Update agent.py

agent.py

@@ -13,7 +13,7 @@ from langchain_community.document_loaders import WikipediaLoader
 from langchain_community.utilities import WikipediaAPIWrapper
 from langchain_community.document_loaders import ArxivLoader
 from langchain_core.messages import SystemMessage, HumanMessage
-from langchain_core.tools import tool
+#from langchain_core.tools import tool
 from langchain.tools.retriever import create_retriever_tool
 from supabase.client import Client, create_client
 from sentence_transformers import SentenceTransformer
@@ -55,6 +55,8 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from huggingface_hub import login
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig
 from langchain_huggingface import HuggingFaceEndpoint
+from langchain.agents.tools import Tool
+
 
 load_dotenv()
 
@@ -643,43 +645,7 @@ def process_all_tasks(tasks: list):
     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)   OLD Code
-# ----------------------------------------------------------------
 def process_question(question: str):
     tasks = planner(question)
     print(f"Tasks to perform: {tasks}")
@@ -712,7 +678,7 @@ def process_question(question: str):
     except Exception as e:
         print(f"❌ Error: {e}")
         return "Sorry, I encountered an error processing your request."
-
+        
 
 
 
@@ -728,7 +694,7 @@ model_config = {
 }
 
 def build_graph(provider, model_config):
-    from langgraph.prebuilt.tool_node import ToolNode
+#    from langgraph.prebuilt.tool_node import ToolNode
 
     def get_llm(provider: str, config: dict):
         if provider == "huggingface":
@@ -745,11 +711,10 @@ def build_graph(provider, model_config):
             
 
     llm = get_llm(provider, model_config)
+    return llm
+    
     llm_with_tools = llm.bind_tools(tools)
 
-    # Continue building graph logic here...
-    # builder = StateGraph(...)
-    # return builder.compile()
 
 
     sys_msg = SystemMessage(content="You are a helpful assistant.")