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[Docs] Grammar and punctuation improvements in LangGraph introduction tutorial (#680)

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* Fix backtick enclosure in section 2

* Remove unnecessary double new line in route_tools() docstring

* Fix typos for describing checkpointer memory in section 3

* Fix grammar mistake when describing checkpointing in section 3

* Replace is with are to describe plural

* Fix incomplete double underscore wrapping for markdown formatting in section 7

---------

Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
This commit is contained in:
Ruan Pretorius
2024-06-18 18:04:34 -07:00
committed by GitHub
co-authored by William FH
parent 4578a653c7
commit 17f1a05b6b
1 changed files with 9 additions and 8 deletions
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examples/introduction.ipynb
+9 -8
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@@ -511,7 +511,7 @@
"source": [
"Next we need to create a function to actually run the tools if they are called. We'll do this by adding the tools to a new node.\n",
"\n",
"Below, implement a `BasicToolNode` that checks the most recent message in the state and calls tools if the message contains `tool_calls. It relies on the LLM's `tool_calling` support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.\n",
"Below, implement a `BasicToolNode` that checks the most recent message in the state and calls tools if the message contains `tool_calls`. It relies on the LLM's `tool_calling` support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.\n",
"\n",
"We will later replace this with LangGraph's prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode) to speed things up, but building it ourselves first is instructive."
]
@@ -587,9 +587,10 @@
"def route_tools(\n",
" state: State,\n",
") -> Literal[\"tools\", \"__end__\"]:\n",
" \"\"\"Use in the conditional_edge to route to the ToolNode if the last message\n",
"\n",
" has tool calls. Otherwise, route to the end.\"\"\"\n",
" \"\"\"\n",
" Use in the conditional_edge to route to the ToolNode if the last message\n",
" has tool calls. Otherwise, route to the end.\n",
" \"\"\"\n",
" if isinstance(state, list):\n",
" ai_message = state[-1]\n",
" elif messages := state.get(\"messages\", []):\n",
@@ -1070,7 +1071,7 @@
"id": "33be4cd8-f96f-4949-9d1f-48054502e5d0",
"metadata": {},
"source": [
"**Notice** that we are't the memory using an external list: it's all handled by the checkpointer! You can inspect the full execution in this [LangSmith trace](https://smith.langchain.com/public/48387889-c002-47a8-9f6a-1f6b298db64b/r) to see what's going on.\n",
"**Notice** that we aren't using an external list for memory: it's all handled by the checkpointer! You can inspect the full execution in this [LangSmith trace](https://smith.langchain.com/public/48387889-c002-47a8-9f6a-1f6b298db64b/r) to see what's going on.\n",
"\n",
"Don't believe me? Try this using a different config."
]
@@ -1165,7 +1166,7 @@
"source": [
"The snapshot above contains the current state values, corresponding config, and the `next` node to process. In our case, the graph has reached an `__end__` state, so `next` is empty.\n",
"\n",
"**Congratulations!** Your chatbot can now maintain conversation state across sessions thanks to LangGraph's checkpointing system. This opens up exciting possibilities for more natural, contextual interactions. LangGraph's checkpointing even handles **arbitrary complex graph states**, which is much more expressive and powerful than simple chat memory.\n",
"**Congratulations!** Your chatbot can now maintain conversation state across sessions thanks to LangGraph's checkpointing system. This opens up exciting possibilities for more natural, contextual interactions. LangGraph's checkpointing even handles **arbitrarily complex graph states**, which is much more expressive and powerful than simple chat memory.\n",
"\n",
"In the next part, we'll introduce human oversight to our bot to handle situations where it may need guidance or verification before proceeding.\n",
" \n",
@@ -1720,7 +1721,7 @@
"source": [
"Now the graph is complete, since we've provided the final response message! Since state updates simulate a graph step, they even generate corresponding traces. Inspec the [LangSmith trace](https://smith.langchain.com/public/c45207bb-bd26-4c9a-b631-928bbeebfbcb/r) of the `update_state` call above to see what's going on.\n",
"\n",
"**Notice** that our new messages is _appended_ to the messages already in the state. Remember how we defined the `State` type?\n",
"**Notice** that our new messages are _appended_ to the messages already in the state. Remember how we defined the `State` type?\n",
"\n",
"```python\n",
"class State(TypedDict):\n",
@@ -2950,7 +2951,7 @@
"id": "b182019e-bae3-4616-ba1b-f845c0ab6636",
"metadata": {},
"source": [
"**Notice** that checkpoints are saved for every step of the graph. This _spans invocations__ so you can rewind across a full thread's history. We've picked out `to_replay` as a state to resume from. This is the state after the `chatbot` node in the second graph invocation above.\n",
"**Notice** that checkpoints are saved for every step of the graph. This __spans invocations__ so you can rewind across a full thread's history. We've picked out `to_replay` as a state to resume from. This is the state after the `chatbot` node in the second graph invocation above.\n",
"\n",
"Resuming from this point should call the **action** node next."
]