[Docs] use END instead of set_finish_point (#903)

examples/tutorials/rag-agent-testing.ipynb

@@ -129,6 +129,7 @@
    "outputs": [],
    "source": [
     "from langchain_openai import ChatOpenAI\n",
+    "\n",
     "model_tested = \"gpt-4o\"\n",
     "metadata = \"CRAG, gpt-4o\"\n",
     "llm = ChatOpenAI(model_name=model_tested, temperature=0)"
@@ -150,6 +151,7 @@
    "outputs": [],
    "source": [
     "from langchain_fireworks import ChatFireworks\n",
+    "\n",
     "model_tested = \"firefunction-v2\"\n",
     "metadata = \"CRAG, firefunction-v\"\n",
     "llm = ChatFireworks(model=\"accounts/fireworks/models/firefunction-v2\", temperature=0)"
@@ -344,9 +346,9 @@
    "source": [
     "@tool\n",
     "def generate_answer(answer: str) -> str:\n",
-    "    \"\"\"You are an assistant for question-answering tasks. \n",
-    "    Use the retrieved documents to answer the user question. \n",
-    "    If you don't know the answer, just say that you don't know. \n",
+    "    \"\"\"You are an assistant for question-answering tasks.\n",
+    "    Use the retrieved documents to answer the user question.\n",
+    "    If you don't know the answer, just say that you don't know.\n",
     "    Use three sentences maximum and keep the answer concise\"\"\"\n",
     "    return f\"Here is the answer to the user question: {answer}\""
    ]
@@ -439,7 +441,7 @@
     "        (\n",
     "            \"system\",\n",
     "            \" You are a helpful assistant tasked with answering user questions using the provided vector store. \"\n",
-    "            \" Use the provided vector store to retrieve documents. Then grade them to ensure they are relevant before answering the question. \"\n",
+    "            \" Use the provided vector store to retrieve documents. Then grade them to ensure they are relevant before answering the question. \",\n",
     "        ),\n",
     "        (\"placeholder\", \"{messages}\"),\n",
     "    ]\n",
@@ -629,7 +631,8 @@
     "    ]\n",
     "    return tool_calls\n",
     "\n",
-    "find_tool_calls_react(response['messages'])"
+    "\n",
+    "find_tool_calls_react(response[\"messages\"])"
    ]
   },
   {
@@ -1080,6 +1083,7 @@
     "# Grade prompt\n",
     "grade_prompt_answer_accuracy = hub.pull(\"langchain-ai/rag-answer-vs-reference\")\n",
     "\n",
+    "\n",
     "def answer_evaluator(run, example) -> dict:\n",
     "    \"\"\"\n",
     "    A simple evaluator for RAG answer accuracy\n",
@@ -1140,6 +1144,7 @@
     "    \"generate_answer\",\n",
     "]\n",
     "\n",
+    "\n",
     "def check_trajectory_react(root_run: Run, example: Example) -> dict:\n",
     "    \"\"\"\n",
     "    Check if all expected tools are called in exact order and without any additional tool calls.\n",

examples/tutorials/tnt-llm/tnt-llm.ipynb

@@ -190,7 +190,7 @@
    "id": "f1f97ea4-53e5-4f55-8d73-b5b2234a47d9",
    "metadata": {},
    "outputs": [],
-   "source": ["from langgraph.graph import StateGraph, START\n\ngraph = StateGraph(TaxonomyGenerationState)\ngraph.add_node(\"summarize\", map_reduce_chain)\ngraph.add_node(\"get_minibatches\", get_minibatches)\ngraph.add_node(\"generate_taxonomy\", generate_taxonomy)\ngraph.add_node(\"update_taxonomy\", update_taxonomy)\ngraph.add_node(\"review_taxonomy\", review_taxonomy)\n\ngraph.add_edge(\"summarize\", \"get_minibatches\")\ngraph.add_edge(\"get_minibatches\", \"generate_taxonomy\")\ngraph.add_edge(\"generate_taxonomy\", \"update_taxonomy\")\n\n\ndef should_review(state: TaxonomyGenerationState) -> str:\n    num_minibatches = len(state[\"minibatches\"])\n    num_revisions = len(state[\"clusters\"])\n    if num_revisions < num_minibatches:\n        return \"update_taxonomy\"\n    return \"review_taxonomy\"\n\n\ngraph.add_conditional_edges(\n    \"update_taxonomy\",\n    should_review,\n    # Optional (but required for the diagram to be drawn correctly below)\n    {\"update_taxonomy\": \"update_taxonomy\", \"review_taxonomy\": \"review_taxonomy\"},\n)\ngraph.set_finish_point(\"review_taxonomy\")\n\ngraph.add_edge(START, \"summarize\")\napp = graph.compile()"]
+   "source": ["from langgraph.graph import StateGraph, START, END\n\ngraph = StateGraph(TaxonomyGenerationState)\ngraph.add_node(\"summarize\", map_reduce_chain)\ngraph.add_node(\"get_minibatches\", get_minibatches)\ngraph.add_node(\"generate_taxonomy\", generate_taxonomy)\ngraph.add_node(\"update_taxonomy\", update_taxonomy)\ngraph.add_node(\"review_taxonomy\", review_taxonomy)\n\ngraph.add_edge(\"summarize\", \"get_minibatches\")\ngraph.add_edge(\"get_minibatches\", \"generate_taxonomy\")\ngraph.add_edge(\"generate_taxonomy\", \"update_taxonomy\")\n\n\ndef should_review(state: TaxonomyGenerationState) -> str:\n    num_minibatches = len(state[\"minibatches\"])\n    num_revisions = len(state[\"clusters\"])\n    if num_revisions < num_minibatches:\n        return \"update_taxonomy\"\n    return \"review_taxonomy\"\n\n\ngraph.add_conditional_edges(\n    \"update_taxonomy\",\n    should_review,\n    # Optional (but required for the diagram to be drawn correctly below)\n    {\"update_taxonomy\": \"update_taxonomy\", \"review_taxonomy\": \"review_taxonomy\"},\n)\ngraph.add_edge(\"review_taxonomy\", END)\n\ngraph.add_edge(START, \"summarize\")\napp = graph.compile()"]
   },
   {
    "cell_type": "code",