Update agent.py

agent.py

@@ -1,6 +1,6 @@
-"""LangGraph Agent – versione senza Supabase"""
+"""LangGraph Agent – retry 5s, 30s, 60s; senza Supabase"""
 
-import os
+import os, time
 from dotenv import load_dotenv
 from langgraph.graph import START, StateGraph, MessagesState
 from langgraph.prebuilt import ToolNode, tools_condition
@@ -8,10 +8,7 @@ from langgraph.prebuilt import ToolNode, tools_condition
 # LLM providers
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_groq import ChatGroq
-from langchain_huggingface import (
-    ChatHuggingFace,
-    HuggingFaceEndpoint,
-)
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
 
 # Tools & loaders
 from langchain_community.tools.tavily_search import TavilySearchResults
@@ -19,29 +16,26 @@ from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
 from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.tools import tool
 
-load_dotenv()  # carica eventuali variabili dal file .env
+load_dotenv()
 
 # --------------------------------------------------------------------------- #
-#  TOOL: operazioni aritmetiche                                               #
+#  TOOLS                                                                      #
 # --------------------------------------------------------------------------- #
 @tool
 def multiply(a: int, b: int) -> int:
     """Multiply two integers and return the product."""
     return a * b
 
-
 @tool
 def add(a: int, b: int) -> int:
     """Add two integers and return the sum."""
     return a + b
 
-
 @tool
 def subtract(a: int, b: int) -> int:
     """Subtract the second integer from the first and return the difference."""
     return a - b
 
-
 @tool
 def divide(a: int, b: int) -> float:
     """Divide a by b and return the quotient (error if b == 0)."""
@@ -49,16 +43,11 @@ def divide(a: int, b: int) -> float:
         raise ValueError("Cannot divide by zero.")
     return a / b
 
-
 @tool
 def modulus(a: int, b: int) -> int:
     """Return the remainder of the division of a by b."""
     return a % b
 
-
-# --------------------------------------------------------------------------- #
-#  TOOL: Wikipedia                                                            #
-# --------------------------------------------------------------------------- #
 @tool
 def wiki_search(query: str) -> str:
     """Search Wikipedia (max 2 docs) and return formatted content."""
@@ -69,10 +58,6 @@ def wiki_search(query: str) -> str:
         for d in docs
     )
 
-
-# --------------------------------------------------------------------------- #
-#  TOOL: Tavily web search                                                    #
-# --------------------------------------------------------------------------- #
 @tool
 def web_search(query: str) -> str:
     """Perform a web search with Tavily (max 3 docs) and return formatted content."""
@@ -83,10 +68,6 @@ def web_search(query: str) -> str:
         for d in docs
     )
 
-
-# --------------------------------------------------------------------------- #
-#  TOOL: ArXiv                                                                #
-# --------------------------------------------------------------------------- #
 @tool
 def arxiv_search(query: str) -> str:
     """Search ArXiv (max 3 docs) and return first 1000 characters per paper."""
@@ -97,7 +78,6 @@ def arxiv_search(query: str) -> str:
         for d in docs
     )
 
-
 # --------------------------------------------------------------------------- #
 #  System prompt                                                              #
 # --------------------------------------------------------------------------- #
@@ -105,67 +85,26 @@ with open("system_prompt.txt", "r", encoding="utf-8") as f:
     system_prompt = f.read()
 sys_msg = SystemMessage(content=system_prompt)
 
-# --------------------------------------------------------------------------- #
-#  Lista tool                                                                 #
-# --------------------------------------------------------------------------- #
 tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arxiv_search,
+    multiply, add, subtract, divide, modulus,
+    wiki_search, web_search, arxiv_search,
 ]
 
+# --------------------------------------------------------------------------- #
+#  Retry parameters                                                           #
+# --------------------------------------------------------------------------- #
+RETRY_DELAYS = [0, 5, 30, 60]        # secondi: tentativo 0, 1, 2, 3
+MAX_ATTEMPTS = len(RETRY_DELAYS)
+
 # --------------------------------------------------------------------------- #
 #  Build LangGraph                                                            #
 # --------------------------------------------------------------------------- #
 def build_graph(provider: str = "groq"):
-    """Return a LangGraph graph without Supabase dependencies."""
-    # ------------ LLM selection ------------------------------------------- #
+    """Return a LangGraph graph with explicit retry logic (5s, 30s, 60s)."""
+
+    # ----------- LLM selection -------------------------------------------- #
     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,
-            )
-        )
-    else:
-        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
-
-    llm_with_tools = llm.bind_tools(tools)
-
-    # ------------------ Nodes -------------------------------------------- #
-    def assistant(state: MessagesState):
-        """Invoke LLM with system prompt prepended."""
-        messages = [sys_msg] + state["messages"]
-        return {"messages": [llm_with_tools.invoke(messages)]}
 
-    # ------------------ Graph -------------------------------------------- #
-    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()
-
-
-# --------------------------------------------------------------------------- #
-#  Test rapido                                                                #
-# --------------------------------------------------------------------------- #
-if __name__ == "__main__":
-    graph = build_graph(provider="groq")
-    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-    messages = [HumanMessage(content=question)]
-    result = graph.invoke({"messages": messages})
-    for m in result["messages"]:
-        m.pretty_print()
+    elif provider == "groq":
+        ll