Add RULER scoring to LG integration example

docs/integrations/langgraph-integration.mdx

@@ -137,7 +137,7 @@ if final_answer:
     correctness_judge_response = await judge_correctness(
         scenario, traj.final_answer.answer
     )
-    traj.metrics["correct"] = float(correctness_judge_response.accept)
+    traj.metrics["correct"] = correctness_judge_response.accept
 ```
 
 ### Training Loop with LangGraph Integration
@@ -179,7 +179,7 @@ for batch in training_iterator:
     # Apply RULER scoring
     judged_groups = []
     for group in finished_groups:
-        judged_group = await ruler_score_group(group, "openai/o4-mini", debug=True)
+        judged_group = await ruler_score_group(group, "openai/o4-mini")
         judged_groups.append(judged_group)
 
     # Train the model
@@ -326,7 +326,7 @@ async def judge_correctness(scenario: Scenario, answer: str) -> CorrectnessJudge
         You are given a question, the reference answer, and an answer generated by an AI assistant.
         Your task is to decide whether the AI answer is correct and should be accepted.
     """)
-    
+
     messages = [
         {"role": "system", "content": system_prompt},
         {
@@ -338,13 +338,13 @@ async def judge_correctness(scenario: Scenario, answer: str) -> CorrectnessJudge
             ),
         },
     ]
-    
+
     response = await acompletion(
         model="openai/gpt-4o-mini",
         messages=messages,
         response_format=CorrectnessJudgeResponse,
     )
-    
+
     return CorrectnessJudgeResponse.model_validate_json(
         response.choices[0].message.content or "{}"
     )
@@ -354,7 +354,7 @@ async def judge_correctness(scenario: Scenario, answer: str) -> CorrectnessJudge
 async def rollout(model: art.Model, email_scenario: EmailScenario) -> ProjectTrajectory:
     scenario = email_scenario.scenario
     MAX_TURNS = 10
-    
+
     traj = ProjectTrajectory(
         reward=0.0,
         messages_and_choices=[],
@@ -363,62 +363,62 @@ async def rollout(model: art.Model, email_scenario: EmailScenario) -> ProjectTra
             "step": email_scenario.step,
         },
     )
-    
+
     system_prompt = dedent(f"""
         You are an email search agent. Use the tools to search emails and find answers.
         User's email address: {scenario.inbox_address}
         Today's date: {scenario.query_date}
-        
+
         When you find the answer, use return_final_answer_tool with the answer and source message IDs.
     """)
-    
+
     final_answer = None
-    
+
     @tool
     def search_inbox_tool(keywords: List[str]) -> List[dict]:
         """Search inbox for emails matching keywords"""
         results = search_emails(scenario.inbox_address, keywords, scenario.query_date)
         return [asdict(result) for result in results]
-    
+
     @tool
     def read_email_tool(message_id: str) -> dict | None:
         """Read a specific email by message ID"""
         email = read_email(message_id)
         return email.model_dump() if email else None
-    
+
     @tool
     def return_final_answer_tool(answer: str, reference_message_ids: List[str]) -> dict:
         """Return final answer with source message IDs"""
         nonlocal final_answer
         final_answer = FinalAnswer(answer=answer, source_ids=reference_message_ids)
         return final_answer.model_dump()
-    
+
     tools = [search_inbox_tool, read_email_tool, return_final_answer_tool]
     chat_model = init_chat_model(model.name, temperature=1.0)
     react_agent = create_react_agent(chat_model, tools)
-    
+
     try:
         config = {
             "configurable": {"thread_id": str(uuid.uuid4())},
             "recursion_limit": MAX_TURNS,
         }
-        
+
         await react_agent.ainvoke({
             "messages": [
                 SystemMessage(content=system_prompt),
                 HumanMessage(content=scenario.question),
             ]
         }, config=config)
-        
+
         if final_answer:
             traj.final_answer = final_answer
             correctness_judge_response = await judge_correctness(scenario, final_answer.answer)
             traj.metrics["correct"] = float(correctness_judge_response.accept)
-    
+
     except Exception as e:
         print(f"Error running agent: {e}")
         traj.messages_and_choices.append({"role": "assistant", "content": f"Error: {str(e)}"})
-    
+
     return traj
 
 # Main training function
@@ -433,17 +433,17 @@ async def main():
             query_date="2024-01-20"
         ),
         Scenario(
-            id="2", 
+            id="2",
             question="Look for urgent project updates",
             answer="Project deadline moved to next month",
             inbox_address="user@company.com",
             query_date="2024-01-20"
         ),
     ]
-    
+
     # Register model with backend
     await model.register(backend)
-    
+
     # Training configuration
     training_config = {
         "groups_per_step": 2,
@@ -452,19 +452,19 @@ async def main():
         "learning_rate": 1e-5,
         "max_steps": 5,
     }
-    
+
     # Training iterator
     training_iterator = iterate_dataset(
         training_scenarios,
         groups_per_step=training_config["groups_per_step"],
         num_epochs=training_config["num_epochs"],
         initial_step=await model.get_step(),
     )
-    
+
     # Training loop
     for batch in training_iterator:
         print(f"Training step {batch.step}, epoch {batch.epoch}")
-        
+
         # Create trajectory groups
         groups = []
         for scenario in batch.items:
@@ -476,22 +476,28 @@ async def main():
                     for _ in range(training_config["rollouts_per_group"])
                 ])
             )
-        
+
         # Gather trajectories
         finished_groups = await art.gather_trajectory_groups(
             groups,
             pbar_desc="gather",
             max_exceptions=training_config["rollouts_per_group"] * len(batch.items),
         )
-
+
+        # Apply RULER scoring
+        judged_groups = []
+        for group in finished_groups:
+            judged_group = await ruler_score_group(group, "openai/o4-mini")
+            judged_groups.append(judged_group)
+
         # Train model
         await model.train(
-            finished_groups,
+            judged_groups,
             config=art.TrainConfig(learning_rate=training_config["learning_rate"]),
         )
-        
+
         print(f"Completed training step {batch.step}")
-        
+
         if batch.step >= training_config["max_steps"]:
             break
 
@@ -503,7 +509,7 @@ This complete example shows how to:
 
 1. **Set up the environment** with model, backend, and data structures
 2. **Define custom tools** for email search and retrieval
-3. **Create a LangGraph ReAct agent** with proper configuration  
+3. **Create a LangGraph ReAct agent** with proper configuration
 4. **Implement trajectory tracking** with custom reward scoring
 5. **Run the full training loop** with proper error handling
 6. **Use wrap_rollout** to automatically capture agent interactions