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docs/docs/integrations/providers/naver.mdx

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+# NAVER
+
+All functionality related to `Naver`, including HyperCLOVA X models, particularly those accessible through `Naver Cloud` [CLOVA Studio](https://clovastudio.ncloud.com/).
+
+> [Naver](https://navercorp.com/) is a global technology company with cutting-edge technologies and a diverse business portfolio including search, commerce, fintech, content, cloud, and AI.
+
+> [Naver Cloud](https://www.navercloudcorp.com/lang/en/) is the cloud computing arm of Naver, a leading cloud service provider offering a comprehensive suite of cloud services to businesses through its [Naver Cloud Platform (NCP)](https://www.ncloud.com/).
+
+Please refer to [NCP User Guide](https://guide.ncloud-docs.com/docs/clovastudio-overview) for more detailed instructions (also in Korean).
+
+## Installation and Setup
+
+- Get both CLOVA Studio API Key and API Gateway Key by [creating your app](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#create-test-app) and set them as environment variables respectively (`NCP_CLOVASTUDIO_API_KEY`, `NCP_APIGW_API_KEY`).
+- Install the integration Python package with:
+
+```bash
+pip install -U langchain-community
+```
+
+## Chat models
+
+### ChatClovaX 
+
+See a [usage example](/docs/integrations/chat/naver).
+
+```python
+from langchain_community.chat_models import ChatClovaX
+```
+
+## Embedding models
+
+### ClovaXEmbeddings
+
+See a [usage example](/docs/integrations/text_embedding/naver).
+
+```python
+from langchain_community.embeddings import ClovaXEmbeddings
+```

docs/docs/integrations/text_embedding/naver.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Naver\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ClovaXEmbeddings\n",
+    "\n",
+    "This notebook covers how to get started with embedding models provided by CLOVA Studio. For detailed documentation on `ClovaXEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/community/embeddings/langchain_community.naver.ClovaXEmbeddings.html).\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "| Provider | Package |\n",
+    "|:--------:|:-------:|\n",
+    "| [Naver](/docs/integrations/providers/naver.mdx) | [langchain-community](https://python.langchain.com/api_reference/community/embeddings/langchain_community.naver.ClovaXEmbeddings.html) |\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Before using embedding models provided by CLOVA Studio, you must go through the three steps below.\n",
+    "\n",
+    "1. Creating [NAVER Cloud Platform](https://www.ncloud.com/) account \n",
+    "2. Apply to use [CLOVA Studio](https://www.ncloud.com/product/aiService/clovaStudio)\n",
+    "3. Find API Keys after creating CLOVA Studio Test App or Service App (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#테스트앱생성).)\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "CLOVA Studio requires 3 keys (`NCP_CLOVASTUDIO_API_KEY`, `NCP_APIGW_API_KEY` and `NCP_CLOVASTUDIO_APP_ID`) for embeddings.\n",
+    "- `NCP_CLOVASTUDIO_API_KEY` and `NCP_CLOVASTUDIO_APP_ID` is issued per serviceApp or testApp\n",
+    "- `NCP_APIGW_API_KEY` is issued per account\n",
+    "\n",
+    "The two API Keys could be found by clicking `App Request Status` > `Service App, Test App List` > `‘Details’ button for each app` in [CLOVA Studio](https://clovastudio.ncloud.com/studio-application/service-app)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c52e8a50-3e67-4272-bc80-3954d98f8dea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"NCP_CLOVASTUDIO_API_KEY\"):\n",
+    "    os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter NCP CLOVA Studio API Key: \"\n",
+    "    )\n",
+    "if not os.getenv(\"NCP_APIGW_API_KEY\"):\n",
+    "    os.environ[\"NCP_APIGW_API_KEY\"] = getpass.getpass(\"Enter NCP API Gateway API Key: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83520d8e-ecf8-4e47-b3bc-1ac205b3a2ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"NCP_CLOVASTUDIO_APP_ID\"] = input(\"Enter NCP CLOVA Studio App ID: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff00653e",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "ClovaXEmbeddings integration lives in the `langchain_community` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99400c9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install package\n",
+    "!pip install -U langchain-community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2651e611-9d5b-4315-9bbd-f99f56be4e19",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our embeddings object and embed query or document:\n",
+    "\n",
+    "- There are several embedding models available in CLOVA Studio. Please refer [here](https://guide.ncloud-docs.com/docs/en/clovastudio-explorer03#임베딩API) for further details.\n",
+    "- Note that you might need to normalize the embeddings depending on your specific use case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "62e0dbc3",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.embeddings import ClovaXEmbeddings\n",
+    "\n",
+    "embeddings = ClovaXEmbeddings(\n",
+    "    model=\"clir-emb-dolphin\",  # set with the model name of corresponding app id. Default is `clir-emb-dolphin`\n",
+    "    # app_id=\"...\"  # set if you prefer to pass app id directly instead of using environment variables\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0493b4a8",
+   "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": 8,
+   "id": "d4d59653",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'CLOVA Studio is an AI development tool that allows you to customize your own HyperCLOVA X models.'"
+      ]
+     },
+     "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 = \"CLOVA Studio is an AI development tool that allows you to customize your own HyperCLOVA X models.\"\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 CLOVA Studio?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1a249e1",
+   "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": "12fcfb4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.094717406, -0.4077411, -0.5513184, 1.6024436, -1.3235079, -1.0720996, -0.44471845, 1.3665184, 0.\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": "8b383b53",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1f2e6104",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.094717406, -0.4077411, -0.5513184, 1.6024436, -1.3235079, -1.0720996, -0.44471845, 1.3665184, 0.\n",
+      "[-0.25525448, -0.84877056, -0.6928286, 1.5867524, -1.2930486, -0.8166254, -0.17934391, 1.4236152, 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "text2 = \"LangChain is the framework for building context-aware reasoning applications\"\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": "eee40d32367cc5c4",
+   "metadata": {},
+   "source": [
+    "## Additional functionalities\n",
+    "\n",
+    "### Service App\n",
+    "\n",
+    "When going live with production-level application using CLOVA Studio, you should apply for and use Service App. (See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#서비스앱신청).)\n",
+    "\n",
+    "For a Service App, corresponding `NCP_CLOVASTUDIO_API_KEY` and `NCP_CLOVASTUDIO_APP_ID` are issued and can only be called with them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08f9f44e-c6a4-4163-8caf-27a0cda345b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update environment variables\n",
+    "\n",
+    "os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
+    "    \"Enter NCP CLOVA Studio API Key for Service App: \"\n",
+    ")\n",
+    "os.environ[\"NCP_CLOVASTUDIO_APP_ID\"] = input(\"Enter NCP CLOVA Studio Service App ID: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86f59698-b3f4-4b19-a9d4-4facfcea304b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = ClovaXEmbeddings(\n",
+    "    service_app=True,\n",
+    "    model=\"clir-emb-dolphin\",  # set with the model name of corresponding app id of your Service App\n",
+    "    # app_id=\"...\"  # set if you prefer to pass app id directly instead of using environment variables\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ddeaee9",
+   "metadata": {},
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `ClovaXEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/latest/api_reference/community/embeddings/langchain_community.embeddings.naver.ClovaXEmbeddings.html)."
+   ]
+  }
+ ],
+ "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"
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}