perf: keywords nodes use Pydantic structured output

app.py

@@ -86,20 +86,17 @@ def classify_individual_topic(topic_name: str):
     def node_runner(state: MultiTopicState):
         llm = ChatOpenAI(model="gpt-4o", temperature=0)
         # Force strict structured output format
-        #structured_llm = llm.with_structured_output(KeywordExtraction)
+        structured_llm = llm.with_structured_output(KeywordExtraction)
         target_sub_tree = SUB_TREE_LOOKUP.get(topic_name, "")
         
         prompt = ChatPromptTemplate.from_messages([
-            #("system", f"You are a specialist in {topic_name} data mapping. Extract all exact matching keyword pathways present in the provided valid path list. Do not modify or truncate the pathway strings."),
-            ("system", f"You are a specialist in {topic_name} data mapping. Extract exact keyword pathways present in the provided list."),
+            ("system", f"You are a specialist in {topic_name} data mapping. Extract all exact matching keyword pathways present in the provided valid path list. Do not modify or truncate the pathway strings."),
             ("user", "Title: {title}\nAbstract: {abstract}\n\nValid Paths:\n{sub_tree}\n\nExtract matching pathways as a structured array list.")
         ])
         
         # Enforce JSON list output natively
-        #result = structured_llm.invoke(prompt.format(title=state["title"], abstract=state["abstract"], sub_tree=target_sub_tree))
-        result = llm.invoke(prompt.format(title=state["title"], abstract=state["abstract"], sub_tree=target_sub_tree))
-        #raw_keywords = [k.strip() for k in result.keywords if k.strip()]
-        raw_keywords = [k.strip() for k in result.content.split(",") if k.strip()]
+        result = structured_llm.invoke(prompt.format(title=state["title"], abstract=state["abstract"], sub_tree=target_sub_tree))
+        raw_keywords = [k.strip() for k in result.keywords if k.strip()]
         
         # Immediate validation pass inside the node branch
         valid_set = set()