From 59407338dde583bcb03abde191bd0e1550ce69be Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Thu, 17 Jul 2025 11:32:34 -0400 Subject: [PATCH] docs: remove AI21 embeddings section (#32084) // no longer exists --- docs/docs/integrations/providers/ai21.mdx | 5 - .../integrations/text_embedding/ai21.ipynb | 272 ------------------ 2 files changed, 277 deletions(-) delete mode 100644 docs/docs/integrations/text_embedding/ai21.ipynb diff --git a/docs/docs/integrations/providers/ai21.mdx b/docs/docs/integrations/providers/ai21.mdx index 140e755da8b..8d3c4124ef1 100644 --- a/docs/docs/integrations/providers/ai21.mdx +++ b/docs/docs/integrations/providers/ai21.mdx @@ -42,11 +42,6 @@ from langchain_ai21 import AI21LLM from langchain_ai21 import AI21ContextualAnswers ``` -### AI21 Embeddings - -```python -from langchain_ai21 import AI21Embeddings -``` ## Text splitters ### AI21 Semantic Text Splitter diff --git a/docs/docs/integrations/text_embedding/ai21.ipynb b/docs/docs/integrations/text_embedding/ai21.ipynb deleted file mode 100644 index 2dfd7b96045..00000000000 --- a/docs/docs/integrations/text_embedding/ai21.ipynb +++ /dev/null @@ -1,272 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "id": "afaf8039", - "metadata": {}, - "source": [ - "---\n", - "sidebar_label: AI21\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "9a3d6f34", - "metadata": {}, - "source": [ - "# AI21Embeddings\n", - "\n", - ":::caution This service is deprecated.\n", - "\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://python.langchain.com/api_reference/ai21/index.html).\n", - "\n", - "## Overview\n", - "### Integration details\n", - "\n", - "import { ItemTable } from \"@theme/FeatureTables\";\n", - "\n", - "\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": 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": [ - "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "39a4953b", - "metadata": {}, - "outputs": [], - "source": [ - "# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n", - "# os.environ[\"LANGSMITH_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(\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](/docs/tutorials/rag/).\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://python.langchain.com/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}