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community: Add Naver chat model & embeddings (#25162)
Reopened as a personal repo outside the organization. ## Description - Naver HyperCLOVA X community package - Add chat model & embeddings - Add unit test & integration test - Add chat model & embeddings docs - I changed partner package(https://github.com/langchain-ai/langchain/pull/24252) to community package on this PR - Could this embeddings(https://github.com/langchain-ai/langchain/pull/21890) be deprecated? We are trying to replace it with embedding model(**ClovaXEmbeddings**) in this PR. Twitter handle: None. (if needed, contact with joonha.jeon@navercorp.com) --- you can check our previous discussion below: > one question on namespaces - would it make sense to have these in .clova namespaces instead of .naver? I would like to keep it as is, unless it is essential to unify the package name. (ClovaX is a branding for the model, and I plan to add other models and components. They need to be managed as separate classes.) > also, could you clarify the difference between ClovaEmbeddings and ClovaXEmbeddings? There are 3 models that are being serviced by embedding, and all are supported in the current PR. In addition, all the functionality of CLOVA Studio that serves actual models, such as distinguishing between test apps and service apps, is supported. The existing PR does not support this content because it is hard-coded. --------- Co-authored-by: Erick Friis <erick@langchain.dev> Co-authored-by: Vadym Barda <vadym@langchain.dev>
This commit is contained in:
CLOVA Studio 개발
GitHub
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docs/docs/integrations/chat/naver.ipynb
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docs/docs/integrations/chat/naver.ipynb
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "afaf8039",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Naver\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c8444f1a-e907-4f07-b8b6-68fbedfb868e",
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"metadata": {},
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"source": [
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"# ChatClovaX\n",
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"\n",
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"This notebook provides a quick overview for getting started with Naver’s HyperCLOVA X [chat models](https://python.langchain.com/docs/concepts/#chat-models) via CLOVA Studio. For detailed documentation of all ChatClovaX features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html).\n",
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"\n",
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"[CLOVA Studio](http://clovastudio.ncloud.com/) has several chat models. You can find information about latest models and their costs, context windows, and supported input types in the CLOVA Studio API Guide [documentation](https://api.ncloud-docs.com/docs/clovastudio-chatcompletions).\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
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"| :--- | :--- |:-----:| :---: |:------------------------------------------------------------------------:| :---: | :---: |\n",
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"| [ChatClovaX](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ |  |  |\n",
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"\n",
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"### Model features\n",
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"| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
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"|:------------------------------------------:| :---: | :---: | :---: | :---: | :---: |:-----------------------------------------------------:| :---: |:------------------------------------------------------:|:----------------------------------:|\n",
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"|❌| ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | \n",
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"\n",
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"## Setup\n",
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"\n",
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"Before using the chat model, you must go through the three steps below.\n",
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"\n",
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"1. Creating [NAVER Cloud Platform](https://www.ncloud.com/) account \n",
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"2. Apply to use [CLOVA Studio](https://www.ncloud.com/product/aiService/clovaStudio)\n",
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"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",
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"\n",
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"### Credentials\n",
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"\n",
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"CLOVA Studio requires 2 keys (`NCP_CLOVASTUDIO_API_KEY` and `NCP_APIGW_API_KEY`).\n",
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" - `NCP_CLOVASTUDIO_API_KEY` is issued per Test App or Service App\n",
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" - `NCP_APIGW_API_KEY` is issued per account, could be optional depending on the region you are using\n",
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"\n",
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"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)\n",
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"\n",
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"You can add them to your environment variables as below:\n",
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"\n",
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"``` bash\n",
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"export NCP_CLOVASTUDIO_API_KEY=\"your-api-key-here\"\n",
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"export NCP_APIGW_API_KEY=\"your-api-key-here\"\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2def81b5-b023-4f40-a97b-b2c5ca59d6a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"if not os.getenv(\"NCP_CLOVASTUDIO_API_KEY\"):\n",
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" os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
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" \"Enter your NCP CLOVA Studio API Key: \"\n",
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" )\n",
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"if not os.getenv(\"NCP_APIGW_API_KEY\"):\n",
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" os.environ[\"NCP_APIGW_API_KEY\"] = getpass.getpass(\n",
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" \"Enter your NCP API Gateway API key: \"\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7c695442",
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"metadata": {},
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"source": [
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"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:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6151aeb6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "17bf9053-90c5-4955-b239-55a35cb07566",
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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"The LangChain Naver integration lives in the `langchain-community` package:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
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"metadata": {},
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"outputs": [],
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"source": [
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"# install package\n",
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"!pip install -qU langchain-community"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a38cde65-254d-4219-a441-068766c0d4b5",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our model object and generate chat completions:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.chat_models import ChatClovaX\n",
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"\n",
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"chat = ChatClovaX(\n",
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" model=\"HCX-003\",\n",
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" max_tokens=100,\n",
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" temperature=0.5,\n",
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" # clovastudio_api_key=\"...\" # set if you prefer to pass api key directly instead of using environment variables\n",
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" # task_id=\"...\" # set if you want to use fine-tuned model\n",
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" # service_app=False # set True if using Service App. Default value is False (means using Test App)\n",
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" # include_ai_filters=False # set True if you want to detect inappropriate content. Default value is False\n",
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" # other params...\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "47752b59",
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"metadata": {},
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"source": [
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"## Invocation\n",
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"\n",
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"In addition to invoke, we also support batch and stream functionalities."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "62e0dbc3",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 1112164354, 'ai_filter': None}, id='run-b57bc356-1148-4007-837d-cc409dbd57cc-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates English to Korean. Translate the user sentence.\",\n",
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" ),\n",
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" (\"human\", \"I love using NAVER AI.\"),\n",
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"]\n",
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"\n",
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"ai_msg = chat.invoke(messages)\n",
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"ai_msg"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "24e7377f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"저는 네이버 AI를 사용하는 것이 좋아요.\n"
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]
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}
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],
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"source": [
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"print(ai_msg.content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
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"metadata": {},
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"source": [
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"## Chaining\n",
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"\n",
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"We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='저는 네이버 AI를 사용하는 것이 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 14, 'seed': 2575184681, 'ai_filter': None}, id='run-7014b330-eba3-4701-bb62-df73ce39b854-0', usage_metadata={'input_tokens': 25, 'output_tokens': 14, 'total_tokens': 39})"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}. Translate the user sentence.\",\n",
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" ),\n",
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" (\"human\", \"{input}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"chain = prompt | chat\n",
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"chain.invoke(\n",
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" {\n",
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" \"input_language\": \"English\",\n",
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" \"output_language\": \"Korean\",\n",
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" \"input\": \"I love using NAVER AI.\",\n",
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" }\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "66e69286",
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"metadata": {},
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"source": [
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"## Streaming"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "2c07af21-dda5-4514-b4de-1f214c2cebcd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Certainly! In Korean, \"Hi\" is pronounced as \"안녕\" (annyeong). The first syllable, \"안,\" sounds like the \"ahh\" sound in \"apple,\" while the second syllable, \"녕,\" sounds like the \"yuh\" sound in \"you.\" So when you put them together, it's like saying \"ahhyuh-nyuhng.\" Remember to pronounce each syllable clearly and separately for accurate pronunciation."
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]
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}
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],
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"source": [
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"system = \"You are a helpful assistant that can teach Korean pronunciation.\"\n",
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"human = \"Could you let me know how to say '{phrase}' in Korean?\"\n",
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"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
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"\n",
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"chain = prompt | chat\n",
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"\n",
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"for chunk in chain.stream({\"phrase\": \"Hi\"}):\n",
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" print(chunk.content, end=\"\", flush=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
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"metadata": {},
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"source": [
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"## Additional functionalities\n",
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"\n",
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"### Using fine-tuned models\n",
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"\n",
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"You can call fine-tuned models by passing in your corresponding `task_id` parameter. (You don’t need to specify the `model_name` parameter when calling fine-tuned model.)\n",
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"\n",
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"You can check `task_id` from corresponding Test App or Service App details."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "cb436788",
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"metadata": {},
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"outputs": [
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{
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"data": {
|
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"text/plain": [
|
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"AIMessage(content='저는 네이버 AI를 사용하는 것이 너무 좋아요.', additional_kwargs={}, response_metadata={'stop_reason': 'stop_before', 'input_length': 25, 'output_length': 15, 'seed': 52559061, 'ai_filter': None}, id='run-5bea8d4a-48f3-4c34-ae70-66e60dca5344-0', usage_metadata={'input_tokens': 25, 'output_tokens': 15, 'total_tokens': 40})"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fine_tuned_model = ChatClovaX(\n",
|
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" task_id=\"5s8egt3a\", # set if you want to use fine-tuned model\n",
|
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" # other params...\n",
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")\n",
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"\n",
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"fine_tuned_model.invoke(messages)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f428deaf",
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"metadata": {},
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"source": [
|
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"### Service App\n",
|
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"\n",
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"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",
|
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"\n",
|
||||
"For a Service App, a corresponding `NCP_CLOVASTUDIO_API_KEY` is issued and can only be called with it."
|
||||
]
|
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},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
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"id": "dcf566df",
|
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"metadata": {},
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"outputs": [],
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"source": [
|
||||
"# Update environment variables\n",
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"\n",
|
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"os.environ[\"NCP_CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
|
||||
" \"Enter NCP CLOVA Studio API Key for Service App: \"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "cebe27ae",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chat = ChatClovaX(\n",
|
||||
" service_app=True, # True if you want to use your service app, default value is False.\n",
|
||||
" # clovastudio_api_key=\"...\" # if you prefer to pass api key in directly instead of using env vars\n",
|
||||
" model=\"HCX-003\",\n",
|
||||
" # other params...\n",
|
||||
")\n",
|
||||
"ai_msg = chat.invoke(messages)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d73e7140",
|
||||
"metadata": {},
|
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"source": [
|
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"### AI Filter\n",
|
||||
"\n",
|
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"AI Filter detects inappropriate output such as profanity from the test app (or service app included) created in Playground and informs the user. See [here](https://guide.ncloud-docs.com/docs/en/clovastudio-playground01#AIFilter) for details. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "32bfbc93",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chat = ChatClovaX(\n",
|
||||
" model=\"HCX-003\",\n",
|
||||
" include_ai_filters=True, # True if you want to enable ai filter\n",
|
||||
" # other params...\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"ai_msg = chat.invoke(messages)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7bd9e179",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(ai_msg.response_metadata[\"ai_filter\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
"\n",
|
||||
"For detailed documentation of all ChatNaver features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.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"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
38
docs/docs/integrations/providers/naver.mdx
Normal file
38
docs/docs/integrations/providers/naver.mdx
Normal file
@@ -0,0 +1,38 @@
|
||||
# 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
|
||||
```
|
||||
318
docs/docs/integrations/text_embedding/naver.ipynb
Normal file
318
docs/docs/integrations/text_embedding/naver.ipynb
Normal file
@@ -0,0 +1,318 @@
|
||||
{
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user