langchain/docs/docs/integrations/text_embedding/zhipuai.ipynb

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   "source": [
    "---\n",
    "sidebar_label: ZhipuAI\n",
    "keywords: [zhipuaiembeddings]\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3d6f34",
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   "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/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": [
    {
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     "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](/docs/tutorials/).\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://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.zhipuai.ZhipuAIEmbeddings.html).\n"
   ]
  }
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