mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
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
parent
d55d99222b
commit
f82c3f622a
@ -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",
|
||||
"## Installation"
|
||||
"## Overview\n",
|
||||
"### Integration details\n",
|
||||
"\n",
|
||||
"import { ItemTable } from \"@theme/FeatureTables\";\n",
|
||||
"\n",
|
||||
"<ItemTable category=\"text_embedding\" item=\"AI21\" />\n",
|
||||
"\n",
|
||||
"## 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": [
|
||||
"!pip install -qU langchain-ai21"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2b4f3e15",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Environment Setup\n",
|
||||
"\n",
|
||||
"We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "62e0dbc3",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"from getpass import getpass\n",
|
||||
"\n",
|
||||
"os.environ[\"AI21_API_KEY\"] = getpass()"
|
||||
"if not os.getenv(\"AI21_API_KEY\"):\n",
|
||||
" os.environ[\"AI21_API_KEY\"] = getpass.getpass(\"Enter your AI21 API key: \")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "74ef9d8b40a1319e",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"id": "c84fb993",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Usage"
|
||||
"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": 5,
|
||||
"id": "12fcfb4b",
|
||||
"execution_count": 3,
|
||||
"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",
|
||||
"The LangChain AI21 integration lives in the `langchain-ai21` package:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "64853226",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install -qU langchain-ai21"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "45dd1724",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Instantiation\n",
|
||||
"\n",
|
||||
"Now we can instantiate our model object and generate chat completions:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "9ea7a09b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_ai21 import AI21Embeddings\n",
|
||||
"\n",
|
||||
"embeddings = AI21Embeddings()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1f2e6104",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings.embed_query(\"My query to look up\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a3465d7e63bfb3d1",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings.embed_documents(\n",
|
||||
" [\"This is a content of the document\", \"This is another document\"]\n",
|
||||
"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": "code",
|
||||
"execution_count": null,
|
||||
"id": "9d60af6d",
|
||||
"cell_type": "markdown",
|
||||
"id": "77d271b6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
"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": 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": {},
|
||||
"source": [
|
||||
"## 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": 9,
|
||||
"id": "0d2befcd",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[0.01913362182676792, 0.004960147198289633, -0.01582135073840618, -0.042474791407585144, 0.040200788\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"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": 10,
|
||||
"id": "2f4d6e97",
|
||||
"metadata": {},
|
||||
"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,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user