diff --git a/docs/docs/integrations/text_embedding/zhipuai.ipynb b/docs/docs/integrations/text_embedding/zhipuai.ipynb new file mode 100644 index 00000000000..78d531b35bd --- /dev/null +++ b/docs/docs/integrations/text_embedding/zhipuai.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "afaf8039", + "metadata": {}, + "source": [ + "---\n", + "sidebar_label: ZhipuAI\n", + "keywords: [zhipuaiembeddings]\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "9a3d6f34", + "metadata": {}, + "source": [ + "# ZhipuAIEmbeddings\n", + "\n", + "This will help you get started with ZhipuAI embedding models using LangChain. For detailed documentation on `ZhipuAIEmbeddings` features and configuration options, please refer to the [API reference](https://bigmodel.cn/dev/api#vector).\n", + "\n", + "## Overview\n", + "### Integration details\n", + "\n", + "| Provider | Package |\n", + "|:--------:|:-------:|\n", + "| [ZhipuAI](/docs/integrations/providers/zhipuai/) | [langchain-community](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.zhipuai.ZhipuAIEmbeddings.html) |\n", + "\n", + "## Setup\n", + "\n", + "To access ZhipuAI embedding models you'll need to create a/an ZhipuAI account, get an API key, and install the `zhipuai` integration package.\n", + "\n", + "### Credentials\n", + "\n", + "Head to [https://bigmodel.cn/](https://bigmodel.cn/usercenter/apikeys) to sign up to ZhipuAI and generate an API key. Once you've done this set the ZHIPUAI_API_KEY environment variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "36521c2a", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "if not os.getenv(\"ZHIPUAI_API_KEY\"):\n", + " os.environ[\"ZHIPUAI_API_KEY\"] = getpass.getpass(\"Enter your ZhipuAI 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": 2, + "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 ZhipuAI integration lives in the `zhipuai` package:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "64853226", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install -qU zhipuai" + ] + }, + { + "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": 4, + "id": "9ea7a09b", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.embeddings import ZhipuAIEmbeddings\n", + "\n", + "embeddings = ZhipuAIEmbeddings(\n", + " model=\"embedding-3\",\n", + " # With the `embedding-3` class\n", + " # of models, you can specify the size\n", + " # of the embeddings you want returned.\n", + " # dimensions=1024\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": 5, + "id": "d817716b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'LangChain is the framework for building context-aware reasoning applications'" + ] + }, + "execution_count": 5, + "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": [ + "[-0.022979736, 0.007785797, 0.04598999, 0.012741089, -0.01689148, 0.008277893, 0.016464233, 0.009246\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": 8, + "id": "2f4d6e97", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.022979736, 0.007785797, 0.04598999, 0.012741089, -0.01689148, 0.008277893, 0.016464233, 0.009246\n", + "[-0.02330017, -0.013916016, 0.00022411346, 0.017196655, -0.034240723, 0.011131287, 0.011497498, -0.0\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 `ZhipuAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.zhipuai.ZhipuAIEmbeddings.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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb b/libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb index 7e9cabaeef9..5babaae5fd2 100644 --- a/libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb +++ b/libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb @@ -19,14 +19,14 @@ "\n", "- [ ] TODO: Make sure API reference link is correct\n", "\n", - "This will help you get started with __ModuleName__ embedding models using LangChain. For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n", + "This will help you get started with __ModuleName__ embedding models using LangChain. For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/__package_name_short__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html).\n", "\n", "## Overview\n", "### Integration details\n", "\n", - "import { ItemTable } from \"@theme/FeatureTables\";\n", - "\n", - "\n", + "| Provider | Package |\n", + "|:--------:|:-------:|\n", + "| [__ModuleName__](/docs/integrations/providers/__package_name_short__/) | [__package_name__](https://python.langchain.com/v0.2/api_reference/__module_name__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html) |\n", "\n", "## Setup\n", "\n",