docs: Update AI21Embeddings Integration docs

docs/docs/integrations/text_embedding/ai21.ipynb

@@ -2,116 +2,248 @@
  "cells": [
   {
    "cell_type": "raw",
-   "id": "c2923bd1",
+   "id": "afaf8039",
    "metadata": {},
    "source": [
     "---\n",
-    "sidebar_label: AI21 Labs\n",
+    "sidebar_label: AI21\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cc3c6ef6bbd57ce9",
-   "metadata": {
-    "collapsed": false
-   },
+   "id": "9a3d6f34",
+   "metadata": {},
    "source": [
     "# AI21Embeddings\n",
     "\n",
-    "This notebook covers how to get started with AI21 embedding models.\n",
+    "This will help you get started with AI21 embedding models using LangChain. For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "import { ItemTable } from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"text_embedding\" item=\"AI21\" />\n",
     "\n",
-    "## Installation"
+    "## Setup\n",
+    "\n",
+    "To access AI21 embedding models you'll need to create an AI21 account, get an API key, and install the `langchain-ai21` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [https://docs.ai21.com/](https://docs.ai21.com/) to sign up to AI21 and generate an API key. Once you've done this set the `AI21_API_KEY` environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "4c3bef91",
+   "execution_count": 2,
+   "id": "36521c2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"AI21_API_KEY\"):\n",
+    "    os.environ[\"AI21_API_KEY\"] = getpass.getpass(\"Enter your AI21 API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c84fb993",
+   "metadata": {},
+   "source": [
+    "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": 3,
+   "id": "39a4953b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -qU langchain-ai21"
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2b4f3e15",
+   "id": "d9664366",
    "metadata": {},
    "source": [
-    "## Environment Setup\n",
+    "### Installation\n",
     "\n",
-    "We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
+    "The LangChain AI21 integration lives in the `langchain-ai21` package:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
+   "id": "64853226",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"AI21_API_KEY\"] = getpass()"
+    "%pip install -qU langchain-ai21"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "74ef9d8b40a1319e",
-   "metadata": {
-    "collapsed": false
-   },
+   "id": "45dd1724",
+   "metadata": {},
    "source": [
-    "## Usage"
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "12fcfb4b",
+   "execution_count": 7,
+   "id": "9ea7a09b",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_ai21 import AI21Embeddings\n",
     "\n",
-    "embeddings = AI21Embeddings()"
+    "embeddings = AI21Embeddings(\n",
+    "    # Can optionally increase or decrease the batch_size\n",
+    "    # to improve latency.\n",
+    "    # Use larger batch sizes with smaller documents, and\n",
+    "    # smaller batch sizes with larger documents.\n",
+    "    # batch_size=256,\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 in the `InMemoryVectorStore`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1f2e6104",
+   "execution_count": 8,
+   "id": "d817716b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'LangChain is the framework for building context-aware reasoning applications'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 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": "e02b9855",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "embeddings.embed_query(\"My query to look up\")"
+    "## 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": null,
-   "id": "a3465d7e63bfb3d1",
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 9,
+   "id": "0d2befcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.01913362182676792, 0.004960147198289633, -0.01582135073840618, -0.042474791407585144, 0.040200788\n"
+     ]
+    }
+   ],
    "source": [
-    "embeddings.embed_documents(\n",
-    "    [\"This is a content of the document\", \"This is another document\"]\n",
-    ")"
+    "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": null,
-   "id": "9d60af6d",
+   "execution_count": 10,
+   "id": "2f4d6e97",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.03029559925198555, 0.002908500377088785, -0.02700909972190857, -0.04616579785943031, 0.0382771529\n",
+      "[0.018214847892522812, 0.011460083536803722, -0.03329407051205635, -0.04951060563325882, 0.032756105\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 `AI21Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n"
+   ]
   }
  ],
  "metadata": {
@@ -130,7 +262,7 @@
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