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## **Description:** This PR was requested after the `langchain-naver` partner-managed packages were completed. We build our package as requested in [this comment](https://github.com/langchain-ai/langchain/pull/29243#issuecomment-2595222791) and the initial version is now uploaded to [pypi](https://pypi.org/project/langchain-naver/). So we've updated some our documents with the additional changed features and how to download our partner-managed package. ## **Dependencies:** https://github.com/langchain-ai/langchain/pull/29243#issuecomment-2595222791 --------- Co-authored-by: ccurme <chester.curme@gmail.com>
357 lines
14 KiB
Plaintext
357 lines
14 KiB
Plaintext
{
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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://guide.ncloud-docs.com/docs/clovastudio-dev-langchain).\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 Guide [documentation](https://guide.ncloud-docs.com/docs/clovastudio-model).\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://guide.ncloud-docs.com/docs/clovastudio-dev-langchain#HyperCLOVAX%EB%AA%A8%EB%8D%B8%EC%9D%B4%EC%9A%A9) | [langchain-naver](https://pypi.org/project/langchain-naver/) | ❌ | ❌ | ❌ |  |  |\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 four 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. Create a CLOVA Studio Test App or Service App of a model to use (See [here](https://guide.ncloud-docs.com/docs/clovastudio-playground-testapp).)\n",
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"4. Issue a Test or Service API key (See [here](https://api.ncloud-docs.com/docs/ai-naver-clovastudio-summary#API%ED%82%A4).)\n",
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"\n",
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"### Credentials\n",
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"\n",
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"Set the `CLOVASTUDIO_API_KEY` environment variable with your API key.\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 CLOVASTUDIO_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": 2,
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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(\"CLOVASTUDIO_API_KEY\"):\n",
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" os.environ[\"CLOVASTUDIO_API_KEY\"] = getpass.getpass(\n",
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" \"Enter your CLOVA Studio 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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"To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
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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[\"LANGSMITH_TRACING\"] = \"true\"\n",
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"# os.environ[\"LANGSMITH_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-naver` 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-naver"
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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": 3,
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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_naver import ChatClovaX\n",
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"\n",
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"chat = ChatClovaX(\n",
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" model=\"HCX-005\",\n",
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" temperature=0.5,\n",
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" max_tokens=None,\n",
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" timeout=None,\n",
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" max_retries=2,\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, `ChatClovaX` 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": 4,
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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='네이버 인공지능을 사용하는 것을 정말 좋아합니다.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 28, 'total_tokens': 39, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'HCX-005', 'system_fingerprint': None, 'id': 'b70c26671cd247a0864115bacfb5fc12', 'finish_reason': 'stop', 'logprobs': None}, id='run-3faf6a8d-d5da-49ad-9fbb-7b56ed23b484-0', usage_metadata={'input_tokens': 28, 'output_tokens': 11, 'total_tokens': 39, 'input_token_details': {}, 'output_token_details': {}})"
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]
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},
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"execution_count": 4,
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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": 5,
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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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"네이버 인공지능을 사용하는 것을 정말 좋아합니다.\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": 6,
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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 인공지능을 사용하는 것을 좋아합니다.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 28, 'total_tokens': 38, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'HCX-005', 'system_fingerprint': None, 'id': 'b7a826d17fcf4fee8386fca2ebc63284', 'finish_reason': 'stop', 'logprobs': None}, id='run-35957816-3325-4d9c-9441-e40704912be6-0', usage_metadata={'input_tokens': 28, 'output_tokens': 10, 'total_tokens': 38, 'input_token_details': {}, 'output_token_details': {}})"
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]
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},
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"execution_count": 6,
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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": 7,
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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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"In Korean, the informal way of saying 'hi' is \"안녕\" (annyeong). If you're addressing someone older or showing more respect, you would use \"안녕하세요\" (annjeonghaseyo). Both phrases are used as greetings similar to 'hello'. Remember, pronunciation is key so make sure to pronounce each syllable clearly: 안-녀-엉 (an-nyeo-eong) and 안-녕-하-세-요 (an-nyeong-ha-se-yo)."
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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 the `task_id` to the `model` parameter as: `ft:{task_id}`.\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": null,
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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='네이버 인공지능을 사용하는 것을 정말 좋아합니다.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 28, 'total_tokens': 39, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'HCX-005', 'system_fingerprint': None, 'id': '2222d6d411a948c883aac1e03ca6cebe', 'finish_reason': 'stop', 'logprobs': None}, id='run-9696d7e2-7afa-4bb4-9c03-b95fcf678ab8-0', usage_metadata={'input_tokens': 28, 'output_tokens': 11, 'total_tokens': 39, 'input_token_details': {}, 'output_token_details': {}})"
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]
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},
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"execution_count": 10,
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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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" model=\"ft:a1b2c3d4\", # set as `ft:{task_id}` with your fine-tuned model's task id\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": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
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"metadata": {},
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"source": [
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"## API reference\n",
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"\n",
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"For detailed documentation of all ChatClovaX features and configurations head to the [API reference](https://guide.ncloud-docs.com/docs/clovastudio-dev-langchain)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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