Create app.py

app.py

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+from typing import TypedDict, Annotated, Sequence, Literal
+
+from functools import lru_cache
+from langchain_core.messages import BaseMessage
+from langchain_anthropic import ChatAnthropic
+from langchain_openai import ChatOpenAI
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import StateGraph, END, add_messages
+
+tools = [TavilySearchResults(max_results=1)]
+
+@lru_cache(maxsize=4)
+def _get_model(model_name: str):
+    if model_name == "openai":
+        model = ChatOpenAI(temperature=0, model_name="gpt-4o")
+    elif model_name == "anthropic":
+        model =  ChatAnthropic(temperature=0, model_name="claude-3-sonnet-20240229")
+    else:
+        raise ValueError(f"Unsupported model type: {model_name}")
+
+    model = model.bind_tools(tools)
+    return model
+
+
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], add_messages]
+
+
+# Define the function that determines whether to continue or not
+def should_continue(state):
+    messages = state["messages"]
+    last_message = messages[-1]
+    # If there are no tool calls, then we finish
+    if not last_message.tool_calls:
+        return "end"
+    # Otherwise if there is, we continue
+    else:
+        return "continue"
+
+
+system_prompt = """Be a helpful assistant"""
+
+# Define the function that calls the model
+def call_model(state, config):
+    messages = state["messages"]
+    messages = [{"role": "system", "content": system_prompt}] + messages
+    model_name = config.get('configurable', {}).get("model_name", "anthropic")
+    model = _get_model(model_name)
+    response = model.invoke(messages)
+    # We return a list, because this will get added to the existing list
+    return {"messages": [response]}
+
+
+# Define the function to execute tools
+tool_node = ToolNode(tools)
+
+# Define the config
+class GraphConfig(TypedDict):
+    model_name: Literal["anthropic", "openai"]
+
+
+# Define a new graph
+workflow = StateGraph(AgentState, config_schema=GraphConfig)
+
+# Define the two nodes we will cycle between
+workflow.add_node("agent", call_model)
+workflow.add_node("action", tool_node)
+
+# Set the entrypoint as `agent`
+# This means that this node is the first one called
+workflow.set_entry_point("agent")
+
+# We now add a conditional edge
+workflow.add_conditional_edges(
+    # First, we define the start node. We use `agent`.
+    # This means these are the edges taken after the `agent` node is called.
+    "agent",
+    # Next, we pass in the function that will determine which node is called next.
+    should_continue,
+    # Finally we pass in a mapping.
+    # The keys are strings, and the values are other nodes.
+    # END is a special node marking that the graph should finish.
+    # What will happen is we will call `should_continue`, and then the output of that
+    # will be matched against the keys in this mapping.
+    # Based on which one it matches, that node will then be called.
+    {
+        # If `tools`, then we call the tool node.
+        "continue": "action",
+        # Otherwise we finish.
+        "end": END,
+    },
+)
+
+# We now add a normal edge from `tools` to `agent`.
+# This means that after `tools` is called, `agent` node is called next.
+workflow.add_edge("action", "agent")
+
+# Finally, we compile it!
+# This compiles it into a LangChain Runnable,
+# meaning you can use it as you would any other runnable
+graph = workflow.compile()