Update agent.py

agent.py

@@ -350,31 +350,6 @@ async def start_questions(request: Request):
     return {"message": f"Loaded {len(docs)} questions", "docs": [doc.page_content for doc in docs]}
 
 
-# -------------------------------
-# Step 3: Create Documents from Each JSON Object
-# -------------------------------
-docs = []
-for task in tasks:
-    # Debugging: Print the keys of each task to ensure 'question' exists
-    print(f"Keys in task: {task.keys()}")
-
-    # Ensure the required field 'question' exists
-    if 'question' not in task:
-        print(f"Skipping task with missing 'question' field: {task}")
-        continue
-
-    content = task.get('question', "").strip()
-    if not content:
-        print(f"Skipping task with empty 'question': {task}")
-        continue
-
-    # Add unique ID to each document
-    task['id'] = str(uuid.uuid4())
-
-    # Create a document from the task data
-    docs.append(Document(page_content=content, metadata=task))
-
-
 
 
 # -------------------------------
@@ -633,8 +608,77 @@ def generate_final_answer(state: dict, task_results: dict) -> str:
         return "🤖 Unable to generate a specific answer."
 
 
+def answer_question(question: str) -> str:
+    """Process a single question and return the answer."""
+    print(f"Processing question: {question[:50]}...")  # Debugging: show first 50 chars
+
+    # Wrap the question in a HumanMessage from langchain_core
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})  # Assuming `graph` is defined elsewhere
+    
+    # Extract the answer from the response
+    answer = messages['messages'][-1].content
+    return answer[14:]  # Assuming 'answer[14:]' is correct based on your example
+
+
+def process_all_tasks(tasks: list):
+    """Process a list of tasks."""
+    results = {}
+    
+    for task in tasks:
+        # Ensure task has a question and process it
+        question = task.get("question", "").strip()
+        if not question:
+            print(f"Skipping task with missing or empty 'question': {task}")
+            continue
+        
+        print(f"\n🟢 Processing Task: {task['task_id']} - Question: {question}")
+        
+        # Call the existing process_question logic
+        response = process_question(question)
+        
+        print(f"✅ Response: {response}")
+        results[task['task_id']] = response
+
+    return results
+
+
+def process_question(question: str):
+    tasks = planner(question)
+    print(f"Tasks to perform: {tasks}")
+
+    task_type = task_classifier(question)
+    print(f"Task type: {task_type}")
+
+    state = {"question": question, "last_response": "", "messages": [HumanMessage(content=question)]}
+    next_task = decide_task(state)
+    print(f"Next task: {next_task}")
+
+    if node_skipper(state):
+        print(f"Skipping task: {next_task}")
+        return "Task skipped."
+
+    try:
+        if task_type == "wiki_search":
+            response = wiki_tool.run(question)
+        elif task_type == "math":
+            # You should dynamically parse these inputs in real use
+            response = calc_tool.run(question)
+        elif task_type == "retriever":
+            retrieval_result = retriever(state)
+            response = retrieval_result["messages"][-1].content
+        else:
+            response = "Default fallback answer."
+
+        return generate_final_answer(state, {task_type: response})
+
+    except Exception as e:
+        print(f"❌ Error: {e}")
+        return "Sorry, I encountered an error processing your request."
+
+        
 # ----------------------------------------------------------------
-# Process Function (Main Agent Runner)
+# Process Function (Main Agent Runner)   OLD Code
 # ----------------------------------------------------------------
 def process_question(question: str):
     tasks = planner(question)