changes to avoid runtime error

agent.py

@@ -18,98 +18,113 @@ from supabase.client import Client, create_client
 
 load_dotenv()
 
-# === TOOLS === #
-
 @tool
-def multiply(a: int, b: int) -> int: return a * b
+def multiply(a: int, b: int) -> int:
+    """Multiply two integers and return the result."""
+    return a * b
 
 @tool
-def add(a: int, b: int) -> int: return a + b
+def add(a: int, b: int) -> int:
+    """Add two integers and return the result."""
+    return a + b
 
 @tool
-def subtract(a: int, b: int) -> int: return a - b
+def subtract(a: int, b: int) -> int:
+    """Subtract the second integer from the first and return the result."""
+    return a - b
 
 @tool
 def divide(a: int, b: int) -> float:
+    """Divide the first integer by the second and return the result as float."""
     if b == 0:
         raise ValueError("Cannot divide by zero.")
     return a / b
 
 @tool
-def modulus(a: int, b: int) -> int: return a % b
+def modulus(a: int, b: int) -> int:
+    """Return the remainder when the first integer is divided by the second."""
+    return a % b
 
 @tool
 def wiki_search(query: str) -> str:
-    docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    return {"wiki_results": "\n\n---\n\n".join(doc.page_content for doc in docs)}
+    """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"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>' for doc in search_docs]
+    )
+    return {"wiki_results": formatted}
 
 @tool
 def web_search(query: str) -> str:
-    docs = TavilySearchResults(max_results=3).invoke(query)
-    return {"web_results": "\n\n---\n\n".join(doc.page_content for doc in docs)}
+    """Search Tavily for a query and return up to 3 results."""
+    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
+    formatted = "\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 {"web_results": formatted}
 
 @tool
 def arvix_search(query: str) -> str:
-    docs = ArxivLoader(query=query, load_max_docs=3).load()
-    return {"arvix_results": "\n\n---\n\n".join(doc.page_content[:1000] for doc in docs)}
-
-# === SYSTEM PROMPT === #
-
+    """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"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>' for doc in search_docs]
+    )
+    return {"arvix_results": formatted}
+
+# Load system prompt
 with open("system_prompt.txt", "r", encoding="utf-8") as f:
     system_prompt = f.read()
 
 sys_msg = SystemMessage(content=system_prompt)
 
-# === EMBEDDING + RETRIEVER === #
-
+# Setup Supabase vector retriever
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
-supabase: Client = create_client(os.getenv("SUPABASE_URL"), os.getenv("SUPABASE_SERVICE_KEY"))
+supabase: Client = create_client(os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_KEY"))
 vector_store = SupabaseVectorStore(
     client=supabase,
     embedding=embeddings,
     table_name="Vector_Test",
     query_name="match_documents_langchain",
 )
+
 create_retriever_tool = create_retriever_tool(
     retriever=vector_store.as_retriever(),
     name="Question Search",
     description="A tool to retrieve similar questions from a vector store."
 )
 
-# === TOOL LIST === #
+# Define tool list
 tools = [
     multiply, add, subtract, divide, modulus,
     wiki_search, web_search, arvix_search
 ]
 
-# === BUILD GRAPH === #
+# Build graph
 def build_graph(provider: str = "groq"):
     if provider == "google":
         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
     elif provider == "groq":
         llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
     elif provider == "huggingface":
-        llm = ChatHuggingFace(llm=HuggingFaceEndpoint(
-            url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
-            temperature=0))
+        llm = ChatHuggingFace(
+            llm=HuggingFaceEndpoint(
+                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
+                temperature=0,
+            )
+        )
     else:
-        raise ValueError("Invalid provider.")
+        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
 
     llm_with_tools = llm.bind_tools(tools)
 
     def assistant(state: MessagesState):
-        response = llm_with_tools.invoke(state["messages"])
-        answer = response.content.strip()
-        if "FINAL ANSWER:" not in answer:
-            answer = f"FINAL ANSWER: {answer.strip().splitlines()[0]}"
-        return {"messages": [AIMessage(content=answer)]}
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
 
     def retriever(state: MessagesState):
         similar = vector_store.similarity_search(state["messages"][0].content)
-        if similar:
-            ref = HumanMessage(content=f"Here is a similar example: \n{similar[0].page_content}")
-            return {"messages": [sys_msg] + state["messages"] + [ref]}
-        return {"messages": [sys_msg] + state["messages"]}
+        example_msg = HumanMessage(content=f"Here I provide a similar question and answer for reference: \n\n{similar[0].page_content}")
+        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
 
     builder = StateGraph(MessagesState)
     builder.add_node("retriever", retriever)
@@ -119,11 +134,13 @@ def build_graph(provider: str = "groq"):
     builder.add_edge("retriever", "assistant")
     builder.add_conditional_edges("assistant", tools_condition)
     builder.add_edge("tools", "assistant")
+
     return builder.compile()
 
 if __name__ == "__main__":
-    graph = build_graph()
-    question = "What is 12 + 4?"
-    result = graph.invoke({"messages": [HumanMessage(content=question)]})
-    for m in result["messages"]:
-        print(m.content)
+    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
+    graph = build_graph("groq")
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()

app.py

@@ -16,6 +16,7 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 
+
 cached_answers = []
 
 class BasicAgent: