diff --git a/docs/core_docs/docs/integrations/llms/ibm.ipynb b/docs/core_docs/docs/integrations/llms/ibm.ipynb
new file mode 100644
index 0000000000000..94a057af8d9ae
--- /dev/null
+++ b/docs/core_docs/docs/integrations/llms/ibm.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "67db2992",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: IBM watsonx.ai\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9597802c",
+   "metadata": {},
+   "source": [
+    "# IBM watsonx.ai\n",
+    "\n",
+    "\n",
+    "This will help you get started with IBM [text completion models (LLMs)](/docs/concepts#llms) using LangChain. For detailed documentation on `IBM watsonx.ai` features and configuration options, please refer to the [IBM watsonx.ai](https://api.js.langchain.com/classes/_langchain_community.llms_ibm.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [PY support](https://python.langchain.com/docs/integrations/llms/ibm_watsonx/) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [`IBM watsonx.ai`](https://api.js.langchain.com/modules/_langchain_community.llms_ibm.html) | [@langchain/community](https://api.js.langchain.com/modules/langchain_community_llms_ibm.html) | ❌ | ✅ | ✅ | ![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",
+    "## Setup\n",
+    "\n",
+    "\n",
+    "To access IBM WatsonxAI models you'll need to create an IBM watsonx.ai account, get an API key or any other type of credentials, and install the `@langchain/community` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "\n",
+    "Head to [IBM Cloud](https://cloud.ibm.com/login) to sign up to IBM watsonx.ai and generate an API key or provide any other authentication form as presented below.\n",
+    "\n",
+    "#### IAM authentication\n",
+    "\n",
+    "```bash\n",
+    "export WATSONX_AI_AUTH_TYPE=iam\n",
+    "export WATSONX_AI_APIKEY=<YOUR-APIKEY>\n",
+    "```\n",
+    "\n",
+    "#### Bearer token authentication\n",
+    "\n",
+    "```bash\n",
+    "export WATSONX_AI_AUTH_TYPE=bearertoken\n",
+    "export WATSONX_AI_BEARER_TOKEN=<YOUR-BEARER-TOKEN>\n",
+    "```\n",
+    "\n",
+    "#### CP4D authentication\n",
+    "\n",
+    "```bash\n",
+    "export WATSONX_AI_AUTH_TYPE=cp4d\n",
+    "export WATSONX_AI_USERNAME=<YOUR_USERNAME>\n",
+    "export WATSONX_AI_PASSWORD=<YOUR_PASSWORD>\n",
+    "export WATSONX_AI_URL=<URL>\n",
+    "```\n",
+    "\n",
+    "Once these are places in your enviromental variables and object is initialized authentication will proceed automatically.\n",
+    "\n",
+    "Authentication can also be accomplished by passing these values as parameters to a new instance.\n",
+    "\n",
+    "## IAM authentication\n",
+    "\n",
+    "```typescript\n",
+    "import { WatsonxLLM } from \"@langchain/community/llms/ibm\";\n",
+    "\n",
+    "const props = {\n",
+    "  version: \"YYYY-MM-DD\",\n",
+    "  serviceUrl: \"<SERVICE_URL>\",\n",
+    "  projectId: \"<PROJECT_ID>\",\n",
+    "  watsonxAIAuthType: \"iam\",\n",
+    "  watsonxAIApikey: \"<YOUR-APIKEY>\",\n",
+    "};\n",
+    "const instance = new WatsonxLLM(props);\n",
+    "```\n",
+    "\n",
+    "## Bearer token authentication\n",
+    "\n",
+    "```typescript\n",
+    "import { WatsonxLLM } from \"@langchain/community/llms/ibm\";\n",
+    "\n",
+    "const props = {\n",
+    "  version: \"YYYY-MM-DD\",\n",
+    "  serviceUrl: \"<SERVICE_URL>\",\n",
+    "  projectId: \"<PROJECT_ID>\",\n",
+    "  watsonxAIAuthType: \"bearertoken\",\n",
+    "  watsonxAIBearerToken: \"<YOUR-BEARERTOKEN>\",\n",
+    "};\n",
+    "const instance = new WatsonxLLM(props);\n",
+    "```\n",
+    "\n",
+    "### CP4D authentication\n",
+    "\n",
+    "```typescript\n",
+    "import { WatsonxLLM } from \"@langchain/community/llms/ibm\";\n",
+    "\n",
+    "const props = {\n",
+    "  version: \"YYYY-MM-DD\",\n",
+    "  serviceUrl: \"<SERVICE_URL>\",\n",
+    "  projectId: \"<PROJECT_ID>\",\n",
+    "  watsonxAIAuthType: \"cp4d\",\n",
+    "  watsonxAIUsername: \"<YOUR-USERNAME>\",\n",
+    "  watsonxAIPassword: \"<YOUR-PASSWORD>\",\n",
+    "  watsonxAIUrl: \"<url>\",\n",
+    "};\n",
+    "const instance = new WatsonxLLM(props);\n",
+    "```\n",
+    "\n",
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:\n",
+    "\n",
+    "```bash\n",
+    "# export LANGCHAIN_TRACING_V2=\"true\"\n",
+    "# export LANGCHAIN_API_KEY=\"your-api-key\"\n",
+    "```\n",
+    "\n",
+    "### Installation\n",
+    "\n",
+    "The LangChain IBM watsonx.ai integration lives in the `@langchain/community` package:\n",
+    "\n",
+    "```{=mdx}\n",
+    "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
+    "import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
+    "\n",
+    "<IntegrationInstallTooltip></IntegrationInstallTooltip>\n",
+    "\n",
+    "<Npm2Yarn>\n",
+    "  @langchain/community @langchain/core\n",
+    "</Npm2Yarn>\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a760037",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0562a13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import { WatsonxLLM } from \"@langchain/community/llms/ibm\";\n",
+    "\n",
+    "const props = {\n",
+    "  decoding_method: \"sample\",\n",
+    "  max_new_tokens: 100,\n",
+    "  min_new_tokens: 1,\n",
+    "  temperature: 0.5,\n",
+    "  top_k: 50,\n",
+    "  top_p: 1,\n",
+    "};\n",
+    "const instance = new WatsonxLLM({\n",
+    "  version: \"YYYY-MM-DD\",\n",
+    "  serviceUrl: process.env.API_URL,\n",
+    "  projectId: \"<PROJECT_ID>\",\n",
+    "  spaceId: \"<SPACE_ID>\",\n",
+    "  idOrName: \"<DEPLOYMENT_ID>\",\n",
+    "  modelId: \"<MODEL_ID>\",\n",
+    "  ...props,\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7498103",
+   "metadata": {},
+   "source": [
+    "Note:\n",
+    "\n",
+    "- You must provide spaceId, projectId or idOrName(deployment id) in order to proceed.\n",
+    "- Depending on the region of your provisioned service instance, use correct serviceUrl.\n",
+    "- You need to specify the model you want to use for inferencing through model_id."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ee90032",
+   "metadata": {},
+   "source": [
+    "## Invocation and generation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "035dea0f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "print('Hello world.')<|endoftext|>\n",
+      "{\n",
+      "  generations: [ [ [Object] ], [ [Object] ] ],\n",
+      "  llmOutput: { tokenUsage: { generated_token_count: 28, input_token_count: 10 } }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "const result = await instance.invoke(\"Print hello world.\");\n",
+    "console.log(result);\n",
+    "\n",
+    "const results = await instance.generate([\n",
+    "  \"Print hello world.\",\n",
+    "  \"Print bye, bye world!\",\n",
+    "]);\n",
+    "console.log(results);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "add38532",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can chain our completion model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "078e9db2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ich liebe Programmieren.\n",
+      "\n",
+      "To express that you are passionate about programming in German,\n"
+     ]
+    }
+   ],
+   "source": [
+    "import { PromptTemplate } from \"@langchain/core/prompts\"\n",
+    "\n",
+    "const prompt = PromptTemplate.fromTemplate(\"How to say {input} in {output_language}:\\n\")\n",
+    "\n",
+    "const chain = prompt.pipe(instance);\n",
+    "await chain.invoke(\n",
+    "  {\n",
+    "    output_language: \"German\",\n",
+    "    input: \"I love programming.\",\n",
+    "  }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c305670",
+   "metadata": {},
+   "source": [
+    "## Props overwrittion\n",
+    "\n",
+    "Passed props at initialization will last for the whole life cycle of the object, however you may overwrite them for a single method's call by passing second argument as below\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bb53235c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "print('Hello world.')<|endoftext|>\n"
+     ]
+    }
+   ],
+   "source": [
+    "const result = await instance.invoke(\"Print hello world.\", {\n",
+    "    modelId: \"<NEW_MODEL_ID>\",\n",
+    "    parameters: {\n",
+    "      max_new_tokens: 20,\n",
+    "    },\n",
+    "  });\n",
+    "  console.log(result);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "577a0583",
+   "metadata": {},
+   "source": [
+    "## Tokenization\n",
+    "This package has it's custom getNumTokens implementation which returns exact amount of tokens that would be used.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "339e237c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "const tokens = await instance.getNumTokens(\"Print hello world.\");\n",
+    "console.log(tokens);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9bdfcef",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all `IBM watsonx.ai` features and configurations head to the API reference: [API docs](https://api.js.langchain.com/modules/_langchain_community.embeddings_ibm.html)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "JavaScript (Node.js)",
+   "language": "javascript",
+   "name": "javascript"
+  },
+  "language_info": {
+   "file_extension": ".js",
+   "mimetype": "application/javascript",
+   "name": "javascript",
+   "version": "20.17.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/core_docs/docs/integrations/text_embedding/ibm.ipynb b/docs/core_docs/docs/integrations/text_embedding/ibm.ipynb
new file mode 100644
index 0000000000000..94c6e7d08ec2b
--- /dev/null
+++ b/docs/core_docs/docs/integrations/text_embedding/ibm.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: IBM watsonx.ai\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a3d6f34",
+   "metadata": {},
+   "source": [
+    "# IBM watsonx.ai\n",
+    "\n",
+    "\n",
+    "This will help you get started with IBM watsonx.ai [embedding models](/docs/concepts#embedding-models) using LangChain. For detailed documentation on `IBM watsonx.ai` features and configuration options, please refer to the [API reference](https://api.js.langchain.com/classes/_langchain_community.embeddings_ibm.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "\n",
+    "| Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/text_embedding/ibm_watsonx/) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
+    "| [`IBM watsonx.ai`](https://api.js.langchain.com/classes/_langchain_community.embeddings_ibm.WatsonxEmbeddings.html) | [@langchain/community](https://api.js.langchain.com/modules/langchain_community_llms_ibm.html)| ❌ | ✅  | ![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",
+    "## Setup\n",
+    "\n",
+    "To access IBM WatsonxAI embeddings you'll need to create an IBM watsonx.ai account, get an API key or any other type of credentials, and install the `@langchain/community` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "\n",
+    "Head to [IBM Cloud](https://cloud.ibm.com/login) to sign up to IBM watsonx.ai and generate an API key or provide any other authentication form as presented below.\n",
+    "\n",
+    "#### IAM authentication\n",
+    "\n",
+    "```bash\n",
+    "export WATSONX_AI_AUTH_TYPE=iam\n",
+    "export WATSONX_AI_APIKEY=<YOUR-APIKEY>\n",
+    "```\n",
+    "\n",
+    "#### Bearer token authentication\n",
+    "\n",
+    "```bash\n",
+    "export WATSONX_AI_AUTH_TYPE=bearertoken\n",
+    "export WATSONX_AI_BEARER_TOKEN=<YOUR-BEARER-TOKEN>\n",
+    "```\n",
+    "\n",
+    "#### CP4D authentication\n",
+    "\n",
+    "```bash\n",
+    "export WATSONX_AI_AUTH_TYPE=cp4d\n",
+    "export WATSONX_AI_USERNAME=<YOUR_USERNAME>\n",
+    "export WATSONX_AI_PASSWORD=<YOUR_PASSWORD>\n",
+    "export WATSONX_AI_URL=<URL>\n",
+    "```\n",
+    "\n",
+    "Once these are places in your enviromental variables and object is initialized authentication will proceed automatically.\n",
+    "\n",
+    "Authentication can also be accomplished by passing these values as parameters to a new instance.\n",
+    "\n",
+    "## IAM authentication\n",
+    "\n",
+    "```typescript\n",
+    "import { WatsonxEmbeddings } from \"@langchain/community/embeddings/ibm\";\n",
+    "\n",
+    "const props = {\n",
+    "  version: \"YYYY-MM-DD\",\n",
+    "  serviceUrl: \"<SERVICE_URL>\",\n",
+    "  projectId: \"<PROJECT_ID>\",\n",
+    "  watsonxAIAuthType: \"iam\",\n",
+    "  watsonxAIApikey: \"<YOUR-APIKEY>\",\n",
+    "};\n",
+    "const instance = new WatsonxEmbeddings(props);\n",
+    "```\n",
+    "\n",
+    "## Bearer token authentication\n",
+    "\n",
+    "```typescript\n",
+    "import { WatsonxEmbeddings } from \"@langchain/community/embeddings/ibm\";\n",
+    "\n",
+    "const props = {\n",
+    "  version: \"YYYY-MM-DD\",\n",
+    "  serviceUrl: \"<SERVICE_URL>\",\n",
+    "  projectId: \"<PROJECT_ID>\",\n",
+    "  watsonxAIAuthType: \"bearertoken\",\n",
+    "  watsonxAIBearerToken: \"<YOUR-BEARERTOKEN>\",\n",
+    "};\n",
+    "const instance = new WatsonxEmbeddings(props);\n",
+    "```\n",
+    "\n",
+    "### CP4D authentication\n",
+    "\n",
+    "```typescript\n",
+    "import { WatsonxEmbeddings } from \"@langchain/community/embeddings/ibm\";\n",
+    "\n",
+    "const props = {\n",
+    "  version: \"YYYY-MM-DD\",\n",
+    "  serviceUrl: \"<SERVICE_URL>\",\n",
+    "  projectId: \"<PROJECT_ID>\",\n",
+    "  watsonxAIAuthType: \"cp4d\",\n",
+    "  watsonxAIUsername: \"<YOUR-USERNAME>\",\n",
+    "  watsonxAIPassword: \"<YOUR-PASSWORD>\",\n",
+    "  watsonxAIUrl: \"<url>\",\n",
+    "};\n",
+    "const instance = new WatsonxEmbeddings(props);\n",
+    "```\n",
+    "\n",
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:\n",
+    "\n",
+    "```bash\n",
+    "# export LANGCHAIN_TRACING_V2=\"true\"\n",
+    "# export LANGCHAIN_API_KEY=\"your-api-key\"\n",
+    "```\n",
+    "\n",
+    "### Installation\n",
+    "\n",
+    "The LangChain IBM watsonx.ai integration lives in the `@langchain/community` package:\n",
+    "\n",
+    "```{=mdx}\n",
+    "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
+    "import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
+    "\n",
+    "<IntegrationInstallTooltip></IntegrationInstallTooltip>\n",
+    "\n",
+    "<Npm2Yarn>\n",
+    "  @langchain/community @langchain/core\n",
+    "</Npm2Yarn>\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45dd1724",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and embed text:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ea7a09b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import { WatsonxEmbeddings } from \"@langchain/community/embeddings/ibm\";\n",
+    "\n",
+    "const embeddings = new WatsonxEmbeddings({\n",
+    "  version: \"YYYY-MM-DD\",\n",
+    "  serviceUrl: process.env.API_URL,\n",
+    "  projectId: \"<PROJECT_ID>\",\n",
+    "  spaceId: \"<SPACE_ID>\",\n",
+    "  idOrName: \"<DEPLOYMENT_ID>\",\n",
+    "  modelId: \"<MODEL_ID>\",\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d271b6",
+   "metadata": {},
+   "source": [
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document using the demo [`MemoryVectorStore`](/docs/integrations/vectorstores/memory)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d817716b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LangChain is the framework for building context-aware reasoning applications\n"
+     ]
+    }
+   ],
+   "source": [
+    "// Create a vector store with a sample text\n",
+    "import { MemoryVectorStore } from \"langchain/vectorstores/memory\";\n",
+    "\n",
+    "const text = \"LangChain is the framework for building context-aware reasoning applications\";\n",
+    "\n",
+    "const vectorstore = await MemoryVectorStore.fromDocuments(\n",
+    "  [{ pageContent: text, metadata: {} }],\n",
+    "  embeddings,\n",
+    ");\n",
+    "\n",
+    "// Use the vector store as a retriever that returns a single document\n",
+    "const retriever = vectorstore.asRetriever(1);\n",
+    "\n",
+    "// Retrieve the most similar text\n",
+    "const retrievedDocuments = await retriever.invoke(\"What is LangChain?\");\n",
+    "\n",
+    "retrievedDocuments[0].pageContent;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02b9855",
+   "metadata": {},
+   "source": [
+    "## Direct Usage\n",
+    "\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embedDocument(...)` and `embeddings.embedQuery(...)` to create embeddings for the text(s) used in `fromDocuments` and the retriever's `invoke` operations, respectively.\n",
+    "\n",
+    "You can directly call these methods to get embeddings for your own use cases.\n",
+    "\n",
+    "### Embed single texts\n",
+    "\n",
+    "You can embed queries for search with `embedQuery`. This generates a vector representation specific to the query:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0d2befcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "   -0.017436018,   -0.01469498,   -0.015685871,  -0.013543149, -0.0011519607,\n",
+      "   -0.008123747,   0.015286108,   -0.023845721,   -0.02454774,    0.07235078,\n",
+      "   -0.032333843, -0.0035843418,   -0.015389036,     0.0455373,  -0.021119863,\n",
+      "   -0.022039745,   0.021746712,   -0.017774817,  -0.008232582,  -0.036727764,\n",
+      "   -0.015734928,    0.03606811,   -0.005108186,  -0.036052454,   0.024462992,\n",
+      "     0.02359307,    0.03273164,    0.009195497, -0.0077208397,    -0.0127943,\n",
+      "   -0.023869334,  -0.029473905,  -0.0080457395, -0.0021337876,    0.04949132,\n",
+      "    0.013950589,  -0.010046689,    0.021029025,  -0.031725302,   0.004251065,\n",
+      "   -0.034171984,   -0.03696642,   -0.014253629,  -0.017757406,  -0.007531065,\n",
+      "     0.07187789,   0.009661725,    0.041889492,   -0.04660478,   0.028036641,\n",
+      "    0.059334517,   -0.04561291,    0.056029715,   -0.00676024,   0.026493236,\n",
+      "      0.0116374,   0.050126843,   -0.018036349,  -0.013711887,   0.042252757,\n",
+      "    -0.04453391,    0.04705777, -0.00044598224,  -0.030227259,   0.029286578,\n",
+      "      0.0252211,   0.011694125,   -0.031404093,    0.02951232,    0.08812359,\n",
+      "    0.023539362,  -0.011082862,    0.008024676, 0.00084492035,  -0.007984158,\n",
+      "  -0.0005008702,  -0.025189219,    0.021000557, -0.0065513053,   0.036524914,\n",
+      "   0.0015150858, -0.0042383806,    0.049065087,   0.000941666,    0.04447001,\n",
+      "    0.012942205,  -0.078316726,    -0.03004237,  -0.025807172,   -0.03446275,\n",
+      "    -0.00932942,  -0.044925686,     0.03190307,   0.010136769,  -0.048854534,\n",
+      "    0.025738232,  -0.017840309,    0.023738133,   0.014214792,   0.030452395\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "    const singleVector = await embeddings.embedQuery(text);\n",
+    "    singleVector.slice(0, 100);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5a7d03",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts for indexing with `embedDocuments`. The internals used for this method may (but do not have to) differ from embedding queries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2f4d6e97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "   -0.017436024,  -0.014695002,    -0.01568589,  -0.013543164, -0.001151976,\n",
+      "   -0.008123703,   0.015286064,   -0.023845702,  -0.024547677,   0.07235076,\n",
+      "   -0.032333862, -0.0035843418,   -0.015389038,   0.045537304, -0.021119865,\n",
+      "    -0.02203975,   0.021746716,    -0.01777481,  -0.008232588,  -0.03672781,\n",
+      "   -0.015734889,   0.036068108,     -0.0051082,  -0.036052432,  0.024462998,\n",
+      "    0.023593083,    0.03273162,    0.009195521,  -0.007720828, -0.012794304,\n",
+      "   -0.023869323,  -0.029473891,   -0.008045726,  -0.002133793,  0.049491342,\n",
+      "    0.013950573,  -0.010046691,     0.02102898,   -0.03172528, 0.0042510596,\n",
+      "   -0.034171965,  -0.036966413,   -0.014253668,  -0.017757434, -0.007531062,\n",
+      "     0.07187787,   0.009661732,    0.041889492,   -0.04660476,  0.028036654,\n",
+      "    0.059334517,  -0.045612894,    0.056029722,   -0.00676024,  0.026493296,\n",
+      "   0.0116374055,   0.050126873,   -0.018036384,  -0.013711868,    0.0422528,\n",
+      "   -0.044533912,   0.047057763, -0.00044596897,  -0.030227251,  0.029286569,\n",
+      "    0.025221113,   0.011694138,    -0.03140413,   0.029512335,   0.08812357,\n",
+      "    0.023539348,  -0.011082865,    0.008024677, 0.00084490055, -0.007984145,\n",
+      "  -0.0005008745,  -0.025189226,    0.021000564, -0.0065513197,  0.036524955,\n",
+      "   0.0015150585, -0.0042383634,    0.049065102,   0.000941638,  0.044469994,\n",
+      "    0.012942193,  -0.078316696,     -0.0300424,  -0.025807157,   -0.0344627,\n",
+      "   -0.009329439,   -0.04492573,    0.031903077,   0.010136808, -0.048854522,\n",
+      "    0.025738247,   -0.01784033,    0.023738142,   0.014214801,  0.030452369\n",
+      "]\n",
+      "[\n",
+      "     0.03278884, -0.017893745, -0.0027520044,   0.016506646,   0.028271576,\n",
+      "    -0.01284331,  0.014344065,  -0.007968607,   -0.03899479,   0.039327156,\n",
+      "   -0.047726233,  0.009559004,   -0.05302522,   0.011498492, -0.0055542476,\n",
+      "  -0.0020940166, -0.029262392,  -0.025919685,   0.024261741, -0.0010863725,\n",
+      "   0.0074619935,  0.014191284,  -0.009054746,  -0.038633537,   0.039744128,\n",
+      "    0.012625762,  0.030490868,   0.013526139,  -0.024638629,  -0.011268263,\n",
+      "   -0.012759613,  -0.04693565,  -0.013087251,   -0.01971696,  0.0125782555,\n",
+      "    0.024156926, -0.011638484,   0.017364893,    -0.0405832, -0.0032466082,\n",
+      "    -0.01611277, -0.022583133,   0.019492855,   -0.03664484,  -0.022627067,\n",
+      "    0.011026938, -0.014631298,   0.043255687,  -0.029447634,   0.017212389,\n",
+      "    0.029366229, -0.041978795,   0.005347565, -0.0106230285,  -0.008334342,\n",
+      "   -0.008841154,  0.045096103,    0.03996879,  -0.002039457, -0.0051824683,\n",
+      "   -0.019464444,  0.092018366,  -0.009283633,  -0.020052811,  0.0043408144,\n",
+      "   -0.029403884,   0.02587689,  -0.027253918,     0.0159064,     0.0421537,\n",
+      "     0.05078811, -0.012380686,  -0.018032575,    0.01711449,    0.03636163,\n",
+      "   -0.014590949, -0.015076142, 0.00018201554,   0.002490666,   0.044776678,\n",
+      "     0.05301749, -0.007891316,   0.028668318, -0.0016632816,    0.04487743,\n",
+      "   -0.032529455, -0.040372133,  -0.020566158,  -0.011109745,   -0.01724949,\n",
+      "  -0.0047519016, -0.041635286,  0.0068111843,   0.039498538,   -0.02491227,\n",
+      "    0.016853934, -0.017926402,  -0.006154979,   0.025893573,   0.015262395\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "    const text2 = \"LangGraph is a library for building stateful, multi-actor applications with LLMs\";\n",
+    "\n",
+    "    const vectors = await embeddings.embedDocuments([text, text2]);\n",
+    "   \n",
+    "    console.log(vectors[0].slice(0, 100));\n",
+    "    console.log(vectors[1].slice(0, 100));\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8938e581",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all __module_name__ features and configurations head to the API reference: __api_ref_module__"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "JavaScript (Node.js)",
+   "language": "javascript",
+   "name": "javascript"
+  },
+  "language_info": {
+   "file_extension": ".js",
+   "mimetype": "application/javascript",
+   "name": "javascript",
+   "version": "20.17.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/libs/langchain-community/package.json b/libs/langchain-community/package.json
index d9d87e2c7959d..86cae9e10a4f5 100644
--- a/libs/langchain-community/package.json
+++ b/libs/langchain-community/package.json
@@ -35,7 +35,7 @@
   "author": "LangChain",
   "license": "MIT",
   "dependencies": {
-    "@ibm-cloud/watsonx-ai": "1.0.1",
+    "@ibm-cloud/watsonx-ai": "^1.1.0",
     "@langchain/openai": ">=0.2.0 <0.4.0",
     "binary-extensions": "^2.2.0",
     "expr-eval": "^2.0.2",
diff --git a/libs/langchain-community/src/embeddings/ibm.ts b/libs/langchain-community/src/embeddings/ibm.ts
index 26bca3cbadc3c..24078929e7b47 100644
--- a/libs/langchain-community/src/embeddings/ibm.ts
+++ b/libs/langchain-community/src/embeddings/ibm.ts
@@ -81,25 +81,21 @@ export class WatsonxEmbeddings
     else return { spaceId: this.spaceId };
   }
 
-  ivocationParams(): EmbeddingParameters {
+  invocationParams(): EmbeddingParameters {
     return {
       truncate_input_tokens: this.truncate_input_tokens,
     };
   }
 
-  async completionWithRetry<T>(callback: () => T) {
-    const caller = new AsyncCaller({
-      maxConcurrency: this.maxConcurrency,
-      maxRetries: this.maxRetries,
-    });
-    return caller.call(async () => callback());
-  }
-
   async listModels() {
     const listModelParams = {
       filters: "function_embedding",
     };
-    const listModels = await this.completionWithRetry(() =>
+    const caller = new AsyncCaller({
+      maxConcurrency: this.maxConcurrency,
+      maxRetries: this.maxRetries,
+    });
+    const listModels = await caller.call(() =>
       this.service.listFoundationModelSpecs(listModelParams)
     );
     return listModels.result.resources?.map((item) => item.model_id);
@@ -110,10 +106,13 @@ export class WatsonxEmbeddings
       inputs,
       modelId: this.modelId,
       ...this.scopeId(),
-      parameters: this.ivocationParams(),
+      parameters: this.invocationParams(),
     };
-
-    const embeddings = await this.completionWithRetry(() =>
+    const caller = new AsyncCaller({
+      maxConcurrency: this.maxConcurrency,
+      maxRetries: this.maxRetries,
+    });
+    const embeddings = await caller.call(() =>
       this.service.embedText(textEmbeddingParams)
     );
     return embeddings.result.results.map((item) => item.embedding);
diff --git a/libs/langchain-community/src/llms/watsonx_ai.ts b/libs/langchain-community/src/llms/watsonx_ai.ts
index ffe75459b6619..0ae7ac2ca51b5 100644
--- a/libs/langchain-community/src/llms/watsonx_ai.ts
+++ b/libs/langchain-community/src/llms/watsonx_ai.ts
@@ -10,6 +10,8 @@ import { getEnvironmentVariable } from "@langchain/core/utils/env";
  * The WatsonxAIParams interface defines the input parameters for
  * the WatsonxAI class.
  */
+
+/** @deprecated Please use newer implementation @langchain/community/llms/ibm instead */
 export interface WatsonxAIParams extends BaseLLMParams {
   /**
    * WatsonX AI Complete Endpoint.
diff --git a/yarn.lock b/yarn.lock
index 2d06c2a21b596..ec43dcfd4a39e 100644
--- a/yarn.lock
+++ b/yarn.lock
@@ -10495,14 +10495,14 @@ __metadata:
   languageName: node
   linkType: hard
 
-"@ibm-cloud/watsonx-ai@npm:1.0.1":
-  version: 1.0.1
-  resolution: "@ibm-cloud/watsonx-ai@npm:1.0.1"
+"@ibm-cloud/watsonx-ai@npm:^1.1.0":
+  version: 1.1.0
+  resolution: "@ibm-cloud/watsonx-ai@npm:1.1.0"
   dependencies:
     "@types/node": ^12.0.8
     extend: 3.0.2
     ibm-cloud-sdk-core: ^4.2.5
-  checksum: 843aa748f2568e5850df7718c38897dd668bc560426a339974cbd481ddf04579a54d661aa16b84d29c2b4d8aab0925ca3fbe78c31beb82e7cdc9ca02bd7a4e07
+  checksum: 0151bb0abe2a7d1dbcd6f8367ea02dfc924f15bdcbe8ec58bb89c8e055fa35c399b2253d6be3b84292f96c9161e49bcd6d6f5e1df0f2cd9adf21d1f3c0bc24b4
   languageName: node
   linkType: hard
 
@@ -11474,7 +11474,7 @@ __metadata:
     "@google-cloud/storage": ^7.7.0
     "@gradientai/nodejs-sdk": ^1.2.0
     "@huggingface/inference": ^2.6.4
-    "@ibm-cloud/watsonx-ai": 1.0.1
+    "@ibm-cloud/watsonx-ai": ^1.1.0
     "@jest/globals": ^29.5.0
     "@langchain/core": "workspace:*"
     "@langchain/openai": ">=0.2.0 <0.4.0"