add telemetry for project import; small telemetry fixes

core/agents/importer.py

@@ -6,6 +6,7 @@ from core.agents.response import AgentResponse, ResponseType
 from core.db.models import Complexity
 from core.llm.parser import JSONParser
 from core.log import get_logger
+from core.telemetry import telemetry
 from core.templates.example_project import EXAMPLE_PROJECT_DESCRIPTION
 
 log = get_logger(__name__)
@@ -84,3 +85,13 @@ class Importer(BaseAgent):
                 "complexity": Complexity.HARD if len(self.current_state.files) > 5 else Complexity.SIMPLE,
             }
         ]
+
+        n_lines = sum(len(f.content.content.splitlines()) for f in self.current_state.files)
+        await telemetry.trace_code_event(
+            "existing-project",
+            {
+                "num_files": len(self.current_state.files),
+                "num_lines": n_lines,
+                "description": llm_response,
+            },
+        )

core/cli/main.py

@@ -28,7 +28,6 @@ async def run_project(sm: StateManager, ui: UIBase) -> bool:
     :return: True if the orchestrator exited successfully, False otherwise.
     """
 
-    telemetry.start()
     telemetry.set("app_id", str(sm.project.id))
     telemetry.set("initial_prompt", sm.current_state.specification.description)
 
@@ -58,7 +57,6 @@ async def run_project(sm: StateManager, ui: UIBase) -> bool:
             source=pythagora_source,
         )
 
-    await telemetry.send()
     return success
 
 
@@ -147,7 +145,6 @@ async def run_pythagora_session(sm: StateManager, ui: UIBase, args: Namespace):
 
     if args.project or args.branch or args.step:
         telemetry.set("is_continuation", True)
-        # FIXME: we should send the project stage and other runtime info to the UI
         success = await load_project(sm, args.project, args.branch, args.step)
         if not success:
             return False

core/llm/base.py

@@ -181,6 +181,7 @@ class BaseLLMClient:
             remaining_retries -= 1
             request_log.messages = convo.messages[:]
             request_log.response = None
+            request_log.status = LLMRequestStatus.SUCCESS
             request_log.error = None
             response = None
 
@@ -278,7 +279,9 @@ class BaseLLMClient:
                     response = parser(response)
                     break
                 except ValueError as err:
-                    log.debug(f"Error parsing GPT response: {err}, asking LLM to retry", exc_info=True)
+                    request_log.error = f"Error parsing response: {err}"
+                    request_log.status = LLMRequestStatus.ERROR
+                    log.debug(f"Error parsing LLM response: {err}, asking LLM to retry", exc_info=True)
                     convo.assistant(response)
                     convo.user(f"Error parsing response: {err}. Please output your response EXACTLY as requested.")
                     continue

core/telemetry/__init__.py

@@ -321,6 +321,7 @@ class Telemetry:
         Note: this method clears all telemetry data after sending it.
         """
         if not self.enabled:
+            log.debug("Telemetry.send(): telemetry is disabled, not sending data")
             return
 
         if self.endpoint is None:

tests/llm/test_openai.py

@@ -114,7 +114,7 @@ async def test_openai_parser_fails(mock_AsyncOpenAI):
 
     llm = OpenAIClient(cfg)
 
-    with pytest.raises(APIError, match="Error parsing LLM response"):
+    with pytest.raises(APIError, match="Error parsing response"):
         await llm(convo, parser=parser, max_retries=1)