Make trio + telco templates compatible with PyRel 1.11

v1/cicd_runner_allocation/README.md

@@ -56,7 +56,7 @@ Finally, it shows **conflict analysis (infeasibility diagnosis)**. A maintenance
 
 ### Tools
 - Python >= 3.10
-- RelationalAI Python SDK (`relationalai`) >= 1.9.0
+- RelationalAI Python SDK (`relationalai`) == 1.11.0
 
 ## Quickstart
 

v1/cicd_runner_allocation/pyproject.toml

@@ -9,7 +9,7 @@ description = "RelationalAI template: cicd_runner_allocation (PyRel v1)"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "relationalai==1.9.0",
+  "relationalai==1.11.0",
   "pandas>=2.0",
 ]
 

v1/factory_production/README.md

@@ -65,7 +65,7 @@ Because the objective is a **maximization**, the *capacity shadow price* is the
 
 ### Tools
 - Python >= 3.10
-- RelationalAI Python SDK (`relationalai`) >= 1.9.0
+- RelationalAI Python SDK (`relationalai`) == 1.11.0
 
 ## Quickstart
 

v1/factory_production/pyproject.toml

@@ -9,7 +9,7 @@ description = "RelationalAI template: factory_production (PyRel v1)"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "relationalai==1.9.0",
+  "relationalai==1.11.0",
   "pandas",
 ]
 

v1/supplier_reliability/README.md

@@ -59,7 +59,7 @@ Finally, the template demonstrates **scenario analysis** by re-solving the probl
 
 ### Tools
 - Python >= 3.10
-- RelationalAI Python SDK (`relationalai`) >= 1.9.0
+- RelationalAI Python SDK (`relationalai`) == 1.11.0
 
 ## Quickstart
 

v1/supplier_reliability/pyproject.toml

@@ -9,7 +9,7 @@ description = "RelationalAI template: supplier_reliability (PyRel v1)"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "relationalai==1.9.0",
+  "relationalai==1.11.0",
   "pandas",
 ]
 

v1/telco_network_recovery/README.md

@@ -54,12 +54,12 @@ Built using **predictive reasoning** (GNN on a heterogeneous graph), **rules-bas
 
 - A Snowflake account with the RelationalAI native app installed.
 - A Snowflake user with permissions on the RAI native app and on `EXP_DATABASE` (the schema for GNN experiment artifacts).
-- A gurobi-enabled prescriptive engine for Stage 4 (the script automatically falls back to the bundled HiGHS solver if gurobi is not configured).
+- A gurobi-enabled prescriptive engine for Stage 4 (if gurobi is unavailable or unlicensed, the script catches the solver error and falls back to the bundled HiGHS solver automatically).
 
 ### Tools
 
 - Python ≥ 3.10.
-- RelationalAI Python SDK (`relationalai == 1.8`).
+- RelationalAI Python SDK (`relationalai == 1.11.0`).
 
 ### One-time Snowflake setup for GNN experiment artifacts
 
@@ -366,7 +366,7 @@ The bundled subscriber data has 50 enterprise (~$300K LTV avg) vs. 1,150 consume
 <details>
 <summary>Gurobi unavailable / unlicensed</summary>
 
-The script attempts the engine's configured solver first. If gurobi is configured but errors at runtime (license, integration), the script catches the failure and retries with `solver="highs"` — no edit needed. To force HiGHS, set the engine's prescriptive solver to `highs` in `raiconfig.yaml`.
+The script requests gurobi first. If gurobi errors at runtime (license, integration), it catches the failure and retries with `solver="highs"` — no edit needed. To always use HiGHS, change `problem.solve("gurobi")` to `problem.solve("highs")`.
 
 </details>
 

v1/telco_network_recovery/pyproject.toml

@@ -9,7 +9,7 @@ description = "RelationalAI template: telco_network_recovery (PyRel v1)"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-  "relationalai==1.8",
+  "relationalai==1.11.0",
   "pandas>=2.0",
 ]
 

v1/telco_network_recovery/telco_network_recovery.py

@@ -522,7 +522,7 @@ def _pos_prob(p):
 NetworkPerformance.latency_ms = model.Property(f"{NetworkPerformance} has {Float:latency_ms}")
 NetworkPerformance.error_rate = model.Property(f"{NetworkPerformance} has {Float:error_rate}")
 NetworkPerformance.for_tower = model.Relationship(
-    f"{NetworkPerformance} for tower {CellTower}", short_name="performance_for_tower"
+    f"{NetworkPerformance} for tower {CellTower}"
 )
 src = model.data(network_perf_df)
 model.define(NetworkPerformance.new(
@@ -672,13 +672,13 @@ def _pos_prob(p):
 # subscriber PageRank graph and for per-tower customer-impact aggregation.
 CallDetailRecord = model.Concept("CallDetailRecord", identify_by={"id": String})
 CallDetailRecord.caller = model.Relationship(
-    f"{CallDetailRecord} has caller {Subscriber}", short_name="cdr_caller"
+    f"{CallDetailRecord} has caller {Subscriber}"
 )
 CallDetailRecord.callee = model.Relationship(
-    f"{CallDetailRecord} has callee {Subscriber}", short_name="cdr_callee"
+    f"{CallDetailRecord} has callee {Subscriber}"
 )
 CallDetailRecord.routed_through = model.Relationship(
-    f"{CallDetailRecord} routed through {CellTower}", short_name="cdr_routed_through"
+    f"{CallDetailRecord} routed through {CellTower}"
 )
 src = model.data(cdr_df)
 model.define(CallDetailRecord.new(
@@ -806,7 +806,7 @@ def _pos_prob(p):
 TowerUpgradeOption.cost = model.Property(f"{TowerUpgradeOption} has {Float:cost}")
 TowerUpgradeOption.install_weeks = model.Property(f"{TowerUpgradeOption} has {Integer:install_weeks}")
 TowerUpgradeOption.for_tower = model.Relationship(
-    f"{TowerUpgradeOption} for tower {CellTower}", short_name="upgrade_for_tower"
+    f"{TowerUpgradeOption} for tower {CellTower}"
 )
 src = model.data(upgrade_options_df)
 model.define(TowerUpgradeOption.new(
@@ -879,25 +879,17 @@ def _pos_prob(p):
     )
 )
 
-# The prescriptive engine's configured solver picks up by default
-# (see raiconfig.yaml `reasoners.prescriptive`). If the engine is set
-# to gurobi but gurobi errors at runtime (license, integration), the
-# except branch catches the failure and retries with HiGHS explicitly.
+# Prefer gurobi (typically faster on MIPs where licensed). If the gurobi
+# solve fails for any reason -- most often gurobi being unavailable or
+# unlicensed on the prescriptive engine -- fall back to the bundled
+# open-source HiGHS solver so the template runs on any engine. If HiGHS
+# also fails, that error (e.g. a genuine model issue) propagates.
 print("\n  Solving...")
 try:
-    problem.solve()
+    problem.solve("gurobi")
 except Exception as exc:
-    # Fall back to the bundled open-source HiGHS solver when gurobi is
-    # unavailable / unlicensed on the prescriptive engine.
-    _msg = str(exc).lower()
-    if "gurobi" in _msg and any(
-        k in _msg for k in ("licen", "unavailable", "not enabled",
-                             "not found", "disabled", "install")
-    ):
-        print(f"  Gurobi unavailable ({exc}); falling back to solver='highs'.")
-        problem.solve(solver="highs")
-    else:
-        raise
+    print(f"  gurobi solve failed ({exc}); falling back to solver='highs'.")
+    problem.solve("highs")
 problem.display()
 
 # Extract selected upgrades. Cast int128 (returned by RAI select) to