feat: local MLflow tracking with SQLite backend

Switch from Databricks-hosted MLflow to local SQLite backend for dev.
Add test script to verify MLflow experiment logging works locally.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: SQLicious

.gitignore

@@ -7,6 +7,8 @@ chroma_data/
 *.egg-info/
 .DS_Store
 mlruns/
+mlflow.db
+mlartifacts/
 spark-warehouse/
 derby.log
 metastore_db/

scripts/test_mlflow_local.py

@@ -0,0 +1,36 @@
+"""Quick test: verify MLflow tracks locally."""
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import mlflow
+
+# Use local file-based tracking (no server needed)
+mlflow.set_tracking_uri("sqlite:///mlflow.db")
+mlflow.set_experiment("queryforge-eval")
+
+with mlflow.start_run(run_name="test-local-tracking"):
+    # Simulate what a real eval run would log
+    mlflow.log_param("prompt_version", "v1")
+    mlflow.log_param("model", "claude-sonnet-4-20250514")
+
+    mlflow.log_metrics({
+        "total_questions": 5,
+        "sql_accuracy": 0.80,
+        "avg_tokens_per_query": 350,
+        "faithfulness": 0.90,
+        "answer_relevancy": 0.85,
+        "context_recall": 0.78,
+    })
+
+    mlflow.log_text("SELECT SUM(balance) FROM accounts WHERE status = 'active'", "sample_sql.sql")
+    mlflow.log_text("What is the total balance of active accounts?", "sample_question.txt")
+
+    run_id = mlflow.active_run().info.run_id
+    print(f"MLflow run logged successfully!")
+    print(f"Run ID: {run_id}")
+    print(f"Experiment: queryforge-eval")
+    print(f"\nTo view the dashboard, run:")
+    print(f"  mlflow ui --backend-store-uri mlruns")
+    print(f"  Then open http://localhost:5000")

src/core/config.py

@@ -5,10 +5,11 @@ class Settings(BaseSettings):
     anthropic_api_key: str = ""
     databricks_host: str = "https://community.cloud.databricks.com"
     databricks_token: str = ""
-    mlflow_tracking_uri: str = "databricks"
-    mlflow_experiment_name: str = "/queryforge-eval"
+    mlflow_tracking_uri: str = "sqlite:///mlflow.db"
+    mlflow_experiment_name: str = "queryforge-eval"
     model_name: str = "claude-sonnet-4-20250514"
     max_tokens: int = 1024
+    prompt_version: str = "v1"
 
     class Config:
         env_file = ".env"