docs/core_docs/docs/concepts/structured_outputs.mdx

@@ -79,7 +79,7 @@ Several more powerful methods that utilizes native features in the model provide
 
 Many [model providers support](/docs/integrations/chat/) tool calling, a concept discussed in more detail in our [tool calling guide](/docs/concepts/tool_calling/).
 In short, tool calling involves binding a tool to a model and, when appropriate, the model can _decide_ to call this tool and ensure its response conforms to the tool's schema.
-With this in mind, the central concept is straightforward: _simply bind our schema to a model as a tool!_
+With this in mind, the central concept is straightforward: _create a tool with our schema and bind it to the model!_
 Here is an example using the `ResponseFormatter` schema defined above:
 
 ```typescript
@@ -90,8 +90,14 @@ const model = new ChatOpenAI({
   temperature: 0,
 });
 
-// Bind ResponseFormatter schema as a tool to the model
-const modelWithTools = model.bindTools([ResponseFormatter]);
+// Create a tool with ResponseFormatter as its schema.
+const responseFormatterTool = tool(async () => {}, {
+  name: "responseFormatter",
+  schema: ResponseFormatter,
+});
+
+// Bind the created tool to the model
+const modelWithTools = model.bindTools([responseFormatterTool]);
 
 // Invoke the model
 const aiMsg = await modelWithTools.invoke(

docs/core_docs/docs/how_to/graph_constructing.ipynb

@@ -102,7 +102,7 @@
         "\n",
         "const model = new ChatOpenAI({\n",
         "    temperature: 0,\n",
-        "    model: \"gpt-4-turbo-preview\",\n",
+        "    model: \"gpt-4o-mini\",\n",
         "});\n",
         "\n",
         "const llmGraphTransformer = new LLMGraphTransformer({\n",

docs/core_docs/docs/how_to/query_high_cardinality.ipynb

@@ -392,7 +392,7 @@
       "metadata": {},
       "source": [
         "```{=mdx}\n",
-        "<ChatModelTabs customVarName=\"llmLong\" openaiParams={`{ model: \"gpt-4-turbo-preview\" }`} />\n",
+        "<ChatModelTabs customVarName=\"llmLong\" openaiParams={`{ model: \"gpt-4o-mini\" }`} />\n",
         "```"
       ]
     },
@@ -635,4 +635,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

docs/core_docs/docs/integrations/chat/openai.ipynb

@@ -625,7 +625,7 @@
       "id": "045668fe",
       "metadata": {},
       "source": [
-        "### Structured output\n",
+        "## Structured output\n",
         "\n",
         "We can also pass `strict: true` to the [`.withStructuredOutput()`](https://js.langchain.com/docs/how_to/structured_output/#the-.withstructuredoutput-method). Here's an example:"
       ]
@@ -664,6 +664,104 @@
         "}]);"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "id": "0194ec1f",
+      "metadata": {},
+      "source": [
+        "### Reasoning models\n",
+        "\n",
+        "```{=mdx}\n",
+        ":::caution Compatibility\n",
+        "\n",
+        "The below points apply to `@langchain/openai>=0.4.0`. Please see here for a [guide on upgrading](/docs/how_to/installation/#installing-integration-packages).\n",
+        "\n",
+        ":::\n",
+        "```\n",
+        "\n",
+        "When using reasoning models like `o1`, the default method for `withStructuredOutput` is OpenAI's built-in method for structured output (equivalent to passing `method: \"jsonSchema\"` as an option into `withStructuredOutput`). JSON schema mostly works the same as other models, but with one important caveat: when defining schema, `z.optional()` is not respected, and you should instead use `z.nullable()`.\n",
+        "\n",
+        "Here's an example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "d2a04807",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{ color: 'No color mentioned' }\n"
+          ]
+        }
+      ],
+      "source": [
+        "import { z } from \"zod\";\n",
+        "import { ChatOpenAI } from \"@langchain/openai\";\n",
+        "\n",
+        "// Will not work\n",
+        "const reasoningModelSchemaOptional = z.object({\n",
+        "  color: z.optional(z.string()).describe(\"A color mentioned in the input\"),\n",
+        "});\n",
+        "\n",
+        "const reasoningModelOptionalSchema = new ChatOpenAI({\n",
+        "  model: \"o1\",\n",
+        "}).withStructuredOutput(reasoningModelSchemaOptional, {\n",
+        "  name: \"extract_color\",\n",
+        "});\n",
+        "\n",
+        "await reasoningModelOptionalSchema.invoke([{\n",
+        "  role: \"user\",\n",
+        "  content: `I am 6'5\" tall and love fruit.`\n",
+        "}]);"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "69854ed4",
+      "metadata": {},
+      "source": [
+        "And here's an example with `z.nullable()`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "5f4bb1bc",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{ color: null }\n"
+          ]
+        }
+      ],
+      "source": [
+        "import { z } from \"zod\";\n",
+        "import { ChatOpenAI } from \"@langchain/openai\";\n",
+        "\n",
+        "// Will not work\n",
+        "const reasoningModelSchemaNullable = z.object({\n",
+        "  color: z.nullable(z.string()).describe(\"A color mentioned in the input\"),\n",
+        "});\n",
+        "\n",
+        "const reasoningModelNullableSchema = new ChatOpenAI({\n",
+        "  model: \"o1\",\n",
+        "}).withStructuredOutput(reasoningModelSchemaNullable, {\n",
+        "  name: \"extract_color\",\n",
+        "});\n",
+        "\n",
+        "await reasoningModelNullableSchema.invoke([{\n",
+        "  role: \"user\",\n",
+        "  content: `I am 6'5\" tall and love fruit.`\n",
+        "}]);"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "af20e756",

examples/src/embeddings/cache_backed_in_memory.ts

@@ -13,7 +13,7 @@ const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore(
   underlyingEmbeddings,
   inMemoryStore,
   {
-    namespace: underlyingEmbeddings.modelName,
+    namespace: underlyingEmbeddings.model,
   }
 );
 

examples/src/embeddings/cache_backed_redis.ts

@@ -20,7 +20,7 @@ const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore(
   underlyingEmbeddings,
   redisStore,
   {
-    namespace: underlyingEmbeddings.modelName,
+    namespace: underlyingEmbeddings.model,
   }
 );
 

examples/src/embeddings/convex/cache_backed_convex.ts

@@ -17,7 +17,7 @@ export const ask = action({
       underlyingEmbeddings,
       new ConvexKVStore({ ctx }),
       {
-        namespace: underlyingEmbeddings.modelName,
+        namespace: underlyingEmbeddings.model,
       }
     );