docs/core_docs/docs/how_to/tool_calling_parallel.ipynb

@@ -56,7 +56,7 @@
         "});\n",
         "\n",
         "const multiplyTool = tool(async ({ a, b }) => {\n",
-        "  return a + b;\n",
+        "  return a * b;\n",
         "}, {\n",
         "  name: \"multiply\",\n",
         "  description: \"Multiplies a and b\",\n",
@@ -220,4 +220,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 4
-}
+}

docs/core_docs/docs/how_to/tool_results_pass_to_model.ipynb

@@ -18,7 +18,7 @@
         ":::\n",
         "```\n",
         "\n",
-        "Some models are capable of [**tool calling**](/docs/concepts/tool_calling) - generating arguments that conform to a specific user-provided schema. This guide will demonstrate how to use those tool cals to actually call a function and properly pass the results back to the model.\n",
+        "Some models are capable of [**tool calling**](/docs/concepts/tool_calling) - generating arguments that conform to a specific user-provided schema. This guide will demonstrate how to use those tool calls to actually call a function and properly pass the results back to the model.\n",
         "\n",
         "![](../../static/img/tool_invocation.png)\n",
         "\n",
@@ -367,4 +367,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 4
-}
+}

docs/core_docs/docs/integrations/chat/ibm.ipynb

@@ -29,7 +29,6 @@
         "| Class | Package | Local | Serializable | [PY support](https://python.langchain.com/docs/integrations/chat/ibm_watsonx/) | Package downloads | Package latest |\n",
         "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
         "| [`ChatWatsonx`](https://api.js.langchain.com/classes/_langchain_community.chat_models_ibm.ChatWatsonx.html) | [@langchain/community](https://www.npmjs.com/package/@langchain/community) | ❌ | ✅ | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/community?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/community?style=flat-square&label=%20&) |\n",
-
         "\n",
         "### Model features\n",
         "\n",
@@ -61,7 +60,7 @@
         "export WATSONX_AI_BEARER_TOKEN=<YOUR-BEARER-TOKEN>\n",
         "```\n",
         "\n",
-        "#### CP4D authentication\n",
+        "#### IBM watsonx.ai software authentication\n",
         "\n",
         "```bash\n",
         "export WATSONX_AI_AUTH_TYPE=cp4d\n",
@@ -104,7 +103,7 @@
         "const instance = new WatsonxLLM(props);\n",
         "```\n",
         "\n",
-        "### CP4D authentication\n",
+        "### IBM watsonx.ai software authentication\n",
         "\n",
         "```typescript\n",
         "import { WatsonxLLM } from \"@langchain/community/llms/ibm\";\n",

docs/core_docs/docs/integrations/document_compressors/ibm.ipynb

@@ -53,7 +53,7 @@
     "export WATSONX_AI_BEARER_TOKEN=<YOUR-BEARER-TOKEN>\n",
     "```\n",
     "\n",
-    "#### CP4D authentication\n",
+    "#### IBM watsonx.ai software authentication\n",
     "\n",
     "```bash\n",
     "export WATSONX_AI_AUTH_TYPE=cp4d\n",
@@ -96,7 +96,7 @@
     "const instance = new WatsonxLLM(props);\n",
     "```\n",
     "\n",
-    "### CP4D authentication\n",
+    "### IBM watsonx.ai software authentication\n",
     "\n",
     "```typescript\n",
     "import { WatsonxLLM } from \"@langchain/community/llms/ibm\";\n",

docs/core_docs/docs/integrations/llms/huggingface_inference.mdx

@@ -11,7 +11,7 @@ import UnifiedModelParamsTooltip from "@mdx_components/unified_model_params_tool
 <UnifiedModelParamsTooltip></UnifiedModelParamsTooltip>
 
 ```typescript
-import { HuggingFaceInference } from "langchain/llms/hf";
+import { HuggingFaceInference } from "@langchain/community/llms/hf";
 
 const model = new HuggingFaceInference({
   model: "gpt2",

docs/core_docs/docs/integrations/llms/ibm.ipynb

@@ -56,7 +56,7 @@
         "export WATSONX_AI_BEARER_TOKEN=<YOUR-BEARER-TOKEN>\n",
         "```\n",
         "\n",
-        "#### CP4D authentication\n",
+        "#### IBM watsonx.ai software authentication\n",
         "\n",
         "```bash\n",
         "export WATSONX_AI_AUTH_TYPE=cp4d\n",
@@ -99,7 +99,7 @@
         "const instance = new WatsonxLLM(props);\n",
         "```\n",
         "\n",
-        "### CP4D authentication\n",
+        "### IBM watsonx.ai software authentication\n",
         "\n",
         "```typescript\n",
         "import { WatsonxLLM } from \"@langchain/community/llms/ibm\";\n",

docs/core_docs/docs/integrations/text_embedding/ibm.ipynb

@@ -55,7 +55,7 @@
         "export WATSONX_AI_BEARER_TOKEN=<YOUR-BEARER-TOKEN>\n",
         "```\n",
         "\n",
-        "#### CP4D authentication\n",
+        "#### IBM watsonx.ai software authentication\n",
         "\n",
         "```bash\n",
         "export WATSONX_AI_AUTH_TYPE=cp4d\n",
@@ -98,7 +98,7 @@
         "const instance = new WatsonxEmbeddings(props);\n",
         "```\n",
         "\n",
-        "### CP4D authentication\n",
+        "### IBM watsonx.ai software authentication\n",
         "\n",
         "```typescript\n",
         "import { WatsonxEmbeddings } from \"@langchain/community/embeddings/ibm\";\n",

docs/core_docs/docs/integrations/tools/google_calendar.mdx

@@ -25,7 +25,7 @@ import IntegrationInstallTooltip from "@mdx_components/integration_install_toolt
 <IntegrationInstallTooltip></IntegrationInstallTooltip>
 
 ```bash npm2yarn
-npm install @langchain/openai @langchain/core
+npm install @langchain/openai @langchain/core @langchain/community @langchain/langgraph
 ```
 
 <CodeBlock language="typescript">{ToolExample}</CodeBlock>

docs/core_docs/src/theme/VectorStoreTabs.js

@@ -54,7 +54,7 @@ const ${vectorStoreVarName} = new MongoDBAtlasVectorSearch(embeddings, {
     {
       value: "PGVector",
       label: "PGVector",
-      text: `import PGVectorStore from "@langchain/community/vectorstores/pgvector";
+      text: `import { PGVectorStore } from "@langchain/community/vectorstores/pgvector";
 
 const ${vectorStoreVarName} = await PGVectorStore.initialize(embeddings, {})`,
       dependencies: "@langchain/community",

examples/src/document_loaders/notionapi.ts

@@ -12,10 +12,12 @@ const pageLoader = new NotionAPILoader({
 
 const splitter = new RecursiveCharacterTextSplitter();
 
-// A page contents is likely to be more than 1000 characters so it's split into multiple documents (important for vectorization)
-const pageDocs = await pageLoader.loadAndSplit(splitter);
+// Load the documents
+const pageDocs = await pageLoader.load();
+// Split the documents using the text splitter
+const splitDocs = await splitter.splitDocuments(pageDocs);
 
-console.log({ pageDocs });
+console.log({ splitDocs });
 
 // Loading a database (each row is a separate document with all properties as metadata)
 const dbLoader = new NotionAPILoader({

examples/src/tools/google_calendar.ts

@@ -1,15 +1,16 @@
-import { initializeAgentExecutorWithOptions } from "langchain/agents";
-import { OpenAI } from "@langchain/openai";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { ChatOpenAI } from "@langchain/openai";
 import { Calculator } from "@langchain/community/tools/calculator";
 import {
   GoogleCalendarCreateTool,
   GoogleCalendarViewTool,
 } from "@langchain/community/tools/google_calendar";
 
 export async function run() {
-  const model = new OpenAI({
+  const model = new ChatOpenAI({
     temperature: 0,
     apiKey: process.env.OPENAI_API_KEY,
+    model: "gpt-4o-mini",
   });
 
   const googleCalendarParams = {
@@ -31,22 +32,26 @@ export async function run() {
     new GoogleCalendarViewTool(googleCalendarParams),
   ];
 
-  const calendarAgent = await initializeAgentExecutorWithOptions(tools, model, {
-    agentType: "zero-shot-react-description",
-    verbose: true,
+  const calendarAgent = createReactAgent({
+    llm: model,
+    tools,
   });
 
   const createInput = `Create a meeting with John Doe next Friday at 4pm - adding to the agenda of it the result of 99 + 99`;
 
-  const createResult = await calendarAgent.invoke({ input: createInput });
+  const createResult = await calendarAgent.invoke({
+    messages: [{ role: "user", content: createInput }],
+  });
   //   Create Result {
   //     output: 'A meeting with John Doe on 29th September at 4pm has been created and the result of 99 + 99 has been added to the agenda.'
   //   }
   console.log("Create Result", createResult);
 
   const viewInput = `What meetings do I have this week?`;
 
-  const viewResult = await calendarAgent.invoke({ input: viewInput });
+  const viewResult = await calendarAgent.invoke({
+    messages: [{ role: "user", content: viewInput }],
+  });
   //   View Result {
   //     output: "You have no meetings this week between 8am and 8pm."
   //   }

langchain-core/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/core",
-  "version": "0.3.28",
+  "version": "0.3.31",
   "description": "Core LangChain.js abstractions and schemas",
   "type": "module",
   "engines": {

langchain-core/src/callbacks/dispatch/web.ts

@@ -39,17 +39,11 @@ export async function dispatchCustomEvent(
   payload: any,
   config?: RunnableConfig
 ) {
-  const callbackManager = await getCallbackManagerForConfig(config);
-  const parentRunId = callbackManager?.getParentRunId();
-  // We want to get the callback manager for the parent run.
-  // This is a work-around for now to be able to dispatch adhoc events from
-  // within a tool or a lambda and have the metadata events associated
-  // with the parent run rather than have a new run id generated for each.
-  if (callbackManager === undefined || parentRunId === undefined) {
+  if (config === undefined) {
     throw new Error(
       [
         "Unable to dispatch a custom event without a parent run id.",
-        "This function can only be called from within an existing run (e.g.,",
+        `"dispatchCustomEvent" can only be called from within an existing run (e.g.,`,
         "inside a tool or a RunnableLambda).",
         `\n\nIf you continue to see this error, please import from "@langchain/core/callbacks/dispatch/web"`,
         "and explicitly pass in a config parameter.",
@@ -60,7 +54,11 @@ export async function dispatchCustomEvent(
       ].join(" ")
     );
   }
+  const callbackManager = await getCallbackManagerForConfig(config);
+  const parentRunId = callbackManager?.getParentRunId();
   // We pass parent id as the current run id here intentionally since events dispatch
   // from within things like RunnableLambda
-  await callbackManager.handleCustomEvent?.(name, payload, parentRunId);
+  if (callbackManager !== undefined && parentRunId !== undefined) {
+    await callbackManager.handleCustomEvent?.(name, payload, parentRunId);
+  }
 }

langchain-core/src/language_models/chat_models.ts

@@ -74,7 +74,18 @@ export type SerializedLLM = {
 /**
  * Represents the parameters for a base chat model.
  */
-export type BaseChatModelParams = BaseLanguageModelParams;
+export type BaseChatModelParams = BaseLanguageModelParams & {
+  /**
+   * Whether to disable streaming.
+   *
+   * If streaming is bypassed, then `stream()` will defer to
+   * `invoke()`.
+   *
+   * - If true, will always bypass streaming case.
+   * - If false (default), will always use streaming case if available.
+   */
+  disableStreaming?: boolean;
+};
 
 /**
  * Represents the call options for a base chat model.
@@ -152,6 +163,8 @@ export abstract class BaseChatModel<
   // Only ever instantiated in main LangChain
   lc_namespace = ["langchain", "chat_models", this._llmType()];
 
+  disableStreaming = false;
+
   constructor(fields: BaseChatModelParams) {
     super(fields);
   }
@@ -220,7 +233,8 @@ export abstract class BaseChatModel<
     // Subclass check required to avoid double callbacks with default implementation
     if (
       this._streamResponseChunks ===
-      BaseChatModel.prototype._streamResponseChunks
+        BaseChatModel.prototype._streamResponseChunks ||
+      this.disableStreaming
     ) {
       yield this.invoke(input, options);
     } else {