diff --git a/docs/docs/integrations/text_embedding/ai21.ipynb b/docs/docs/integrations/text_embedding/ai21.ipynb index c44e55f2b497d..e861ff98b06c2 100644 --- a/docs/docs/integrations/text_embedding/ai21.ipynb +++ b/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", + "\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 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.9.6" } }, "nbformat": 4,