docs: update integration docs for openai embeddings (#25249)

Related issue: https://github.com/langchain-ai/langchain/issues/24856

```json
   {
      "provider": "openai",
      "js":  true,
      "local": false,
     "serializable": false,
"async_native": true
  }
```

---------

Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>

docs/docs/integrations/text_embedding/openai.ipynb

@@ -2,42 +2,88 @@
  "cells": [
   {
    "cell_type": "raw",
-   "id": "ae8077b8",
-   "metadata": {
-    "vscode": {
-     "languageId": "raw"
-    }
-   },
+   "id": "afaf8039",
+   "metadata": {},
    "source": [
     "---\n",
+    "sidebar_label: OpenAI\n",
     "keywords: [openaiembeddings]\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "278b6c63",
+   "id": "9a3d6f34",
    "metadata": {},
    "source": [
-    "# OpenAI\n",
+    "# OpenAIEmbeddings\n",
     "\n",
-    "Let's load the OpenAI Embedding class."
+    "This will help you get started with OpenAI embedding models using LangChain. For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html).\n",
+    "\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "import { ItemTable } from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"text_embedding\" item=\"OpenAI\" />\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access OpenAI embedding models you'll need to create a/an OpenAI account, get an API key, and install the `langchain-openai` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [platform.openai.com](https://platform.openai.com) to sign up to OpenAI and generate an API key. Once you’ve done this set the OPENAI_API_KEY environment variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "36521c2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"OPENAI_API_KEY\"):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter your OpenAI API key: \")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "40ff98ff-58e9-4716-8788-227a5c3f473d",
+   "id": "c84fb993",
    "metadata": {},
    "source": [
-    "## Setup\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:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "39a4953b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9664366",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
     "\n",
-    "First we install langchain-openai and set the required env vars"
+    "The LangChain OpenAI integration lives in the `langchain-openai` package:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c66c4613-6c67-40ca-b3b1-c026750d1742",
+   "id": "64853226",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,171 +91,55 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "62e3710e-55a0-44fb-ba51-2f1d520dfc38",
+   "cell_type": "markdown",
+   "id": "45dd1724",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "import getpass\n",
-    "import os\n",
+    "## Instantiation\n",
     "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0be1af71",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import OpenAIEmbeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2c66e5da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "01370375",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"This is a test document.\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f012c222-3fa9-470a-935c-758b2048d9af",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "### Embed query"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "bfb6142c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning: model not found. Using cl100k_base encoding.\n"
-     ]
-    }
-   ],
-   "source": [
-    "query_result = embeddings.embed_query(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-0.014380056377383358,\n",
-       " -0.027191711627651764,\n",
-       " -0.020042716111860304,\n",
-       " 0.057301379620345545,\n",
-       " -0.022267658631828974]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "query_result[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b733391-1e23-438b-a6bc-0d77eed9426e",
-   "metadata": {},
-   "source": [
-    "## Embed documents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0356c3b7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning: model not found. Using cl100k_base encoding.\n"
-     ]
-    }
-   ],
-   "source": [
-    "doc_result = embeddings.embed_documents([text])"
+    "Now we can instantiate our model object and generate chat completions:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
+   "id": "9ea7a09b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-0.014380056377383358,\n",
-       " -0.027191711627651764,\n",
-       " -0.020042716111860304,\n",
-       " 0.057301379620345545,\n",
-       " -0.022267658631828974]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "doc_result[0][:5]"
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings(\n",
+    "    model=\"text-embedding-3-large\",\n",
+    "    # With the `text-embedding-3` class\n",
+    "    # of models, you can specify the size\n",
+    "    # of the embeddings you want returned.\n",
+    "    # dimensions=1024\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e7dc464a-6fa2-4cff-ab2e-49a0566d819b",
+   "id": "77d271b6",
    "metadata": {},
    "source": [
-    "## Specify dimensions\n",
+    "## Indexing and Retrieval\n",
     "\n",
-    "With the `text-embedding-3` class of models, you can specify the size of the embeddings you want returned. For example by default `text-embedding-3-large` returned embeddings of dimension 3072:"
+    "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 in the `InMemoryVectorStore`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "f7be1e7b-54c6-4893-b8ad-b872e6705735",
+   "id": "d817716b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3072"
+       "'LangChain is the framework for building context-aware reasoning applications'"
       ]
      },
      "execution_count": 11,
@@ -218,61 +148,111 @@
     }
    ],
    "source": [
-    "len(doc_result[0])"
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "33287142-0835-4958-962f-385ae4447431",
+   "id": "e02b9855",
    "metadata": {},
    "source": [
-    "But by passing in `dimensions=1024` we can reduce the size of our embeddings to 1024:"
+    "## Direct Usage\n",
+    "\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `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 single texts or documents with `embed_query`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "854ee772-2de9-4a83-84e0-908033d98e4e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_1024 = OpenAIEmbeddings(model=\"text-embedding-3-large\", dimensions=1024)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "3b464396-8d94-478b-8329-849b56e1ae23",
+   "execution_count": 12,
+   "id": "0d2befcd",
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Warning: model not found. Using cl100k_base encoding.\n"
+      "[-0.019276829436421394, 0.0037708976306021214, -0.03294256329536438, 0.0037671267054975033, 0.008175\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1024"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(embeddings_1024.embed_documents([text])[0])"
+    "single_vector = embeddings.embed_query(text)\n",
+    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5a7d03",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2f4d6e97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.019260549917817116, 0.0037612367887049913, -0.03291035071015358, 0.003757466096431017, 0.0082049\n",
+      "[-0.010181212797760963, 0.023419594392180443, -0.04215526953339577, -0.001532090245746076, -0.023573\n"
+     ]
+    }
+   ],
+   "source": [
+    "text2 = (\n",
+    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+    ")\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
+    "for vector in two_vectors:\n",
+    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98785c12",
+   "metadata": {},
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html).\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -284,12 +264,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
-   }
+   "version": "3.11.4"
   }
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  "nbformat": 4,