Update agent.py

agent.py

@@ -1,4 +1,4 @@
-"""LangGraph Agent – retry 5s, 30s, 60s; senza Supabase"""
+"""LangGraph Agent – retry 5s, 30s, 60s • no Supabase"""
 
 import os
 import time
@@ -20,7 +20,7 @@ from langchain_core.tools import tool
 load_dotenv()
 
 # --------------------------------------------------------------------------- #
-#  TOOLS                                                                      #
+#  TOOL DEFINITIONS                                                           #
 # --------------------------------------------------------------------------- #
 @tool
 def multiply(a: int, b: int) -> int:
@@ -94,37 +94,38 @@ tools = [
 # --------------------------------------------------------------------------- #
 #  Retry parameters                                                           #
 # --------------------------------------------------------------------------- #
-RETRY_DELAYS = [0, 5, 30, 60]   # 4 tentativi complessivi
+RETRY_DELAYS = [0, 5, 30, 60]   # seconds for attempts 0-3
 MAX_ATTEMPTS = len(RETRY_DELAYS)
 
 # --------------------------------------------------------------------------- #
 #  Build LangGraph                                                            #
 # --------------------------------------------------------------------------- #
 def build_graph(provider: str = "groq"):
-    """Return a LangGraph graph with custom retry logic."""
+    """Return a LangGraph graph with explicit retry logic."""
 
     # ----------- LLM selection -------------------------------------------- #
     if provider == "google":
-        llm_selected = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
 
     elif provider == "groq":
-        llm_selected = ChatGroq(
+        llm = ChatGroq(
             model="qwen-qwq-32b",
             temperature=0,
-            max_retries=0,  # gestiamo noi i retry
+            max_retries=0,  # we handle retries manually
         )
 
     elif provider == "huggingface":
-        llm_selected = ChatHuggingFace(
+        llm = ChatHuggingFace(
             llm=HuggingFaceEndpoint(
-                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
+                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_selected.bind_tools(tools)
+    llm_with_tools = llm.bind_tools(tools)
 
     # ---------------- Retry wrapper -------------------------------------- #
     def invoke_with_retry(messages):
@@ -136,11 +137,10 @@ def build_graph(provider: str = "groq"):
             try:
                 return llm_with_tools.invoke(messages)
             except Exception as e:
-                err_text = str(e)
-                if ("503" in err_text or "Service Unavailable" in err_text) and attempt < MAX_ATTEMPTS - 1:
+                if ("503" in str(e) or "Service Unavailable" in str(e)) and attempt < MAX_ATTEMPTS - 1:
                     last_err = e
-                    continue  # retry
-                raise  # altro errore o tentativi finiti
+                    continue
+                raise
         raise last_err or RuntimeError("Unknown error during LLM invocation")
 
     # ---------------- Nodes ---------------------------------------------- #
@@ -164,12 +164,10 @@ def build_graph(provider: str = "groq"):
 #  Stand-alone test                                                           #
 # --------------------------------------------------------------------------- #
 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?"
-    )
-    msgs = [HumanMessage(content=question)]
-    result = graph.invoke({"messages": msgs})
-    for m in result["messages"]:
+    g = build_graph(provider="groq")
+    q = ("When was a picture of St. Thomas Aquinas first added to the Wikipedia "
+         "page on the Principle of double effect?")
+    msgs = [HumanMessage(content=q)]
+    res = g.invoke({"messages": msgs})
+    for m in res["messages"]:
         m.pretty_print()