docs[patch]: Update tutorials (langchain-ai#5473)

* Update tutorials

* Fix LangSmith section:

docs/core_docs/.gitignore

@@ -49,6 +49,8 @@ docs/tutorials/extraction.md
 docs/tutorials/extraction.mdx
 docs/tutorials/classification.md
 docs/tutorials/classification.mdx
+docs/tutorials/chatbot.md
+docs/tutorials/chatbot.mdx
 docs/how_to/tools_prompting.md
 docs/how_to/tools_prompting.mdx
 docs/how_to/tools_builtin.md

docs/core_docs/docs/how_to/structured_output.ipynb

@@ -44,35 +44,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "070bf702",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{\n",
-       "  setup: \u001b[32m\"Why was the cat sitting on the computer?\"\u001b[39m,\n",
-       "  punchline: \u001b[32m\"Because it wanted to keep an eye on the mouse!\"\u001b[39m,\n",
-       "  rating: \u001b[33m8\u001b[39m\n",
+       "  setup: \u001b[32m\"Why don't cats play poker in the wild?\"\u001b[39m,\n",
+       "  punchline: \u001b[32m\"Too many cheetahs.\"\u001b[39m,\n",
+       "  rating: \u001b[33m7\u001b[39m\n",
        "}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import { z } from \"zod\";\n",
     "\n",
-    "const Joke = z.object({\n",
+    "const joke = z.object({\n",
     "    setup: z.string().describe(\"The setup of the joke\"),\n",
     "    punchline: z.string().describe(\"The punchline to the joke\"),\n",
     "    rating: z.number().optional().describe(\"How funny the joke is, from 1 to 10\"),\n",
     "});\n",
     "\n",
-    "const structuredLlm = model.withStructuredOutput(Joke);\n",
+    "const structuredLlm = model.withStructuredOutput(joke);\n",
+    "\n",
+    "await structuredLlm.invoke(\"Tell me a joke about cats\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe6efeab",
+   "metadata": {},
+   "source": [
+    "One key point is that though we set our Zod schema as a variable named `joke`, Zod is not able to access that variable name, and therefore cannot pass it to the model. Though it is not required, we can pass a name for our schema in order to give the model additional context as to what our schema represents, improving performance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f3d01a1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{\n",
+       "  setup: \u001b[32m\"Why don't cats play poker in the wild?\"\u001b[39m,\n",
+       "  punchline: \u001b[32m\"Too many cheetahs!\"\u001b[39m,\n",
+       "  rating: \u001b[33m7\u001b[39m\n",
+       "}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "const structuredLlm = model.withStructuredOutput(joke, { name: \"joke\" });\n",
     "\n",
     "await structuredLlm.invoke(\"Tell me a joke about cats\")"
    ]
@@ -155,27 +190,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "df0370e3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{\n",
-       "  setup: \u001b[32m\"Why was the cat sitting on the computer?\"\u001b[39m,\n",
-       "  punchline: \u001b[32m\"To keep an eye on the mouse!\"\u001b[39m\n",
+       "  setup: \u001b[32m\"Why don't cats play poker in the jungle?\"\u001b[39m,\n",
+       "  punchline: \u001b[32m\"Too many cheetahs!\"\u001b[39m\n",
        "}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "const structuredLlm = model.withStructuredOutput(Joke, {\n",
-    "    method: \"json_schema\"\n",
+    "const structuredLlm = model.withStructuredOutput(joke, {\n",
+    "    method: \"json_mode\",\n",
+    "    name: \"joke\",\n",
     "})\n",
     "\n",
     "await structuredLlm.invoke(\n",
@@ -203,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "6e514455",
    "metadata": {},
    "outputs": [],
@@ -256,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "3d73d33d",
    "metadata": {},
    "outputs": [
@@ -290,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "8d6b3d17",
    "metadata": {},
    "outputs": [
@@ -300,7 +336,7 @@
        "{ people: [ { name: \u001b[32m\"Anna\"\u001b[39m, height_in_meters: \u001b[33m1.83\u001b[39m } ] }"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "id": "525721b3",
    "metadata": {},
    "outputs": [],
@@ -399,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "c8a30d0e",
    "metadata": {},
    "outputs": [
@@ -432,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "id": "e1e7baf6",
    "metadata": {},
    "outputs": [
@@ -444,7 +480,7 @@
        "]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }

docs/core_docs/docs/langsmith/index.mdx → docs/core_docs/docs/langsmith.mdx

@@ -1,15 +1,9 @@
----
-sidebar_class_name: hidden
----
-
 # 🦜🛠️ LangSmith
 
 [LangSmith](https://smith.langchain.com) helps you trace and evaluate your language model applications and intelligent agents to help you
 move from prototype to production.
 
-Check out the [interactive walkthrough](/docs/langsmith/walkthrough) to get started.
-
-For more information, please refer to the [LangSmith documentation](https://docs.smith.langchain.com/).
+To get started, please refer to the [LangSmith documentation](https://docs.smith.langchain.com/).
 
 For tutorials and other end-to-end examples demonstrating ways to integrate LangSmith in your workflow,
 check out the [LangSmith Cookbook](https://github.com/langchain-ai/langsmith-cookbook). Some of the guides therein include: