aggiunt file agent.py con la definizione dell'agent

agent.py

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+import os
+from dotenv import load_dotenv
+import traceback
+
+from typing import Annotated,Sequence, TypedDict
+
+from langchain_core.messages import BaseMessage
+from langgraph.graph.message import add_messages # helper function to add messages to the state
+from langchain_core.messages import ToolMessage
+from langchain_core.runnables import RunnableConfig
+from langgraph.graph import StateGraph, END
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+# Local imports
+from tools import get_search_tool, get_wikipedia_tool
+
+# Nota: per i test in locale si usa il .env
+#       su HuggingFace invece si usano le variabili definite in Settings/"Variables and secrets"
+load_dotenv()
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
+GEMINI_MODEL = os.environ.get("GEMINI_MODEL")
+GEMINI_BASE_URL = os.environ.get("GEMINI_BASE_URL")
+
+#
+# Inizializza il modello e gli associa i tool
+#
+
+# ChatGoogleGenerativeAI è il package ufficiale di LangChain per interagire con i modelli Gemini
+# https://python.langchain.com/docs/integrations/chat/google_generative_ai/
+chat = ChatGoogleGenerativeAI(
+    model=GEMINI_MODEL,
+    google_api_key=GEMINI_API_KEY)
+
+# Imposta i tool
+search_tool = get_search_tool()
+wikipedia_tool = get_wikipedia_tool()
+
+tools = [search_tool, wikipedia_tool]
+
+# Bind tools to the model
+model = chat.bind_tools(tools)
+
+tools_by_name = {tool.name: tool for tool in tools}
+
+
+#
+# Definisce il grafo
+#
+
+class AgentState(TypedDict):
+    """The state of the agent."""
+    messages: Annotated[Sequence[BaseMessage], add_messages]
+    number_of_steps: int
+
+
+# Define our tool node
+def call_tool(state: AgentState):
+    outputs = []
+    # Iterate over the tool calls in the last message
+    for tool_call in state["messages"][-1].tool_calls:
+        # Get the tool by name
+        tool_result = tools_by_name[tool_call["name"]].invoke(tool_call["args"])
+        outputs.append(
+            ToolMessage(
+                content=tool_result,
+                name=tool_call["name"],
+                tool_call_id=tool_call["id"],
+            )
+        )
+    return {"messages": outputs}
+
+
+def call_model( state: AgentState, config: RunnableConfig):
+    # Invoke the model with the system prompt and the messages
+    response = model.invoke(state["messages"], config)
+    # We return a list, because this will get added to the existing messages state using the add_messages reducer
+    return {"messages": [response]}
+
+
+# Define the conditional edge that determines whether to continue or not
+def should_continue(state: AgentState):
+    messages = state["messages"]
+    # If the last message is not a tool call, then we finish
+    if not messages[-1].tool_calls:
+        return "end"
+    # default to continue
+    return "continue"
+
+
+def get_agent():
+    # Creazione del grafo
+    workflow = StateGraph(AgentState)
+
+    # 1. Add our nodes 
+    workflow.add_node("llm", call_model)
+    workflow.add_node("tools",  call_tool)
+    # 2. Set the entrypoint as `agent`, this is the first node called
+    workflow.set_entry_point("llm")
+    # 3. Add a conditional edge after the `llm` node is called.
+    workflow.add_conditional_edges(
+        # Edge is used after the `llm` node is called.
+        "llm",
+        # The function that will determine which node is called next.
+        should_continue,
+        # Mapping for where to go next, keys are strings from the function return, and the values are other nodes.
+        # END is a special node marking that the graph is finish.
+        {
+            # If `tools`, then we call the tool node.
+            "continue": "tools",
+            # Otherwise we finish.
+            "end": END,
+        },
+    )
+    # 4. Add a normal edge after `tools` is called, `llm` node is called next.
+    workflow.add_edge("tools", "llm")
+
+    # 5. Now we can compile our graph
+    react_graph = workflow.compile()
+
+    return react_graph
+
+
+# Riferimenti
+#
+# https://ai.google.dev/gemini-api/docs/langgraph-example