From c73fbc20b07f13be857ecfc4120192a0ee1459db Mon Sep 17 00:00:00 2001 From: jacoblee93 Date: Thu, 8 Aug 2024 14:00:24 -0700 Subject: [PATCH 1/2] Update Google Vertex AI embeddings docs --- .../integrations/chat/google_vertex_ai.ipynb | 2 +- .../text_embedding/google_vertex_ai.ipynb | 356 ++++++++++++++++++ .../text_embedding/google_vertex_ai.mdx | 41 -- 3 files changed, 357 insertions(+), 42 deletions(-) create mode 100644 docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.ipynb delete mode 100644 docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.mdx diff --git a/docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb b/docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb index a874e569f2a1..0eb1fcc75305 100644 --- a/docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb +++ b/docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb @@ -10,7 +10,7 @@ }, "source": [ "---\n", - "sidebar_label: Google VertexAI\n", + "sidebar_label: Google Vertex AI\n", "---" ] }, diff --git a/docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.ipynb b/docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.ipynb new file mode 100644 index 000000000000..8c1de34ac0c0 --- /dev/null +++ b/docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.ipynb @@ -0,0 +1,356 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "afaf8039", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "---\n", + "sidebar_label: Google Vertex AI\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "9a3d6f34", + "metadata": {}, + "source": [ + "# VertexAIEmbeddings\n", + "\n", + "This will help you get started with Google Vertex AI [embedding models](/docs/concepts#embedding-models) using LangChain. For detailed documentation on `VertexAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.js.langchain.com/classes/langchain_google_vertexai.GoogleVertexAIEmbeddings.html).\n", + "\n", + "## Overview\n", + "### Integration details\n", + "\n", + "| Class | Package | Local | [Py support](https://python.langchain.com/v0.2/docs/integrations/text_embedding/google_vertex_ai_palm/) | Package downloads | Package latest |\n", + "| :--- | :--- | :---: | :---: | :---: | :---: |\n", + "| [`VertexAIEmbeddings`](https://api.js.langchain.com/classes/langchain_google_vertexai.GoogleVertexAIEmbeddings.html) | [`@langchain/google-vertexai`](https://npmjs.com/@langchain/google-vertexai) | ❌ | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/google-vertexai?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/google-vertexai?style=flat-square&label=%20&) |\n", + "\n", + "## Setup\n", + "\n", + "LangChain.js supports two different authentication methods based on whether\n", + "you're running in a Node.js environment or a web environment.\n", + "\n", + "To access `ChatVertexAI` models you'll need to setup Google VertexAI in your Google Cloud Platform (GCP) account, save the credentials file, and install the `@langchain/google-vertexai` integration package.\n", + "\n", + "### Credentials\n", + "\n", + "Head to your [GCP account](https://console.cloud.google.com/) and generate a credentials file. Once you've done this set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable:\n", + "\n", + "```bash\n", + "export GOOGLE_APPLICATION_CREDENTIALS=\"path/to/your/credentials.json\"\n", + "```\n", + "\n", + "If running in a web environment, you should set the `GOOGLE_VERTEX_AI_WEB_CREDENTIALS` environment variable as a JSON stringified object, and install the `@langchain/google-vertexai-web` package:\n", + "\n", + "```bash\n", + "GOOGLE_VERTEX_AI_WEB_CREDENTIALS={\"type\":\"service_account\",\"project_id\":\"YOUR_PROJECT-12345\",...}\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 `VertexAIEmbeddings` integration lives in the `@langchain/google-vertexai` 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/google-vertexai\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "45dd1724", + "metadata": {}, + "source": [ + "## Instantiation\n", + "\n", + "Now we can instantiate our model object and embed text:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9ea7a09b", + "metadata": {}, + "outputs": [], + "source": [ + "import { VertexAIEmbeddings } from \"@langchain/google-vertexai\";\n", + "// Uncomment the following line if you're running in a web environment:\n", + "// import { VertexAIEmbeddings } from \"@langchain/google-vertexai-web\"\n", + "\n", + "const embeddings = new VertexAIEmbeddings({\n", + " model: \"text-embedding-004\",\n", + " // ...\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", + " -0.02831101417541504, 0.022063178941607475, -0.07454229146242142,\n", + " 0.006448323838412762, 0.001955120824277401, -0.017617391422390938,\n", + " 0.018649872392416, -0.05262855067849159, 0.0006953597767278552,\n", + " -0.0018249079585075378, 0.022437218576669693, 0.0036489504855126143,\n", + " 0.0018086736090481281, 0.016940006986260414, -0.007894322276115417,\n", + " -0.04187627509236336, 0.039501357823610306, 0.06918870657682419,\n", + " -0.006931832991540432, 0.049655742943286896, 0.021211417391896248,\n", + " -0.029322246089577675, -0.04546992480754852, -0.01769082061946392,\n", + " 0.046703994274139404, 0.03127637133002281, 0.006355373188853264,\n", + " 0.014901148155331612, -0.006893016863614321, -0.05992589890956879,\n", + " -0.009733330458402634, 0.015709295868873596, -0.017982766032218933,\n", + " -0.0852997675538063, -0.032453566789627075, 0.0014507169835269451,\n", + " 0.03345133736729622, 0.048862338066101074, 0.006664620712399483,\n", + " -0.06287197023630142, -0.02109423652291298, 0.018176473677158356,\n", + " -0.022175665944814682, 0.03340170532464981, -0.008905526250600815,\n", + " -0.03492079675197601, -0.03819998353719711, -0.05230168625712395,\n", + " -0.05247239023447037, 0.048254698514938354, 0.046494755893945694,\n", + " -0.029708227142691612, -0.002180763054639101, 0.051957979798316956,\n", + " -0.05483679473400116, 0.00700812041759491, -0.08181990683078766,\n", + " -0.02295914851129055, 0.026530204340815544, 0.04028692841529846,\n", + " -0.05230272561311722, -0.057705819606781006, -0.015022763051092625,\n", + " 0.002143724123016, 0.06361843645572662, -0.027828887104988098,\n", + " 0.006870461627840996, -0.016140831634402275, -0.034440942108631134,\n", + " -0.004059414379298687, -0.042537953704595566, -0.00984653178602457,\n", + " -0.07701274752616882, 0.09815558046102524, -0.025801729410886765,\n", + " -0.008693721145391464, -0.0010926402173936367, -0.027235493063926697,\n", + " 0.06945550441741943, 0.023456251248717308, -0.02160717360675335,\n", + " 0.03252667561173439, 0.05874639376997948, -0.001329384627752006,\n", + " 0.03664775192737579, -0.07353461533784866, -0.028453022241592407,\n", + " -0.05666429176926613, -0.012955721467733383, -0.041723109781742096,\n", + " 0.07209191471338272, 0.0326194241642952, -0.0496046207845211,\n", + " -0.025037819519639015, 0.004625750705599785, -0.03622527793049812,\n", + " -0.022546149790287018, 0.0053468807600438595, 0.03879072889685631,\n", + " 0.03238753229379654\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", + " -0.02831101417541504, 0.022063178941607475, -0.07454229146242142,\n", + " 0.006448323838412762, 0.001955120824277401, -0.017617391422390938,\n", + " 0.018649872392416, -0.05262855067849159, 0.0006953597767278552,\n", + " -0.0018249079585075378, 0.022437218576669693, 0.0036489504855126143,\n", + " 0.0018086736090481281, 0.016940006986260414, -0.007894322276115417,\n", + " -0.04187627509236336, 0.039501357823610306, 0.06918870657682419,\n", + " -0.006931832991540432, 0.049655742943286896, 0.021211417391896248,\n", + " -0.029322246089577675, -0.04546992480754852, -0.01769082061946392,\n", + " 0.046703994274139404, 0.03127637133002281, 0.006355373188853264,\n", + " 0.014901148155331612, -0.006893016863614321, -0.05992589890956879,\n", + " -0.009733330458402634, 0.015709295868873596, -0.017982766032218933,\n", + " -0.0852997675538063, -0.032453566789627075, 0.0014507169835269451,\n", + " 0.03345133736729622, 0.048862338066101074, 0.006664620712399483,\n", + " -0.06287197023630142, -0.02109423652291298, 0.018176473677158356,\n", + " -0.022175665944814682, 0.03340170532464981, -0.008905526250600815,\n", + " -0.03492079675197601, -0.03819998353719711, -0.05230168625712395,\n", + " -0.05247239023447037, 0.048254698514938354, 0.046494755893945694,\n", + " -0.029708227142691612, -0.002180763054639101, 0.051957979798316956,\n", + " -0.05483679473400116, 0.00700812041759491, -0.08181990683078766,\n", + " -0.02295914851129055, 0.026530204340815544, 0.04028692841529846,\n", + " -0.05230272561311722, -0.057705819606781006, -0.015022763051092625,\n", + " 0.002143724123016, 0.06361843645572662, -0.027828887104988098,\n", + " 0.006870461627840996, -0.016140831634402275, -0.034440942108631134,\n", + " -0.004059414379298687, -0.042537953704595566, -0.00984653178602457,\n", + " -0.07701274752616882, 0.09815558046102524, -0.025801729410886765,\n", + " -0.008693721145391464, -0.0010926402173936367, -0.027235493063926697,\n", + " 0.06945550441741943, 0.023456251248717308, -0.02160717360675335,\n", + " 0.03252667561173439, 0.05874639376997948, -0.001329384627752006,\n", + " 0.03664775192737579, -0.07353461533784866, -0.028453022241592407,\n", + " -0.05666429176926613, -0.012955721467733383, -0.041723109781742096,\n", + " 0.07209191471338272, 0.0326194241642952, -0.0496046207845211,\n", + " -0.025037819519639015, 0.004625750705599785, -0.03622527793049812,\n", + " -0.022546149790287018, 0.0053468807600438595, 0.03879072889685631,\n", + " 0.03238753229379654\n", + "]\n", + "[\n", + " -0.00007261172140715644, 0.03209814056754112, -0.10099327564239502,\n", + " -0.0017932605696842074, -0.0016863049240782857, 0.009428824298083782,\n", + " 0.023065969347953796, -0.018305035308003426, 0.03765229508280754,\n", + " 0.03357342258095741, 0.0018431750359013677, 0.03230319544672966,\n", + " 0.024983661249279976, 0.02752346731722355, -0.027390114963054657,\n", + " -0.01945030689239502, -0.05770668387413025, 0.046621184796094894,\n", + " -0.03308689966797829, 0.03985097259283066, -0.021250328049063683,\n", + " -0.001940526650287211, -0.06034174561500549, -0.05026412755250931,\n", + " 0.02385033667087555, -0.03279203176498413, 0.02966252714395523,\n", + " 0.01294293999671936, -0.009747475385665894, -0.07896383106708527,\n", + " -0.013269499875605106, -0.011228476651012897, 0.022224457934498787,\n", + " -0.018957728520035744, -0.05092151463031769, -0.043285638093948364,\n", + " 0.016826728358864784, 0.010665969923138618, 0.021219193935394287,\n", + " -0.08588971197605133, -0.038367897272109985, 0.012244532816112041,\n", + " 0.009497410617768764, 0.017629485577344894, 0.0013116559712216258,\n", + " -0.016468070447444916, -0.04423798993229866, -0.04043079912662506,\n", + " -0.05485917255282402, -0.007577189709991217, 0.028067218139767647,\n", + " -0.022974666208028793, 0.0006999042234383523, 0.009812192991375923,\n", + " -0.05387532711029053, -0.016531387344002724, -0.015153753571212292,\n", + " 0.03397523611783981, -0.0018232968868687749, 0.01200891938060522,\n", + " -0.013123664073646069, -0.043459296226501465, -0.01856262981891632,\n", + " 0.018269911408424377, 0.016155652701854706, -0.05597233399748802,\n", + " -0.05852395296096802, 0.020076945424079895, -0.033808667212724686,\n", + " -0.008225022815167904, -0.014589417725801468, -0.01408824510872364,\n", + " -0.06293410807847977, 0.026668129488825798, -0.01397104375064373,\n", + " -0.017627086490392685, -0.03409220278263092, -0.018559949472546577,\n", + " 0.07163946330547333, 0.015611495822668076, -0.034166790544986725,\n", + " -0.005098687019199133, 0.04163505882024765, -0.010681619867682457,\n", + " 0.027817489579319954, -0.031076539307832718, -0.006825212389230728,\n", + " -0.06810358166694641, -0.03793689236044884, -0.03981738165020943,\n", + " 0.09524374455213547, -0.03607913851737976, 0.003638653317466378,\n", + " 0.02828306518495083, 0.018808560445904732, -0.047244682908058167,\n", + " -0.06114668399095535, -0.02395530976355076, 0.036157332360744476,\n", + " 0.0422002375125885\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 `VertexAIEmbeddings` features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_google_vertexai.GoogleVertexAIEmbeddings.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/google_vertex_ai.mdx b/docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.mdx deleted file mode 100644 index 54e57020113d..000000000000 --- a/docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.mdx +++ /dev/null @@ -1,41 +0,0 @@ -import CodeBlock from "@theme/CodeBlock"; - -# Google Vertex AI - -The `GoogleVertexAIEmbeddings` class uses Google's Vertex AI PaLM models -to generate embeddings for a given text. - -The Vertex AI implementation is meant to be used in Node.js and not -directly in a browser, since it requires a service account to use. - -Before running this code, you should make sure the Vertex AI API is -enabled for the relevant project in your Google Cloud dashboard and that you've authenticated to -Google Cloud using one of these methods: - -- You are logged into an account (using `gcloud auth application-default login`) - permitted to that project. -- You are running on a machine using a service account that is permitted - to the project. -- You have downloaded the credentials for a service account that is permitted - to the project and set the `GOOGLE_APPLICATION_CREDENTIALS` environment - variable to the path of this file. - -import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx"; - - - -```bash npm2yarn -npm install google-auth-library @langchain/community -``` - -import GoogleVertexAIExample from "@examples/models/embeddings/googlevertexai.ts"; - -{GoogleVertexAIExample} - -**Note:** The default Google Vertex AI embeddings model, `textembedding-gecko`, has a different number of dimensions than OpenAI's `text-embedding-ada-002` model -and may not be supported by all vector store providers. - -## Related - -- Embedding model [conceptual guide](/docs/concepts/#embedding-models) -- Embedding model [how-to guides](/docs/how_to/#embedding-models) From 37713e4d6f7ffe6aaaf0874a9a08cd645972fd5f Mon Sep 17 00:00:00 2001 From: jacoblee93 Date: Thu, 8 Aug 2024 14:29:35 -0700 Subject: [PATCH 2/2] Hide PaLM --- docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb | 2 +- docs/core_docs/docs/integrations/llms/google_palm.mdx | 2 +- docs/core_docs/docs/integrations/llms/google_vertex_ai.ipynb | 2 +- .../core_docs/docs/integrations/text_embedding/google_palm.mdx | 3 ++- .../docs/integrations/text_embedding/google_vertex_ai.ipynb | 2 ++ 5 files changed, 7 insertions(+), 4 deletions(-) diff --git a/docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb b/docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb index 0eb1fcc75305..16f236349335 100644 --- a/docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb +++ b/docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb @@ -21,7 +21,7 @@ "source": [ "# ChatVertexAI\n", "\n", - "[Google Vertex](https://cloud.google.com/vertex-ai) is a service that exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc.\n", + "[Google Vertex](https://cloud.google.com/vertex-ai) is a service that exposes all foundation models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc.\n", "\n", "This will help you getting started with `ChatVertexAI` [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatVertexAI` features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain_google_vertexai.ChatVertexAI.html).\n", "\n", diff --git a/docs/core_docs/docs/integrations/llms/google_palm.mdx b/docs/core_docs/docs/integrations/llms/google_palm.mdx index 28948cad384e..6815599c5afb 100644 --- a/docs/core_docs/docs/integrations/llms/google_palm.mdx +++ b/docs/core_docs/docs/integrations/llms/google_palm.mdx @@ -5,7 +5,7 @@ sidebar_class_name: hidden import CodeBlock from "@theme/CodeBlock"; -# Google PaLM +# Google PaLM (Legacy) :::warning The Google PaLM API is deprecated and will be removed in 0.3.0. Please use the [Google GenAI](/docs/integrations/chat/google_generativeai) or [VertexAI](/docs/integrations/llms/google_vertex_ai) integrations instead. diff --git a/docs/core_docs/docs/integrations/llms/google_vertex_ai.ipynb b/docs/core_docs/docs/integrations/llms/google_vertex_ai.ipynb index e0a3b5357af5..00c09cd63b2a 100644 --- a/docs/core_docs/docs/integrations/llms/google_vertex_ai.ipynb +++ b/docs/core_docs/docs/integrations/llms/google_vertex_ai.ipynb @@ -31,7 +31,7 @@ "\n", "```\n", "\n", - "[Google Vertex](https://cloud.google.com/vertex-ai) is a service that exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc.\n", + "[Google Vertex](https://cloud.google.com/vertex-ai) is a service that exposes all foundation models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc.\n", "\n", "This will help you get started with VertexAI completion models (LLMs) using LangChain. For detailed documentation on `VertexAI` features and configuration options, please refer to the [API reference](https://api.js.langchain.com/classes/langchain_google_vertexai.VertexAI.html).\n", "\n", diff --git a/docs/core_docs/docs/integrations/text_embedding/google_palm.mdx b/docs/core_docs/docs/integrations/text_embedding/google_palm.mdx index 36f1fc2d9eda..22b41b2b1e1f 100644 --- a/docs/core_docs/docs/integrations/text_embedding/google_palm.mdx +++ b/docs/core_docs/docs/integrations/text_embedding/google_palm.mdx @@ -1,10 +1,11 @@ --- sidebar_label: Google PaLM +sidebar_class_name: hidden --- import CodeBlock from "@theme/CodeBlock"; -# Google PaLM +# Google PaLM (Legacy) :::warning The Google PaLM API is deprecated and will be removed in 0.3.0. Please use the [Google GenAI](/docs/integrations/text_embedding/google_generativeai) integration instead. diff --git a/docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.ipynb b/docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.ipynb index 8c1de34ac0c0..6da3c72657ef 100644 --- a/docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.ipynb +++ b/docs/core_docs/docs/integrations/text_embedding/google_vertex_ai.ipynb @@ -21,6 +21,8 @@ "source": [ "# VertexAIEmbeddings\n", "\n", + "[Google Vertex](https://cloud.google.com/vertex-ai) is a service that exposes all foundation models available in Google Cloud.\n", + "\n", "This will help you get started with Google Vertex AI [embedding models](/docs/concepts#embedding-models) using LangChain. For detailed documentation on `VertexAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.js.langchain.com/classes/langchain_google_vertexai.GoogleVertexAIEmbeddings.html).\n", "\n", "## Overview\n",