Update agent.py

agent.py

@@ -1,21 +1,38 @@
-"""LangGraph Agent – versione GPT-4.1 / Hugging Face Spaces"""
+"""LangGraph Agent – GPT-4.1 / Hugging Face Spaces (import lazy)"""
 import os
 from dotenv import load_dotenv
 from langgraph.graph import START, StateGraph, MessagesState
-from langgraph.prebuilt import tools_condition
-from langgraph.prebuilt import ToolNode
-
-# LLM providers
-from langchain_openai import ChatOpenAI            # NEW (GPT-4.1)
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
-from langchain_huggingface import (
-    ChatHuggingFace,
-    HuggingFaceEndpoint,
-    HuggingFaceEmbeddings,
-)
+from langgraph.prebuilt import tools_condition, ToolNode
+from langchain_openai import ChatOpenAI
+
+# --------------------------------------------------------------------------- #
+#  Import facoltativi (se il pacchetto non c'è, il provider viene disattivato) #
+# --------------------------------------------------------------------------- #
+def _lazy_import(name):
+    try:
+        module = __import__(name, fromlist=["*"])
+        return module
+    except ModuleNotFoundError:
+        return None
 
-# Tools & loaders
+lg_google = _lazy_import("langchain_google_genai")
+lg_groq = _lazy_import("langchain_groq")
+lg_hf = _lazy_import("langchain_huggingface")
+
+if lg_google:
+    ChatGoogleGenerativeAI = lg_google.ChatGoogleGenerativeAI
+if lg_groq:
+    ChatGroq = lg_groq.ChatGroq
+if lg_hf:
+    ChatHuggingFace = lg_hf.ChatHuggingFace
+    HuggingFaceEndpoint = lg_hf.HuggingFaceEndpoint
+    HuggingFaceEmbeddings = lg_hf.HuggingFaceEmbeddings
+else:
+    from langchain_huggingface import HuggingFaceEmbeddings  # solo embeddings
+
+# --------------------------------------------------------------------------- #
+#  Tools & loaders                                                            #
+# --------------------------------------------------------------------------- #
 from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
 from langchain_community.vectorstores import SupabaseVectorStore
@@ -24,38 +41,26 @@ from langchain_core.tools import tool
 from langchain.tools.retriever import create_retriever_tool
 from supabase.client import Client, create_client
 
-# --------------------------------------------------------------------------- #
-#  Carica variabili d'ambiente (.env eventuale + secrets di HF Spaces)        #
-# --------------------------------------------------------------------------- #
-load_dotenv()  # nei Spaces le secrets sono già in os.environ
+load_dotenv()  # Secrets di HF Spaces
 
-# --------------------------------------------------------------------------- #
-#  TOOL di esempio (aritmetica)                                               #
-# --------------------------------------------------------------------------- #
+# -------------------- TOOL di esempio -------------------- #
 @tool
 def multiply(a: int, b: int) -> int: return a * b
-
 @tool
 def add(a: int, b: int) -> int: return a + b
-
 @tool
 def subtract(a: int, b: int) -> int: return a - b
-
 @tool
 def divide(a: int, b: int) -> float:
     if b == 0:
         raise ValueError("Cannot divide by zero.")
     return a / b
-
 @tool
 def modulus(a: int, b: int) -> int: return a % b
 
-# --------------------------------------------------------------------------- #
-#  TOOL: Wikipedia                                                             #
-# --------------------------------------------------------------------------- #
+# -------------------- Wikipedia -------------------------- #
 @tool
 def wiki_search(query: str) -> str:
-    """Search Wikipedia (max 2 docs) and return formatted result."""
     docs = WikipediaLoader(query=query, load_max_docs=2).load()
     return "\n\n---\n\n".join(
         f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
@@ -63,12 +68,9 @@ def wiki_search(query: str) -> str:
         for d in docs
     )
 
-# --------------------------------------------------------------------------- #
-#  TOOL: Tavily web search                                                     #
-# --------------------------------------------------------------------------- #
+# -------------------- Tavily ----------------------------- #
 @tool
 def web_search(query: str) -> str:
-    """Search Tavily (max 3 docs) and return formatted result."""
     docs = TavilySearchResults(max_results=3).invoke(query=query)
     return "\n\n---\n\n".join(
         f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
@@ -76,12 +78,9 @@ def web_search(query: str) -> str:
         for d in docs
     )
 
-# --------------------------------------------------------------------------- #
-#  TOOL: ArXiv                                                                 #
-# --------------------------------------------------------------------------- #
+# -------------------- ArXiv ------------------------------ #
 @tool
 def arxiv_search(query: str) -> str:
-    """Search ArXiv (max 3 docs) and return formatted snippet."""
     docs = ArxivLoader(query=query, load_max_docs=3).load()
     return "\n\n---\n\n".join(
         f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
@@ -90,14 +89,14 @@ def arxiv_search(query: str) -> str:
     )
 
 # --------------------------------------------------------------------------- #
-#  System prompt                                                               #
+#  System prompt                                                              #
 # --------------------------------------------------------------------------- #
 with open("system_prompt.txt", "r", encoding="utf-8") as f:
     system_prompt = f.read()
 sys_msg = SystemMessage(content=system_prompt)
 
 # --------------------------------------------------------------------------- #
-#  Vector store per il retriever                                               #
+#  Vector store / retriever                                                   #
 # --------------------------------------------------------------------------- #
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 supabase: Client = create_client(
@@ -117,49 +116,38 @@ question_search_tool = create_retriever_tool(
 )
 
 # --------------------------------------------------------------------------- #
-#  Registrazione tool list                                                    #
+#  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,
     question_search_tool,
 ]
 
 # --------------------------------------------------------------------------- #
-#  Costruzione del graph LangGraph                                             #
+#  Costruzione graph                                                          #
 # --------------------------------------------------------------------------- #
 def build_graph(provider: str = "openai"):
-    """Restituisce un graph LangGraph pronto all'uso.
-
-    provider: "openai" (default), "google", "groq", "huggingface"
-    """
-    # --- Selezione LLM ------------------------------------------------------ #
+    # ------------------- LLM selection ------------------------------------- #
     if provider == "openai":
-        openai_key = os.getenv("OPENAI_KEY")
-        if not openai_key:
-            raise ValueError(
-                "❌ Environment variable OPENAI_KEY mancante. "
-                "Aggiungi la secret dal tab 'Secrets' dello Space."
-            )
-        llm = ChatOpenAI(
-            model_name="gpt-4.1",
-            temperature=0,
-            openai_api_key=openai_key,
-        )
+        key = os.getenv("OPENAI_KEY")
+        if not key:
+            raise ValueError("OPENAI_KEY mancante: aggiungi la secret nello Space.")
+        llm = ChatOpenAI(model_name="gpt-4.1", temperature=0, openai_api_key=key)
 
     elif provider == "google":
+        if not lg_google:
+            raise ImportError("langchain_google_genai non installato.")
         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
 
     elif provider == "groq":
+        if not lg_groq:
+            raise ImportError("langchain_groq non installato.")
         llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
 
     elif provider == "huggingface":
+        if not lg_hf:
+            raise ImportError("langchain_huggingface non installato.")
         llm = ChatHuggingFace(
             llm=HuggingFaceEndpoint(
                 url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
@@ -167,32 +155,25 @@ def build_graph(provider: str = "openai"):
             )
         )
     else:
-        raise ValueError(
-            "Invalid provider. Choose 'openai', 'google', 'groq' or 'huggingface'."
-        )
+        raise ValueError("Provider non valido.")
 
-    # Abilita tool calling
     llm_with_tools = llm.bind_tools(tools)
 
-    # ------------------------- NODES --------------------------------------- #
+    # ------------------- Nodes -------------------------------------------- #
     def assistant(state: MessagesState):
-        """Invoca il modello."""
         return {"messages": [llm_with_tools.invoke(state["messages"])]}
 
     def retriever(state: MessagesState):
-        """Aggiunge alla history un Q/A simile come esempio."""
         similar = vector_store.similarity_search(state["messages"][0].content)
         if similar:
-            example_msg = HumanMessage(
-                content=(
-                    "Here I provide a similar question and answer for reference:\n\n"
-                    f"{similar[0].page_content}"
-                )
+            example = HumanMessage(
+                content=("Here I provide a similar question and answer for reference:\n\n"
+                         f"{similar[0].page_content}")
             )
-            return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+            return {"messages": [sys_msg] + state["messages"] + [example]}
         return {"messages": [sys_msg] + state["messages"]}
 
-    # --------------------------- GRAPH ------------------------------------- #
+    # ------------------- Graph -------------------------------------------- #
     builder = StateGraph(MessagesState)
     builder.add_node("retriever", retriever)
     builder.add_node("assistant", assistant)
@@ -205,14 +186,13 @@ def build_graph(provider: str = "openai"):
 
     return builder.compile()
 
-
 # --------------------------------------------------------------------------- #
-#  Quick test (python agent.py)                                               #
+#  Test rapido                                                                #
 # --------------------------------------------------------------------------- #
 if __name__ == "__main__":
-    graph = build_graph(provider="openai")
-    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()
+    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()