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docs: Update AI21Embeddings Integration docs (langchain-ai#25298)
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Update AI21 Integration docs

Issue: langchain-ai#24856

---------

Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
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@eyurtsev @isahers1 @olgamurraft
3 people authored and olgamurraft committed Aug 16, 2024
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228 changes: 180 additions & 48 deletions docs/docs/integrations/text_embedding/ai21.ipynb
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"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": {
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},
"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",
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"id": "a3465d7e63bfb3d1",
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"execution_count": 9,
"id": "0d2befcd",
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{
"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": [],
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"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": {
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