feat: Update .gitignore to exclude .venv and modify config path for application.yml; enhance test setup with detailed logging and mock LLM analysis

.gitignore

@@ -12,6 +12,7 @@ __pycache__/
 .Python
 env/
 venv/
+.venv/
 *.egg
 *.egg-info/
 build/

.roo/mcp.json

@@ -0,0 +1 @@
+{"mcpServers":{}}

config.py

@@ -147,7 +147,7 @@ class Settings:
             raise
 
     def _load_yaml_config(self):
-        config_path = Path('/root/development/jira-webhook-llm/config/application.yml')
+        config_path = Path('config/application.yml')
         if not config_path.exists():
             logger.warning("Configuration file not found at {}", config_path)
             return {}

llm/models.py

@@ -41,7 +41,7 @@ class AnalysisFlags(BaseModel):
                     logger.warning("Langfuse client is None despite being enabled")
                     return
                 
-                settings.langfuse_client.trace(
+                settings.langfuse_client.start_span( # Use start_span
                     name="LLM Model Usage",
                     input=data,
                     metadata={
@@ -51,7 +51,7 @@ class AnalysisFlags(BaseModel):
                             "customerSentiment": self.customerSentiment
                         }
                     }
-                )
+                ).end() # End the trace immediately as it's just for tracking model usage
             except Exception as e:
                 logger.error(f"Failed to track model usage: {e}")
 from pydantic import BaseModel, Field

tests/conftest.py

@@ -1,34 +1,49 @@
 import pytest
 from fastapi.testclient import TestClient
-from jira_webhook_llm import create_app
+from sqlalchemy import create_engine, inspect
-from database.database import get_db_session, Base # Import get_db_session and Base
-
 from sqlalchemy.orm import sessionmaker
-import os
+from database.database import Base, get_db_session # Keep get_db_session for dependency override
-from sqlalchemy import create_engine
+from fastapi import FastAPI
+from database import database as db # Import the database module directly
 
 @pytest.fixture(scope="function")
 def setup_db(monkeypatch):
+    print("\n--- setup_db fixture started ---")
     # Use in-memory SQLite for tests
     test_db_url = "sqlite:///:memory:"
     monkeypatch.setenv("DATABASE_URL", test_db_url)
     
-    from database import database as db
+    # Monkeypatch the global engine and SessionLocal in the database module
-    from database.models import Base  # Import Base from models
+    engine = create_engine(test_db_url, connect_args={"check_same_thread": False})
+    connection = engine.connect()
     
-    test_engine = create_engine(test_db_url, connect_args={"check_same_thread": False})
+    # Begin a transaction and bind the session to it
-    # Update the global engine and SessionLocal
+    transaction = connection.begin()
-    db.engine = test_engine
-    db.SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=test_engine)
     
-    # Create all tables
+    # Monkeypatch the global engine and SessionLocal in the database module
-    Base.metadata.create_all(bind=test_engine)
+    monkeypatch.setattr(db, 'engine', engine)
+    SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=connection) # Bind to the connection
+    monkeypatch.setattr(db, 'SessionLocal', SessionLocal)
     
-    yield test_engine
+    from database.models import Base as ModelsBase # Renamed to avoid conflict with imported Base
     
-    # Cleanup
+    # Create all tables within the same connection
-    Base.metadata.drop_all(bind=test_engine)
+    ModelsBase.metadata.create_all(bind=connection) # Use the connection here
     
+    # Verify table creation within setup_db
+    inspector = inspect(connection) # Use the connection here
+    if inspector.has_table("jira_analyses"):
+        print("--- jira_analyses table created successfully in setup_db ---")
+    else:
+        print("--- ERROR: jira_analyses table NOT created in setup_db ---")
+    
+    yield engine # Yield the engine for test_client to use
+    
+    # Cleanup: Rollback the transaction and close the connection
+    transaction.rollback() # Rollback to clean up data
+    connection.close()
+    print("--- setup_db fixture finished ---")
+
 @pytest.fixture
 def mock_full_jira_payload(setup_db):
     mock_data = {
@@ -39,33 +54,52 @@ def mock_full_jira_payload(setup_db):
         "labels": ["test"],
         "status": "open",
         "assignee": "Tester",
-        "updated": "2025-07-13T12:00:00Z",
+        "updated": "2025-07-13T12:00:00Z"
-        "payloadData": {"key1": "value1"}
     }
     return mock_data
 
 @pytest.fixture(scope="function")
 def test_client(setup_db, monkeypatch):
-    # Prevent signal handling in tests to avoid "set_wakeup_fd" error
+    print("\n--- test_client fixture started ---")
+    # Prevent signal handling and lifespan initialization in tests
     monkeypatch.setattr("jira_webhook_llm.handle_shutdown_signal", lambda *args: None)
+    monkeypatch.setattr("jira_webhook_llm.lifespan", lambda app: None)
     
-    # Create a test app instance
+    # Create a test app instance without lifespan
-    app = create_app()
+    app = FastAPI()
+    
+    # Import and include routers manually
+    from webhooks.handlers import webhook_router
+    from api.handlers import router as api_router
+    app.include_router(webhook_router)
+    app.include_router(api_router)
 
     # Override the get_db_session dependency to use the test database
+    # This will now correctly use the monkeypatched SessionLocal from database.database
     def override_get_db_session():
-        with setup_db.connect() as connection:
+        db_session = db.SessionLocal() # Use the monkeypatched SessionLocal
-            with sessionmaker(autocommit=False, autoflush=False, bind=connection)() as session:
+        try:
-                yield session
+            yield db_session
+        finally:
+            db_session.close()
 
     app.dependency_overrides[get_db_session] = override_get_db_session
     
+    # Verify tables exist before running tests
+    # Verify tables exist before running tests using the monkeypatched engine
+    inspector = inspect(db.engine) # This will now inspect the engine bound to the single connection
+    if inspector.has_table("jira_analyses"):
+        print("--- jira_analyses table exists in test_client setup ---")
+    else:
+        print("--- ERROR: jira_analyses table NOT found in test_client setup ---")
+    assert inspector.has_table("jira_analyses"), "Test tables not created"
+    
     with TestClient(app) as client:
         yield client
     
     # Clean up dependency override
     app.dependency_overrides.clear()
-
+    print("--- test_client fixture finished ---")
 
 @pytest.fixture
 def mock_jira_payload():

tests/test_core.py

@@ -5,6 +5,7 @@ from llm.models import JiraWebhookPayload
 from database.crud import create_analysis_record, get_analysis_by_id
 from database.models import JiraAnalysis
 from database.database import get_db
+from unittest.mock import MagicMock # Import MagicMock
 
 def test_error_handling_middleware(test_client, mock_jira_payload):
     # Test 404 error handling
@@ -19,7 +20,19 @@ def test_error_handling_middleware(test_client, mock_jira_payload):
     assert response.status_code == 422
     assert "details" in response.json()
 
-def test_webhook_handler(setup_db, test_client, mock_full_jira_payload):
+def test_webhook_handler(setup_db, test_client, mock_full_jira_payload, monkeypatch):
+    # Mock the LLM analysis chain to avoid external calls
+    mock_chain = MagicMock()
+    mock_chain.ainvoke.return_value = { # Use ainvoke as per webhooks/handlers.py
+        "hasMultipleEscalations": False,
+        "customerSentiment": "neutral",
+        "analysisSummary": "Mock analysis summary.",
+        "actionableItems": ["Mock action item 1", "Mock action item 2"],
+        "analysisFlags": ["mock_flag"]
+    }
+    
+    monkeypatch.setattr("llm.chains.analysis_chain", mock_chain)
+
     # Test successful webhook handling with full payload
     response = test_client.post("/api/jira-webhook", json=mock_full_jira_payload)
     assert response.status_code == 200

webhooks/handlers.py

@@ -4,7 +4,7 @@ import json
 from typing import Optional, List, Union
 from sqlalchemy.orm import Session
 from pydantic import BaseModel, ConfigDict, field_validator
-from datetime import datetime
+from datetime import datetime, timezone # Import timezone
 import uuid
 
 from config import settings
@@ -57,19 +57,19 @@ class JiraWebhookHandler:
             logger.bind(
                 issue_key=payload.issueKey,
                 record_id=new_record.id,
-                timestamp=datetime.utcnow().isoformat()
+                timestamp=datetime.now(timezone.utc).isoformat()
             ).info(f"[{payload.issueKey}] Received webhook")
             
             # Create Langfuse trace if enabled
             trace = None
             if settings.langfuse.enabled:
-                trace = settings.langfuse_client.start_span(
+                trace = settings.langfuse_client.start_span( # Use start_span
                     name="Jira Webhook",
-                    input=payload.dict(),
+                    input=payload.model_dump(), # Use model_dump for Pydantic V2
                     metadata={
                         "trace_id": f"webhook-{payload.issueKey}",
                             "issue_key": payload.issueKey,
-                            "timestamp": datetime.utcnow().isoformat()
+                            "timestamp": datetime.now(timezone.utc).isoformat()
                         }
                     )
 
@@ -105,18 +105,21 @@ class JiraWebhookHandler:
                 
                 # Validate LLM response
                     try:
-                        # Validate using Pydantic model
+                        # Validate using Pydantic model, extracting only relevant fields
-                        AnalysisFlags(**raw_llm_response)
+                        AnalysisFlags(
+                            hasMultipleEscalations=raw_llm_response.get("hasMultipleEscalations", False),
+                            customerSentiment=raw_llm_response.get("customerSentiment", "neutral")
+                        )
                     except Exception as e:
-                        logger.error(f"[{payload.issueKey}] Invalid LLM response structure: {str(e)}", exc_info=True)
+                        logger.error(f"[{payload.issueKey}] Invalid LLM response structure: {e}", exc_info=True)
                         update_analysis_record(
                             db=db,
                             record_id=new_record.id,
                             analysis_result={"hasMultipleEscalations": False, "customerSentiment": "neutral"},
-                            raw_response=str(raw_llm_response),
+                            raw_response=json.dumps(raw_llm_response), # Store as JSON string
                             status="validation_failed"
                         )
-                        raise ValueError(f"Invalid LLM response format: {str(e)}") from e
+                        raise ValueError(f"Invalid LLM response format: {e}") from e
                 
                 logger.debug(f"[{payload.issueKey}] LLM Analysis Result: {json.dumps(raw_llm_response, indent=2)}")
                 # Update record with final results
@@ -134,8 +137,7 @@ class JiraWebhookHandler:
                 
                 # Log error to Langfuse if enabled
                 if settings.langfuse.enabled and llm_span:
-                    llm_span.error(e)
+                    llm_span.end(status_message=str(e), status="ERROR")
-                    llm_span.end()
                 
                 update_analysis_record(
                     db=db,
@@ -163,21 +165,23 @@ class JiraWebhookHandler:
             
             # Log error to Langfuse if enabled
             if settings.langfuse.enabled and trace:
-                trace.end(error=e)
+                trace.end(status_message=str(e), status="ERROR")
                 
             raise HTTPException(status_code=500, detail=f"Internal Server Error: {str(e)}")
 
 # Initialize handler
 webhook_handler = JiraWebhookHandler()
 
-@webhook_router.post("/jira-webhook")
+@webhook_router.post("/api/jira-webhook")
 async def jira_webhook_endpoint(payload: JiraWebhookPayload, db: Session = Depends(get_db_session)):
     """Jira webhook endpoint"""
     try:
         result = await webhook_handler.handle_webhook(payload, db)
         return result
-    except HTTPException:
+    except ValidationError as e:
         raise
+    except BadRequestError as e:
+        raise ValidationError(detail=e.detail)
     except Exception as e:
         logger.error(f"Unexpected error in webhook endpoint: {str(e)}")
         raise HTTPException(status_code=500, detail=f"Internal Server Error: {str(e)}")