Samples: improvements for ticket classification sample

Author: radu-mocanu

samples/ticket-classification/agent.mermaid

@@ -1,16 +1,19 @@
-%%{init: {'flowchart': {'curve': 'linear'}}}%%
+---
+config:
+  flowchart:
+    curve: linear
+---
 graph TD;
 	__start__([<p>__start__</p>]):::first
 	classify(classify)
-	create_action(create_action)
-	human_approval(human_approval)
+	human_approval_node(human_approval_node)
 	notify_team(notify_team)
 	__end__([<p>__end__</p>]):::last
 	__start__ --> classify;
-	classify --> create_action;
-	create_action --> human_approval;
-	human_approval --> notify_team;
+	classify --> human_approval_node;
 	notify_team --> __end__;
+	human_approval_node -.-> classify;
+	human_approval_node -.-> notify_team;
 	classDef default fill:#f2f0ff,line-height:1.2
 	classDef first fill-opacity:0
 	classDef last fill:#bfb6fc

samples/ticket-classification/escalation app/escalation_agent_app.uiapp

@@ -1,6 +1,6 @@
 import logging
 import os
-from typing import Literal, Optional
+from typing import Literal, Optional, List
 
 from langchain_openai import AzureChatOpenAI
 from langchain_core.output_parsers import PydanticOutputParser
@@ -10,14 +10,15 @@
 from pydantic import BaseModel, Field
 
 from uipath_sdk import UiPathSDK
-
+from uipath_sdk._models import CreateAction
 logger = logging.getLogger(__name__)
 
 uipath = UiPathSDK()
 
 class GraphInput(BaseModel):
     message: str
     ticket_id: str
+    assignee: Optional[str]
 
 class GraphOutput(BaseModel):
     label: str
@@ -26,9 +27,11 @@ class GraphOutput(BaseModel):
 class GraphState(BaseModel):
     message: str
     ticket_id: str
+    assignee: Optional[str] = None
     label: Optional[str] = None
     confidence: Optional[float] = None
-
+    rejected_categories: List[str] = []
+    human_approval: Optional[bool] = None
 
 class TicketClassification(BaseModel):
     label: Literal["security", "error", "system", "billing", "performance"] = Field(
@@ -78,15 +81,23 @@ def get_azure_openai_api_key() -> str:
 
     return api_key
 
+def decide_next_node(state: GraphState) -> Literal["classify", "notify_team"]:
+    if state.human_approval_result is True:
+        return "notify_team"
+    return "classify"
 
 async def classify(state: GraphState) -> GraphState:
+    logger.info("classify")
+
     """Classify the support ticket using LLM."""
     llm = AzureChatOpenAI(
         azure_deployment="gpt-4o-mini",
         api_key=get_azure_openai_api_key(),
         azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
         api_version="2024-10-21"
     )
+    if len(state.rejected_categories) > 0:
+        prompt.append(("user", f"The ticket is 100% not part of the following categories '{state.rejected_categories}'. Choose another one."))
     _prompt = prompt.partial(
         format_instructions=output_parser.get_format_instructions()
     )
@@ -109,17 +120,22 @@ async def classify(state: GraphState) -> GraphState:
 
 async def wait_for_human(state: GraphState) -> GraphState:
     logger.info("Wait for human approval")
-    feedback = interrupt(f"Label: {state.label} Confidence: {state.confidence}")
-
-    if isinstance(feedback, bool) and feedback is True:
-        return Command(goto="notify_team")
-    else:
-        return Command(goto=END)
+    action_data = interrupt(CreateAction(name="escalation_agent_app",
+                                         title="Action Required: Review classification",
+                                         data={
+                                             "AgentOutput": f"This is how I classified the ticket: '{state.ticket_id}', with message '{state.message}' \n Label: '{state.label}' Confidence: '{state.confidence}'",
+                                             "AgentName": "ticket-classification "},
+                                         app_version=1,
+                                         assignee=state.assignee,
+                                         ))
+    state.human_approval = isinstance(action_data["Answer"], bool) and action_data["Answer"] is True
+    if not state.human_approval:
+        state.rejected_categories.append(state.label)
+    return state
 
-async def notify_team(state: GraphState) -> GraphState:
+async def notify_team(state: GraphState) -> GraphOutput:
     logger.info("Send team email notification")
-    print(state)
-    return state
+    return GraphOutput(label=state.label, confidence=state.confidence)
 
 """Process a support ticket through the workflow."""
 
@@ -131,7 +147,7 @@ async def notify_team(state: GraphState) -> GraphState:
 
 builder.add_edge(START, "classify")
 builder.add_edge("classify", "human_approval")
-builder.add_edge("human_approval", "notify_team")
+builder.add_conditional_edges("human_approval", decide_next_node)
 builder.add_edge("notify_team", END)
 
 
@@ -140,3 +156,4 @@ async def notify_team(state: GraphState) -> GraphState:
 memory = MemorySaver()
 
 graph = builder.compile(checkpointer=memory)
+

samples/ticket-classification/main.py

@@ -9,8 +9,8 @@ dependencies = [
     "langchain-openai>=0.3.9",
     "langgraph-checkpoint-sqlite>=2.0.3",
     "python-dotenv>=1.0.1",
-    "uipath-sdk>=0.0.89",
-    "uipath-langchain>=0.0.62",
+    "uipath-sdk==0.0.110",
+    "uipath-sdk==0.0.110",
     "pydantic>=2.10.6",
     "aiohttp>=3.11.12",
     "typing-extensions>=4.12.2",