 
         path: str = file_object["path"]
         flow: FlowKernelRequestParams

tests/utilities/test_flow_decorator.py

@@ -84,11 +84,15 @@ def test_decorators_without_flow(
         ]
     )
     # WHEN exporting the spans
+    # Wait for the prompt span to be exported; It was waiting
+    # on the OpenAI call span to finish first
+    time.sleep(1)
     spans = exporter.get_finished_spans()
     # THEN 3 spans arrive at the exporter in the following order:
     #   0. Intercepted OpenAI call, which is ignored by the exporter
     #   1. Tool Span (called after the OpenAI call but before the Prompt Span finishes)
     #   2. Prompt Span
+    print("WOW", [span.name for span in spans])
     assert len(spans) == 3
     assert read_from_opentelemetry_span(
         span=spans[1],
@@ -146,6 +150,10 @@ def test_flow_decorator_flow_in_flow(
     # WHEN Calling the _test_flow_in_flow function with specific messages
     _flow_over_flow(call_llm_messages)
 
+    # Wait for the Prompt span to be exported; It was asynchronously waiting
+    # on the OpenAI call span to finish first
+    time.sleep(1)
+
     # THEN 5 spans are arrive at the exporter in the following order:
     #   0. Intercepted OpenAI call, which is ignored by the exporter
     #   1. Tool Span (called after the OpenAI call but before the Prompt Span finishes)

tests/utilities/test_prompt_decorator.py

@@ -1,4 +1,5 @@
 import os
+import time
 from typing import Optional
 
 import cohere
@@ -158,6 +159,11 @@ def test_prompt_decorator(
         model=model,
         messages=call_llm_messages,
     )
+
+    # Wait for the Prompt span to be exported, it is waiting
+    # asynchronously for the LLM provider call span to finish
+    time.sleep(1)
+
     # THEN two spans are created: one for the OpenAI LLM provider call and one for the Prompt
     spans = exporter.get_finished_spans()
     assert len(spans) == 2
@@ -189,7 +195,13 @@ def test_prompt_decorator_with_hl_processor(
         model=model,
         messages=call_llm_messages,
     )
+
     # THEN two spans are created: one for the OpenAI LLM provider call and one for the Prompt
+
+    # Wait for the Prompt span to be exported, it is waiting
+    # asynchronously for the LLM provider call span to finish
+    time.sleep(1)
+
     spans = exporter.get_finished_spans()
     assert len(spans) == 2
     assert not is_humanloop_span(span=spans[0])
@@ -237,6 +249,11 @@ def test_prompt_decorator_with_defaults(
         model=model,
         messages=call_llm_messages,
     )
+
+    # Wait for the Prompt span to be exported, it is waiting
+    # asynchronously for the LLM provider call span to finish
+    time.sleep(1)
+
     spans = exporter.get_finished_spans()
     # THEN the Prompt span is enhanced with information and forms a correct PromptKernel
     prompt = PromptKernelRequest.model_validate(
@@ -289,6 +306,10 @@ def test_prompt_attributes(
         messages=call_llm_messages,
     )
 
+    # Wait for the Prompt span to be exported, it is waiting
+    # asynchronously for the LLM provider call span to finish
+    time.sleep(1)
+
     assert len(exporter.get_finished_spans()) == 2
 
     prompt_kernel = PromptKernelRequest.model_validate(

tests/utilities/test_tool_decorator.py

@@ -1,4 +1,5 @@
 import sys
+import time
 from typing import Any, Optional, TypedDict, Union
 
 import pytest
@@ -450,6 +451,9 @@ def calculator(operation: str, num1: float, num2: float) -> float:
     higher_order_fn_tool(operation="add", num1=1, num2=2)
     calculator(operation="add", num1=1, num2=2)
 
+    # Processor handles HL spans asynchronously, wait for them
+    time.sleep(1)
+
     assert len(spans := exporter.get_finished_spans()) == 2
 
     hl_file_higher_order_fn = read_from_opentelemetry_span(