diff --git a/examples/introduction.ipynb b/examples/introduction.ipynb index aa27ff7c5..6caea9cd8 100644 --- a/examples/introduction.ipynb +++ b/examples/introduction.ipynb @@ -569,7 +569,7 @@ "\n", "Below, call define a router function called `route_tools`, that checks for tool_calls in the chatbot's output. Provide this function to the graph by calling `add_conditional_edges`, which tells the graph that whenever the `chatbot` node completes to check this function to see where to go next. \n", "\n", - "The condition will route to `action` if tool calls are present and \"`__end__`\" if not.\n", + "The condition will route to `tools` if tool calls are present and \"`__end__`\" if not.\n", "\n", "Later, we will replace this with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition) to be more concise, but implementing it ourselves first makes things more clear. " ] @@ -624,9 +624,9 @@ "id": "a2aa67c2-dd1b-4bf2-8c64-eea44296d15f", "metadata": {}, "source": [ - "**Notice** that conditional edges start from a single node. This tells the graph \"any time the '`chatbot`' node runs, either go to 'action' if it calls a tool, or end the loop if it responds directly. \n", + "**Notice** that conditional edges start from a single node. This tells the graph \"any time the '`chatbot`' node runs, either go to 'tools' if it calls a tool, or end the loop if it responds directly. \n", "\n", - "The prebuilt `tools_condition` returns the \"`__end__`\" string if no tool calls are made. When the graph transitions to `__end__`, it has no more tasks to complete and ceases execution. Because the condition can return `__end__`, we don't need to explicitly set a `finish_point` this time. Our graph already has a way to finish!\n", + "Like the prebuilt `tools_condition`, our function returns the \"`__end__`\" string if no tool calls are made. When the graph transitions to `__end__`, it has no more tasks to complete and ceases execution. Because the condition can return `__end__`, we don't need to explicitly set a `finish_point` this time. Our graph already has a way to finish!\n", "\n", "Let's visualize the graph we've built. The following function has some additional dependencies to run that are unimportant for this tutorial." ] @@ -639,7 +639,7 @@ "outputs": [ { "data": { - "image/jpeg": 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", 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