Update graph_builder.py

graph_builder.py

@@ -1,28 +1,56 @@
 from langgraph.graph import StateGraph
+from langchain_core.tools import Tool
+from langchain_community.chat_models import ChatOpenAI
+from langchain_tavily import TavilySearchAPIWrapper
 
 def build_graph():
     graph = StateGraph(dict)
 
-    def llm_step(state):
+    # Setup tools
+    search = TavilySearchAPIWrapper(api_key="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")
+
+    def search_step(state):
         question = state.get("question")
         if not question:
-            raise ValueError("State is missing 'question'")
-        # This is where you integrate your LLM call.
-        llm_output = f"Dummy answer to: {question}"
-        state["llm_output"] = llm_output
+            raise ValueError("Missing 'question' in state")
+        result = search.run(question)
+        state["search_result"] = result
+        return state
+
+    def llm_step(state):
+        llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
+        question = state.get("question")
+        search_info = state.get("search_result", "")
+        prompt = f"""
+You are solving a GAIA benchmark question.
+Here’s the question:
+{question}
+
+Here’s retrieved web info:
+{search_info}
+
+ONLY return the final exact answer (no explanation, no prefix).
+"""
+        response = llm.invoke(prompt)
+        state["llm_output"] = response.content.strip()
         return state
 
     def formatter_step(state):
         llm_output = state.get("llm_output")
         if not llm_output:
-            raise ValueError("State is missing 'llm_output'")
-        # For now, we just pass it through.
-        state["final_answer"] = llm_output.strip()
+            raise ValueError("Missing 'llm_output' in state")
+        cleaned = llm_output.strip()
+        state["final_answer"] = cleaned
         return state
 
+    # Add nodes
+    graph.add_node("search", search_step)
     graph.add_node("llm", llm_step)
     graph.add_node("formatter", formatter_step)
-    graph.set_entry_point("llm")
+
+    # Define flow
+    graph.set_entry_point("search")
+    graph.add_edge("search", "llm")
     graph.add_edge("llm", "formatter")
     graph.set_finish_point("formatter")