diff --git a/docs/core_docs/docs/integrations/text_embedding/mistralai.ipynb b/docs/core_docs/docs/integrations/text_embedding/mistralai.ipynb
new file mode 100644
index 000000000000..cba77a87fee8
--- /dev/null
+++ b/docs/core_docs/docs/integrations/text_embedding/mistralai.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+ {
+ "cell_type": "raw",
+ "id": "afaf8039",
+ "metadata": {
+ "vscode": {
+ "languageId": "raw"
+ }
+ },
+ "source": [
+ "---\n",
+ "sidebar_label: MistralAI\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9a3d6f34",
+ "metadata": {},
+ "source": [
+ "# MistralAIEmbeddings\n",
+ "\n",
+ "This will help you get started with MistralAIEmbeddings [embedding models](/docs/concepts#embedding-models) using LangChain. For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.js.langchain.com/classes/langchain_mistralai.MistralAIEmbeddings.html).\n",
+ "\n",
+ "## Overview\n",
+ "### Integration details\n",
+ "\n",
+ "| Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/text_embedding/mistralai/) | Package downloads | Package latest |\n",
+ "| :--- | :--- | :---: | :---: | :---: | :---: |\n",
+ "| [MistralAIEmbeddings](https://api.js.langchain.com/classes/langchain_mistralai.MistralAIEmbeddings.html) | [@langchain/mistralai](https://api.js.langchain.com/modules/langchain_mistralai.html) | ❌ | ✅ |  |  |\n",
+ "\n",
+ "## Setup\n",
+ "\n",
+ "To access MistralAI embedding models you'll need to create a MistralAI account, get an API key, and install the `@langchain/mistralai` integration package.\n",
+ "\n",
+ "### Credentials\n",
+ "\n",
+ "Head to [console.mistral.ai](https://console.mistral.ai/) to sign up to `MistralAI` and generate an API key. Once you've done this set the `MISTRAL_API_KEY` environment variable:\n",
+ "\n",
+ "```bash\n",
+ "export MISTRAL_API_KEY=\"your-api-key\"\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 MistralAIEmbeddings integration lives in the `@langchain/mistralai` package:\n",
+ "\n",
+ "```{=mdx}\n",
+ "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
+ "import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " @langchain/mistralai\n",
+ "\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45dd1724",
+ "metadata": {},
+ "source": [
+ "## Instantiation\n",
+ "\n",
+ "Now we can instantiate our model object and generate chat completions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9ea7a09b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import { MistralAIEmbeddings } from \"@langchain/mistralai\";\n",
+ "\n",
+ "const embeddings = new MistralAIEmbeddings({\n",
+ " model: \"mistral-embed\", // Default value\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": 3,
+ "id": "0d2befcd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\n",
+ " -0.04443359375, 0.01885986328125, 0.018035888671875,\n",
+ " -0.00864410400390625, 0.049652099609375, -0.001190185546875,\n",
+ " 0.028900146484375, -0.035675048828125, -0.00702667236328125,\n",
+ " 0.00016105175018310547, -0.027587890625, 0.029388427734375,\n",
+ " -0.053253173828125, -0.0003020763397216797, -0.046112060546875,\n",
+ " 0.0258026123046875, -0.0010776519775390625, 0.02703857421875,\n",
+ " 0.040985107421875, -0.004547119140625, -0.020172119140625,\n",
+ " -0.02606201171875, -0.01457977294921875, 0.01220703125,\n",
+ " -0.0078582763671875, -0.0084228515625, -0.02056884765625,\n",
+ " -0.071044921875, -0.0404052734375, 0.00923919677734375,\n",
+ " 0.01407623291015625, -0.0210113525390625, 0.0006284713745117188,\n",
+ " -0.01465606689453125, 0.0186309814453125, -0.015838623046875,\n",
+ " 0.0007920265197753906, -0.04437255859375, 0.008758544921875,\n",
+ " -0.0172119140625, 0.01312255859375, -0.01358795166015625,\n",
+ " -0.0212860107421875, -0.000035822391510009766, -0.0226898193359375,\n",
+ " -0.01390838623046875, -0.007659912109375, -0.016021728515625,\n",
+ " 0.025909423828125, -0.034515380859375, -0.0372314453125,\n",
+ " 0.020355224609375, -0.02606201171875, -0.0158843994140625,\n",
+ " -0.037994384765625, 0.00450897216796875, 0.0142822265625,\n",
+ " -0.012725830078125, -0.0770263671875, 0.02630615234375,\n",
+ " -0.048614501953125, 0.006072998046875, 0.00417327880859375,\n",
+ " -0.005138397216796875, 0.02557373046875, 0.0311279296875,\n",
+ " 0.026519775390625, -0.0103607177734375, -0.0108489990234375,\n",
+ " -0.029510498046875, 0.022186279296875, 0.0256500244140625,\n",
+ " -0.0186309814453125, 0.0443115234375, -0.0304107666015625,\n",
+ " -0.03131103515625, 0.007427215576171875, 0.0234527587890625,\n",
+ " 0.0224761962890625, 0.00463104248046875, -0.0037021636962890625,\n",
+ " 0.0302581787109375, 0.0733642578125, -0.0121612548828125,\n",
+ " -0.0172576904296875, 0.019317626953125, 0.029052734375,\n",
+ " -0.0024871826171875, 0.0174713134765625, 0.026092529296875,\n",
+ " 0.04425048828125, -0.0004563331604003906, 0.0146026611328125,\n",
+ " -0.00748443603515625, 0.06146240234375, 0.02294921875,\n",
+ " -0.016845703125, -0.0014057159423828125, -0.01435089111328125,\n",
+ " 0.06097412109375\n",
+ "]\n"
+ ]
+ }
+ ],
+ "source": [
+ "const singleVector = await embeddings.embedQuery(text);\n",
+ "\n",
+ "console.log(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": 4,
+ "id": "2f4d6e97",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\n",
+ " -0.04443359375, 0.01885986328125, 0.0180511474609375,\n",
+ " -0.0086517333984375, 0.049652099609375, -0.00121307373046875,\n",
+ " 0.0289154052734375, -0.03570556640625, -0.007015228271484375,\n",
+ " 0.0001499652862548828, -0.0276641845703125, 0.0294036865234375,\n",
+ " -0.05322265625, -0.0002808570861816406, -0.04608154296875,\n",
+ " 0.02581787109375, -0.0011072158813476562, 0.027099609375,\n",
+ " 0.040985107421875, -0.004547119140625, -0.0201873779296875,\n",
+ " -0.0260772705078125, -0.0146026611328125, 0.0121917724609375,\n",
+ " -0.007843017578125, -0.0084381103515625, -0.0205535888671875,\n",
+ " -0.07110595703125, -0.04046630859375, 0.00931549072265625,\n",
+ " 0.01409912109375, -0.02099609375, 0.0006232261657714844,\n",
+ " -0.014678955078125, 0.0186614990234375, -0.0158233642578125,\n",
+ " 0.000812530517578125, -0.04437255859375, 0.00873565673828125,\n",
+ " -0.0172119140625, 0.013092041015625, -0.0135498046875,\n",
+ " -0.0212860107421875, -0.000006735324859619141, -0.0226898193359375,\n",
+ " -0.01389312744140625, -0.0076751708984375, -0.0160064697265625,\n",
+ " 0.0259246826171875, -0.0345458984375, -0.037200927734375,\n",
+ " 0.020355224609375, -0.0260009765625, -0.0159149169921875,\n",
+ " -0.03802490234375, 0.004489898681640625, 0.0143280029296875,\n",
+ " -0.01274871826171875, -0.07708740234375, 0.0263214111328125,\n",
+ " -0.04864501953125, 0.00608062744140625, 0.004192352294921875,\n",
+ " -0.005115509033203125, 0.0255889892578125, 0.0311279296875,\n",
+ " 0.0265045166015625, -0.0103607177734375, -0.01084136962890625,\n",
+ " -0.0294952392578125, 0.022186279296875, 0.0256500244140625,\n",
+ " -0.0186767578125, 0.044342041015625, -0.030426025390625,\n",
+ " -0.03131103515625, 0.007396697998046875, 0.0234527587890625,\n",
+ " 0.0224609375, 0.004634857177734375, -0.003643035888671875,\n",
+ " 0.0302886962890625, 0.07342529296875, -0.01221466064453125,\n",
+ " -0.017303466796875, 0.0193023681640625, 0.029052734375,\n",
+ " -0.0024890899658203125, 0.0174407958984375, 0.026123046875,\n",
+ " 0.044219970703125, -0.0004944801330566406, 0.01462554931640625,\n",
+ " -0.007450103759765625, 0.06146240234375, 0.022979736328125,\n",
+ " -0.016845703125, -0.001445770263671875, -0.0143890380859375,\n",
+ " 0.06097412109375\n",
+ "]\n",
+ "[\n",
+ " -0.02032470703125, 0.02606201171875, 0.051605224609375,\n",
+ " -0.0281982421875, 0.055755615234375, 0.001987457275390625,\n",
+ " 0.031982421875, -0.0131378173828125, -0.0252685546875,\n",
+ " 0.001010894775390625, -0.024017333984375, 0.053375244140625,\n",
+ " -0.042816162109375, 0.005584716796875, -0.04132080078125,\n",
+ " 0.03021240234375, 0.01324462890625, 0.016876220703125,\n",
+ " 0.041961669921875, -0.004299163818359375, -0.0273895263671875,\n",
+ " -0.039642333984375, -0.021575927734375, 0.0309295654296875,\n",
+ " -0.0099945068359375, -0.0163726806640625, -0.00968170166015625,\n",
+ " -0.07733154296875, -0.030364990234375, -0.003864288330078125,\n",
+ " 0.016387939453125, -0.0389404296875, -0.0026702880859375,\n",
+ " -0.0176544189453125, 0.0264434814453125, -0.01226806640625,\n",
+ " -0.0022220611572265625, -0.039703369140625, -0.00907135009765625,\n",
+ " -0.0260467529296875, 0.03155517578125, -0.0004324913024902344,\n",
+ " -0.019500732421875, -0.0120697021484375, -0.008544921875,\n",
+ " -0.01654052734375, 0.00067138671875, -0.0134735107421875,\n",
+ " 0.01080322265625, -0.034759521484375, -0.06201171875,\n",
+ " 0.012359619140625, -0.006237030029296875, -0.0168914794921875,\n",
+ " -0.0183563232421875, 0.0236053466796875, -0.0021419525146484375,\n",
+ " -0.0164947509765625, -0.052581787109375, 0.022125244140625,\n",
+ " -0.045745849609375, -0.0009088516235351562, 0.0097808837890625,\n",
+ " -0.0009326934814453125, 0.041656494140625, 0.0269775390625,\n",
+ " 0.016845703125, -0.0022335052490234375, -0.0182342529296875,\n",
+ " -0.0245208740234375, 0.0036602020263671875, -0.0188751220703125,\n",
+ " -0.0023956298828125, 0.0238800048828125, -0.034942626953125,\n",
+ " -0.033782958984375, 0.0046234130859375, 0.0318603515625,\n",
+ " 0.0251007080078125, -0.0023288726806640625, -0.0225677490234375,\n",
+ " 0.0004394054412841797, 0.064208984375, -0.0254669189453125,\n",
+ " -0.0234222412109375, 0.0009264945983886719, 0.01464080810546875,\n",
+ " 0.006626129150390625, -0.007450103759765625, 0.02642822265625,\n",
+ " 0.0260009765625, 0.00536346435546875, 0.01479339599609375,\n",
+ " -0.0032253265380859375, 0.0498046875, 0.048248291015625,\n",
+ " -0.01519012451171875, 0.00605010986328125, 0.019744873046875,\n",
+ " 0.0296478271484375\n",
+ "]\n"
+ ]
+ }
+ ],
+ "source": [
+ "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));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8938e581",
+ "metadata": {},
+ "source": [
+ "## API reference\n",
+ "\n",
+ "For detailed documentation of all MistralAIEmbeddings features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_mistralai.MistralAIEmbeddings.html"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "TypeScript",
+ "language": "typescript",
+ "name": "tslab"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "mode": "typescript",
+ "name": "javascript",
+ "typescript": true
+ },
+ "file_extension": ".ts",
+ "mimetype": "text/typescript",
+ "name": "typescript",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/core_docs/docs/integrations/text_embedding/mistralai.mdx b/docs/core_docs/docs/integrations/text_embedding/mistralai.mdx
deleted file mode 100644
index d1edf1251182..000000000000
--- a/docs/core_docs/docs/integrations/text_embedding/mistralai.mdx
+++ /dev/null
@@ -1,28 +0,0 @@
----
-sidebar_label: Mistral AI
----
-
-# Mistral AI
-
-The `MistralAIEmbeddings` class uses the Mistral AI API to generate embeddings for a given text.
-
-## Setup
-
-In order to use the Mistral API you'll need an API key. You can sign up for a Mistral account and create an API key [here](https://console.mistral.ai/).
-
-You'll first need to install the [`@langchain/mistralai`](https://www.npmjs.com/package/@langchain/mistralai) package:
-
-import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
-
-
-
-```bash npm2yarn
-npm install @langchain/mistralai
-```
-
-## Usage
-
-import CodeBlock from "@theme/CodeBlock";
-import MistralExample from "@examples/models/embeddings/mistral.ts";
-
-{MistralExample}