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docs: Update AI21Embeddings Integration docs (#25298)
Update AI21 Integration docs Issue: https://github.com/langchain-ai/langchain/issues/24856 --------- Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com> Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
This commit is contained in:
Eugene Yurtsev
GitHub
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"cells": [
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"cell_type": "raw",
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"cell_type": "raw",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"---\n",
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"---\n",
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"sidebar_label: AI21 Labs\n",
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"sidebar_label: AI21\n",
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"---"
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"---"
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"cell_type": "markdown",
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"cell_type": "markdown",
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"id": "cc3c6ef6bbd57ce9",
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"id": "9a3d6f34",
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"metadata": {
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"collapsed": false
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"source": [
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"source": [
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"# AI21Embeddings\n",
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"# AI21Embeddings\n",
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"\n",
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"\n",
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"This notebook covers how to get started with AI21 embedding models.\n",
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"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",
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"\n",
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"\n",
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"## Installation"
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"import { ItemTable } from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"text_embedding\" item=\"AI21\" />\n",
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"\n",
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"## Setup\n",
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"\n",
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"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",
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"\n",
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"### Credentials\n",
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"\n",
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"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:"
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]
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]
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"id": "4c3bef91",
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"id": "36521c2a",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"!pip install -qU langchain-ai21"
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"import getpass\n",
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]
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},
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"cell_type": "markdown",
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"id": "2b4f3e15",
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"metadata": {},
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"source": [
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"## Environment Setup\n",
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"\n",
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"We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
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]
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"cell_type": "code",
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"execution_count": null,
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"id": "62e0dbc3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import os\n",
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"from getpass import getpass\n",
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"\n",
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"\n",
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"os.environ[\"AI21_API_KEY\"] = getpass()"
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"if not os.getenv(\"AI21_API_KEY\"):\n",
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" os.environ[\"AI21_API_KEY\"] = getpass.getpass(\"Enter your AI21 API key: \")"
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]
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]
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},
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"cell_type": "markdown",
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"cell_type": "markdown",
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"id": "74ef9d8b40a1319e",
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"id": "c84fb993",
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"metadata": {
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"metadata": {},
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"collapsed": false
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},
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"source": [
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"source": [
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"## Usage"
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"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:"
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]
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]
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 3,
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"id": "12fcfb4b",
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"id": "39a4953b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
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]
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},
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"cell_type": "markdown",
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"id": "d9664366",
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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"The LangChain AI21 integration lives in the `langchain-ai21` package:"
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"id": "64853226",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-ai21"
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]
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},
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"cell_type": "markdown",
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"id": "45dd1724",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our model object and generate chat completions:"
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]
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "9ea7a09b",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"from langchain_ai21 import AI21Embeddings\n",
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"from langchain_ai21 import AI21Embeddings\n",
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"\n",
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"\n",
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"embeddings = AI21Embeddings()"
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"embeddings = AI21Embeddings(\n",
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]
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" # Can optionally increase or decrease the batch_size\n",
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},
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" # to improve latency.\n",
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{
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" # Use larger batch sizes with smaller documents, and\n",
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"cell_type": "code",
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" # smaller batch sizes with larger documents.\n",
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"execution_count": null,
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" # batch_size=256,\n",
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"id": "1f2e6104",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings.embed_query(\"My query to look up\")"
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]
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"cell_type": "code",
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"execution_count": null,
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"id": "a3465d7e63bfb3d1",
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"embeddings.embed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\n",
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")"
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")"
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]
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]
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},
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"cell_type": "code",
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"cell_type": "markdown",
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"execution_count": null,
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"id": "77d271b6",
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"id": "9d60af6d",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"source": [
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"source": []
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"## Indexing and Retrieval\n",
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"\n",
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"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",
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"\n",
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"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`."
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]
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},
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"cell_type": "code",
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"execution_count": 8,
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"id": "d817716b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'LangChain is the framework for building context-aware reasoning applications'"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Create a vector store with a sample text\n",
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"from langchain_core.vectorstores import InMemoryVectorStore\n",
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"\n",
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"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
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"\n",
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"vectorstore = InMemoryVectorStore.from_texts(\n",
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" [text],\n",
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" embedding=embeddings,\n",
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")\n",
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"\n",
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"# Use the vectorstore as a retriever\n",
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"retriever = vectorstore.as_retriever()\n",
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"\n",
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"# Retrieve the most similar text\n",
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"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
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"\n",
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"# show the retrieved document's content\n",
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"retrieved_documents[0].page_content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e02b9855",
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"metadata": {},
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"source": [
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"## Direct Usage\n",
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"\n",
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"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",
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"\n",
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"You can directly call these methods to get embeddings for your own use cases.\n",
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"\n",
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"### Embed single texts\n",
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"\n",
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"You can embed single texts or documents with `embed_query`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "0d2befcd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0.01913362182676792, 0.004960147198289633, -0.01582135073840618, -0.042474791407585144, 0.040200788\n"
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]
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}
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],
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"source": [
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"single_vector = embeddings.embed_query(text)\n",
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"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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"cell_type": "markdown",
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"id": "1b5a7d03",
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"metadata": {},
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"source": [
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"### Embed multiple texts\n",
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"\n",
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"You can embed multiple texts with `embed_documents`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "2f4d6e97",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0.03029559925198555, 0.002908500377088785, -0.02700909972190857, -0.04616579785943031, 0.0382771529\n",
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"[0.018214847892522812, 0.011460083536803722, -0.03329407051205635, -0.04951060563325882, 0.032756105\n"
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]
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}
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],
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"source": [
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"text2 = (\n",
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" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
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")\n",
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"two_vectors = embeddings.embed_documents([text, text2])\n",
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"for vector in two_vectors:\n",
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" print(str(vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "markdown",
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"id": "98785c12",
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"metadata": {},
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"source": [
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"## API Reference\n",
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"\n",
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"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"
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]
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}
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}
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],
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"metadata": {
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"metadata": {
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@ -130,7 +262,7 @@
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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"version": "3.9.6"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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