Update agent.py

agent.py

@@ -4,32 +4,35 @@ from langgraph.graph import START, StateGraph, MessagesState
 from langgraph.prebuilt import tools_condition
 from langgraph.prebuilt import ToolNode
 from langchain_community.tools.duckduckgo_search import DuckDuckGoSearchResults
-from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_community.document_loaders import ArxivLoader
 from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.tools import tool
 from langchain_google_genai import ChatGoogleGenerativeAI
 
+# .env laden (falls lokal)
 load_dotenv()
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 
-# --- Define Tools ---
+# Google API Key aus Environment
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 
-@tool 
+# --- Tools definieren ---
+@tool
 def multiply(a: int, b: int) -> int:
     """Multiplies two numbers."""
     return a * b
 
-@tool 
+@tool
 def add(a: int, b: int) -> int:
     """Adds two numbers."""
     return a + b
 
-@tool 
+@tool
 def subtract(a: int, b: int) -> int:
     """Subtracts two numbers."""
     return a - b
 
-@tool 
+@tool
 def divide(a: int, b: int) -> float:
     """Divides two numbers."""
     if b == 0:
@@ -38,43 +41,40 @@ def divide(a: int, b: int) -> float:
 
 @tool
 def modulo(a: int, b: int) -> int:
-    """Returns the remainder of dividing two numbers."""
+    """Returns the remainder after division."""
     return a % b
 
 @tool
 def wiki_search(query: str) -> str:
     """Search Wikipedia for a query and return up to 2 results."""
     search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    formatted = "\n\n---\n\n".join(
-        [f'<Document source="{doc.metadata["source"]}">\n{doc.page_content}\n</Document>'
-         for doc in search_docs]
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}">\n{doc.page_content}\n</Document>'
+            for doc in search_docs
+        ]
     )
-    return formatted
+    return {"wiki_results": formatted_search_docs}
 
 @tool
 def arxiv_search(query: str) -> str:
-    """Search Arxiv for scientific papers matching the query."""
+    """Search Arxiv for a query and return up to 3 results."""
     search_docs = ArxivLoader(query=query, load_max_docs=3).load()
-    formatted = "\n\n---\n\n".join(
-        [f'<Document source="{doc.metadata["source"]}">\n{doc.page_content[:1000]}\n</Document>'
-         for doc in search_docs]
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}">\n{doc.page_content[:1000]}\n</Document>'
+            for doc in search_docs
+        ]
     )
-    return formatted
+    return {"arxiv_results": formatted_search_docs}
 
 @tool
 def web_search(query: str) -> str:
-    """Search the web using DuckDuckGo."""
-    search = DuckDuckGoSearchResults()
+    """Search DuckDuckGo for a query and return results."""
+    search = DuckDuckGoSearchResults(max_results=5)
     return search.run(query)
 
-# --- Load System Prompt ---
-
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    system_prompt = f.read()
-
-sys_msg = SystemMessage(content=system_prompt)
-
-# --- Define Tools List ---
+# Tools-Liste
 tools = [
     multiply,
     add,
@@ -86,26 +86,41 @@ tools = [
     web_search,
 ]
 
-# --- Build Graph Function ---
+# System Prompt
+system_prompt = (
+    "You are a highly accurate AI assistant. "
+    "Use tools when needed. Be very concise and precise. "
+    "Do not hallucinate information."
+)
+sys_msg = SystemMessage(content=system_prompt)
 
+# --- Build Graph ---
 def build_graph():
     llm = ChatGoogleGenerativeAI(
         model="gemini-2.0-flash",
         google_api_key=GOOGLE_API_KEY,
         temperature=0,
         max_output_tokens=2048,
-        system_message=sys_msg
+        system_message=sys_msg,
     )
     llm_with_tools = llm.bind_tools(tools)
 
     def assistant(state: MessagesState):
         """Assistant Node"""
         return {"messages": [llm_with_tools.invoke(state["messages"])]}
-    
+
     builder = StateGraph(MessagesState)
     builder.add_node("assistant", assistant)
     builder.add_node("tools", ToolNode(tools))
     builder.add_edge(START, "assistant")
     builder.add_conditional_edges("assistant", tools_condition)
     builder.add_edge("tools", "assistant")
-    return builder.compile()
+
+    return builder.compile()
+
+# --- Agent Executor für app.py ---
+def agent_executor(question: str) -> str:
+    graph = build_graph()
+    messages = [HumanMessage(content=question)]
+    result = graph.invoke({"messages": messages})
+    return result["messages"][-1].content