[Docs] Add ruff linting to .ipynb files (#645)

examples/learning.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "When running LangGraph agents, you can easily save good threads and use them in the future.\n",
     "\n",
-    "**Note:** this requires passing in a checkpointer."
+    "**Note:** this requires passing in a checkpointer.\n"
    ]
   },
   {
@@ -19,7 +19,7 @@
    "source": [
     "## Setup\n",
     "\n",
-    "First we need to install the packages required"
+    "First we need to install the packages required\n"
    ]
   },
   {
@@ -47,7 +47,7 @@
    "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d",
    "metadata": {},
    "source": [
-    "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)"
+    "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)\n"
    ]
   },
   {
@@ -66,8 +66,8 @@
     }
    ],
    "source": [
-    "import os\n",
     "import getpass\n",
+    "import os\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
     "os.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily API Key:\")"
@@ -78,7 +78,7 @@
    "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c",
    "metadata": {},
    "source": [
-    "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability."
+    "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability.\n"
    ]
   },
   {
@@ -150,7 +150,7 @@
     "1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.\n",
     "2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.\n",
     "\n",
-    "Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example."
+    "Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example.\n"
    ]
   },
   {
@@ -170,7 +170,6 @@
    "id": "a77995c0-bae2-4cee-a036-8688a90f05b9",
    "metadata": {},
    "source": [
-    "\n",
     "After we've done this, we should make sure the model knows that it has these tools available to call.\n",
     "We can do this using the `.bind_tools()` method, common to many of LangChain's chat models.\n"
    ]
@@ -209,7 +208,7 @@
     "   b. If the agent said that it was finished, then it should finish\n",
     "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n",
     "\n",
-    "Let's define the nodes, as well as a function to decide how what conditional edge to take."
+    "Let's define the nodes, as well as a function to decide how what conditional edge to take.\n"
    ]
   },
   {
@@ -237,7 +236,7 @@
    "source": [
     "## Define the graph\n",
     "\n",
-    "We can now put it all together and define the graph!"
+    "We can now put it all together and define the graph!\n"
    ]
   },
   {
@@ -247,11 +246,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langgraph.graph import StateGraph, END\n",
+    "from typing import Annotated, TypedDict\n",
+    "\n",
+    "from langchain_core.messages import (\n",
+    "    AIMessage,\n",
+    "    AnyMessage,\n",
+    "    HumanMessage,\n",
+    "    SystemMessage,\n",
+    "    ToolMessage,\n",
+    ")\n",
+    "\n",
+    "from langgraph.graph import END, StateGraph\n",
     "from langgraph.graph.message import add_messages\n",
     "from langgraph.managed.few_shot import FewShotExamples\n",
-    "from typing import TypedDict, Annotated\n",
-    "from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage\n",
     "\n",
     "\n",
     "class BaseState(TypedDict):\n",
@@ -259,9 +266,6 @@
     "    examples: Annotated[list, FewShotExamples]\n",
     "\n",
     "\n",
-    "from langchain_core.messages import AIMessage, ToolMessage\n",
-    "\n",
-    "\n",
     "def _render_message(m):\n",
     "    if isinstance(m, HumanMessage):\n",
     "        return \"Human: \" + m.content\n",
@@ -299,9 +303,7 @@
     "\n",
     "{examples}\n",
     "\n",
-    "Assist the user as they require!\"\"\".format(\n",
-    "            examples=_examples\n",
-    "        )\n",
+    "Assist the user as they require!\"\"\".format(examples=_examples)\n",
     "\n",
     "    else:\n",
     "        system_message = \"\"\"You are a helpful assistant\"\"\"\n",
@@ -350,7 +352,7 @@
    "source": [
     "**Persistence**\n",
     "\n",
-    "To add in persistence, we pass in a checkpoint when compiling the graph"
+    "To add in persistence, we pass in a checkpoint when compiling the graph\n"
    ]
   },
   {
@@ -383,7 +385,7 @@
    "id": "e8aff75b-563e-42b1-969b-742201514fc3",
    "metadata": {},
    "source": [
-    "## Preview the graph"
+    "## Preview the graph\n"
    ]
   },
   {
@@ -435,8 +437,6 @@
     }
    ],
    "source": [
-    "from langchain_core.messages import HumanMessage\n",
-    "\n",
     "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
     "for event in app.stream(\n",
     "    {\"messages\": [HumanMessage(content=\"whats the weather in sf?\")]}, thread\n",